Miracle-dz/UniFAR-FTUs huggingface dataset:
AutoDSE: Enabling Software Programmers Design Efficient FPGA Accelerators

Adopting FPGA as an accelerator in datacenters is becoming mainstream for customized computing, but the fact that FPGAs are hard to program creates a steep learning curve for software programmers. Even with the help of high-level synthesis (HLS), accelerator designers still must manually perform code reconstruction and cumbersome parameter tuning to achieve the optimal performance. While many learning models have been leveraged by existing work to automate the design of efficient accelerators, the unpredictability of modern HLS tools becomes a major obstacle for them to maintain high accuracy. We address this problem by incorporating an automated DSE framework - AutoDSE - that leverages bottleneck-guided gradient optimizer to systematically find a better design point. AutoDSE finds the bottleneck of the design in each step and focuses on high-impact parameters to overcome that, which is like the approach an expert would take. The experimental results show that AutoDSE is able to find the design point that achieves, on the geometric mean, 19.9x speedup over one CPU core for Machsuite and Rodinia benchmarks and 1.04x over the manually designed HLS accelerated vision kernels in Xilinx Vitis libraries yet with 26x reduction of their optimization pragmas.
Predicting Metamorphic Relations for Matrix Calculation Programs

Matrices often represent important information in scientific applications and are involved in performing complex calculations. But systematically testing these applications is hard due to the oracle problem. Metamorphic testing is an effective approach to test such applications because it uses metamorphic relations to determine whether test cases have passed or failed. Metamorphic relations are typically identified with the help of a domain expert and is a labor intensive task. In this work we use a graph kernel based machine learning approach to predict metamorphic relations for matrix calculation programs. Previously, this graph kernel based machine learning approach was used to successfully predict metamorphic relations for programs that perform numerical calculations. Results of this study show that this approach can be used to predict metamorphic relations for matrix calculation programs as well.
NENet: Monocular Depth Estimation via Neural Ensembles

Depth estimation is getting a widespread popularity in the computer vision community, and it is still quite difﬁ-cult to recover an accurate depth map using only one single RGB image. In this work, we observe a phenomenon that existing methods tend to exhibit asymmetric errors, which might open up a new direction for accurate and robust depth estimation. We carefully investigate into the phenomenon, and construct a two-level ensemble scheme, NENet, to integrate multiple predictions from diverse base predictors. The NENet forms a more reliable depth estimator, which substantially boosts the performance over base predictors. Notably, this is the ﬁrst attempt to introduce ensemble learning and evaluate its utility for monocular depth estimation to the best of our knowledge. Extensive experiments demonstrate that the proposed NENet achieves better results than previous state-of-the-art approaches on the NYU-Depth-v2 and KITTI datasets. In particular, our method improves previous state-of-the-art methods from 0.365 to 0.349 on the metric RMSE on the NYU dataset. To validate the generalizability across cameras, we directly apply the models trained on the NYU dataset to the SUN RGB-D dataset without any ﬁne-tuning, and achieve the superior results, which indicate its strong generalizability. The source code and trained models will be publicly available upon the acceptance.
The development of a satellite‐based insolation model for the tropical western Pacific Ocean

Concurrent pyranometer and GMS (Japanese Geostationary Meteorological Satellite) data were collected for the western tropical Pacific during a 21-month period in 1987 and 1988. Data from 10 pyranometer stations in 1987 were used to build a satellite insolation model. Such processes as surface albedo, narrow band to broad band conversion, and depletion by water vapour and aerosols were taken into account. The model was then tested with an independent pyranometer data set comprising 9 months in 1988. Results show that aerosol depletion is site-specific, with typical long-term values ranging from 19·8 per cent of the extraterrestrial value for Kuala Lampur, to 4·8 per cent for Willis Island. Monthly insolation may be mapped over the oceanic regions of the study area assuming that the Willis Island aerosol depletion values apply. The model gave root-mean-square (RMS) errors of 14·08 W m−2 when tested against all monthly average pyranometer data for 1988. Corresponding RMS errors are 11·67 W m−2 when only Willis Island data are used. Sources of errors are discussed, as are suggestions for future research.
Integrating machine learning techniques and high-resolution imagery to generate GIS-ready information for urban water consumption studies

