Miracle-dz/UniFAR-FTUs huggingface dataset:
TOWARD CONTINUOUS DELIVERY OF A NONTRIVIAL DISTRIBUTED SOFTWARE SYSTEM ∗

The MeerKAT Control and Monitoring(CAM) solution is a mature software system that has undergone multiple phases of construction and expansion. It is a distributed system with a run-time environment of 15 logical nodes featuring dozens of interdependent, short-lived processes that interact with a number of long-running services. This presents a challenge for the development team to balance operational goals with the continued discovery and development of useful enhancements for its users (astronomers, telescope operators). Continuous Delivery is a set of practices designed to always keep software in a releasable state. It employs the discipline of release engineering to optimise the process of taking changes from source control to production. In this paper, we review the current path to production (build, test and release) of CAM, identify shortcomings and introduce approaches to support further incremental development of the system. By implementing patterns such as deployment pipelines* and immutable release candidates we hope to simplify the release process and demonstrate increased throughput of changes, quality and stability in the future.
Fear memory-associated synaptic and mitochondrial changes revealed by deep learning-based processing of electron microscopy data

Serial section electron microscopy (ssEM) can provide comprehensive 3D ultrastructural information of the brain with exceptional computational cost. Targeted reconstruction of subcellular structures from ssEM datasets is less computationally demanding but still highly informative. We thus developed a Region-CNN-based deep learning method to identify, segment, and reconstruct synapses and mitochondria to explore the structural plasticity of synapses and mitochondria in the auditory cortex of mice subjected to fear conditioning. Upon reconstructing over 135,000 mitochondria and 160,000 synapses, we found that fear conditioning significantly increases the number of mitochondria but decreases their size, and promotes the formation of multi-contact synapses comprising a single axonal bouton and multiple postsynaptic sites from different dendrites. Modeling indicates that such multi-contact configuration increases the information storage capacity of new synapses by over 50%. With high accuracy and speed in reconstruction, our method yields structural and functional insight into cellular plasticity associated with fear learning.
Exploring the Distributed Max Flow Algorithm for IoT Networks

— For any kind of IoT network, we want maximum flow from source to destination. It is a classical optimization problem. In case of the static network, we can easily implement the classical max-flow algorithm. However, in the distributed environment, we can’t implement classical max-flow algorithm directly. The main challenges are any node can join or leave at any time and node mobility. To get the max-flow in the distributed network with minimum overhead, we investigate the flooding method considering the three different types of distributed network environment based on the well-known classical max-flow algorithm such as Dinic’s, Ford-Fulkerson and Edmond-Karp algorithm. However, our implementation is not restricted to any kind of particular max-flow algorithm.
The effect of selecting for different behavioral traits on the evolved gaits of modular robots

Moving around in the environment is a fundamental skill for mobile robots. This makes the evolution of an appropriate gait, a pivotal problem in evolutionary robotics. Whereas the majority of the related studies concern robots with predeﬁned modular or legged morphologies and locomotion speed as the optimization objective, here we investigate robots with evolv-able morphologies and behavioral traits included in the ﬁtness function. To analyze the effects we consider morphological as well as behavioral features of the evolved robots. To this end, we introduce novel behavioral measures that describe how the robot locomotes and look into the trade-off between them. Our main goal is to gain insights into differences in possible gaits of modular robots and to provide tools to steer evolution towards objectives beyond ‘simple’ speed.
Political News Propagation Headline Matching A Deep Learning-Based Short Text Matching Approach

