Reviewerly/exHarmony huggingface dataset:
Mining Similar Methods for Test Adaptation

Developers may choose to implement a library despite the existence of similar libraries, considering factors such as computational performance, language or platform dependency, accuracy, convenience, and completeness of an API. As a result, GitHub hosts several library projects that have overlaps in their functionalities. These overlaps have been of interest to developers from the perspective of code reuse or the preference of one implementation over the other. Through an empirical study, we explore the extent and nature of existence of these similarities in the library functions. We have further studied whether the similarity of functions across different libraries and their associated test suites can be leveraged to reveal defects in one another. We see scope for effectively using the mining of test suites from the perspective of revealing defects in a program or its documentation. Another noteworthy observation made in the study is that similar functions may exist across libraries implemented in the same language as well as in different languages. Identifying the challenges that lie in building a testing tool, we automate the entire process in <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Metallicus</small> , a test mining and recommendation tool. <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Metallicus</small> returns a test suite for the given input of a query function and a template for its test suite. On a dataset of query functions taken from libraries implemented in Java or Python, <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Metallicus</small> revealed 46 defects.
Centralized collision-free polynomial trajectories and goal assignment for aerial swarms

Computationally tractable methods are developed for centralized goal assignment and planning of collision-free polynomial-in-time trajectories for systems of multiple aerial robots. The method first assigns robots to goals to minimize total time-in-motion based on initial trajectories. By coupling the assignment and trajectory generation, the initial motion plans tend to require only limited collision resolution. The plans are then refined by checking for potential collisions and resolving them using either start time delays or altitude assignment. Numerical experiments using both methods show significant reductions in the total time required for agents to arrive at goals with only modest additional computational effort in comparison to state-of-the-art prior work, enabling planning for thousands of agents.
Video Search with Collage Queries

Nowadays, popular web search portals enable users to find available images corresponding to a provided free-form text description. With such sources of example images, a suitable composition/collage of images can be constructed as an appropriate visual query input to a known-item search system. In this paper, we investigate a querying approach enabling users to search videos with a multi-query consisting of positioned example images, so-called collage query, depicting expected objects in a searched scene. The approach relies on images from external search engines, partitioning of preselected representative video frames, relevance scoring based on deep features extracted from images/frames, and is currently integrated into the open-source version of the SOMHunter system providing additional browsing capabilities.
Optimization of Unsupervised Learning in Machine Learning

Abstract The Ombudsman of the Republic of Indonesia (hereinafter referred to as the Ombudsman) is a state institution (independent) that has the authority to oversee the administration of public services. The purpose of this study is to analyze the completion of reports/complaints from the public by using unsupervised learning techniques in machine learning. The data source used is the statistical report/public complaints based on the classification of the reporter and how to submit it in each provincial regional office (simpel.ombudsman.go.id). The unsupervised learning techniques in machine learning that are used are clustering (k-medoids) and classification (C4.5) which are part of data mining. k-medoids is tasked with mapping community reports/complaints based on provincial regional offices. The results of the mapping will be classified to get the range of values from the existing mapping. The calculation process uses the help of RapidMiner software. The distribution labels used were 4 clusters namely the percentage of completion of the “very good” report (C1) of 9 provinces; percentage of “good” report completion (C2) of 10 provinces; percentage of completion of “lacking” reports (C3) of 11 provinces; percentage of “bad” report completion (C4) of 3 provinces. The Davies-Bouldin Index value on the map is 0.530 (optimal). The results of the mapping can be information in improving the quality of public services in the completion of the report including the provinces included in the C3 and C4 clusters with the percentage of report completion classification (C4 = 0 - 20.70% and 20.70% &gt; C3 &lt; 47.69%).
A non-Discriminatory Approach to Ethical Deep Learning

Artificial neural networks perform state-of-the-art in an ever-growing number of tasks, nowadays they are used to solve an incredibly large variety of tasks. However, typical training strategies do not take into account lawful, ethical and discriminatory potential issues the trained ANN models could incur in. In this work we propose NDR, a non-discriminatory regularization strategy to prevent the ANN model to solve the target task using some discriminatory features like, for example, the ethnicity in an image classification task for human faces. In particular, a part of the ANN model is trained to hide the discriminatory information such that the rest of the network focuses in learning the given learning task. Our experiments show that NDR can be exploited to achieve non-discriminatory models with both minimal computational overhead and performance loss.
Hadoop-Based University Ideological and Political Big Data Platform Design and Behavior Pattern Mining

