Miracle-dz/UniFAR-FTUs huggingface dataset:
A qualitative analysis of the Wikipedia N-Substate Algorithm's Enhancement Terms

Automatic Search Query Enhancement (ASQE) is the process of modifying a user submitted search query and identifying terms that can be added or removed to enhance the relevance of documents retrieved from a search engine. ASQE differs from other enhancement approaches as no human interaction is required. ASQE algorithms typically rely on a source of a priori knowledge to aid the process of identifying relevant enhancement terms. This paper describes the results of a qualitative analysis of the enhancement terms generated by the Wikipedia NSubstate Algorithm (WNSSA) for ASQE. The WNSSA utilises Wikipedia as the sole source of a priori knowledge during the query enhancement process. As each Wikipedia article typically represents a single topic, during the enhancement process of the WNSSA, a mapping is performed between the user’s original search query and Wikipedia articles relevant to the query. If this mapping is performed correctly, a collection of potentially relevant terms and acronyms are accessible for ASQE. This paper reviews the results of a qualitative analysis process performed for the individual enhancement term generated for each of the 50 test topics from the TREC-9 Web Topic collection. The contributions of this paper include: (a) a qualitative analysis of generated WNSSA search query enhancement terms and (b) an analysis of the concepts represented in the TREC-9 Web Topics, detailing interpretation issues during query-to-Wikipedia article mapping performed by the WNSSA.
Automated, near real-time inspection of commercial sUAS imagery using deep learning

Commercial small Unmanned Aerial Systems (sUAS) have become popular for real-time inspection tasks due to their cost-effectiveness at covering large areas quickly. They can produce vast amounts of image data at high resolution, with little user involvement. However, manual review of this information can’t possibly keep pace with data collection rates. For time-sensitive applications, automated tools are required to locate objects of interest. These tools must perform at very low false alarm rates to avoid overwhelming the user. We approach real-time inspection as a semi-automated problem where a single user can provide limited feedback to guide object detection algorithms.
Wind farm power short-term prediction using WRF model and Kalman filtering

The growing wind power industry requires more reliable power forecasting due tohigher penetration of this technology in electricity generation. Due to the stochasticbehaviour of wind, additional tools for improving the electricity forecast are required.This research parameterizes WRF model for wind power forecasting in Qollpana windfarm -Bolivia. Qollpana is a wind farm over a very complex terrain and 2800 metersabove sea level (masl) located on the Andes. Wind power forecasting is performed fordifferent periods of the year, such as, January, April, June, and October. Finally, theresults are improved using a Kalman filter and compared with the power generated inQollpana wind farm. The simulated WRF results of energy generated in the wind farmshowed between 50 and 30 % of error. However using a Kalman filter the errorsdiminished up to 10 % of the energy generated by the wind farm. The time of thehighest errors (at sunlight time) suggests that topographic conditions play an importantrole in the wind farm electricity production.
Assisting Software Developers With License Compliance

Open source licensing determines how open source systems are reused, distributed, and modified from a legal perspective. While it facilitates rapid development, it can present di culty for developers in understanding due to the legal language of these licenses. Because of misunderstandings, systems can incorporate licensed code in a way that violates the terms of the license. Such incompatibilities between licensing can result in the inability to reuse a particular library without either relicensing the system or redesigning the architecture of the system. Prior e↵orts have predominantly focused on license identification or understanding the underlying phenomena without reasoning about compatibility in a broad scale. The work in this dissertation first investigates the rationale of developers and identifies the areas that developers struggle with respect to free/open source software licensing. First, we investigate the di↵usion of licenses and the prevalence of license changes in a large scale empirical study of 16,221 Java systems. We observed a clear lack of traceability and a lack of standardized licensing that led to di culties and confusion for developers trying to reuse source code. We further investigated the di culty by surveying the developers of the systems with license changes to understand why they first adopted a license and then changed licenses. Additionally, we performed an analysis on issue trackers and legal mailing lists to extract licensing bugs. From these works, we identified key areas in which developers struggled and needed support. While developers need support to identify license incompatibilities and understand both the cause and implications of the incompatibilities, we observed that state-of-the-art license identification tools did not identify license exceptions. Since these exceptions directly modify the license terms (either the permissions granted by the license or the restrictions imposed by the license), we proposed an approach to complement current license identification techniques in order to classify license exceptions. The approach relies on supervised machine learners to classify the licensing text to identify the particular license exceptions or the lack of a license exception. Subsequently, we built an infrastructure to assist developers with evaluating license compliance warnings for their system. The infrastructure evaluates compliance across the dependency tree of a system to ensure it is compliant with all of the licenses of the dependencies. When an incompatibility is present, it notes the specific library/libraries and the conflicting license(s) so that the developers can investigate these compliance warnings, which would prevent distribution of their software, in their system. We conduct a study on 121,094 open source projects spanning 6 programming languages, and we demonstrate that the infrastructure is able to identify license incompatibilities between these projects and their dependencies.
A dynamic CSFICA and feature‐based collision detection method in multichannel space‐based AIS

