Reviewerly/exHarmony huggingface dataset:
A three-step synthetic extraction algorithm for transmission lines

The combination of vision and unmanned aerial vehicle is an economic and effective solution for transmission line inspection. The key in the process of transmission line inspection is real-time extraction and recognition of transmission lines regardless of the complicated background. The horizontal and vertical disturbances are numerous in the complicated natural background. At the same time, transmission line crossing and different shooting angles of unmanned aerial vehicle lead to the phenomenon of overlapping lines, which makes it more difficult to extract and track transmission lines. Therefore, a three-step synthetic extraction algorithm for transmission lines is proposed in the article. Firstly, in view of the edge extraction problem of the image, a complementary edge feature extraction algorithm combining the Sobel operator with the Log operator is proposed to solve the problems such as discontinuous and uneven edges and low-detection efficiency caused by the general Canny operator for edge detection. Secondly, in view of the line feature extraction, a coarse-fine directional optimization algorithm is proposed. The horizontal and vertical interferences of the extraction of transmission lines under complex background and the problem of line crossing in the image are eliminated. The algorithm has higher detection effect and efficiency than Hough transform. Finally, a similarity determination mechanism is proposed to merge the adjacent lines, which avoids the loss of the tracking line caused by overlapping lines in the image. A quadrotor unmanned aerial vehicle is used to carry out the experiment of transmission line detection in the article. The experimental results demonstrate that the three-step method of line extraction is more effective and efficient than the current algorithms.
Using Computational Approaches to Integrate Endangered Language Legacy Data into Documentation Corpora: Past Experiences and Challenges Ahead

The systematic integration of pre-digital published transcriptions of legacy language materials offers many possiblities to enrich documentary corpora with data that is often very comparable to contemporary collections, and often originating from the same speech communities reesearchers currently work with. Especially recent advances in text recognition technologies make the reuse of old materials a very attractive and accessible task. However, the output of text recognition needs to be connected to further parts of the pipeline, namely forced alignment and speeech recognition. The workflows discussed here attempt to reach a maximally useful situation where legacy data is transformed into a usable and comparable format, but not transformed into a time aligned corpus.
Towards an Editor for VR-Oriented Educational Scenarios

VRLE (Virtual Reality Learning Environment) has long been used as an education tool. Our research work aims to propose solutions for assisting teachers to design, reuse and deploy their pedagogical scenarios in VRLE. In this demonstration paper, we present a VR-oriented pedagogical scenario editor that embeds our model allowing teachers to design and adapt their situations (in scenario form) and generate their own VRLE.
Personal Assistance and Monitoring Devices Applications

During almost the whole day, people are carrying or wearing one or more personal devices, such as smartphone, smartwatch, smartband, activity tracker, or smart ring.From one side, these devices provide an extraordinary source of information.ey can collect health parameters, such as heart rate and blood pressure, and measure the activity of the user, for example, by counting the number of steps, track position, and speed of the user.From the other side, the personal devices offer an intuitive and implicit interaction with the user.eir interface is designed to recognize the user's actions and respond to them.A natural communication, based on touch, gestures, voice, and sounds, is established with the user in order to recognize his/her actions and respond to them.Furthermore, the personal devices can be aware of the usage context, for example, by automatically recognizing the activity performed by the user.eir behaviour can be proactive: by recording previous routine and needs of the user, they can propose specific suggestions related to the current context, so anticipating the explicit user input.New
Labeling for linearization

Abstract The primary goal of the present paper is to argue for the hypothesis that labeling is required for linearization, which is called Labeling for Linearization ( LfL ). To achieve this goal, it is first argued that labels are not necessary for semantic interpretation. It is then proposed that labels are necessary for linearization at the PF-interface in that they serve as a device to encode structural asymmetries that are employed to determine precedence relations, which are asymmetric as well. It is also shown that LfL can remove several problems of the original labeling framework. Building on the idea that Spell-Out applies to the whole phase but not its subpart, it is illustrated that the LfL -based analysis can solve the problem concerning the variable ways of applying Spell-Out, which arises in the standard phase theory. Extending the LfL -based framework to Japanese, a novel analysis of particle-stranding ellipsis is also proposed. Incorporating some insights of recent approaches that particle-stranding ellipsis arises through a PF-deletion process, it is shown that the proposed analysis based on LfL offers a theoretically more suitable characterization of the PF-deletion process. In this way, the present article contributes to not only sharpening the core theoretical notions regarding structure building and linearization in terms of labeling but also deepening our understanding of the structure of Japanese.
Development of Fitness Function of Electrical Network Evolutionary Model for Computation of Settling Behavior by Method of Genetic Algorithm

