Miracle-dz/UniFAR-FTUs huggingface dataset:
Modeling Interconnected Social and Technical Risks in Open Source Software Ecosystems

Open source software ecosystems consist of thousands of interdependent libraries, which users can combine to great effect. Recent work has pointed out two kinds of risks in these systems: that technical problems like bugs and vulnerabilities can spread through dependency links, and that relatively few developers are responsible for maintaining even the most widely used libraries. However, a more holistic diagnosis of systemic risk in software ecosystem should consider how these social and technical sources of risk interact and amplify one another. Motivated by the observation that the same individuals maintain several libraries within dependency networks, we present a methodological framework to measure risk in software ecosystems as a function of both dependencies and developers. In our models, a library’s chance of failure increases as its developers leave and as its upstream dependencies fail. We apply our method to data from the Rust ecosystem, highlighting several systemically important libraries that are overlooked when only considering technical dependencies. We compare potential interventions, seeking better ways to deploy limited developer resources with a view to improving overall ecosystem health and software supply chain resilience.
Self-Serviced IoT: Practical and Private IoT Computation Offloading with Full User Control

The rapid increase in the adoption of Internet-of-Things (IoT) devices raises critical privacy concerns as these devices can access a variety of sensitive data. The current status quo of relying on manufacturers’ cloud services to process this data is especially problematic since users cede control once their data leaves their home. Multiple recent incidents further call into question if vendors can indeed be trusted with users’ data. At the same time, users desire compelling features supported by IoT devices and ML based cloud inferences which compels them to subscribe to manufacturer-managed cloud services. An alternative to use a local in-home hub requires substantial hardware investment, management, and scalability limitations. This paper proposes Self-Serviced IoT (SSIoT), a clean-slate approach of using a hybrid hub-cloud setup to enable privacy-aware computation ofﬂoad for IoT applications. Uniquely, SSIoT enables opportunistic computation ofﬂoad to public cloud providers (e.g. Amazon AWS) while still ensuring that the end-user retains complete end-to-end control of their private data reducing the trust required from public cloud providers. We show that SSIoT can leverage emerging function-as-a-service computation (e.g. AWS Lambda) to make these ofﬂoads cost-efﬁcient, scalable and high performance as long as key limitations of being stateless, limited resources, and security isolation can be addressed. We build an end-to-end prototype of SSIoT and evaluate it using several micro-benchmarks and example applications representing real-world IoT use cases. Our results show that SSIoT is highly scalable, as compared to local-only approaches which struggle with as little as 2–4 apps in parallel. We also show that SSIoT is cost-efﬁcient (operating a smart doorbell for $10 a year) at the cost of minimal additional latency as compared to a local-only hub, even with a hardware ML accelerator.
On off-line and on-line Bayesian filtering for uncertainty quantification of structural deterioration

Abstract Data-informed predictive maintenance planning largely relies on stochastic deterioration models. Monitoring information can be utilized to update sequentially the knowledge on model parameters. In this context, on-line (recursive) Bayesian filtering algorithms typically fail to properly quantify the full posterior uncertainty of time-invariant model parameters. Off-line (batch) algorithms are—in principle—better suited for the uncertainty quantification task, yet they are computationally prohibitive in sequential settings. In this work, we adapt and investigate selected Bayesian filters for parameter estimation: an on-line particle filter, an on-line iterated batch importance sampling filter, which performs Markov Chain Monte Carlo (MCMC) move steps, and an off-line MCMC-based sequential Monte Carlo filter. A Gaussian mixture model approximates the posterior distribution within the resampling process in all three filters. Two numerical examples provide the basis for a comparative assessment. The first example considers a low-dimensional, nonlinear, non-Gaussian probabilistic fatigue crack growth model that is updated with sequential monitoring measurements. The second high-dimensional, linear, Gaussian example employs a random field to model corrosion deterioration across a beam, which is updated with sequential sensor measurements. The numerical investigations provide insights into the performance of off-line and on-line filters in terms of the accuracy of posterior estimates and the computational cost, when applied to problems of different nature, increasing dimensionality and varying sensor information amount. Importantly, they show that a tailored implementation of the on-line particle filter proves competitive with the computationally demanding MCMC-based filters. Suggestions on the choice of the appropriate method in function of problem characteristics are provided.
FC3: Feasibility-Based Control Chain Coordination