To detect message collisions in multichannel spaced‐based automatic identification system (AIS), a dynamic complex symmetric fast independent component analysis (CSFICA) and feature‐based collision detection method is proposed in this paper. A fast and stable blind source separation algorithm, dynamic CSFICA, is utilized to separate signals dynamically and improve the signal‐to‐interference ratio (SIR) in each channel. A frequency and phase offset insensitive feature detection algorithm is used to calculate the test statistics in each channel. The false alarm is suppressed by applying the arithmetic to geometric mean (AGM) method, and test statistics of channels with sufficient signal quality are extracted to detect the preamble. Simulation results show that the proposed algorithm outperforms the reference feature detection algorithm under collision conditions and is insensitive to the SIR. The proposed algorithm is more resistant to false alarm caused by signal (FAS) than the differential correlation (DC) algorithm.
Straddling the crevasse: A review of microservice software architecture foundations and recent advancements

Microservice architecture style has been gaining wide impetus in the software engineering industry. Researchers and practitioners have adopted the microservices concepts into several application domains such as the internet of things, cloud computing, service computing, and healthcare. Applications developed in alignment with the microservices principles require an underlying platform with management capabilities to coordinate the different microservice units and ensure that the application functionalities are delivered to the user. A multitude of approaches has been proposed for the various tasks in microservices‐based systems. However, since the field is relatively young, there is a need to organize the different research works. In this study, we present a comprehensive review of the research approaches directed toward microservice architectures and propose a multilevel taxonomy to categorize the existing research. The study also discusses the different distributed computing paradigms employing microservices and identifies the open research challenges in the domain.
Physically Aware Affinity-Driven Multiplier Implementation

Optimized hardware for the execution of large dot-product (DP) calculations is central to many of today’s integrated circuits. These arithmetic blocks are often implemented with the parallel fused DP (FDP) approach, and to achieve high performance, are realized with a tree-based compression algorithm, using on commercially available synthesis macros. However, these macros are based on performance optimization of the gate-level netlist, and fail to take into account the consequences of the applied heuristics on the physical-implementation (layout) of these large circuits. In this article, we propose a physical-aware approach to FDP implementation based on the affinity between the logic gates that make up the gate-level structure. The proposed clustered DP (CDP) algorithm, enables the place and route tools to cluster gates with high-affinity, leading to higher placement utilization and lower routing congestion. DP calculations with up to 78 multipliers were implemented with a 65-nm CMOS standard cell library, providing power reduction of up to 63%, up to 60% lower area, and performance improvements as high as $2.5\times $ , as compared to similar implementations based on commercial macros based on post-layout results.
A MULTIPHASE MIXED METHOD APPROACH TO ASSESS INTERNATIONALISATON OF HIGHER EDUCATION POLICIES

Higher education internationalization (HEI) has become a significant priority to policy makers and education providers around the globe. Motivated by globalization, HEI has become politically strategic and economically promising for nations, governments and universities to position themselves in a global market. The paper’s aim is to provide a methodology for the study of a dynamic and reformative South African HEI framework based on the transformative force (i.e. adjustment of change, learning, shared knowledge, internationalization, globalization an institutionalized memory) of HEI in the People’s Republic of China. A multiphase-mixed methods approach is used in determining a South African higher education reformative internationalization framework based on Chinese experiences. The interpretivist paradigm constitute the underlying research approach. Multiphase mixed methods will be employed to collect, analyze and integrate quantitative and qualitative data and will allow for a more complete and synergistic usage of data than separate quantitative and qualitative methodology. The multiphase mixed methods design includes three phases that come together to answer the focal research question. The focal research question and will be addressed through content analysis (Phase I), in-depth interviews (Phase II) and surveys (Phase III). The significance of this research is aimed at arousing a better understanding of higher education policy issues in South Africa from a transformation perspective. This research will contribute to theory with respect to how internationalization and globalization influence higher education and the position of higher education in economic and social development. Furthermore, this research may also have practical significance for higher education policy and practice in South Africa and other comparatively positioned countries. It might expand knowledge and awareness of how internationalization influences the unique circumstances and challenges of higher education reform in developing countries, especially a country in search of positioning itself in a globalized knowledge economy.
Research on Autonomous Driving Simulator Control and Decision Algorithms based on Computer Vision Methods