The combination of higher education and big data technology is not only the focus of the application of big data technology, but also an emerging field of self-development in the field of higher education. This article is dedicated to building a big data processing platform through Hadoop big data storage architecture, Hive, flume data collection technology, and Sqoop data synchronization technology to achieve efficient processing of big data sets. The traditional data mining algorithm is implemented using Map Reduce programming, and the implementation of the data mining algorithm of the Hadoop platform is studied, mainly to analyze its execution efficiency and scalability. We select the data clustering task in data mining as a representative, and write its Map Reduce version to test and verify its effect on the Hadoop platform. Through comparative experiments of different cluster sizes and different data sizes, it is concluded that the use of Hadoop distributed systems for data mining tasks has a good acceleration ratio and efficiency, and the extended performance analysis of computing power also shows that it has great potential.
Design and Analysis of a Public Key Infrastructure for SBAS Data Authentication

Integral to any authentication system is the design of its public key infrastructure (PKI), allowing the system to divide and allocate the responsibility of security between different entities. Moreover, any PKI is closely tied to its associated key management (KM) architecture that is responsible for the distribution, maintenance, and revocation of cryptographic keys. This paper develops two candidate authentication schemes: one each for an L5 I-channel and L5 Q-channel implementation. A PKI is then proposed along with its associated KM architecture. A simulator, introduced as MCOS, is developed to enable large scale Monte Carlo tests of these different PKI and KM designs. The schemes are tested using MCOS producing results concerning key performance metrics of these authentication schemes and their associated PKI. This work concludes that a strictly over-the-air method of delivering PKI information is feasible for SBAS data authentication systems using the proposed PKI and KM architecture.
AnCoins: Image-Based Automated Identification of Ancient Coins Through Transfer Learning Approaches

The identification of ancient coins is a time consuming and complex task with huge experience demands. The analysis of numismatic evidence through patterns detection executed by Machine Learning methods has started to be recognized as approaches that can provide archaeologists with a wide range of tools, which, especially in the fields of numismatics, can be used to ascertain distribution, continuity, change in engraving style and imitation. In this paper we introduce what we call the Ancient Coins (AnCoins-12) dataset. Α set of images composed of 12 different classes of Greek ancient coins from the area of ancient Thrace, aiming for the automatic identification of their issuing authority. In this context we describe the methodology of data acquisition and dataset organization emphasizing the small number of images available in this field. In addition to that we apply deep learning approaches based on popular CNN architectures to classify the images of the new introduced dataset. Pre-trained CNNs, through transfer learning approaches, achieved a top-1 validation accuracy of 98.32% and top-5 validation accuracy of 99.99%. For a better diffusion of the results in the archaeological community, we introduce a responsive web-based application with an extension asset focusing in the identification of common characteristics in different coin types. We conclude the paper, by stressing some of the most importance key elements of the proposed approaches and by highlighting some future challenges.
A FPGA-based end-to-end acceleration framework for fast deployment of Convolutional Neural Networks

Abstract Nowadays, CNNs has delivered the state-of-the-art performance in the field of computer vision, image classification, etc. As CNNs going deeper, it becomes more difficult to implement CNNs applications based on general-purpose computing platforms. Recently, many FPGA-based CNNs accelerators have been proposed, these accelerators achieved high performance on specific CNNs models, however they are somewhat lack of reconfigurability to fit different applications. To deal with this problem, an end-to-end acceleration framework was proposed in this paper, which consists of a parameterized hardware accelerator and a fully automatic software framework. Parallel computation and pipeline optimization are deployed in the hardware design to achieve high performance. Simultaneously, runtime reconfigurability is implemented by using a global register list. By encapsulating the underlying driver, a three-layer software framework is provided for users to deploy their pre-trained models. A typical CNNs model used for handwritten digital recognition was selected to test and verify the accelerator. The experimental result shows that the accelerator can reach a recognition speed of 22.65FPS under the clock frequency of 100MHz, comparing with ARM Cortex-A9 working at 650MHz, it can achieve 25.9 times of acceleration effect, with only 1.59W power consumption.
Small Object Detection in Optical Remote Sensing Video with Motion Guided R-CNN