Urban sprawl driven by shifts in tourism development produces new suburban landscapes of water consumption on Mediterranean coasts. Golf courses, ornamental, 'Atlantic' gardens and swimming pools are the most striking artefacts of this transformation, threatening the local water supply systems and exacerbating water scarcity. In the face of climate change, urban landscape irrigation is becoming increasingly important from a resource management point of view. This paper adopts urban remote sensing towards a targeted mapping approach using machine learning techniques and highresolution satellite imagery (WorldView-2) to generate GIS-ready information for urban water consumption studies. Swimming pools, vegetation and – as a subgroup of vegetation – turf grass are extracted as important determinants of water consumption. For image analysis, the complex nature of urban environments suggests spatial-spectral classification, i.e. the complementary use of the spectral signature and spatial descriptors. Multiscale image segmentation provides means to extract the spatial descriptors – namely object feature layers – which can be concatenated at pixel level to the spectral signature. This study assesses the value of object features using different machine learning techniques and amounts of labeled information for learning. The results indicate the benefit of the spatial-spectral approach if combined with appropriate classifiers like tree-based ensembles or support vector machines, which can handle high dimensionality. Finally, a Random Forest classifier was chosen to deliver the classified input data for the estimation of evaporative water loss and net landscape irrigation requirements.
DEPTH INTERVALS AND TOPSOIL MOISTURE MEASUREMENT WITH THE NEUTRON DEPTH PROBE

From measurements and calculations using a depth probe, the relative response is estimated for soil layers of different thicknesses. For a 10 cm thick layer of soil with the instrument centre in the middle, about 40 to 70 per cent of the response is caused by this particular layer at a water content of 8 to 40 per cent by volume. For a 20 cm layer the response for similar conditions is about 65 to 85 per cent. A special calibration function for the upper (0–20 cm) soil layer is demonstrated for the neutron depth probe. It is found that the water content in this soil layer can be estimated in this way with about the same degree of accuracy as can be expected by conventional methods.
Investigating dynamical complexity in the magnetosphere using various entropy measures

[1] The complex system of the Earth's magnetosphere corresponds to an open spatially extended nonequilibrium (input-output) dynamical system. The nonextensive Tsallis entropy has been recently introduced as an appropriate information measure to investigate dynamical complexity in the magnetosphere. The method has been employed for analyzing Dst time series and gave promising results, detecting the complexity dissimilarity among different physiological and pathological magnetospheric states (i.e., prestorm activity and intense magnetic storms, respectively). This paper explores the applicability and effectiveness of a variety of computable entropy measures (e.g., block entropy, Kolmogorov entropy, T complexity, and approximate entropy) to the investigation of dynamical complexity in the magnetosphere. We show that as the magnetic storm approaches there is clear evidence of significant lower complexity in the magnetosphere. The observed higher degree of organization of the system agrees with that inferred previously, from an independent linear fractal spectral analysis based on wavelet transforms. This convergence between nonlinear and linear analyses provides a more reliable detection of the transition from the quiet time to the storm time magnetosphere, thus showing evidence that the occurrence of an intense magnetic storm is imminent. More precisely, we claim that our results suggest an important principle: significant complexity decrease and accession of persistency in Dst time series can be confirmed as the magnetic storm approaches, which can be used as diagnostic tools for the magnetospheric injury (global instability). Overall, approximate entropy and Tsallis entropy yield superior results for detecting dynamical complexity changes in the magnetosphere in comparison to the other entropy measures presented herein. Ultimately, the analysis tools developed in the course of this study for the treatment of Dst index can provide convenience for space weather applications.
Smartphone-Based Vehicle Telematics: A Ten-Year Anniversary