Researches on perception and control technologies are the most crucial area in autonomous driving systems and is the core to achieve fully autonomous driving. Simulations, especially hardware-in-loop and algorithms-in-loop could accelerate the testing process. In this article, based on the second World Intelligent Driving Challenge took place in Tianjin, held by CATARC in May 2018, with the combination of computer vision, edge detection, objection detection, Kalman filter and vehicle dynamics methods, constructed in the Automotive Artificial Intelligence Simulator environment, aiming at paving the road to the better research on simulation testing and autonomous driving
Model-Based Bayesian Sparse Sampling for Data Efficient Control

In this work, we propose a novel Bayesian-inspired model-based policy search algorithm for data efficient control. In contrast to other model-based approaches, our algorithm makes use of approximate Gaussian processes in the form of random Fourier features for fast online systems identification and computationally efficient posterior updates via rank one Cholesky updates. Furthermore, fast and tractable posterior updates permits policy optimization to leverage knowledge from posterior evolution tracking for a directed Bayesian approach to the exploration-exploitation dilemma. To address the optimization formulation involving belief monitoring as well as the potentiality of a loss surface with zero gradients everywhere, we leverage a blackbox optimizer in the form of covariance matrix adaptation evolution strategy (CMA-ES). We test our algorithm on four challenging control tasks and report the superior data efficiency as well as the exploration capabilities of our model.
A Crowd Behavior Identification Method Combining the Streakline With the High-Accurate Variational Optical Flow Model

A crowd behavior identification method is proposed by combining the streakline based on fluid mechanics with a high-accurate variational optical flow model in this paper. Firstly, calculated by the high-accurate variational optical flow model, the streaklines are used to acquire the crowd motion trajectory information. The angular histogram and the regions of interest in the scenes are obtained by calculating and clustering the dasymetric dot maps of the starting and ending points of the trajectory, and then, combining the dasymetric dot map and angular histogram information to analyze whether there are specific crowd behaviors in the regions of interest, and thus to identify different types of crowd behavior in such scene. Finally, experimental comparison and analysis are made to verify the effectiveness and accuracy of the method proposed in this paper.
Context-aware task scheduling in distributed computing systems

These days, the popularity of technologies such as machine learning, augmented reality, and big data analytics is growing dramatically. This leads to a higher demand of computational power not only for IT professionals but also for ordinary device users who benefit from new applications. At the same time, the computational performance of end-user devices increases to meet the demands of these resource-hungry applications. As a result, there is a coexistence of a huge demand of computational power on the one side and a large pool of computational resources on the other side. Bringing these two sides together is the idea of computational resource sharing systems which allow applications to forward computationally intensive workload to remote resources. This technique is often used in cloud computing where customers can rent computational power. However, we argue that not only cloud resources can be used as offloading targets. Rather, idle CPU cycles from end-user administered devices at the edge of the network can be spontaneously leveraged as well. Edge devices, however, are not only heterogeneous in their hardware and software capabilities, they also do not provide any guarantees in terms of reliability or performance. Does it mean that either the applications that require further guarantees or the unpredictable resources need to be excluded from such a sharing system? In this thesis, we propose a solution to this problem by introducing the Tasklet system, our approach for a computational resource sharing system. The Tasklet system supports computation offloading to arbitrary types of devices, including stable cloud instances as well as unpredictable end-user owned edge resources. Therefore, the Tasklet system is structured into multiple layers. The lowest layer is a best-effort resource sharing system which provides lightweight task scheduling and execution. Here, best-effort means that in case of a failure, the task execution is dropped and that tasks are allocated to resources randomly. To provide execution guarantees such as a reliable or timely execution, we add a Quality of Computation (QoC) layer on top of the best-effort execution layer. The QoC layer enforces the guarantees for applications by using a context-aware task scheduler which monitors the available resources in the computing environment and performs the matchmaking between resources and tasks based on the current state of the system. As edge resources are controlled by individuals, we consider the fact that these users need to be able to decide with whom they want to share their resources and for which price. Thus, we add a social layer on top of the system that allows users to establish friendship connections which can then be leveraged for social-aware task allocation and accounting of shared computation.
Segmented and Non-Segmented Skin Lesions Classification Using Transfer Learning and Adaptive Moment Learning Rate Technique Using Pretrained Convolutional Neural Network

