Miracle-dz/UniFAR-FTUs huggingface dataset:
An intelligent strategy for faults location in distribution networks with distributed generation

The exact location of faults in the electrical distribution systems is a problem that affects not only the users, but also the companies providing the electric service. With greater time invested in this period, the losses due to unbilled energy and inconvenience to users increases, thus decreasing the quality of service. One of the causes of the growth in time is the misunderstanding that might exist in the localization systems that act under the presence of distributed generation sources in the distribution networks. In this sense, the present research develops an intelligent diagnosis of faults in distribution systems with distributed generation. Three stages are defined: Identification of the type of fault, the location of the zone, and the exact point of fault. A mixed method based on artificial intelligence and mathematical algorithms is applied. Eleven different types of faults that can occur in a distribution system are considered with six different values of fault resistances ranging from 5 to 30 . The errors found are less than 2% in the location of the fault point with robustness to variations in the load and the penetration of distributed generation.
Chi-MIC-share: a new feature selection algorithm for quantitative structure–activity relationship models

Quantitative structure–activity relationship models are used in toxicology to predict the effects of organic compounds on aquatic organisms. Common filter feature selection methods use correlation statistics to rank features, but this approach considers only the correlation between a single feature and the response variable and does not take into account feature redundancy. Although the minimal redundancy maximal relevance approach considers the redundancy among features, direct removal of the redundant features may result in loss of prediction accuracy, and cross-validation of training sets to select an optimal subset of features is time-consuming. In this paper, we describe the development of a feature selection method, Chi-MIC-share, which can terminate feature selection automatically and is based on an improved maximal information coefficient and a redundant allocation strategy. We validated Chi-MIC-share using three environmental toxicology datasets and a support vector regression model. The results show that Chi-MIC-share is more accurate than other feature selection methods. We also performed a significance test on the model and analyzed the single-factor effects of the reserved descriptors.
GrandSLAm: Guaranteeing SLAs for Jobs in Microservices Execution Frameworks

The microservice architecture has dramatically reduced user effort in adopting and maintaining servers by providing a catalog of functions as services that can be used as building blocks to construct applications. This has enabled datacenter operators to look at managing datacenter hosting microservices quite differently from traditional infrastructures. Such a paradigm shift calls for a need to rethink resource management strategies employed in such execution environments. We observe that the visibility enabled by a microservices execution framework can be exploited to achieve high throughput and resource utilization while still meeting Service Level Agreements, especially in multi-tenant execution scenarios. In this study, we present GrandSLAm, a microservice execution framework that improves utilization of datacenters hosting microservices. GrandSLAm estimates time of completion of requests propagating through individual microservice stages within an application. It then leverages this estimate to drive a runtime system that dynamically batches and reorders requests at each microservice in a manner where individual jobs meet their respective target latency while achieving high throughput. GrandSLAm significantly increases throughput by up to 3x compared to the our baseline, without violating SLAs for a wide range of real-world AI and ML applications.
Using Randomness to Improve Robustness of Tree-Based Models Against Evasion Attacks

Machine learning models have been widely used in security applications. However, it is well-known that adversaries can adapt their attacks to evade detection. There has been some work on making machine learning models more robust to such attacks. However, one simple but promising approach called randomization is under-explored. In addition, most existing works focus on models with differentiable error functions while tree-based models do not have such error functions but are quite popular because they are easy to interpret. This paper proposes a novel randomization-based approach to improve robustness of tree-based models against evasion attacks. The proposed approach incorporates randomization into both model training time and model application time (meaning when the model is used to detect attacks). We also apply this approach to random forest, an existing ML method which already has incorporated randomness at training time but still often fails to generate robust models. We proposed a novel weighted-random-forest method to generate more robust models and a clustering method to add randomness at model application time. We also proposed a theoretical framework to provide a lower bound for adversaries’ effort. Experiments on intrusion detection and spam filtering data show that our approach further improves robustness of random-forest method.
Load Balancing of Distributed Actors in an Asynchronous Message Processing Boundary

Reactive Systems, especially in the Big Data Era, are undergoing a revolution. The increasing interest is due to their responsiveness, elasticity, resiliency and message-driven trait. In particular, a reactive message-driven system has one core feature asynchronous communication assuring a non-blocking behavior. A particular case of such systems is the use of actors to assure encapsulation and support fast decisions, failure handling and progess. The Actor Model meets the requirements for buiding a highly conccurent, scalable, distributed system, and Akka's tools provide the means to build it. However, to the best of our knowledge the effect of intra-actor asynchronous message processing on the routing strategies and the load balancing issue has not been addressed in the literature yet. The aim of this study is to analyse the behavior of the routers and routees over the existing routing strategies when each routee processes its messages asynchronously. Moreover, we study how the load is balanced among the routees and propose our own routing strategy that can reduce the processing time overhead by considering the complexity of the messages. For that, we build a distributed system to offline process massive trajectories, initially stored in HDFS, with Akka, the Play Framework, and MongoDB. Specifically, we leverage the Geolife projectâĂrŹs GPS trajectory dataset.
Facial age analysis using an absorptive rough laminar light scattering model

