Miracle-dz/UniFAR-FTUs huggingface dataset:
The General Index of Software Engineering Papers

We introduce the General Index of Software Engineering Papers, a dataset of fulltext-indexed papers from the most prominent scientific venues in the field of Software Engineering. The dataset includes both complete bibliographic information and indexed n-grams (sequence of contiguous words after removal of stopwords and non-words, for a total of 577 276 382 unique n-grams in this release) with length 1 to 5 for 44 581 papers retrieved from 34 venues over the 1971–2020 period. The dataset serves use cases in the field of meta-research, allowing to introspect the output of software engineering research even when access to papers or scholarly search engines is not possible (e.g., due to contractual reasons). The dataset also contributes to making such analyses reproducible and independently verifiable, as opposed to what happens when they are conducted using 3rd-party and non-open scholarly indexing services. The dataset is available as a portable Postgres database dump and released as open data.
People Capability Maturity Model (P-CMM) Facing the Key HR Challenges of MSME in India: A Theoretical Approach

Micro Small and Medium Enterprises (MSME) have a significant role in the growth of Indian economy and contribute to generating employment especially in the rural areas. But, the performances of the MSME in the globalization period have not been satisfactory as the number of sick MSME is increasing year by year. The MSME in India now face a tough competition from large industries and are not adequately equipped to compete with the global players. There are many challenges in the MSME of India and HR practices are one of the major problems. Many industries claimed that the P-CMM is a strategy for transforming an organization in the modern era by improving workforce practices progressively. Therefore, this paper specifically tries to enlighten the important of P-CMM and its relevant applications to solve the HR problems faced by the MSME of India.
L2G: A Simple Local-to-Global Knowledge Transfer Framework for Weakly Supervised Semantic Segmentation

Mining precise class-aware attention maps, a.k. a, class activation maps, is essential for weakly supervised semantic segmentation. In this paper, we present L2G, a simple online local-to-global knowledge transfer framework for high-quality object attention mining. We observe that classification models can discover object regions with more details when replacing the input image with its local patches. Taking this into account, we first leverage a local classification network to extract attentions from multiple local patches randomly cropped from the input image. Then, we utilize a global network to learn complementary attention knowledge across multiple local attention maps online. Our framework conducts the global network to learn the captured rich object detail knowledge from a global view and thereby produces high-quality attention maps that can be directly used as pseudo annotations for semantic segmentation networks. Experiments show that our method attains 72.1% and 44.2% mIoU scores on the validation set of PASCAL VOC 2012 and MS COCO 2014, respectively, setting new state-of-the-art records. Code is available at https://github.com/PengtaoJiang/L2G.
Satisfiability transition in asymmetric neural networks

Asymmetry in the synaptic interactions between neurons plays a crucial role in determining the memory storage and retrieval properties of recurrent neural networks. In this work, we analyze the problem of storing random memories in a network of neurons connected by a synaptic matrix with a definite degree of asymmetry. We study the corresponding satisfiability and clustering transitions in the space of solutions of the constraint satisfaction problem associated with finding synaptic matrices given the memories. We find, besides the usual SAT/UNSAT transition at a critical number of memories to store in the network, an additional transition for very asymmetric matrices, where the competing constraints (definite asymmetry vs memories storage) induce enough frustration in the problem to make it impossible to solve. This finding is particularly striking in the case of a single memory to store, where no quenched disorder is present in the system.
Supertranslation invariance of angular momentum at null infinity in double null gauge

The supertranslation invariance of the Chen-Wang-Yau (CWY) angular momentum in the Bondi-Sachs formalism/gauge was ascertained by the authors in [12, 13]. In this article, we study the corresponding problem in the double null gauge. In particular, supertranslation ambiguity of this gauge is identiﬁed and the CWY angular momentum is proven to be free of this ambiguity. A similar result is obtained for the CWY center of mass integral.
Task-Aware Active Learning for Endoscopic Image Analysis

Semantic segmentation of polyps and depth estimation are two important research problems in endoscopic image analysis. One of the main obstacles to conduct research on these research problems is lack of annotated data. Endoscopic annotations necessitate the special-ist knowledge of expert endoscopists and due to this, it can be diﬃcult to organise, expensive and time consuming. To address this problem, we investigate an active learning paradigm to reduce the number of training examples by selecting the most discriminative and diverse unlabelled examples for the task taken into consideration. Most of the existing active learning pipelines are task-agnostic in nature and are often sub-optimal to the end task. In this paper, we propose a novel task-aware active learning pipeline and applied for two important tasks in endoscopic image analysis: semantic segmentation and depth estimation. We compared our method with the competitive baselines. From the exper-imental results, we observe a substantial improvement over the compared baselines. Codes are available at https://github.com/thetna/ endo-active-learn .
On the Effectiveness of Pretrained Models for API Learning

