Miracle-dz/UniFAR-FTUs huggingface dataset:
Annealing Effects on XLPE Insulation of Retired High-Voltage Cable

In this paper, the performance of several annealing methods on three retired cables and the annealing effects on the improvement in the thermal and electrical properties of cross-linked polyethylene (XLPE) insulation were discussed. The cable insulation layer was peeled, and the peels near the inner semi-conductive layer were used as the test samples. Isothermal treatment and heat recycling treatment were performed at temperatures of 85, 90, 95, and 100 °C, and the temperatures were held in the heat recycling treatment for 8, 16, and 24 h, respectively. Each heat recycling treatment was repeated 20 times, and the duration of the isothermal treatment was the same as that for the heat recycling treatment with a 24 h temperature holding hour. Then, Fourier transform infrared spectroscopy (FTIR) and differential scanning calorimetry (DSC) were performed, and the dielectric spectrum, DC conduction current, and dielectric breakdown strength $E_{B}$ were measured. The results showed that damage involving molecular changes is linearly related to the cable service year. As the annealing temperature increases, the melting range and electrical conductivity decrease; the melting point, crystallinity, lamellar thickness, and dielectric breakdown strength increase; and the optimal values appear for the samples annealed at 95 °C. With an increased temperature holding hour, the peels annealed at 95 °C exhibit a decreased melting range and electrical conductivity and an increased crystallinity, melting point, lamellar thickness, and dielectric breakdown strength. As a result, the two different treatments are verified to effectively improve the thermal and electrical properties for the XLPE as early research on cable rejuvenation by heat treatment.
Corpusen etiketatze linguistikoa

In this article, we shall comment on the steps that have to be taken to give a linguistic label to a corpus and the difficulties that appear in this process. Our main objective was to highlight the importance of the labelling when preparing a corpus that is useful for linguistic research, and the need to establish criteria and to take the decisions that this entails. We also explain how semi-automatic methods are applied and how the manual revision that guarantees the quality of the corpus is carried out. Once the corpus has been revised and labelled, it will be useful both for carrying out linguistic analyses and for improving or assessing the linguistic tools and resources, and also for channelling automatic study.
Data envelopment analysis with neutrosophic inputs and outputs

Since the recent appearance of neutrosophic theory as a generalization of fuzzy and intuitionistic fuzzy theories, many multicriteria decision methods have adopted this theory to deal with incomplete and indeterminate data. However, it has not yet been applied to the data envelopment analysis (DEA) methodology. Therefore, this study presents a DEA model with triangular neutrosophic inputs and outputs that considers the truth, indeterminacy, and falsity degrees of each data value. As an alternative, a parametric approach based on what we term the variation degree of a triangular neutrosophic number is developed. This approach transforms a neutrosophic DEA model into an interval DEA model that can be solved using one of many existing techniques. Interval efficiency scores obtained from our numerical example show the flexibility and authenticity of the proposed approach.
Database of Historical Atlases: An Interactive Web Application

Abstract. Within the currently solved project of Czech Historical Atlas, an extensive analytic research of existing historical atlases was conducted with the aim of creating a large database of Czech (Czechoslovak) and foreign historical atlases released after 1950. During the analysis, the team collected information about more than 400 items. Beside the standard bibliographic description (title, author, country of origin, language, availability in the library, etc.), the database contains additional detail information on the content of the atlases, such as a chronological or spatial determination of the maps or applied methods of thematic cartography, that cannot be found in any library catalogue. The database is published as a complementary web application to Czech Historical Atlas web portal. Besides the large amount of collected information, the database of historical atlases in the form of an interactive web application provides a user-friendly visual analytic platform that enables the user to analyse information about historical atlases easily and effectively across fully interactive dashboards. The database can be used not only by academics in the field of historical and atlas cartography but also by broad public interested in this topic. The web application exists in Czech and English version to meet both, Czech and international public.
An Interpretable Disease Onset Predictive Model Using Crossover Attention Mechanism From Electronic Health Records

Analysis of patients’ Electronic Health Records (EHRs) can help guide the prevention of diseases and personalization of treatment. Therefore, it is an important task to predict the disease onset information (referred to as medical codes in this paper) within the upcoming visit based on patients’ EHR data. In order to achieve this objective, the real-time nature and high dimensionality of EHR data must be addressed. Moreover, the prediction results of the model must be interpretable. Existing methods mainly use Recurrent Neural Networks (RNNs) to model EHR data and adopt attention mechanism to provide interpretability. However, diagnosis and treatment information have usually been regarded as the same kind of information, the difference and relationship between the two parts being ignored. This has led to unclear analysis about the patient’s disease development and inaccurate prediction results. To address this limitation, we propose a CrossOver Attention Model (COAM). This model adopts two RNNs to process diagnosis and treatment information, respectively, and then deploys a crossover attention mechanism to improve prediction accuracy by leveraging the correlation between the two parts of information. It can learn effective representations of personal medical diagnosis and treatment, and provide interpretable prediction results. Experiments demonstrate that COAM can significantly improve the accuracy of prediction and provide clinically meaningful explanations.
Parallel architectures and runtime systems co-design for task-based programming models

