Reviewerly/exHarmony huggingface dataset:
Inheritance of the Social Media Accounts in Poland

The challenges for the existing legal framework, entailed by the Internet revolution, to a growing extent tackle also on the problems of inheritance law. The Bundesgerichtshof’s decision of 12 July 2018 (III ZR 183/17) serves as a clear epitome of the complexity of questions triggered by the increasing transfer of private and market activity into the digital realm. The problem of inheritability of digital assets consists of at least three more particular dimensions. (a) Above all, it is the issue of getting access to the deceased’s account – especially, to its publicly unavailable content. By answering this question, BGH tackled also on two other dimensions of the problem. (b) As has been implicitly claimed in the judgment, the question of inheritance of an account is also interconnected with succession over particular assets stored on this account. In particular, access to an account may be a necessary prerequisite for exercising entitlements over particular assets, e.g. to download an item subjected to one’s intellectual property right, such as a photograph or a video recording. (c) The problem of digital inheritance exceeds also beyond the traditional domain of private law succession rules and constitutes a compound nexus, which combines elements of inheritance, contract, and public law (in the latter regard e.g. privacy and data protection).Observed from the Polish perspective, the judgment opens a new interesting chapter in the discussion over the concept and legal framework of ‘digital inheritance’, initiated a few years ago and encompassing so far several contributions that attempt to incorporate this notion into the existing schemes of private law. The BGH judgment brought the issue into wider public attention and triggered media coverage. The matter still lacks, however, a direct voice of Polish courts. From this perspective, the decision of BGH, along with previous US case-law, may provide a clear point of reference for the Polish legal system. To discuss its relevance, the following observations will focus on the issues related to a particular fraction of digital assets – i.e. the accounts on online platforms and the items stored at them by them by users. In major part, these remarks may be referred also to post-mortem fate of other types of a digital assets.
Thermal energy diagnosis of boiler plant by computer image processing and neural network technology

The paper aims to study the identification and diagnosis of infrared thermal fault of airborne circuit board of equipment, expand the application of intelligent algorithm in infrared thermal fault diagnosis, and promote the development of computer image processing technology and neural network technology in the field of thermal diagnosis. Taking the airborne circuit board in the boiler plant as the research object, first, the sequential analysis method was selected to collect the temperature changes during the operation of the circuit board. Second, on the basis of convolutional neural network, the program was written in Python, and the Relu function was used as the activation function establish the thermal fault diagnosis method of the on-board circuit board of the boiler plant equipment based on the convolutional neural network model. Third, based on the support vector machine intelligent algorithm, genetic algorithm was used to optimize the parameters, and combined with the grey prediction model, the infrared thermal fault diagnosis scheme of the circuit board of the multistage support vector machine boiler plant equipment was constructed. The results showed that the accuracy of the model after 6000 iterations was stable between 0.92-0.96, and the loss function value was stable at about 0.17. After the optimization of genetic algorithm, the accuracy of thermal fault diagnosis based on support vector machine model was optimized. Compared with grey prediction model, the accuracy of support vector machine model for fault diagnosis was higher, mean square error value was 0.0258, and the correlation coefficient was 91.55%. To sum up, the support vector machin model shows higher accuracy than grey prediction model, which can be used for thermal fault diagnosis.
Stochastic Modeling and Performance Analysis of Energy-Aware Cloud Data Center Based on Dynamic Scalable Stochastic Petri Net

The characteristics of cloud computing, such as large-scale, dynamics, heterogeneity and diversity, present a range of challenges for the study on modeling and performance evaluation on cloud data centers. Performance evaluation not only finds out an appropriate trade-off between cost-benefit and quality of service (QoS) based on service level agreement (SLA), but also investigates the influence of virtualization technology. In this paper, we propose an Energy-Aware Optimization (EAO) algorithm with considering energy consumption, resource diversity and virtual machine migration. In addition, we construct a stochastic model for Energy-Aware Migration-Enabled Cloud (EAMEC) data centers by introducing Dynamic Scalable Stochastic Petri Net (DSSPN). Several performance parameters are defined to evaluate task backlogs, throughput, reject rate, utilization, and energy consumption under different runtime and machines. Finally, we use a tool called SPNP to simulate analytical solutions of these parameters. The analysis results show that DSSPN is applicable to model and evaluate complex cloud systems, and can help to optimize the performance of EAMEC data centers.
An Advantage Actor-Critic Algorithm with Confidence Exploration for Open Information Extraction