Developers frequently use APIs to implement certain functionalities, such as parsing Excel Files, reading and writing text files line by line, etc. Developers can greatly benefit from automatic API usage sequence generation based on natural language queries for building applications in a faster and cleaner manner. Existing approaches utilize information retrieval models to search for matching API sequences given a query or use RNN-based encoder-decoder to generate API sequences. As it stands, the first approach treats queries and API names as bags of words. It lacks deep comprehension of the semantics of the queries. The latter approach adapts a neural language model to encode a user query into a fixed-length context vector and generate API sequences from the context vector. We want to understand the effectiveness of recent Pre-trained Transformer based Models (PTMs) for the API learning task. These PTMs are trained on large natural language corpora in an unsupervised manner to retain contextual knowledge about the language and have found success in solving similar Natural Language Processing (NLP) problems. However, the applicability of PTMs has not yet been explored for the API sequence generation task. We use a dataset that contains 7 million annotations collected from GitHub to evaluate the PTMs empirically. This dataset was also used to assess previous approaches. Based on our results, PTMs generate more accurate API sequences and outperform other related methods by ∼11%. We have also identified two different tokenization approaches that can contribute to a significant boost in PTMs' performance for the API sequence generation task.
Offline Reinforcement Learning for Safer Blood Glucose Control in People with Type 1 Diabetes

The widespread adoption of effective hybrid closed loop systems would represent an important milestone of care for people living with type 1 diabetes (T1D). These devices typically utilise simple control algorithms to select the optimal insulin dose for maintaining blood glucose levels within a healthy range. Online reinforcement learning (RL) has been utilised as a method for further enhancing glucose control in these devices. Previous approaches have been shown to reduce patient risk and improve time spent in the target range when compared to classical control algorithms, but are prone to instability in the learning process, often resulting in the selection of unsafe actions. This work presents an evaluation of offline RL for developing effective dosing policies without the need for potentially dangerous patient interaction during training. This paper examines the utility of BCQ, CQL and TD3-BC in managing the blood glucose of the 30 virtual patients available within the FDA-approved UVA/Padova glucose dynamics simulator. When trained on less than a tenth of the total training samples required by online RL to achieve stable performance, this work shows that offline RL can significantly increase time in the healthy blood glucose range from 61.6±0.3% to 65.3±0.5% when compared to the strongest state-of-art baseline (p<0.001). This is achieved without any associated increase in low blood glucose events. Offline RL is also shown to be able to correct for common and challenging control scenarios such as incorrect bolus dosing, irregular meal timings and compression errors. The code for this work is available at: https://github.com/hemerson1/offline-glucose.
Generalised Gibbs Ensemble for spherically constrained harmonic models

We build and analytically calculate the Generalised Gibbs Ensemble partition function of the integrable Soft Neumann Model. This is the model of a classical particle which is constrained to move, on average over the initial conditions, on an NN dimensional sphere, and feels the effect of anisotropic harmonic potentials. We derive all relevant averaged static observables in the (thermodynamic) N\to\inftyN→∞ limit. We compare them to their long-term dynamic averages finding excellent agreement in all phases of a non-trivial phase diagram determined by the characteristics of the initial conditions and the amount of energy injected or extracted in an instantaneous quench. We discuss the implications of our results for the proper Neumann model in which the spherical constraint is imposed strictly.
Towards Fair Evaluation of Dialogue State Tracking by Flexible Incorporation of Turn-level Performances

Dialogue State Tracking (DST) is primarily evaluated using Joint Goal Accuracy (JGA) defined as the fraction of turns where the ground-truth dialogue state exactly matches the prediction. Generally in DST, the dialogue state or belief state for a given turn contain all the intents shown by the user till that turn. Due to this cumulative nature of the belief state, it is difficult to get a correct prediction once a misprediction has occurred. Thus, although being a useful metric, it can be harsh at times and underestimate the true potential of a DST model. Moreover, an improvement in JGA can sometimes decrease the performance of turn-level or non-cumulative belief state prediction due to inconsistency in annotations. So, using JGA as the only metric for model selection may not be ideal for all scenarios. In this work, we discuss various evaluation metrics used for DST along with their shortcomings. To address the existing issues, we propose a new evaluation metric named Flexible Goal Accuracy (FGA). FGA is a generalized version of JGA. But unlike JGA, it tries to give penalized rewards to mispredictions that are locally correct i.e. the root cause of the error is an earlier turn. By doing so, FGA considers the performance of both cumulative and turn-level prediction flexibly and provides a better insight than the existing metrics. We also show that FGA is a better discriminator of DST model performance.
Incremental Prototype Prompt-tuning with Pre-trained Representation for Class Incremental Learning