Just as it has irrevocably reshaped social life, the fast growth of smartphone ownership is now beginning to revolutionize the driving experience and change how we think about automotive insurance, vehicle safety systems, and traffic research. This paper summarizes the first ten years of research in smartphone-based vehicle telematics, with a focus on user-friendly implementations and the challenges that arise due to the mobility of the smartphone. Notable academic and industrial projects are reviewed, and system aspects related to sensors, energy consumption, and human–machine interfaces are examined. Moreover, we highlight the differences between traditional and smartphone-based automotive navigation, and survey the state of the art in smartphone-based transportation mode classification, vehicular ad hoc networks, cloud computing, driver classification, and road condition monitoring. Future advances are expected to be driven by improvements in sensor technology, evidence of the societal benefits of current implementations, and the establishment of industry standards for sensor fusion and driver assessment.
Cancer-Net BCa: Breast Cancer Pathologic Complete Response Prediction using Volumetric Deep Radiomic Features from Synthetic Correlated Diffusion Imaging

Breast cancer is the second most common type of cancer in women in Canada and the United States, representing over 25% of all new female cancer cases. Neoadjuvant chemotherapy treatment has recently risen in usage as it may result in a patient having a pathologic complete response (pCR), and it can shrink inoperable breast cancer tumors prior to surgery so that the tumor becomes operable, but it is difﬁcult to predict a patient’s pathologic response to neoadjuvant chemotherapy. In this paper, we investigate the efﬁcacy of leveraging learnt volumetric deep features from a newly introduced magnetic resonance imaging (MRI) modality called synthetic correlated diffusion imaging (CDI s ) for the purpose of pCR prediction. More speciﬁcally, we leverage a volumetric convolutional neural network to learn volumetric deep radiomic features from a pre-treatment cohort and construct a predictor based on the learnt features using the post-treatment response. As the ﬁrst study to explore the utility of CDI s within a deep learning perspective for clinical decision support, we evaluated the proposed approach using the ACRIN-6698 study against those learnt using gold-standard imaging modalities, and found that the proposed approach can provide enhanced pCR prediction performance and thus may be a useful tool to aid oncologists in improving recommendation of treatment of patients. Subsequently, this approach to leverage volumetric deep radiomic features (which we name Cancer-Net BCa) can be further extended to other applications of CDI
Experimentation of an Expectation Maximization Algorithm for Probabilistic Logic Programs

Statistical Relational Learning and Probabilistic Inductive Logic Programming are two emerging fields that use representation languages able to combine logic and probability. In the field of Logic Programming, the distribution semantics is one of the prominent approaches for representing uncertainty and underlies many languages such as ICL, PRISM, ProbLog and LPADs. Learning the parameters for such languages requires an Expectation Maxi-mization algorithm since their equivalent Bayesian networks contain hidden variables. EMBLEM (EM over BDDs for probabilistic Logic programs Efficient Mining) is an EM algorithm for languages following the distribution semantics that computes expectations directly on the Binary Decision Diagrams that are built for inference. In this paper we present experiments comparing EMBLEM with LeProbLog, Alchemy, CEM, RIB and LFI-ProbLog on six real world datasets. The results show that EMBLEM is able to solve problems on which the other systems fail and it often achieves significantly higher areas under the Precision Recall and the ROC curves in a similar time.
Grace++: Loss-Resilient Real-Time Video Communication under High Network Latency