Open Information Extraction (OIE) is a task of generating the structured representations of information from natural language sentences. Recently years, many works have trained an End-to-End OIE extractor based on Sequence-to-Sequence (Seq2Seq) model and applied Reinforce Algorithm to update the model. However, the model performance often suffers from a large training variance and limited exploration. This paper introduces a reinforcement learning framework that enables an Advantage Actor-Critic (AAC) algorithm to update the Seq2Seq model with samples from a novel Confidence Exploration (CE). The AAC algorithm reduces the training variance with a fine-grained evaluation of each individual word. The confidence exploration provides effective training samples by exploring the word at key positions. Empirical evaluations demonstrate the leading performance of our Advantage Actor-Critic algorithm and Confidence Exploration over other comparison methods.MSC codesOpen Information ExtractionSeq2Seq modelActor-CriticConfidence Exploration
Modified Sobel Mask to Locate Knee Joint Boundaries

Sobel masking algorithm is a very important technique to detect edges in an image.Comparing the Sobel gradient operator with other edge/boundary detection operators used repeatedly; Making an additional study on the traditional Sobel gradient operator, the benefits of Sobel mask are its quick speed of detecting edges.Meanwhile, it has also an impact on suppressing and smoothing noise.In addition, Sobel operator has a standard effect on detecting the edges.Although Sobel gradient operator has some advantages in different aspects, it exists some problems.The Existing Sobel masking technique is a type of edge detection in vertical and horizontal directions only and it ignores the boundary points in other directions.It cannot attain a true location of edge points in an image.In this paper, the existing sobel technique is improved by adding an increase of 315 degrees and 360 degrees in horizontal and vertical directions.This will have an effect of detecting the knee joint space of osteoarthritis.According to simulation results, they show this method is very simple and feasible, and the outcomes are more abundant than traditional Sobel edge detection.In this paper edge detection and noise interference problems are improved.
Query Rewriting and Semantic Annotation in Semantic-Based Image Retrieval under Heterogeneous Ontologies of Big Data

In the era of big data, it is of great significance to retrieve the semantic features from images by big data technique.However, most semantic query models perform poorly in actual images, which are distributed heterogeneously.Image ontology mapping provides a solution to the problem.This paper applies the H-Match algorithm to find the mapping relationship between image ontologies in peer-to-peer (P2P) environment, and rewrite user queries for heterogeneous image ontologies.The H-Match algorithm was developed under the framework called Helios evolving interaction-based ontology knowledge sharing (Helios).The weights of semantic annotation were calculated by a novel method, involving word frequency, position and feedback.The research results have great application potentials in various fields.
Combined ANFIS–Wavelet Technique to Improve the Estimation Accuracy of the Power Output of Neighboring PV Systems during Cloud Events

The short-term variability of photovoltaic (PV) system-generated power due to ambient conditions, such as passing clouds, represents a key challenge for network planners and operators. Such variability can be reduced using a geographical smoothing technique based on installing multiple PV systems over certain locations at distances of meters to kilometers. To accurately estimate the PV system’s generated power during cloud events, a variability reduction index (VRI), which is a function of several parameters, should be calculated precisely. In this paper, the Wavelet Transform Technique (WTT) along with Adaptive Neuro Fuzzy Inference System (ANFIS) are used to develop new models to estimate the PV system’s power output during cloud events. In this context, irradiance data collected from one PV system along with other parameters, including ambient conditions, were used to develop the proposed models. Ultimately, the models were validated through their application on a 0.7 km2 PV plant with 16 rooftop PV systems in Brisbane, Australia.
PELATIHAN PEMBUATAN BAHAN AJAR BERBASIS MULTIMEDIA BAGI GURU-GURU AL YASINI PASURUAN

&lt;p class="AbstractText"&gt;Multimedia provides opportunities for educators to develop learning techniques so as to produce maximum results, with multi media it is hoped that they will be easier to determine with what and how students can absorb information quickly and efficiently. Until now, interactive learning media has not been developed optimally in Indonesia. One obstacle in the development of interactive learning media is the lack of mastery of interactive media development technology by teachers, so the development of interactive learning materials with computers is less than optimal. This online-based learning media development training that will be developed and applied for teachers at the Al Yasini Pasuruan Foundation is expected to provide new insights in making instructional media, given the ability of this program in terms of making animations for attractive online-based learning media in a relatively easy way . With the mastery of online-based learning media by the teachers at the Al Yasini Pasuruan Foundation, it is hoped that it will trigger students' interest to be more enthusiastic in following the lessons, and ultimately will improve the overall quality of learning.&lt;/p&gt;
A Methodology to Compare the Usability of Information Systems