Russian computational propaganda techniques have evolved to include the use of English-language news sites that serve as proxies for Russian propaganda. Information sharing and content propagation studies currently examine exact article copies but do not capture key semantics employed by proxy sites. We propose a short text matching deep learning (DL)-based model that seeks to capture the semantic similarity between article headlines in different media ecosystems. Our model captures named entities, word co-occurrences, and phrase sequences, and represents them in a propagandist context to capture both lexical and semantic similarities between article headlines. We benchmark our model against DL-based models from several architecture categories: Deep Neural Networks, Convolutional Neural Networks, Long-Short-Term-Memory Networks, and Attention-based architectures. Our proposed model consistently outperforms benchmark models by statistically significant margins across two datasets. Our results indicate that using several representations of text, combined with contextualization of content, can identify semantically similar article headlines with greater precision than some prevailing models.
Performance Analysis of Filtered OFDM Based Downlink and Uplink NOMA System over Nakagami-m Fading Channel

Efficient consumption of available resources and fulfillment of increasing demands are the two main challenges which are addressed by exploring advanced multiple access schemes along with efficient modulation techniques. To this end, non-orthogonal multiple access (NOMA) is discussed as a promising scheme for future 5G traffic. NOMA enables the users to share same resource block, permitting certain level of interference. In this paper, we propose filtered OFDM (F-OFDM) as a transmission waveform for NOMA systems, as it offers all the advantages of OFDM with the additional provision of sub-band filtering to satisfy the diverse services of the users. We examine F-OFDM in both downlink and uplink NOMA systems. Error-related performances of both downlink and uplink F-OFDM NOMA systems are analyzed and compared with conventional OFDM NOMA system over Nakagami-m fading channel. The results show that the error performance of F-OFDM NOMA is better than that of OFDM NOMA. An improvement of about 2 dB and 1 dB in bit error rate is achieved in downlink and uplink F-OFDM NOMA, respectively. Monte Carlo simulations are conducted for different values of fading parameter m, supporting the obtained analytical results
Actionable Insights in Multivariate Time-series for Urban Analytics

Multivariate time-series data are gaining popularity in various urban applications, such as emergency management, public health, etc. Segmentation algorithms mostly focus on identifying discrete events with changing phases in such data. For example, consider a power outage scenario during a hurricane. Each time-series can represent the number of power failures in a county for a time period. Segments in such time-series are found in terms of different phases, such as, when a hurricane starts, counties face severe damage, and hurricane ends. Disaster management domain experts typically want to identify the most affected counties (time-series of interests) during these phases. These can be effective for retrospective analysis and decision-making for resource allocation to those regions to lessen the damage. However, getting these actionable counties directly (either by simple visualization or looking into the segmentation algorithm) is typically hard. Hence we introduce and formalize a novel problem RaTSS (Rationalization for time-series segmentation) that aims to find such time-series (rationalizations), which are actionable for the segmentation. We also propose an algo-rithm Find-RaTSS to find them for any black-box segmentation. We show Find-RaTSS outperforms non-trivial baselines on generalized synthetic and real data, also provides actionable insights in multiple urban domains, especially disasters and public health.
Seismic performance analysis of self-centering segment piers with mortise-tenon shear connectors based on cyclic pseudo-static test

In the past decades, more and more precast piers have been used in actual engineering, which brings convenience in construction and standardization of production. However, precast piers still have some shortcomings, such as shear slip between segments and poor energy dissipation. Thus, a self-centering mortise-tenon segmental bridge pier was proposed in this paper. To research the seismic performance of the self-centering mortise-tenon segment piers, three scaled model piers with their scale ratio as 1:3 were designed and prefabricated, namely as a cast-in-place model pier (CP) and 2 self-centering mortise-tenon segment model piers (designated as MTSP1 and MTSP2) with the initial pretension of 1302 MPa and 1488 MPa, respectively. The cyclic pseudo-static tests of the three piers were carried out. The comparison analysis was made respecting to the test results including the damage form, hysteretic characteristics, skeleton curve, energy dissipation capacity, residual displacement and equivalent stiffness of the three specimens. It was shown that MTSP specimens had higher horizontal bearing capacity. While the yield load, ultimate load, displacement ductility coefficient and initial stiffness of MTSP1 and MTSP2 were higher than those of CP. The MTSP had better performance considering the above aspects. The mean value of the residual displacement of MTSP2 was lower than that of CP 21.14 % and 29. 72 % respectively, as the drift ratio reaching 5 %. The MTSP specimens had better self-centering capacity due to the increased pretension stress which reducing the residual displacement greatly. The energy dissipation reinforcements improved the energy dissipation capacity of the segmental assembled piers. Based on the ABAQUS model, the numerical simulation was carried out and compared with the experimental data. MTSP had great energy dissipation capacity and self-centering capacity. It was suggested that MTSP should be used to make up for the deficiency of segmental assembled pier and improve the seismic performance of segmental assembled pier.
Parallel and Distributed Structured SVM Training