The increasing parallelism levels in modern computing systems has extolled the need for a holistic vision when designing multiprocessor architectures taking in account the needs of the programming models and applications. Nowadays, system design consists of several layers on top of each other from the architecture up to the application software. Although this design allows to do a separation of concerns where it is possible to independently change layers due to a well-known interface between them, it is hampering future systems design as the Law of Moore reaches to an end. Current performance improvements on computer architecture are driven by the shrinkage of the transistor channel width, allowing faster and more power efficient chips to be made. However, technology is reaching physical limitations were the transistor size will not be able to be reduced furthermore and requires a change of paradigm in systems design. This thesis proposes to break this layered design, and advocates for a system where the architecture and the programming model runtime system are able to exchange information towards a common goal, improve performance and reduce power consumption. By making the architecture aware of runtime information such as a Task Dependency Graph (TDG) in the case of dataflow task-based programming models, it is possible to improve power consumption by exploiting the critical path of the graph. Moreover, the architecture can provide hardware support to create such a graph in order to reduce the runtime overheads and making possible the execution of fine-grained tasks to increase the available parallelism. Finally, the current status of inter-node communication primitives can be exposed to the runtime system in order to perform a more efficient communication scheduling, and also creates new opportunities of computation and communication overlap that were not possible before. An evaluation of the proposals introduced in this thesis is provided and a methodology to simulate and characterize the application behavior is also presented.
SWNF: Sign Prediction of Weak Ties Based on the Network Features

Most of existing community detection algorithms group nodes with more connections into the same community, and they are more concerned with links within the community. However, the weak ties between different communities are also important, because they can reflect the relationships between different communities, including helpful, friendly or negative, and adverse. Few studies focus on weak ties, although they are important. In this paper, we propose a novel sign prediction model based on the nodes features in the network, including the Jaccard similarity and the ratio of the negative degrees of all nodes, and the autoencoder technology that self-defines its loss function with the features of the communities. The proposed model maps the original network to a low-dimensional space so that the weak ties can be represented by low-dimensional vectors. We conduct experiments on the Epinions and Slashdot datasets and find that the proposed model outperforms the challenging state-of-the-art graph embedding methods in the sign prediction of weak ties in terms of accuracy and F1 score measurement.
A novel method for merging academic social network ontologies using formal concept analysis and hybrid semantic similarity measure

Purpose The purpose of this paper is to merge the ontologies that remove the redundancy and improve the storage efficiency. The count of ontologies developed in the past few eras is noticeably very high. With the availability of these ontologies, the needed information can be smoothly attained, but the presence of comparably varied ontologies nurtures the dispute of rework and merging of data. The assessment of the existing ontologies exposes the existence of the superfluous information; hence, ontology merging is the only solution. The existing ontology merging methods focus only on highly relevant classes and instances, whereas somewhat relevant classes and instances have been simply dropped. Those somewhat relevant classes and instances may also be useful or relevant to the given domain. In this paper, we propose a new method called hybrid semantic similarity measure (HSSM)-based ontology merging using formal concept analysis (FCA) and semantic similarity measure. Design/methodology/approach The HSSM categorizes the relevancy into three classes, namely highly relevant, moderate relevant and least relevant classes and instances. To achieve high efficiency in merging, HSSM performs both FCA part and the semantic similarity part. Findings The experimental results proved that the HSSM produced better results compared with existing algorithms in terms of similarity distance and time. An inconsistency check can also be done for the dissimilar classes and instances within an ontology. The output ontology will have set of highly relevant and moderate classes and instances as well as few least relevant classes and instances that will eventually lead to exhaustive ontology for the particular domain. Practical implications In this paper, a HSSM method is proposed and used to merge the academic social network ontologies; this is observed to be an extremely powerful methodology compared with other former studies. This HSSM approach can be applied for various domain ontologies and it may deliver a novel vision to the researchers. Originality/value The HSSM is not applied for merging the ontologies in any former studies up to the knowledge of authors.
Color Image Encryption and Decryption Based Pixel Shuffling with 3D Blowfish Algorithm