The tendency of active introducing of the artificial intelligence methods into the absolute majority of spheres of human activity is due to the permanent development of society and, as a result, to the complication of a wide range of tasks of prime importance, in particular, in the electric power industry. One of these tasks is computing the electrical network settling behavior (EN SB), on the correct solution of which the proper level of functioning of the entire electric power complex depends. Due to the complexity of the structures of existing electrical networks, there is a need for new methods which are more modern in terms of approach and implementation of computing, the principle of which stipulates for using computer resources more than traditional (iterative) calculation methods. That is why such directions in artificial intelligence as evolutionary modeling, and one of the methods of such modeling which consists in genetic algorithms, are the most relevant areas of research in relation to the problems of electric power industry, in particular, to the task of computing the electrical network modes, since this method, being a priori optimizing in view of the implementation specifics, makes it possible to obtain an optimal result in the end, which is difficult to accomplish using traditional iterative methods, as it is necessary to further formulate and subsequently precisely solve the task of optimization. Such an element as the fitness function is of particular interest in reaching power goals using the method of a genetic algorithm, since it is precisely through the fitness function that the electrical network model representation is realized and implemented in the analytical form. Also, the degree of correctness of the results depends on the completeness of formation of the fitness function. This paper presents the results of the development of the genetic algorithm fitness function for computing the electrical network settling behavior.
Imitation-Projected Policy Gradient for Programmatic Reinforcement Learning

We present Imitation-Projected Policy Gradient (IPPG), an algorithmic framework for learning policies that are parsimoniously represented in a structured programming language. Such programmatic policies can be more interpretable, generalizable, and amenable to formal verification than neural policies; however, designing rigorous learning approaches for programmatic policies remains a challenge. IPPG, our response to this challenge, is based on three insights. First, we view our learning task as optimization in policy space, modulo the constraint that the desired policy has a programmatic representation, and solve this optimization problem using a lift-and-project perspective that takes a gradient step into the unconstrained policy space and then projects back onto the constrained space. Second, we view the unconstrained policy space as mixing neural and programmatic representations, which enables employing state-of-the-art deep policy gradient approaches. Third, we cast the projection step as program synthesis via imitation learning, and exploit contemporary combinatorial methods for this task. We present theoretical convergence results for IPPG, as well as an empirical evaluation in three continuous control domains. The experiments show that IPPG can significantly outperform state-of-the-art approaches for learning programmatic policies.
Mineralogical and major oxide characterization of Panyam clays, North - Central Nigeria

Clay minerals are form over long periods of time, through weathering as a result of gradual chemical breakdown of rocks rich in feldspar. The extent of weathering is determined by many factors. Most common constituent mineral of clays is often one of the members of the mica mineral group. They include illite, sericite, muscovite, and biotite. Others are kaolinite, smectite, mixed-layer clays and chlorite. This paper characterizes eight clay samples from Panyam, North-Central Nigeria. The characterization is based on their mineralogy and major oxide chemistry. This is aimed at determining the extent of weathering and/or maturity and origin or source of the clay materials. Compositional mineralogical and oxide analysis was performed using X-Ray Diffraction (XRD) and X-Ray Fluorescence (XRF) respectively. The X-ray data revealed that the primary constituents of the rocks such as feldspars, biotite and other minerals have been altered to Illite and kaolinite. Nesbitt and Young weathering trend model was used for characterizing the samples using their major oxides; four samples classified as muscovite, two as kaolinite and two as illite. From the Nesbitt and Young model, it was discovered that all samples had a granitic origin with a slower rate of weathering.Key words: Panyam, clay characterization, smectite, kaolinite, oxides.
Power Grid Cloud Resource Status Data Compression Method Based on Deep-Learning