Structured Support Vector Machines (structured SVMs) are a fundamental machine learning algorithm, and have solid theoretical foundation and high effectiveness in applications such as natural language parsing and computer vision. However, training structured SVMs is very time-consuming, due to the large number of constraints and inferior convergence rates, especially for large training data sets. The high cost of training structured SVMs has hindered its adoption to new applications. In this article, we aim to improve the efficiency of structured SVMs by proposing a parallel and distributed solution (namely FastSSVM) for training structured SVMs building on top of MPI and OpenMP. FastSSVM exploits a series of optimizations (e.g., optimizations on data storage and synchronization) to efficiently use the resources of the nodes in a cluster and the cores of the nodes. Moreover, FastSSVM tackles the large constraint set problem by batch processing and addresses the slow convergence challenge by adapting stop conditions based on the improvement of each iteration. We theoretically prove that our solution is guaranteed to converge to a global optimum. A comprehensive experimental study shows that FastSSVM can achieve at least four times speedup over the existing solutions, and in some cases can achieve two to three orders of magnitude speedup.
The Effect of Hyperparameter Tuning on the Comparative Evaluation of Unsupervised Anomaly Detection Methods

Anomaly detection aims at finding observations in a dataset that do not conform to expected behavior. Researchers have proposed a large variety of anomaly detection algorithms and their performance is greatly affected by how a user sets each algorithm’s hyperparameters. However, the anomaly detection literature does not agree on how to set these hyperparameters when experimentally comparing different algorithms. Most papers compare either performance using “default” settings, or maximal performance under optimal settings. In this paper, we argue that both strategies fail to capture what practitioners are actually interested in: how well does the algorithm perform in practice? They are either too pessimistic, assuming no tuning, or unrealistically optimistic, assuming optimal tuning; and they often result in methodologically unsound and irre-producible comparisons between algorithms. We therefore propose to use a small validation set to tune an anomaly detector’s hyperpa-rameters on a per dataset basis. We argue this is realistic, striking the balance between keeping the cost of acquiring labeled data low and selecting the hyperparameters in a fair, sound, and reproducible manner. We provide a theoretical lower bound on the validation set size based on probability of an anomaly detector achieving a higher area under the ROC curve than a random detector. Using a benchmark of 16 datasets, we experimentally show that different
Quantitative Analysis of EEG Slow Wave Activity Based on MinPeakProminence Method

Persistent, unchanging, and non-reactive focal or generalized abnormal Slow Wave (SW) activities in an awake adult patient are examined pathologically. Although these waves in Electroencephalogram (EEG) are much less prominent than transient activities in some areas, it is not possible to understand them easily by looking at the EEG. For this reason, reliable computer programs that can sort out Slow Waves (SWs) correctly are needed. In this study, a new method based on MinPeakProminence that can detect abnormal SW activities was developed. To test the performance of the study, the data collected from Selcuk University Hospital (22 subjects - epilepsy and various neurological diseases) and Bonn Hospital (only normal A dataset) were used. Various statistical performance measurement methods were used to search the results. The results of this analysis revealed that the classification success, sensitivity and specificity values obtained with the SUH dataset were 96.5%, 93.3% and 96.1%, respectively. In the results of the experiments made with the Bonn dataset, 100% classification success was achieved. Besides, according to the analyses, it was found that SWs are frequently seen in the posterior regions of the brain, especially in the parietal and occipital regions in the SUH dataset.
Towards NLU Model Robustness to ASR Errors at Scale