Hierarchical coordination of controllers often uses symbolic state representations that fully abstract their underlying low-level controllers, treating them as “black boxes” to the symbolic action abstraction. This paper proposes a framework to realize robust behavior, which we call Feasibility-based Control Chain Coordination (FC3). Our controllers expose the geometric features and constraints they operate on. Based on this, FC3 can reason over the controllers' feasibility and their sequence feasibility. For a given task, FC3 first automatically constructs a library of potential controller chains using a symbolic action tree, which is then used to coordinate controllers in a chain, evaluate task feasibility, as well as switching between controller chains if necessary. In several real-world experiments we demonstrate FC3, s robustness and awareness of the task's feasibility through its own actions and gradual responses to different interferences.
HierAttn: Effectively Learn Representations from Stage Attention and Branch Attention for Skin Lesions Diagnosis

examination of is for the early diagnosis and of The visual feature of the skin lesions varies signiﬁcantly because skin images are collected from patients with di ﬀ erent skin colours by using various devices. Recent studies have developed ensembled convolutional neural networks (CNNs) to classify the images for early diagnosis. However, the practical use of CNNs is limited because their network structures are heavyweight and ne-glect contextual information. Vision transformers (ViTs) learn the global features by self-attention mechanisms, but they also have comparatively large model sizes (more than 100M). To address these limitations, we introduce HierAttn, a lite and e ﬀ ective neural network with hierarchical and self attention. HierAttn applies a novel strat-egy based on learning local and global features by a multi-stage and hierarchical network. The e ﬃ cacy of HierAttn was evaluated by using the dermoscopy images dataset ISIC2019 and smartphone photos dataset PAD-UFES-20. The experimental results show that HierAttn achieves the best top-1 accuracy and AUC among state-of-the-art mobile networks, including MobileNetV3 and MobileViT. The code is available at
An Effective Scheme for Maize Disease Recognition based on Deep Networks

In the last decades, the area under cultivation of maize products has increased because of its essential role in the food cycle for humans, livestock, and poultry. Moreover, the diseases of plants impact food safety and can significantly reduce both the quality and quantity of agricultural products. There are many challenges to accurate and timely diagnosis of the disease. This research presents a novel scheme based on a deep neural network to overcome the mentioned challenges. Due to the limited number of data, the transfer learning technique is employed with the help of two well-known architectures. In this way, a new effective model is adopted by a combination of pre-trained MobileNetV2 and Inception Networks due to their effective performance on object detection problems. The convolution layers of MoblieNetV2 and Inception modules are parallelly arranged as earlier layers to extract crucial features. In addition, the imbalance problem of classes has been solved by an augmentation strategy. The proposed scheme has a superior performance compared to other state-of-the-art models published in recent years. The accuracy of the model reaches 97%, approximately. In summary, experimental results prove the method's validity and significant performance in diagnosing disease in plant leaves.
Scheduled Multi-task Learning for Neural Chat Translation

Neural Chat Translation (NCT) aims to translate conversational text into different languages. Existing methods mainly focus on modeling the bilingual dialogue characteristics (e.g., coherence) to improve chat translation via multi-task learning on small-scale chat translation data. Although the NCT models have achieved impressive success, it is still far from satisfactory due to insufficient chat translation data and simple joint training manners. To address the above issues, we propose a scheduled multi-task learning framework for NCT. Specifically, we devise a three-stage training framework to incorporate the large-scale in-domain chat translation data into training by adding a second pre-training stage between the original pre-training and fine-tuning stages. Further, we investigate where and how to schedule the dialogue-related auxiliary tasks in multiple training stages to effectively enhance the main chat translation task. Extensive experiments on four language directions (English-Chinese and English-German) verify the effectiveness and superiority of the proposed approach. Additionally, we will make the large-scale in-domain paired bilingual dialogue dataset publicly available for the research community.
Scanning Electron Microscopy and Metabolite Measurement Revealed the Stress Mechanism of PS-COOH Microplastics on Rhodotorula mucilaginosa AN5

: Microplastics in the marine environment have been paid more and more attention by researchers, and the impact of these substances on marine microorganisms can not be ignored. Studies have shown that PS-COOH Microplastics are harmful to marine molluscs, algae and monads. This study explore the effect and mechanism of microplastics (80 nm PS-COOH) on Antarctic marine yeast, Rhodotorula mucilaginosa AN5 by bacterial count, Scanning Electron Microscopy (SEM) and metabolite analysis. The results illustrates that a 50 mg/L concentration of PS-COOH could inhibit 36.15 ± 4.58% growth of yeast cells and 10 mg/L inhibit 80.20 ± 3.27%. Microplastics stress causes changes in the content of some oxidative stress substances, including reactive oxygen species (ROS) 42.86% , malondialdehyde (MDA) 54.06% content and the activities of antioxidant enzymes such as catalase (CAT) 36.00% , peroxidase (POD) 66.67% and superoxide dismutase (SOD) 25.40%. These results revealed the possible stress effect of microplastic pollution on marine yeast and may affect bottom layer of marine ecosystem.
Bayesian estimation of correlation functions