Facial aging research concerns the way aging aﬀects a person’s appearance and how we can use knowledge of this process. It has been an interesting topic for ﬁelds such as human perception, pattern recognition, computer vision, graphics, and skin optics. Most studies acknowledge that facial appearance changes with age. As a person grows older, certain characteristics of their skin will change, notably the light scattering. If a model is used to predict a person’s skin light scattering, its parameter(s) may be used to predict the age of its owner. The aim of this thesis is to observe whether a light scattering model parameter is suitable to be used as an age estimator/classiﬁer. This is done by investigating and analyzing the relationship between the parameter of an analytical-based light scattering model and skins of various ages. In the end, this thesis has shown that the parameter(s) for an analytical-based light scattering model can be used as an alternative method for estimating/classifying a person’s age.
The Difficulties of Learning Object-Oriented Analysis and Design: An Exploratory Study

Object-oriented analysis and design (OOAD) techniques are becoming increasingly popular. Advocates argue that OOAD produces systems that are more maintainable, more reusable, and of higher quality. In addition, OOAD proponents argue that OOAD is easier because the techniques are more “natural. ” However, recent research suggests that learning OOAD is difficult. Moreover, no clear pedagogy has emerged to assist instructors in industry and at universities in teaching OOAD. Here we report our efforts to understand students' difficulties in learning object-oriented concepts of OOAD. We studied students' perceptions of their understanding of OOAD concepts with a questionnaire. Students reported more difficulty in understanding and modeling the behavioral aspects of OOAD than the structural aspects. We learned that subjects' overall confidence in understanding OOAD depended on understanding object behavior and the models used in OOAD. On the basis of these findings we make several recommendations to improve OOAD instruction.
Energy-efficient hierarchical cooperative caching optimisation for 5G networks

The caching in fifth generation (5G) networks has been considered as a promising technique to reduce the duplicate traffic transmission and improve the users' quality of experience (QoE). Although many works have been done for caching in 5G networks, most of them focus on the content caching policy design to optimise the users' QoE, the issue to realise the energy efficiency of the whole network is not fully considered. Therefore, in this study, by introducing the hierarchical cooperative caching property, i.e. the core gateway and the base stations can cooperative cache the content, the authors study the problem of the hierarchical cooperative caching policy to realise the energy efficiency. They then formulate the hierarchical cooperative content placement problem as an integer programming problem to minimise the total energy consumption. Also, to reduce the computation complexity, the optimisation algorithm based on the idea of quantum-inspired evolutionary is proposed, which has the fast convergence and approximate to the optimal solution. Finally, extensive simulation results are illustrated to demonstrate the performance of the proposed scheme.
A Computer System Quality metric for Infrastructure with Configuration Files' Changes

We propose a system quality metric for computer infrastructure development. The feature of the metric is the usage of configuration files. The growths and changes of configuration files present characteristics of infrastructure development. The proposed metric consists of 4 element metrics and a total metric. The proposed metric was applied to a real large-scale project. The metric helps us understand how the configuration files were developed. There are two patterns, one is growing little by little, and another is suddenly large growth and suddenly decreasing. For evaluation of the metric, we compared the metric values with system faults that occurred in the real running. As a result, the two patterns of configuration files' development are not related to the system faults. However, the one element metric Scale is related to the system faults. Therefore, the Scale element metric may present system quality in the target project.
Checkpointing and Migration of IoT Edge Functions

The serverless and functions as a service (FaaS) paradigms are currently trending among cloud providers and are now increasingly being applied to the network edge, and to the Internet of Things (IoT) devices. The benefits include reduced latency for communication, less network traffic and increased privacy for data processing. However, there are challenges as IoT devices have limited resources for running multiple simultaneous containerized functions, and also FaaS does not typically support long-running functions. Our implementation utilizes Docker and CRIU for checkpointing and suspending long-running blocking functions. The results show that checkpointing is slightly slower than regular Docker pause, but it saves memory and allows for more long-running functions to be run on an IoT device. Furthermore, the resulting checkpoint files are small, hence they are suitable for live migration and backing up stateful functions, therefore improving availability and reliability of the system.
Replayable Execution Optimized for Page Sharing for a Managed Runtime Environment