In real-time videos, resending any packets, especially in networks with high latency, can lead to stuttering, poor video quality, and user frustration. Despite extensive research, current real-time video systems still use redundancy to handle packet loss, thus compromising on quality in the the absence of packet loss. Since predicting packet loss is challenging, these systems only enhance their resilience to packet loss after it occurs, leaving some frames insufficiently protected against burst packet losses. They may also add too much redundancy even after the packet loss has subsided. We present Grace++ , a new real-time video communication system. With Grace++ , (i) a video frame can be decoded, as long as any non-empty subset of its packets are received, and (ii) the quality gracefully degrades as more packets are lost, and (iii) approximates that of a standard codec (like H.265) in absence of packet loss. To achieve this, Grace++ encodes and decodes frames by using neural networks (NNs). It uses a new packetization scheme that makes packet loss appear to have the same effect as randomly masking (zero-ing) a subset of elements in the NN-encoded output, and the NN encoder and decoder are specially trained to achieve decent quality if a random subset of elements in the NN-encoded output are masked. Using various test videos and real network traces, we show that the quality of Grace++ is slightly lower than H.265 when no packets are lost, but significantly reduces the 95th percentile of frame delay (be-tween encoding
PAGAR: Imitation Learning with Protagonist Antagonist Guided Adversarial Reward

Imitation learning (IL) algorithms often rely on inverse reinforcement learning (IRL) to ﬁrst learn a reward function from expert demonstrations. However, IRL can suffer from identiﬁability issues and there is no performance or efﬁciency guarantee when training with the learned reward function. In this paper, we propose Protagonist Antagonist Guided Adversarial Reward (PAGAR), a semi-supervised learning paradigm for designing rewards for policy training. PAGAR employs an iterative adversarially search for reward functions to maximize the performance gap between a protagonist policy and an antagonist policy. This allows the protagonist policy to perform well across a set of possible reward functions despite the identiﬁability issue. When integrated with IRL-based IL, PAGAR guarantees that the trained policy succeeds in the underlying task. Furthermore, we introduce a practical on-and-off policy approach to IL with PAGAR. This approach maximally utilizes samples from both the protagonist and antagonist policies for the optimization of policy and reward functions. Experimental results demonstrate that our algorithm achieves higher training efﬁciency compared to state-of-the-art IL/IRL baselines in standard settings, as well as zero-shot learning from demonstrations in transfer environments.
Counterfactual Risk Assessments under Unmeasured Confounding

Statistical risk assessments inform consequential decisions, such as pretrial release in criminal justice and loan approvals in consumer finance, by counterfactually predicting an outcome under a proposed decision (e.g., would the applicant default if we approved this loan? ). There may, however, have been unmeasured confounders that jointly affected decisions and outcomes in the historical data. We propose a mean outcome sensitivity model that bounds the extent to which unmeasured confounders could affect outcomes on average. The mean outcome sensitivity model partially identifies the conditional likelihood of the outcome under the proposed decision and therefore also partially identifies popular measures of predictive performance and disparities. We derive their identified sets and develop procedures for the confounding-robust learning and evaluation of statistical risk assessments. We propose 1) a nonparametric regression procedure for the bounds on the conditional likelihood of the outcome under the proposed decision and 2) estimators for the bounds on predictive performance and disparities. Applying our methods to a real-world credit-scoring task from a large Australian financial institution, we show how varying assumptions on unmeasured confounding lead to substantive changes in the credit score’s predictions and evaluations of its predictive disparities.
PIT: a practical mutation testing tool for Java (demo)

Mutation testing introduces artificial defects to measure the adequacy of testing. In case candidate tests can distinguish the behaviour of mutants from that of the original program, they are considered of good quality -- otherwise developers need to design new tests. While, this method has been shown to be effective, industry-scale code challenges its applicability due to the sheer number of mutants and test executions it requires. In this paper we present PIT, a practical mutation testing tool for Java, applicable on real-world codebases. PIT is fast since it operates on bytecode and optimises mutant executions. It is also robust and well integrated with development tools, as it can be invoked through a command line interface, Ant or Maven. PIT is also open source and hence, publicly available at \url{http://pitest.org/}
Fantômas: Evaluating Reversibility of Face Anonymizations Using a General Deep Learning Attacker