We apply Bayesian statistics to the estimation of correlation functions. We give the probability distributions of auto- and cross-correlations as functions of the data. Our procedure uses the measured data optimally and informs about the certainty level of the estimation. Our results apply to general stationary processes and their essence is a non-parametric estimation of spectra. It allows one to better understand the statistical noise fluctuations, assess the correlations between two variables, and postulate parametric models of spectra that can be further tested. We also propose a method to numerically generate correlated noise with a given spectrum.
Near-Optimal Deterministic Vertex-Failure Connectivity Oracles

We revisit the vertex-failure connectivity oracle problem. This is one of the most basic graph data structure problems under vertex updates, yet its complexity is still not well-understood. We essentially settle the complexity of this problem by showing a new data structure whose space, preprocessing time, update time, and query time are simultaneously optimal up to sub-polynomial factors assuming popular conjectures. Moreover, the data structure is deterministic.More precisely, for any integer $d_{\star}$, the data structure preprocesses a graph G with n vertices and m edges in $\hat{O}\left(m d_{\star}\right)$ time and uses $\tilde{O}\left(\min \left\{m, n d_{\star}\right\}\right)$ space. Then, given the vertex set D to be deleted where $|D|=d \leq d_{\star}$, it takes $\hat{O}\left(d^{2}\right)$ updates time. Finally, given any vertex pair $(u, v)$, it checks if u and v are connected in $G \backslash D$ in $O(d)$ time. This improves the previously best deterministic algorithm by Duan and Pettie [SICOMP 2020] in both space and update time by a factor of d. It also significantly speeds up the $\Omega\left(\min \left\{m n, n^{\omega}\right\}\right)$ preprocessing time of all known (even randomized) algorithms with update time at most $\tilde{O}\left(d^{5}\right)$.
The Integrated Ship Database: A Rapid-Access Database of Naval Ship Information-A Users Manual

Abstract : Analytical and information support for Navy decision-makers and staff action officers demands easy, quick access to reliable information about ships. In this CNA Interactive Software (CIS) product, we offer a relatively quick and easy-to-learn way to find information on naval shipsfuture, current, and pastfrom authoritative sources on the Internet. We present the data in a simple database titled the Integrated Ship Database. The database resides in a Microsoft Excel workbook. Our fundamental data source is the on-line Naval Vessel Register (NVR)The Official Inventory of US Naval Ships and Craft [1]. The NVR contains a wealth of information on ships and service craft over the course of a vessels life cycle. Although the on-line information is quite useful, it is not available in the standard database format of fields and records. Therefore, it is of only limited use to researchers and others who must search, sort, query, and count. We believe the information could be even more useful if it were available in such form.
On the Use of Bert for Automated Essay Scoring: Joint Learning of Multi-Scale Essay Representation

In recent years, pre-trained models have become dominant in most natural language processing (NLP) tasks. However, in the area of Automated Essay Scoring (AES), pre-trained models such as BERT have not been properly used to outperform other deep learning models such as LSTM. In this paper, we introduce a novel multi-scale essay representation for BERT that can be jointly learned. We also employ multiple losses and transfer learning from out-of-domain essays to further improve the performance. Experiment results show that our approach derives much benefit from joint learning of multi-scale essay representation and obtains almost the state-of-the-art result among all deep learning models in the ASAP task. Our multi-scale essay representation also generalizes well to CommonLit Readability Prize data set, which suggests that the novel text representation proposed in this paper may be a new and effective choice for long-text tasks.
Robust parameter estimation from pulsar timing data

Recently, global pulsar timing arrays have released results from searching for a nano-Hertz gravitational wave background signal. Although there has not been any definite evidence of the presence of such a signal in residuals of pulsar timing data yet, with more and improved data in future, a statistically significant detection is expected to be made. Stochastic algorithms are used to sample a very large parameter space to infer results from data. In this paper, we attempt to rule out effects arising from the stochasticity of the sampler in the inference process. We compare different configurations of nested samplers and the more commonly used markov chain monte carlo method to sample the pulsar timing array parameter space and account for times taken by the different samplers on same data. Although we obtain consistent results on parameters from different sampling algorithms, we propose two different samplers for robustness checks on data in the future to account for cross-checks between sampling methods as well as realistic run-times.
Correlation-Robust Optimal Auctions