Background: As the "three-type two-net, world-class" strategy proposed, the number of cloud resources in power grid continues to grow, there is a large amount of data to be filed every day. Which are key issues to be addressed, the long-term preservation of data, using back-up data for the operation and maintenance, fault recovery, fault drill and tracking of cloud platform are essential. The traditional compression algorithm faces severe challenges. Method: In this case, this paper proposes the deep-learning method for data compression. First, a more accurate and complete grid cloud resource status data is gathered through data cleaning, correction, and standardization. The preprocessed data is then compressed by SaDE-MSAE. Result: Experiments show that the SaDE-MSAE method can compress data faster. The data compression ratio based on neural network is basically between 45% and 60%, which is relatively stable and stronger than the traditional compression algorithm. Conclusion:: The paper can complete the compressed data quickly and efficiently in a large amount of power data. Improve the speed and accuracy of the algorithm while ensuring that the data is correct and complete, and improve the compression time and efficiency through the neural network. It gives better compression schemes cloud resource data grid.
A New Approach to Information Extraction in User-Centric E-Recruitment Systems

In modern society, people are heavily reliant on information available online through various channels, such as websites, social media, and web portals. Examples include searching for product prices, news, weather, and jobs. This paper focuses on an area of information extraction in e-recruitment, or job searching, which is increasingly used by a large population of users in across the world. Given the enormous volume of information related to job descriptions and users’ profiles, it is complicated to appropriately match a user’s profile with a job description, and vice versa. Existing information extraction techniques are unable to extract contextual entities. Thus, they fall short of extracting domain-specific information entities and consequently affect the matching of the user profile with the job description. The work presented in this paper aims to extract entities from job descriptions using a domain-specific dictionary. The extracted information entities are enriched with knowledge using Linked Open Data. Furthermore, job context information is expanded using a job description domain ontology based on the contextual and knowledge information. The proposed approach appropriately matches users’ profiles/queries and job descriptions. The proposed approach is tested using various experiments on data from real life jobs’ portals. The results show that the proposed approach enriches extracted data from job descriptions, and can help users to find more relevant jobs.
The changing use-cases of medium and large-scale geological maps in Hungary

Abstract. Systematic geological mapping of a country is done mainly by state authorities often involving industrial and academic partners in the process. The resolution of the result is controlled primarily by the financial background of the firms and the scale of the available base maps. In Hungary the state authority for geological mapping was established in 1869. Prior to this, geological maps were aimed to give an overview of the country. Later the mapping campaigns focused on providing support for the industry, agriculture and civil engineering. The need for detailed maps of raw materials led to increased efforts and founding, but from the end of the 1970s the mining industry did not need more maps and the geological mapping campaigns lost their inertia. Parallel with this, in the Cold War era, the ban to use detailed topography on geological maps ruined the ergonomic value of the produced cartographic materials. After the collapse of the socialist economy in 1989 the state authority concentrated on digitizing the existing data instead of systematic geological mapping. Geological maps became an illustration of extensive contributed books about certain regions, which were made by scientists for scientist. Though, many of these medium-scale geological maps are now provided via internet, large-scale maps are not available. In the last decade, the use-case of the geological maps seem to be changing again. The geological information interests not only the professionals, but the larger audience as well. Also, the spreading of geotourism, created a need to compile geotourist maps.
Transforming the Archives of the Great East Japan Earthquake into Global Natural Disaster Archives

Abstract Soon after the devastation brought by Great East Japan Earthquake (GEJE), a variety of Japanese agencies and organizations began to build digital archives of the disaster. Seven years onwards, over fifty digital archives collect and release millions of earthquake records. These earthquake disaster digital archive created for the Great East Japan Earthquake have the potential to lead the way for more general measures and countermeasures to reduce the impact of future ‘natural’ disasters. 1 We also believe that the records and knowledge of ‘natural’ disasters must be systematized and shared worldwide. As a first step, this research sheds light on examples and subjects of the digital archive of GEJE and report on basic knowledge that serves for the development of digital archive of ‘natural’ disasters’ in the future.
An efficient real-time data collection framework on petascale systems

High efficiency data collection remains a great challenge for HPC reliability and resilience, yet may pave the way to overcome the barrier before fault prediction. Not only for increasing scalability up to exascale systems but even for contemporary supercomputer architectures does it require substantial efforts to efficiently collect and analyze data that contains system fault information within the framework of faults prediction. In this term, the article mainly focuses on efficient data collection and data preprocessing, preferring to optimize an effective framework to improve the efficiency of data collection in petascale system. The core of our framework includes a data collection acceleration layer scheduled by H2FS, a further detailed information get by performance analysis tool, as well as a new method for log template extraction, which all attribute to a more efficient and convenient framework for the real-time data collection. Hereafter, we conducted extensive tests based on a petascale system to verify the solution, and the experimental results demonstrate to be effectiveness and scalability of our framework.
Towards a Multimodal Time-Based Empathy Prediction System