A skin lesion is a very severe problem, especially in coastal countries. Early detection by a highly reliable classification of skin lesion causes a great reduction in the mortality rate. Recognition of melanoma is a complicated issue due to the high degree of visual similarities between melanoma and non-melanoma lesions. Various studies are carried out to overcome this problem and to obtain accurate screening of skin lesion, where the most recent method for segmenting and classifying the lesion is based on a deep learning algorithm. In this paper, (GoogleNet) and (AlexNet) are employed with transfer learning and optimization gradient descent adaptive momentum learning rate (ADAM). The proposed method is applied on Archive International Skin Imaging Collaboration (ISIC) database to classify images into three main classes (benign, melanoma, seborrheic keratosis) under the two scenarios; segmented and non-segmented lesion images. The overall accuracy of the non-segmented classification database is 92.2% and 89.8% for the non-segmented dataset. Utilizing optimization algorithm (ADAM) leads to a significant improvement in the classification results when they are compared with previous studies.
Digital Library of the Artistic Production of ECA USP IFLA 2019

The Digital Library of Artistic Production of the School of Communications and Arts of the University of São Paulo will gather images of the artistic works created by the artists of the School presented as dissertations and theses. The dissemination of this production is a request from the researcher users of the collection. The search for solutions for the development of the Digital Library included: choice of the software; analysis of international metadata standards for the description of works of art; study of protocols for use of images. As results, the chosen tools and the characteristics of the developed prototype are presented: software Omeka for content management; VRA core metadata standard and Cataloging Cultural Objects for image description; adopting the International Image Interoperability Framework (IIIF). Conclusion: the creation of the Digital Library of Artistic Production will value the institutional artistic production, democratize access and to increase the visibility of works by ECA's young artists.
Towards Low-Carbon Conferencing: Acceptance of Virtual Conferencing Solutions and Other Sustainability Measures in the ALIFE Community

The latest report from the Intergovernmental Panel on Climate Change (IPCC) estimated that humanity has a time window of about 12 years in order to prevent anthropogenic climate change of catastrophic magnitude. Green house gas emission from air travel, which is currently rising, is possibly one of the factors that can be most readily reduced. Within this context, we advocate for the re-design of academic conferences in order to decrease their environmental footprint. Today, virtual technologies hold the promise to substitute many forms of physical interactions and increasingly make their way into conferences to reduce the number of travelling delegates. Here, we present the results of a survey in which we gathered the opinion on this topic of academics worldwide. Results suggest there is ample room for challenging the (dangerous) business-as-usual inertia of scientific lifestyle.
Collective Named Entity Recognition in User Comments via Parameterized Label Propagation

Named entity recognition (NER) in the past has focused on extracting mentions in a local region, within a sentence or short paragraph. When dealing with user‐generated text, the diverse and informal writing style makes traditional approaches much less effective. On the other hand, in many types of text on social media such as user comments, tweets, or question–answer posts, the contextual connections between documents do exist. Examples include posts in a thread discussing the same topic, tweets that share a hashtag about the same entity. Our idea in this work is utilizing the related contexts across documents to perform mention recognition in a collective manner. Intuitively, within a mention coreference graph, the labels of mentions are expected to propagate from more confidence cases to less confidence ones. To this end, we propose a novel semisupervised inference algorithm named parameterized label propagation. In our model, the propagation weights between mentions are learned by an attention‐like mechanism, given their local contexts and the initial labels as input. We study the performance of our approach in the Yahoo! News data set, where comments and articles within a thread share similar context. The results show that our model significantly outperforms all other noncollective NER baselines.
Neuroevolution of Humanoids that Walk Further and Faster with Robust Gaits

Bipedal locomotion requires precise rhythm and balance. Here we demonstrate two fitness-function enhancements applied to OpenAI’s 3D Humanoid-v1 walking task using a replica of Salimans et al.’s evolution strategy (Salimans et al., 2017). The first enhancement reduces control cost, following a start-up period, and the second enhancement penalises poor balance. Individually, each enhancement results in improved gaits and doubles both median speed and median distance walked. Combining the two enhancements results in little further improvement in the absence of noise but is shown to produce gaits that are much more robust to noise in their actions, with median speed, distance and time two to five times those of the default and individual-enhancement gaits at an intermediate noise level.