Biometric data is a rich source of information that can be used to identify individuals and infer private information about them. To mitigate this privacy risk, anonymization techniques employ transformations on clear data to obfuscate sensitive information, all while retaining some utility of the data. Albeit published with impressive claims, they sometimes are not evaluated with convincing methodology. We hence are interested to which extent recently suggested anonymization techniques for obfuscating facial images are effective. More speciﬁcally, we test how easily they can be automatically reverted, to estimate the privacy they can provide. Our approach is agnostic to the anonymization technique as we learn a machine learning model on the clear and corresponding anonymized data. We ﬁnd that 10 out of 14 tested face anonymization techniques are at least partially reversible, and six of them are at least highly reversible.
Operating System I/O Speculation: How Two Invocations Are Faster Than One

We present an in-kernel disk prefetcher which uses speculative execution to determine what data an application is likely to require in the near future. By placing our design within the operating system, we provide several benefits compared to the previous application-level design. Not only is our system easier to implement and deploy, but by handling page faults as well as traditional file-access methods we are able to apply speculative execution to swapping applications, which often spend the majority of their execution time fetching non-resident pages. We also present two new OS features that further improve the performance of speculative execution for applications that have large page tables and working sets. These are a fast method for synchronizing an errant speculative process with normal execution, and a modified form of copy-on-write which preserves application semantics without delaying normal execution. Finally, by leveraging OS knowledge about memory usage and contention, we design a mechanism for estimating and limiting the memory overhead of speculative executions. Our implementation for Linux 2.4. 8 provides benefits of up to 60% for a wide range of explicit-I/O and swapping applications. Our results show that our support mechanisms for swapping applications provide significant performance benefits, and in some cases prevent speculative execution from hurting performance. We further demonstrate that our memory control mechanism effectively limits speculative overheads while allowing beneficial speculative executions to proceed un-
ARFA: a program for annotating bacterial release factor genes, including prediction of programmed ribosomal frameshifting

UNLABELLED Correct annotation of genes encoding release factors in bacterial genomes is often complicated by utilization of +1 programmed ribosomal frameshifting during synthesis of release factor 2, RF2. In the absence of robust computational approaches for predicting ribosomal frameshifting, the success of proper annotation depends on annotators' familiarity with this phenomenon. Here we describe a novel computer tool that allows automatic discrimination of genes encoding class-I bacterial release factors, RF1, RF2 and RFH. Most usefully, this program identifies and automatically annotates +1 frameshifting in RF2 encoding genes. Comparison of ARFA performance with existing annotations of bacterial genomes revealed that only 20% of RF2 genes utilizing ribosomal frameshifting during their expression are annotated correctly. AVAILABILITY The PHP based web interface of ARFA and the source code are located at http://recode.genetics. utah.edu/arfa
MAGOS: multiple alignment and modelling server

UNLABELLED MAGOS is a web server allowing automated protein modelling coupled to the creation of a hierarchical and annotated multiple alignment of complete sequences. MAGOS is designed for an interactive approach of structural information within the framework of the evolutionary relevance of mined and predicted sequence information. AVAILABILITY The web server is freely available at http://pig-pbil.ibcp. fr/magos.
YAAB (Yet another AST browser): using OCL to navigate ASTs

In the last decades several tools and environments defined and introduced languages for querying, navigating and transforming abstract syntax trees. These environments were meant to support software maintenance, reengineering and program comprehension activities. Instead of introducing a new language, this paper proposes to adopt the Object Constraint Language (OCL) to express queries over an object model representing the abstract syntax tree of the code to be analyzed. OCL is part of the UML lingua franca and thus several advantages can be readily obtained. Central to the idea is to shift the analysis paradigm from a tree-based to an object-oriented paradigm, and to provide a meta-model decoupling the query language from the target language. This paper presents the current status in implementing an OCL interpreter with the ability of querying an object model representing the abstract syntax tree, as well as some interesting applications, such as extracting software metrics or computing clones.