Deep learning (DL) based object detection methods have been making great achievements for natural images, which guides the vehicle detection of optical remote sensing videos (ORSV). Compared with natural images, objects in ORSV are smaller and blurrier, and most of vehicles are crowded. Thus, it is difficult for DL to detect these small objects only using the single-frame image. To address this problem, a motion guided R-CNN (MG-RCNN) is proposed. In MG-RCNN, motion information from consecutive frames is extracted by the mean differencing method and merged into apparent information to obtain motion-related discriminative features. Then, high-quality proposals are generated on the feature maps by mini-region proposal network (MRPN). For small targets, an improved loss function is defined by incorporating smooth factor, which makes the regression of shapes more stable. Experiments on ORSV demonstrate the proposed method shows superior detection performance over state-of-the-art deep learning methods.
Towards a Secure Proxy-based Architecture for Collaborative AI Engineering

In this paper, we investigate how to design a security architecture of a Platform-as-a-Service (PaaS) solution, denoted as Secure Virtual Premise (SVP), for collaborative and distributed AI engineering using AI artifacts and Machine Learning (ML) pipelines. Artifacts are re-usable software objects which are a) tradeable in marketplaces, b) implemented by containers, c) offer AI functions as microservices, and, d) can form service chains, denoted as AI pipelines. Collaborative engineering is facilitated by the trading and (re-)using artifacts and, thus, accelerating the AI application design. The security architecture of the SVP is built around the security needs of collaborative AI engineering and uses a proxy concept for microservices. The proxy shields the AI artifact and pipelines from outside adversaries as well as from misbehaving users, thus building trust among the collaborating parties. We identify the security needs of collaborative AI engineering, derive the security challenges, outline the SVP's architecture, and describe its security capabilities and its implementation, which is currently in use with several AI developer communities. Furthermore, we evaluate the SVP's Technology Readiness Level (TRL) with regard to collaborative AI engineering and data security.
Model-Invariant State Abstractions for Model-Based Reinforcement Learning

Accuracy and generalization of dynamics models is key to the success of model-based reinforcement learning (MBRL). As the complexity of tasks increases, so does the sample inefficiency of learning accurate dynamics models. However, many complex tasks also exhibit sparsity in the dynamics, i.e., actions have only a local effect on the system dynamics. In this paper, we exploit this property with a causal invariance perspective in the single-task setting, introducing a new type of state abstraction called \textit{model-invariance}. Unlike previous forms of state abstractions, a model-invariance state abstraction leverages causal sparsity over state variables. This allows for compositional generalization to unseen states, something that non-factored forms of state abstractions cannot do. We prove that an optimal policy can be learned over this model-invariance state abstraction and show improved generalization in a simple toy domain. Next, we propose a practical method to approximately learn a model-invariant representation for complex domains and validate our approach by showing improved modelling performance over standard maximum likelihood approaches on challenging tasks, such as the MuJoCo-based Humanoid. Finally, within the MBRL setting we show strong performance gains with respect to sample efficiency across a host of other continuous control tasks.
Analysis and Research of Weibo Public Opinion Based on Text

Abstract With the rapid development of social networks, social platforms such as Weibo and WeChat have become important places for the development and evolution of public opinion events. Therefore, analyze the trend of online public opinions through social platforms. Managers can analyze user preferences and grasp user needs, and help decision-makers to better improve their ability to deal with emergencies. The paper uses the python automatically to get the data from the Weibo. Using the Xunfei sentiment analysis API to identify the emotional polarity, and result feedback to the user. By using the Weibo emotional tendency analysis, the government can track social conditions and public opinions, study and judge online public opinions, guide the direction of public opinions and resolve crisis public opinions.
Word Frequency Data Analysis in Virtual Reality Technology Industrialization

Abstract Virtual reality technology is increasingly in demand for economic, social and management applications, with Moore’s Law gradually failing, it’s difficult to balance the relationship between realistic needs and advanced technology by relying solely on technological progress, and even falling into the “IT black hole”, hindering the industrialisation of virtual reality technology. This paper uses word frequency analysis to extract the four most frequently occurring keywords from current policies, literature and other secondary sources, and abstracts them into four social support concepts: conceptual awareness, education, regulation and finance, to explore the inner connection between virtual reality technology and social support systems through comparison, and to realise the socialisation of virtual reality from the perspective of virtual reality. By comparing and exploring the intrinsic links between virtual reality technology and social support systems, the socialisation of virtual reality technology is realised from the perspective of virtual reality, thus realising the socialisation of technology.
Machine Learning, Market Manipulation and Collusion on Capital Markets: Why the 'Black Box' matters