In a large-scale Spoken Language Understanding system, Natural Language Understanding (NLU) models are typically decoupled, i.e, trained and updated independently, from the upstream Automatic Speech Recognition (ASR) system that provides textual hypotheses for the user’s voice signal as input to NLU. Such ASR hypotheses often contain errors causing severe performance degradation as the downstream NLU models are trained on clean human-annotated transcripts. Furthermore, as the ASR model updates, the error distribution drifts making it even harder for NLU models to recover and making manual annotation of erroneous ASR hypotheses impractical. In this paper, we investigate data-efficient techniques applicable to a wide variety of NLU models employed in large-scale production environments to make them robust to ASR errors. We measure the effectiveness of such techniques as both the ASR error distribution and usage patterns change over time.
EdgeFlow—Developing and Deploying Latency-Sensitive IoT Edge Applications

Demanding latency-sensitive IoT applications have stringent requirements, such as low latency, better privacy, and security. To meet such requirements, researchers proposed a new paradigm, i.e., edge computing. Edge computing consists of distributed computational resources and enables the execution of IoT applications closer to the edge of the network. However, the distributed nature of this paradigm makes the application deployment and development process more challenging since the developer must divide the application’s functionality into multiple parts, assigning for each a set of requirements. As a result, the developer must: 1) define the application’s requirements and validate them at design time and 2) find a deployment strategy on the target edge computing platform. In this article, we propose EdgeFlow, a new IoT framework capable of assisting the developer in the application development process. Specifically, we introduce a methodology for latency-sensitive IoT applications development and deployment, consisting of three different stages, i.e., the development, validation, and deployment. To this end, we propose an extension of the flow-based programming paradigm with new timing requirements and provide a resource allocation technique to assist with the deployment and validation of latency-sensitive IoT applications. Finally, we evaluate EdgeFlow by: 1) presenting the application development methodology and 2) performing a quantitative evaluation demonstrating our resource allocation technique’s capabilities to find feasible and optimal deployment strategies. The experimental results illustrate the effectiveness of our methodology to assist the developer throughout the entire application development process.
Improved Mapping of Information Processing in the Brain During Naturalistic Experiments

Modern cognitive neuroscience has focused on mapping what information is processed both where and when in the brain as an initial step towards understanding how information is processed by the brain. Brain mapping is most commonly performed using the linear encoding model a computational tool that can be used to align brain measurements with numerical stimulus representations that can be informative of the underlying neural processes. But as we move towards more naturalistic experimental settings and start to work with increasingly complex stimulus representations, it becomes important to test the boundaries of what we are able to learn using existing computational tools so we can adjust them accordingly. In this work, we examine two potential limitations of the linear encoding model. First, we look at a specific problem setting that shows that the existing framework does not always allow us to disentangle where specific stimulus-related information is processed in the brain. As an initial step towards addressing this issue, we propose a new framework that can be used to group together areas of the brain that are responsible for processing similar information. Second, we explore whether using a more expressive, nonlinear encoding model can allow us to better align the internal representational spaces of artificial neural networks, from which we can derive more complex and potentially more informative stimulus representations, with those of the brain. Overall, our proposed extensions to conventional encoding approaches can help neuroscientists unlock a richer and more complete space of brain mappings.
Two-Stage Voice Application Recommender System for Unhandled Utterances in Intelligent Personal Assistant