Class incremental learning has attracted much attention, but most existing works still continually ﬁne-tune the entire representation model, resulting in much catastrophic forgetting. Instead of struggling to ﬁght against such forgetting by replaying or distillation like most of the existing methods, we take the pre-train-and-prompt-tuning paradigm to sequentially learn new visual concepts based on a ﬁxed semantic rich pre-trained representation model by incremental prototype prompt-tuning (IPP), which substantially reduces the catastrophic forgetting. In addition, an example prototype classiﬁcation is proposed to compensate for semantic drift, the problem caused by learning bias at different phases. Extensive experiments conducted on the mainstream incremental learning benchmarks demonstrate that our method consistently outperforms other state-of-the-art methods with a large margin.
Flexible Sampling for Long-tailed Skin Lesion Classification

Most of the medical tasks naturally exhibit a long-tailed distribution due to the complex patient-level conditions and the existence of rare diseases. Existing long-tailed learning methods usually treat each class equally to re-balance the long-tailed distribution. However, considering that some challenging classes may present diverse intra-class distributions, re-balancing all classes equally may lead to a significant performance drop. To address this, in this paper, we propose a curriculum learning-based framework called Flexible Sampling for the long-tailed skin lesion classification task. Specifically, we initially sample a subset of training data as anchor points based on the individual class prototypes. Then, these anchor points are used to pre-train an inference model to evaluate the per-class learning difficulty. Finally, we use a curriculum sampling module to dynamically query new samples from the rest training samples with the learning difficulty-aware sampling probability. We evaluated our model against several state-of-the-art methods on the ISIC dataset. The results with two long-tailed settings have demonstrated the superiority of our proposed training strategy, which achieves a new benchmark for long-tailed skin lesion classification.
Thermal Equilibrium Solutions of Black Hole Accretion Flows: Outflows versus Advection

Observations and numerical simulations have shown that outflows generally exist in the accretion process. We revisit the thermal equilibrium solutions of black hole accretion flows by including the role of outflows. Our study focuses on the comparison of the cooling rate of outflows with that of advection. Our results show that, except for the inner region, outflows can dominate over advection in a wide range of flows, which is in good agreement with previous numerical simulations. We argue that an advection-dominated inner region together with an outflow-dominated outer region should be a general radial distribution for both super-Eddington accretion flows and optically thin flows with low accretion rates.
Surface Vision Transformers: Flexible Attention-Based Modelling of Biomedical Surfaces

Recent state-of-the-art performances of Vision Transformers (ViT) in computer vision tasks demonstrate that a general-purpose architecture, which implements long-range self-attention, could replace the local feature learning operations of convolutional neural networks. In this paper, we extend ViTs to surfaces by reformulating the task of surface learning as a sequence-to-sequence learning problem, by proposing patching mechanisms for general surface meshes. Sequences of patches are then processed by a transformer encoder and used for classification or regression. We validate our method on a range of different biomedical surface domains and tasks: brain age prediction in the developing Human Connectome Project (dHCP), fluid intelligence prediction in the Human Connectome Project (HCP), and coronary artery calcium score classification using surfaces from the Scottish Computed Tomography of the Heart (SCOT-HEART) dataset, and investigate the impact of pretraining and data augmentation on model performance. Results suggest that Surface Vision Transformers (SiT) demonstrate consistent improvement over geometric deep learning methods for brain age and fluid intelligence prediction and achieve comparable performance on calcium score classification to standard metrics used in clinical practice. Furthermore, analysis of transformer attention maps offers clear and individualised predictions of the features driving each task. Code is available on Github: https://github.com/metrics-lab/surface-vision-transformers
Algebraic Structure of the Weak Stage Order Conditions for Runge-Kutta Methods

Runge-Kutta (RK) methods may exhibit order reduction when applied to stiff problems. For linear problems with time-independent operators, order reduction can be avoided if the method satisfies certain weak stage order (WSO) conditions, which are less restrictive than traditional stage order conditions. This paper outlines the first algebraic theory of WSO, and establishes general order barriers that relate the WSO of a RK scheme to its order and number of stages for both fully-implicit and DIRK schemes. It is shown in several scenarios that the constructed bounds are sharp. The theory characterizes WSO in terms of orthogonal invariant subspaces and associated minimal polynomials. The resulting necessary conditions on the structure of RK methods with WSO are then shown to be of practical use for the construction of such schemes.
Leveraging Real Conversational Data for Multi-Channel Continuous Speech Separation