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Application of Collaborative Filtering Personalized Recommendation Algorithms to Website Navigation

Abstract This article builds a personalized management and push design for the functional and performance requirements of website personalized recommendations. The system uses PHPstorm as the development platform and Hadoop distributed file system combined with DBMS design algorithm to achieve data level research, and uses a combination of customer-based collaborative filtering and project-based collaborative filtering to categorize clients and web pages in personalized recommendation, which improves the calculation and personalization of website navigation to a certain extent.
The Anomaly Detection Mechanism Using Extreme Learning Machine for Service Function Chaining

The age of the wireless network already advances to the fifth generation (5G) era. With software-defined networking (SDN) and network function virtualization (NFV), various scenarios can be implemented in the 5G network. Cloud computing, for example, is one of the important application scenarios for implementing SDN/NFV solutions. The emerging container technologies, such as Docker, can provide more agile service provisioning than virtual machines can do in cloud environments. It is a trend that virtual network functions (VNFs) tend to be deployed in the form of containers. The services provided by clouds can be formed by service function chaining (SFC) consisting of containerized VNFs. Nevertheless, the challenges and limitation regarding SFCs are reported in the literature. Various network services are bound to rely heavily on these novel technologies, however, the development of related technologies often emphasizes functions and ignores security issues. One noticeable issue is the SFC integrity. In brief, SFC integrity concerns whether the paths that traffic flows really pass by and the ones of service chains that are predefined are consistent. In order to examine SFC integrity in the cloud-native environment of 5G network, we propose a framework that can be integrated with NFV management and orchestration (MANO) in this work. The core of this framework is the anomaly detection mechanism for SFC integrity. The learning algorithm of our mechanism is based on extreme learning machine (ELM). The proposed mechanism is evaluated by its performance such as the accuracy of our ELM model. This paper concludes with discussions and future research work.
WITHDRAWN: Bug classification using machine and Deep Learning Algorithms

DevOps is a method used to automate the process between development team and the IT team through which they can develop, test and release their software in a reliable way. Bugs during this stage slows the entire release cycle. To overcome this, Machine Learning and Deep Learning Algorithms are used to analyze and arrive at the possible cause of the bug. This reduces the dependency on the developers and in turn speeds up the release cycle. The bug dataset is fed to various classification algorithms like CNN, Random Forest, Decision Tree, SVM and Naïve Bayes for bug classification.
Research on Hubei Tourism Talent Demand and University Tourism Education Training Model Based on Computer Big Data Mining Technology

Abstract The data processing technology in the training and construction of tourism talents can well integrate the complex data, make it more concrete and visualized and effectively reflect the real situation of campus construction. The use of computers can organically link the parents of students with the school. Parents of students can understand the learning situation of the students at any time and carry out education work for the students in time, which is conducive to the improvement of student performance. At the same time, data processing technology still has certain drawbacks in the actual use process and relevant educators need to continuously explore new processing schemes and improve education. The data processing technology of the campus should also fully integrate the actual situation of the school, implement the data processing work seriously and build the training of tourism talents. The scope of tourism talent training is becoming wider and wider and data processing technology plays an important role in it. Therefore, in the process of tourism talent training and construction, it is necessary to accurately grasp the data processing technology and explore more efficient office models. In the actual construction process of tourism talent training, it can be found that there are still many problems, because the construction of tourism talent training sites still lacks certain theoretical guidance and the construction of tourism talent training is blind, which causes the construction of tourism talent training to not give full play. Its theoretical effect.
To the question of assessing universal competences of students

The subject of this research is the assessment of competences that are crucial in modern life, known as 4K-competences (critical thinking, creativity, communication and cooperation) among the first-year students. Within the framework of the federal state educational standards of higher education, these competences are referred to as universal competences, while in public schools &amp;ndash; in the list of metasubject learning results. The goal of this work consists in the analysis of experience of assessing general cultural/universal competences of the first-year students (recent high school graduates). Research methodology is comprised of the methods of theoretical analysis of this problem in scientific literature, examination and generalization of the approaches towards assessment of general cultural/universal competences. The novelty of research consists in actualization of the need to assess general cultural/universal competences of first-year students, as well as in justification of application of assessment method with regards to these competences. The conclusion is made that the input assessment allows detecting the level of formation of universal competences, and if necessary, adjusting individual educational routes of the students. In the course of assessment, the students were immersed into project activities for the purpose of revealing the elements of universal competences, as well as learning teamwork, communication skills and critical thinking.