We describe our system for empathic emotion recognition. It is based on deep learning on multiple modalities in a late fusion architecture. We describe the modules of our system and discuss the evaluation results. Our code is also available for the research community.
Identifying implied security requirements from functional requirements

The elicitation of software security requirements in early stages of software development life cycle is an essential task. Using security requirements templates could help practitioners to identify implied software security requirements from functional requirements in the context of a software system. In this paper, we replicated a previous study that analyzed the effectiveness of security requirements templates to support the identification of security requirements. Our objective was to evaluate this approach and compare the applicability of the previous findings. We conducted the first replication of the controlled experiment in 2015, and subsequently conducted two differentiated replications in 2018. We evaluated the responses of 33 participants in terms of quality, coverage, relevance and efficiency and discussed insights regarding the impact of context factors. Participants were divided into treatment (security requirements templates) and control groups (no templates). Our findings support some previous results: treatment group performed significantly better than the control group in terms of the coverage of the identified security requirements. Besides, the requirements elicitation process performed significantly better in relevance and efficiency metrics in two of the three replications. Security requirements templates supported participants to identify a core set of the security requirements and participants were favorable towards the use of templates in identifying security requirements.
Analysis of Energy Consumption Model in Cloud Computing Environments

Cloud computing offers software as a service (SaaS), infrastructure as a service (IaaS), and platform as service (PaaS) on pay-as-you-go model over the Internet. Although Cloud have been attractive to businesses and other domains to accommodate their increasing demand for computational power on demand bases, the high energy consumption of Cloud data centers has recently become a serious issue. The high energy consumption not only causes the energy wastes and system instability but also generates low return on the investment (ROI) and adverse effects on the environment. Therefore, it is extremely necessary to reduce energy consumption while meeting the quality of service (QoS). This chapter presents a fine-grained energy consumption model and analyzes its effectiveness in energy consumption of data centers.
Building Open Access to Research (OAR) Data Infrastructure at NIST

As a National Metrology Institute (NMI), the USA National Institute of Standards and Technology (NIST) scientists, engineers and technology experts conduct research across a full spectrum of physical science domains. NIST is a non-regulatory agency within the U.S. Department of Commerce with a mission to promote U.S. innovation and industrial competitiveness by advancing measurement science, standards, and technology in ways that enhance economic security and improve our quality of life. NIST research results in the production and distribution of standard reference materials, calibration services, and datasets. These are generated from a wide range of complex laboratory instrumentation, expert analyses, and calibration processes. In response to a government open data policy, and in collaboration with the broader research community, NIST has developed a federated Open Access to Research (OAR) scientific data infrastructure aligned with FAIR (Findable, Accessible, Interoperable, Reusable) data principles. Through the OAR initiatives, NIST’s Material Measurement Laboratory Office of Data and Informatics (ODI) recently released a new scientific data discovery portal and public data repository. These science-oriented applications provide dissemination and public access for data from across the broad spectrum of NIST research disciplines, including chemistry, biology, materials science (such as crystallography, nanomaterials, etc.), physics, disaster resilience, cyberinfrastructure, communications, forensics, and others. NIST’s public data consist of carefully curated Standard Reference Data, legacy high valued data, and new research data publications. The repository is thus evolving both in content and features as the nature of research progresses. Implementation of the OAR infrastructure is key to NIST’s role in sharing high integrity reproducible research for measurement science in a rapidly changing world.
A Study of Camera Tip Position Estimating Methods in Transnasal Endoscopic Surgery

In this study, we developed a navigation system for transnasal endoscopic surgery based on estimations of tip positioning of laparoscopic cameras from related studies and research of orientation control methods using a 3D model display of organs during the process. In transnasal endoscopic surgery, to remove pituitary tumors, a nasoscope and surgical instruments are inserted into the nasal cavity, and an incision is made in the sphenoidal sinus (paranasal sinus) and Turkish saddle (sella turcica), so that removal of the tumor can be performed from the bottom of the pituitary gland. The advantage of this technique is that it is less invasive for the patient than a craniotomy. However, this approach requires surgeons to use advanced techniques. Thus, we have aimed to develop an AR surgical navigation system that superimposes information about the affected area in the image of the operating area. In this research, to develop an AR surgical navigation system, we take on one of the necessary tasks of investigating methods for estimating the tip position of the endoscopic camera.