In recent years, With the wide development in communications networks and applied the e-government in most field depending on the internet and its technologies, with the development in the hackers ability to intruder the communication channels. Consequently, cryptographic techniques are required to accomplish a sufficient level of security, integrity, confidentiality as well as, to prevent unauthorized users from accessing of important information during storage, recovery and transmission of data. To meet this challenges, a novel and efficient color image encryption method depending on the diffusion and confusion mechanesim presented in this papper. a permutation manner existing by 2D Arnold cat map transform with row_colunm wise methods to make more distortion of the relationship among connected pixels of original image by hide the statistical structure of pixels. Moreover, the proposed method applied a blowfish algorithm on image to presented confusion and diffusion on it with 3D logistic mapping in order to diffuse the relation between plain and encrypted images by changing the gray value of original image pixels. The strongest of Encryption algorithm is evaluated dependent on the relation between the plain and cipher image must be contradicting by hiding the natural feature of image. Also, the algorithm in this study has been tested on some images and showed good results. These results evaluated using security and statistical analysis such as Entropy function, NPCR and UACI Factors with the degree of randomness.
A new semi-analytical approach for numerical solving of cauchy problem for differential equations with delay

In the paper, we present new semi-analytical approach for FDE’s consisting in combination of the method of steps and a technique called differential transformation method (DTM). This approach reduces the original Cauchy problem for delayed or neutral differential equation to Cauchy problem for ordinary differential equation for which DTM is convenient and efficient method. Moreover, there is no need of any symbolic calculations or initial approximation guesstimates in contrast to methods like the homotopy analysis method, the homotopy perturbation method, the variational iteration method or the Adomian decomposition method. The efficiency of the proposed method is shown on certain classes of FDE’s with multiple constant delays including FDE of neutral type. We also compare it to the current approach of using DTM and the Adomian decomposition method where Cauchy problem is not well posed.
Towards Automatic Diagram Description for the Blind

Conventional methods for describing complex diagrams to blind people are either ineffective or inefficient. We describe our preliminary work on how one can describe a diagram to a blind person with minimal supervision. We explain our text localization method which beats commercially available off-the-shelf state-of-the-art systems. We also provide a prototype user interface which can effectively describe diagrams based on our user study.
Performance Evaluation of Continuous and Discrete Particle Swarm Optimization in Job-Shop Scheduling Problems

The Particle Swarm Optimization (PSO) is an optimization method that was modeled based on the social behavior of organisms, such as bird flocks or swarms of bees. It was initially applied for cases defined over continuous spaces, but it can also be modified to solve problems in discrete spaces. Such problems include scheduling problems, where the Job-shop Scheduling Problem (JSP) is among the hardest combinatorial optimization problems. Although the JSP is a discrete problem, the continuous version of PSO has been able to handle the problem through a suitable mapping. Subsequently, its modified model, namely the discrete PSO, has also been proposed to solve it. In this paper, the performance of continuous and discrete PSO in solving JSP are evaluated and compared. The benchmark tests used are FT06 and FT10 problems available in the OR-library, where the goal is to minimize the maximum completion time of all jobs, i.e. the makespan. The experimental results show that the discrete PSO outperforms the continuous PSO for both benchmark problems.
Surge-Heading Guidance-Based Finite-Time Path Following of Underactuated Marine Vehicles

In this paper, a surge-heading guidance-based finite-time path-following control (SHG-FPC) scheme is created for an underactuated marine vehicle with complex unknowns. In lieu of line-of-sight guidance, both surge and heading can be guided by adapting to path-following errors and this can greatly enhance the decision-making autonomy pertaining to guidance of kinematics and dynamics, simultaneously. Complex unknowns can be exactly identified by virtue of the devised finite-time uncertainty observer (FUO), by which FUO-based nonsmooth control laws are able to achieve accurate guidance tracking, and thereby, contributing to entire finite-time path-following performance with strong robustness. Simulation results demonstrate the remarkable performance of the proposed SHG-FPC scheme.
Recognizing Human Activities From Video Using Weakly Supervised Contextual Features

Human activity recognition from the video is an important problem due to its potential applications in remote surveillance, content-based video retrieval, and in humanoid robots. Most of the visual activity recognition research in the last decade focus on recognizing basic human actions from well-constrained laboratory videos and depend upon fully annotated video dataset for model training. Whereas activity recognition from unconstrained videos is still very challenging due to large variations in object appearance and pose, occlusion, and inter and intraclass variations. It is an extremely laborious task to prepare large scale realistic video activity dataset with detailed annotations of the human, object, and their mutual interactions in each frame. Although it is intuitive to model contextual relationships form fully annotated dataset, however, it is unknown as to how reliably multilevel contextual features can be extracted in the absence of annotated dataset. To mitigate these challenges, we propose a weakly supervised approach for complex human activity recognition from realistic videos. The proposed approach requires only activity labels for each video to train the model. A novel multilevel contextual features and context estimation procedure from the un-annotated dataset is also introduced. Restricted Boltzman machine is used to systematically integrate multilevel contextual features. We evaluate the proposed approach on benchmark realistic surveillance video datasets for human–human and human–object interaction activity recognition. The experimental results show improved accuracies on benchmark datasets without using fully annotated datasets.
Localization of Moving Robots By Using Omnidirectional Camera in State Space Framework