Existing multi-channel continuous speech separation (CSS) models are heavily dependent on supervised data - either simulated data which causes data mismatch between the training and real-data testing, or the real transcribed overlapping data, which is difficult to be acquired, hindering further improvements in the conversational/meeting transcription tasks. In this paper, we propose a three-stage training scheme for the CSS model that can leverage both supervised data and extra large-scale unsupervised real-world conversational data. The scheme consists of two conventional training approaches -- pre-training using simulated data and ASR-loss-based training using transcribed data -- and a novel continuous semi-supervised training between the two, in which the CSS model is further trained by using real data based on the teacher-student learning framework. We apply this scheme to an array-geometry-agnostic CSS model, which can use the multi-channel data collected from any microphone array. Large-scale meeting transcription experiments are carried out on both Microsoft internal meeting data and the AMI meeting corpus. The steady improvement by each training stage has been observed, showing the effect of the proposed method that enables leveraging real conversational data for CSS model training.
Free-Floating Planets, the Einstein Desert, and 'Oumuamua

: We complete the survey for ﬁnite-source/point-lens (FSPL) giant-source events in 2016-2019 KMTNet microlensing data. The 30 FSPL events show a clear gap in Einstein radius, 9 µ as < θ E < 26 µ as, which is consistent with the gap in Einstein timescales near t E ∼ 0 . 5 days found by Mr´oz et al. (2017) in an independent sample of point-source/point-lens (PSPL) events. We demonstrate that the two surveys are consistent. We estimate that the 4 events below this gap are due to a power-law distribution of free-ﬂoating planet candidates (FFPs) with 0 . 2. There are substantially more FFPs than known bound planets, implying that the bound planet power-law index γ = 0 . 6 is likely shaped by the ejection process at least as much as by formation. The mass density per decade of FFPs in the Solar neighborhood is of the same order as that of ’Oumuamua-like objects. In particular, if we assume that ’Oumuamua is part of the same process that ejected the FFPs to very wide or unbound orbits, the power-law index is p = 0 . 92 ± 0 . 06. If the Solar System’s endowment of Neptune-mass objects in Neptune-like orbits is typical, which is consistent with the results of Poleski et al. (2021), then these could account for a substantial fraction of the FFPs in the Neptune-mass range.
Development Of The Persistence In Engineering (Pie) Survey Instrument

Method and Background The Persistence In Engineering (PIE) survey was developed to identify and characterize the fundamental factors that influence students’ intentions to pursue an engineering degree over the course of their undergraduate career, and upon graduation, to practice engineering as a profession. The PIE survey is one of five data-gathering methods used in the APS. This research brief discusses the initial development and refinement of the PIE survey instrument and its role as part of the Academic Pathways Study (APS) of the Center for the Advancement of Engineering Education (CAEE).
Swarm behavior tracking based on a deep vision algorithm

The intelligent swarm behavior of social insects (such as ants) springs up in different environments, promising to provide insights for the study of embodied intelligence. Researching swarm behavior requires that researchers could accurately track each individual over time. Obviously, manually labeling individual insects in a video is labor-intensive. Automatic tracking methods, however, also poses serious challenges: (1) individuals are small and similar in appearance; (2) frequent interactions with each other cause severe and long-term occlusion. With the advances of artiﬁcial intelligence and computing vision technologies, we are hopeful to provide a tool to automate monitor multiple insects to address the above challenges. In this paper, we propose a detection and tracking framework for multi-ant tracking in the videos by: (1) adopting a two-stage object detection framework using ResNet-50 as backbone and coding the position of regions of interest to locate ants accurately; (2) using the ResNet model to develop the appearance descriptors of ants; (3) constructing long-term appearance sequences and combining them with motion information to achieve online tracking. To validate our method, we construct an ant database including 10 videos of ants from different indoor and outdoor scenes. We achieve a state-of-the-art performance of 95.7% mMOTA and 81.1% mMOTP in indoor videos, 81.8% mMOTA and 81.9% mMOTP in outdoor videos. Additionally, Our method runs 6-10 times faster than existing methods for insect tracking. Experimental results demonstrate that our method provides a powerful tool for accelerating the unraveling of the mechanisms underlying the swarm behavior of social insects.
A Framework for Distributed Estimation with Reduced Communication via Event-Based Strategies

This paper considers the problem of distributed estimation in a sensor network, where multiple sensors are deployed to infer the state of a linear time-invariant (LTI) Gaussian system. By proposing a lossless decomposition of Kalman filter, a framework of event-based distributed estimation is developed, where each sensor node runs a local filter using solely its own measurement, alongside with an event-based synchronization algorithm to fuse the neighboring information. One novelty of the proposed framework is that it decouples the local filter from synchronization process. By doing so, we prove that a general class of triggering strategies can be applied in our framework, which yields stable distributed estimators under the minimal requirements of network connectivity and collective system observability. As compared with existing works, the proposed algorithm enjoys lower data size for each transmission. Moreover, the developed results can be generalized to achieve a distributed implementation of any Luenberger observer. By solving a semi-definite programming (SDP), we further present a low-rank estimator design to obtain the optimal gain of Luenberger observer such that the distributed estimation is realized under the constraint of message complexity. Numerical examples are finally provided to demonstrate the proposed methods.