Intelligent personal assistants (IPA) enable voice applications that facilitate people's daily tasks. However, due to the complexity and ambiguity of voice requests, some requests may not be handled properly by the standard natural language understanding (NLU) component. In such cases, a simple reply like “Sorry, I don't know” hurts the user's experience and limits the functionality of IPA. In this paper, we propose a two-stage shortlister-reranker recommender system to match third-party voice applications (skills) to unhandled utterances. In this approach, a skill shortlister is proposed to retrieve candidate skills from the skill catalog by calculating both lexical and semantic similarity between skills and user requests. We also illustrate how to build a new system by using observed data collected from a baseline rule-based system, and how the exposure biases can generate discrepancy between offline and human metrics. Lastly, we present two relabeling methods that can handle the incomplete ground truth, and mitigate exposure bias. We demonstrate the effectiveness of our proposed system through extensive offline experiments. Furthermore, we present online A/B testing results that show a significant boost on user experience satisfaction.
Multi-Source Transformer Architectures for Audiovisual Scene Classification

In this technical report, the systems we submitted for subtask 1B of the DCASE 2021 challenge, regarding audiovisual scene classiﬁcation, are described in detail. They are essentially multi-source transformers employing a combination of auditory and visual features to make predictions. These models are evaluated utilizing the macro-averaged multi-class cross-entropy and accuracy metrics. In terms of the macro-averaged multi-class cross-entropy, our best model achieved a score of 0.620 on the validation data. This is slightly better than the performance of the baseline system (0.658). With regard to the accuracy measure, our best model achieved a score of 77.1% on the validation data, which is about the same as the performance obtained by the baseline system (77.0%).
OPINION Brillouin microscopy: assessing ocular tissue biomechanics

ow naded rom httpsournals.lw w om /cophthalm oogy by hD M fePH KazEoum tQ fN 4a+khEZgbsIH o4XM hC yw C X1AW YQ /IlQ rH D 3z+H cjN BC nrpeeM pD 3N 8/1Z+Q W C q/6pvW R C FQ = on 051/2018 Downloadedfromhttps://journals.lww. com/co-ophthalmologybyBhDMf5ePHKav1zEoum1tQfN4a+kJLhEZgbsIHo4XMi0hCywCX1AWnYQp/IlQrHD30z+H3cjNBCnr5peeMpD3N8/Z1Z+QWCq/TB6pvWRC5FQ=on05/21/2018 CE: Tripti; ICU/290405; Total nos of Pages: 7; ICU 290405
Detecting Node.js prototype pollution vulnerabilities via object lookup analysis

Prototype pollution is a type of vulnerability specific to prototype-based languages, such as JavaScript, which allows an adversary to pollute a base object’s property, leading to a further consequence such as Denial of Service (DoS), arbitrary code execution, and session fixation. On one hand, the only prior work in detecting prototype pollution adopts dynamic analysis to fuzz package inputs, which inevitably has code coverage issues in triggering some deeply embedded vulnerabilities. On the other hand, it is challenging to apply state-of-the-art static analysis in detecting prototype pollution because of the involvement of prototype chains and fine-grained object relations including built-in ones. In this paper, we propose a flow-, context-, and branch-sensitive static taint analysis tool, called ObjLupAnsys, to detect prototype pollution vulnerabilities. The key of ObjLupAnsys is a so-called object lookup analysis, which gradually expands the source and sink objects into big clusters with a complex inner structure by performing targeted object lookups in both clusters so that a system built-in function can be redefined. Specifically, at the source cluster, ObjLupAnsys proactively creates new object properties based on how the target program uses the initial source object; at the sink cluster, ObjLupAnsys assigns property values in object lookups to decrease the number of object lookups to reach a system built-in function. We implemented an open-source tool and applied it for the detection of prototype pollution among Node.js packages. Our evaluation shows that ObjLupAnsys finds 61 zero-day, previously-unknown, exploitable vulnerabilities as opposed to 18 by the state-of-the-art dynamic fuzzing tool and three by a state-of-the-art static analysis tool that is modified to detect prototype pollution. To date, 11 vulnerable Node.js packages are assigned with CVE numbers and five have already been patched by their developers. In addition, ObjLupAnsys also discovered seven applications or packages including a real-world, online website, which are indirectly vulnerable due to the inclusion of vulnerable packages found by ObjLupAnsys.