I study the design of auctions in which the auctioneer is assumed to have information only about the marginal distribution of a generic bidder’s valuation, but does not know the correlation structure of the joint distribution of bidders’ valuations. I assume that a generic bidder’s valuation is bounded and ¯ v is the maximum valuation of a generic bidder. The performance of a mechanism is evaluated in the worst case over the uncertainty of joint distributions that are consistent with the marginal distribution. For the two-bidder case, the second-price auction with the uniformly distributed random reserve maximizes the worst-case expected revenue across all dominant-strategy mechanisms under certain regularity conditions. For the N -bidder ( N ≥ 3 ) case, the second-price auction with the ¯ v − scaled Beta ( 1 N − 1 , 1) distributed random reserve maximizes the worst-case expected revenue across standard (a bidder whose bid is not the highest will never be allocated) dominant-strategy mechanisms under certain regularity conditions. When the probability mass condition (part of the regularity conditions) does not hold, the second-price auction with the s ∗ − scaled Beta ( 1 N − 1 , 1) distributed random reserve maximizes the worst-case expected revenue across standard dominant-strategy mechanisms, where s ∗ ∈ (0 , ¯ v ) .
Federated Random Reshuffling with Compression and Variance Reduction

Random Reshuffling (RR), which is a variant of Stochastic Gradient Descent (SGD) employing sampling without replacement, is an immensely popular method for training supervised machine learning models via empirical risk minimization. Due to its superior practical performance, it is embedded and often set as default in standard machine learning software. Under the name FedRR, this method was recently shown to be applicable to federated learning (Mishchenko et al.,2021), with superior performance when compared to common baselines such as Local SGD. Inspired by this development, we design three new algorithms to improve FedRR further: compressed FedRR and two variance reduced extensions: one for taming the variance coming from shuffling and the other for taming the variance due to compression. The variance reduction mechanism for compression allows us to eliminate dependence on the compression parameter, and applying additional controlled linear perturbations for Random Reshuffling, introduced by Malinovsky et al.(2021) helps to eliminate variance at the optimum. We provide the first analysis of compressed local methods under standard assumptions without bounded gradient assumptions and for heterogeneous data, overcoming the limitations of the compression operator. We corroborate our theoretical results with experiments on synthetic and real data sets.
SYSTEMIC APPROACH (SA) TO CURRICULUM DEVELOPMENT (IMPLICATION AND APPLICATION OF SYSTEMS THINKING TO CURRICULUM DEVELOPMENT)

This paper will address the application of systemic approach to curriculum development. This approach is inspired by systems or systemic thinking which is holistic-comprehensive in nature as opposed to taxonomic thinking which is partial-fragmentary. Systemic thinking considers this world as a system: education is a system; curriculum and curriculum development are sub-systems of education. Every system has its own elements; all elements in the system are equally important and together will determine the success of a system to achieve its goal. The success of a system is determined by the intersection, interrelation, and interaction of all its elements. The failure of language teaching to meet its goal might be because it is based on a curriculum which is not systemically developed; some elements are not considered or involved. Therefore, the application of systemic approach to curriculum development might be an alternative solution.
DRAM Refresh with Master Wordline Granularity Control of Refresh Intervals: Position Paper

DRAM cells need to be periodically refreshed to preserve the charge stored in them. There are multiple mechanisms causing the loss of charge. These mechanisms also vary in strength. The retention time is therefore not the same for all DRAM cells but follows a distribution with multiple orders of magnitude difference between the retention time of cells with the highest charge loss and the cells with the lowest charge loss. Today's DRAM standards have one refresh interval that is set based on the retention time of the weakest cells that are not replaced by redundancy or repaired with ECC. Refresh adds overhead to DRAM energy consumption and command bandwidth and blocks access to banks currently being refreshed. This overhead increases with larger DRAM capacity and shrinking DRAM feature size. In this paper we propose a method and corresponding circuit implementation that allows using different refresh intervals based on the required minimum retention time of cells on those wordlines. We show that this method can increase the effective refresh interval of a modern DRAM between 40% and 80% without loss of reliability and a corresponding reduction of the contribution of refresh to energy consumption and command bandwidth. Our evaluation shows that the method can be implemented with a moderate DRAM die size impact (between 1% and 2.5%). The method does not require the memory controller to keep track of refresh addresses. After initialization of the DRAM devices, the memory controller needs only to issue refresh commands as today, albeit a smaller number than without our approach.
Multimodal Optimization of Edge Server Placement Considering System Response Time

Mobile edge computing (MEC) deploys computing and storage resources close to mobile devices, enabling resource demanding applications to run on mobile devices with short network latency. In the past few years, large numbers of research works focused on the research hotspots in MEC, such as computation offloading and energy efficiency. However, few researchers have investigated the deployment of edge servers. On the one hand, blindly deploying numerous edge servers will result in a large amount of capital expenditure. On the other hand, the deployment of edge servers is a multimodal problem that should provide decision makers with multiple deployment options to deal with the impact of unmeasured real-world factors. Considering these factors, we study the multimodal optimization problem of edge server placement (MESP) with the goal of minimizing the average system response time in this work. Regarding the difficulty of the MESP problem, we propose a heuristic algorithm that combines particle swarm optimization and niching technology to obtain a set of competitive placement solutions. Extensive experiments over a real-world dataset show that the proposed algorithm can significantly reduce the system response time.