We present Replayable Execution, a system for improving the efficiency of Function-as-a-Service (FaaS) frameworks. It takes advantage of standard kernel features to reduce memory usage and accelerate cold startup speed without changes to the OS kernel, language runtimes, and the surrounding FaaS deployment environment. Replayable Execution exploits the intensive-deflated execution characteristics of the majority of target applications. It uses checkpointing to save an image of an application, allowing this image to be shared across containers and resulting in speedy restoration at service startup. We apply Replayable Execution to a representative FaaS Java framework to create a ReplayableJVM execution, which together with benefits from deterministic execution of a warmed up runtime, offers 2X memory footprint reduction, and over 10X startup time improvement.
REAPER: Real-time App Analysis for Augmenting the Android Permission System

Android's app ecosystem relies heavily on third-party libraries as they facilitate code development and provide a steady stream of revenue for developers. However, while Android has moved towards a more fine-grained run time permission system, users currently lack the required resources for deciding whether a specific permission request is actually intended for the app itself or is requested by possibly dangerous third-party libraries. In this paper we present Reaper, a novel dynamic analysis system that traces the permissions requested by apps in real time and distinguishes those requested by the app's core functionality from those requested by third-party libraries linked with the app. We implement a sophisticated UI automator and conduct an extensive evaluation of our system's performance and find that Reaper introduces negligible overhead, rendering it suitable both for end users (by integrating it in the OS) and for deployment as part of an official app vetting process. Our study on over 5K popular apps demonstrates the large extent to which personally identifiable information is being accessed by libraries and highlights the privacy risks that users face. We find that an impressive 65% of the permissions requested do not originate from the core app but are issued by linked third-party libraries, 37.3% of which are used for functionality related to ads, tracking, and analytics. Overall, Reaper enhances the functionality of Android's run time permission model without requiring OS or app modifications, and provides the necessary contextual information that can enable users to selectively deny permissions that are not part of an app's core functionality.
New Insights into the role of the phonological loop in task switching: evidence from three different age groups

ABSTRACT The phonological loop plays an important role in task switching. Until now, however, it has been unclear whether it is the maintenance of the task sequence or the retrieval of the task goal that requires phonological processing. In the present study, 60 participants (20 children, 20 adolescents, 20 young adults) performed three phonological working-memory tests before they switched between two tasks in a cue-present condition (random task sequence) and in a cue-absent condition (predictable task sequence). Results revealed that better phonological and central-executive working-memory performance predicted lower switch costs. In line with age-differentiation of cognitive abilities switching was less coupled with working memory in adults compared to children and adolescents. Interestingly, however, relations were only demonstrated in the cue-present but not in the cue-absent condition, indicating that it is more the retrieval of the task goal than the maintenance of the task sequence that requires the involvement of phonological processing.
Implementation of a CG-Based Simulator to Evaluate Dynamic Relocation of UAVs in Image-Assisted Routing

The authors are trying to construct a real-time vital sensing system during exercise based on image-assisted routing. In order to estimate locations of target exercisers based on image processing required for multi-hop networking, robust personal identification is indispensable because visual target tracking executed after detection sometimes fails in practical scenes. To solve this problem, dynamic relocation of UAVs to obtain visual cues to identify who is a target is adopted to our system. In this paper, a CG-based simulator that has functions necessary for evaluation of dynamic relocation algorithm of UAVs. Experimental results using a basic scenario show that the implemented simulator works well.
Overlapped Fingerprint Separation Based on Deep Learning

Biometrics and artificial intelligence play the important roles of recent technology. In biometrics, fingerprint is one of the most widely used identification methods. However, most of this kind applications only focus on single fingerprint processing but lack discussion of recognition of overlapped fingerprint due to its complexity. In fact, overlapped fingerprints are much more common on the criminal spot and nowadays we still rely on the inefficient manual operation to separate those overlapped fingerprints. So, we purpose our automatic, accurate, and even more efficient method using convolutional neural network to deal with the overlapped fingerprints problem. In experimental result, not only the single and multi-fingerprint latent test has 92.39% and 97.1% average accurate rate respectively, but we also got 92.19% and 95.84% correct rate respectively in the overlapped and non-overlapped range detection tests. The result shows that we could actually assist the fingerprint separation work automatically and efficiently with our own method.
μLayer: Low Latency On-Device Inference Using Cooperative Single-Layer Acceleration and Processor-Friendly Quantization