This paper proposes a localization method of mobile robots with omnidirectional camera. An omnidirectional camera captures images of 360 ◦ , hence the view is invariant with respect to the direction of the robot’s sensor if the image is processed into correlation. The localization algorithm consists of two steps: regression from the correlation image into the position, and applying Kalman ﬁlter by incorporating the dynamic model of the mobile robot. The experiment shows the eﬀec-tiveness of the proposed approach.
A 115–135-GHz 8PSK Receiver Using Multi-Phase RF-Correlation-Based Direct-Demodulation Method

This paper presents the theory, design, and implementation of an 8PSK direct-demodulation receiver based on a novel multi-phase RF-correlation concept. The output of this RF-to-bits receiver architecture is demodulated bits, obviating the need for power-hungry high-speed-resolution data converters. A single-channel 115–135-GHz receiver prototype was fabricated in a 55-nm SiGe BiCMOS process. A max conversion gain of 32 dB and a min noise figure (NF) of 10.3 dB were measured. A data rate of 36 Gb/s was wirelessly measured at 30-cm distance with the received 8PSK signal being directly demodulated on-chip at a bit-error rate (BER) of 1e-6. The measured receiver sensitivity at this BER is −41.28 dBm. The prototype occupies 2.5 $\times $ 3.5 mm2 of die area, including PADs and test circuits (2.5-mm2 active area), and consumes a total dc power of 200.25 mW.
Volumetric Seam Carving

In volumetric image analysis and visualization, challenges have be induced by the increasing size of volume over recent years. Rendering and interacting with a volume with reduced size is preferable and highly needed. The primary concern in producing such downsized volumetric images is to preserve the important structures and those of the user’s interest, such as boundaries between materials. Typical volume reduction approaches usually perform uniform subsampling without the awareness of user-specified parameters such as the opacity and color transfer functions. However, it is also handy for the algorithm to have “global” encoding and control over the entire volume, meanwhile revealing some features of the data while it is being downsized. This thesis aims at providing a means of such type, extended from the famous seam carving operator that has been used widely in the task of image and video retargeting. Our work applies and extends the seam carving algorithm for videos proposed by Rubinstein et al. to downsize three-dimensional volumetric images. This extended technique computes and removes from the volume two-dimensional seams, or what we name and define as sheets, to reduce the size of the volume with minimum loss of important details measured by gradient. We aim at learning through experimentation the visual quality of seam carved volumetric images, making improvements based on feedback and potentially paving ways towards applications. With the great flexibility of the graph cut formulation, we implement in our algorithm the existing backward and forward energy optimization, and add extensions including isosurface protection and the encoding of the opacity transfer function. At the visual level, experimental results tell us when applied alone with fixed parameters, volumetric seam carving outperforms trivial approaches in preserving important structures only for part of the datasets, on which discussions are included at the best knowledge of the author.
A new energy-gap cost functional approach for the exterior Bernoulli free boundary problem

The solution to a free boundary problem of Bernoulli type, also known as Alt-Caffarelli problem, is studied via shape optimization techniques. In particular, a novel energy-gap cost functional approach with a state constraint consisting of a Robin condition is proposed as a shape optimization reformulation of the problem. Accordingly, the shape derivative of the cost is explicitly determined, and using the gradient information, a Lagrangian-like method is used to formulate an efficient boundary variation algorithm to numerically solve the minimization problem. The second order shape derivative of the cost is also computed, and through its characterization at the solution of the Bernoulli problem, the ill-posedness of the shape optimization formulation is proved. The analysis of the proposed formulation is completed by addressing the existence of optimal solution of the shape optimization problem and is accomplished by proving the continuity of the solution of the state problems with respect to the domain. The feasibility of the newly proposed method and its comparison with the classical energy-gap type cost functional approach is then presented through various numerical results. The numerical exploration issued in the study also includes results from a second-order optimization procedure based on a Newton-type method for resolving such minimization problem. This computational scheme put forward in the paper utilizes the Hessian information at the optimal solution and thus offers a state-of-the-art numerical approach for solving such free boundary problem via shape optimization setting.