Emerging mobile services heavily utilize Neural Networks (NNs) to improve user experiences. Such NN-assisted services depend on fast NN execution for high responsiveness, demanding mobile devices to minimize the NN execution latency by efficiently utilizing their underlying hardware resources. To better utilize the resources, existing mobile NN frameworks either employ various CPU-friendly optimizations (e.g., vectorization, quantization) or exploit data parallelism using heterogeneous processors such as GPUs and DSPs. However, their performance is still bounded by the performance of the single target processor, so that realtime services such as voice-driven search often fail to react to user requests in time. It is obvious that this problem will become more serious with the introduction of more demanding NN-assisted services. In this paper, we propose μLayer, a low latency on-device inference runtime which significantly improves the latency of NN-assisted services. μLayer accelerates each NN layer by simultaneously utilizing diverse heterogeneous processors on a mobile device and by performing computations using processor-friendly quantization. Two key findings motivate our work: 1) the existing frameworks are limited by single-processor performance as they execute an NN layer using only a single processor, and 2) the CPU and the GPU on the same mobile device achieve comparable computational throughput, making cooperative acceleration highly promising. First, to accelerate an NN layer using both the CPU and the GPU at the same time, μLayer employs a layer distribution mechanism which completely removes redundant computations between the processors. Next, μLayer optimizes the per-processor performance by making the processors utilize different data types that maximize their utilization. In addition, to minimize potential latency increases due to overly aggressive workload distribution, μLayer selectively increases the distribution granularity to divergent layer paths. Our experiments using representative NNs and mobile devices show that μLayer significantly improves the speed and the energy efficiency of on-device inference by up to 69.6% and 58.1%, respectively, over the state-of-the-art NN execution mechanism.
Biotable: A Tool to Extract Semantic Structure of Table in Biology Literature

The publication of biological literature increasing year by year. And the important information in biomedical articles may only appear in tables. However, research on information extraction from tables is rare. Nowadays, there are two ways to do table mining. The first way is that researchers convert the document to HTML format, but the performance of conversion is terrible. The second way is that researchers use documents in XML format directly, but the number of XML documents are limited. To solve this problem, we propose Biotable, a tool for mining biological tables in PDF documents. We use the concept of Connected Value to locate the table boundary and locate each cell after converting each page of the PDF into a picture. In the analysis of the table header field, we convert all the heterogeneous table headers into one row. Then we will have better understanding of the semantics of each column. Based on Biotable and the pipeline QTLMiners proposed, we performed a table mining experiment on QTLMiner's dataset. The precision value of the table detection is 98.12% and the recall value of table detection is 93.14%. The recall value of QTL statements is 86.53%.
Fast String Searching on PISA

This paper presents PPS, a system for locating occurrences of string keywords stored in the payload of packets using a programmable network ASIC. The PPS compiler first converts keywords into Deterministic Finite Automata (DFA) representations, and then maps the DFA into a sequence of forwarding tables in the switch pipeline. Our design leverages several hardware primitives (e.g., TCAM, hashing, parallel tables) to achieve high throughput. Our evaluation shows that PPS demonstrates significantly higher throughput and lower latency than string searches running on CPUs, GPUs, or FPGAs.
Keeping Master Green at Scale

Giant monolithic source-code repositories are one of the fundamental pillars of the back end infrastructure in large and fast-paced software companies. The sheer volume of everyday code changes demands a reliable and efficient change management system with three uncompromisable key requirements --- always green master, high throughput, and low commit turnaround time. Green refers to a master branch that always successfully compiles and passes all build steps, the opposite being red. A broken master (red) leads to delayed feature rollouts because a faulty code commit needs to be detected and rolled backed. Additionally, a red master has a cascading effect that hampers developer productivity--- developers might face local test/build failures, or might end up working on a codebase that will eventually be rolled back. This paper presents the design and implementation of SubmitQueue. It guarantees an always green master branch at scale: all build steps (e.g., compilation, unit tests, UI tests) successfully execute for every commit point. SubmitQueue has been in production for over a year, and can scale to thousands of daily commits to giant monolithic repositories.
Symptoms of anxiety and depression among colorectal cancer survivors from the population based, longitudinal PROFILES registry Prevalence, Predictors, and Impact on Quality of Life Mols,

Citation for published version (APA): Mols, F. , Schoormans, D. , de Hingh, I. H. J. T. , Oerlemans, S. , & Husson, O. (2018). Symptoms of anxiety and depression among colorectal cancer survivors from the population based, longitudinal PROFILES registry Prevalence, Predictors, and Impact on Quality of Life: Prevalence, predictors and impact on quality of life. Cancer: A journal of the American Cancer Society, 124(12), 2621-2628. DOI: 10.1002/cncr.31369