The usability of customer facing software interfaces is a source of competitive advantage for organisations. The usability of systems has also shown to encourage the effective and efficient completion of tasks and in consequence, operations. Furthermore, competitive analysis of the usability of software products has been shown to be a useful tool in the adoption of the user centred design philosophy within organisations. However, low adoption of usability evaluation is prevalent due to a lack of methodologies to support organisations in their endeavours to achieve better usability. Therefore, the purpose of this study is to present a methodology to compare the usability of information systems. By using such a methodology, organisations will be able to gauge the standard of the usability of their information systems, by comparing it to others.
Attention-Based Gated Scaling Adaptive Acoustic Model for CTC-Based Speech Recognition

In this paper, we propose a novel adaptive technique that uses an attention-based gated scaling (AGS) scheme to improve deep feature learning for connectionist temporal classification (CTC) acoustic modeling. In AGS, the outputs of each hidden layer of the main network are scaled by an auxiliary gate matrix extracted from the lower layer by using an attention mechanism. Furthermore, the auxiliary AGS layer and the main network are jointly trained without requiring second-pass model training or additional speaker information, such as i-vector. On the Mandarin AISHELL-1 dataset, the proposed AGS yields a 7.94% character error rate (CER). To the best of our knowledge, the results obtained when training on the full AISHELL-1 training set, are the best published currently for the end-to-end systems.
Extremal matrices for the Bruhat-graph order

We consider the class A 0 sym (n, k) of symmetric (0, 1)-matrices with zero trace and constant row sums k which can be identified with the class of the adjacency matrices of k-regular undirected graphs.In a previous paper, two partial orders, the Bruhat and the Bruhatgraph order, have been introduced in this class.In fact, when k = 1 or k = 2, it was shown that the two orders coincide, while for k ≥ 3 the two orders are distinct.In this paper we give general properties of minimal and maximal matrices for these orders on A 0 sym (n, k) and study the minimal and maximal matrices when k = 1, 2 or 3.
Discriminant Subspace Alignment for Cross-Project Defect Prediction

Cross-project defect prediction (CPDP) aims at recognizing defective software modules in a target project with the utilization of historical data from other source projects. Lately, CPDP has attracted much research interest. However, distribution discrepancy between the source and targert projects is known to have a negative effect on CPCP performance. Furthermore, most of the CPDP methods don't consider to explore the class label information in source data, thus their prediction performance may be limited. In the paper, we first introduce a new subspace alignment (SA) based domain adaptation method into CPDP, which can reduce the data distribution discrepancy between the source and target projects. Then, we propose a discriminant SA (DSA) approach for CPDP, the class label information of source project can be fully used. Experimental results from five public projects of NASA dataset demonstrate that DSA outperforms the related competing methods.
Hardware/Software Co-Design of Fractal Features Based Fall Detection System

Falls are a leading cause of death in older adults and result in high levels of mortality, morbidity and immobility. Fall Detection Systems (FDS) are imperative for timely medical aid and have been known to reduce death rate by 80%. We propose a novel wearable sensor FDS which exploits fractal dynamics of fall accelerometer signals. Fractal dynamics can be used as an irregularity measure of signals and our work shows that it is a key discriminant for classification of falls from other activities of life. We design, implement and evaluate a hardware feature accelerator for computation of fractal features through multi-level wavelet transform on a reconfigurable embedded System on Chip, Zynq device for evaluating wearable accelerometer sensors. The proposed FDS utilises a hardware/software co-design approach with hardware accelerator for fractal features and software implementation of Linear Discriminant Analysis on an embedded ARM core for high accuracy and energy efficiency. The proposed system achieves 99.38% fall detection accuracy, 7.3× speed-up and 6.53× improvements in power consumption, compared to the software only execution with an overall performance per Watt advantage of 47.6×, while consuming low reconfigurable resources at 28.67%.
MARC to BIBFRAME: Converting the PCC to Linked Data

The Program for Cooperative Cataloging (PCC) has formal relationships with the Library of Congress (LC), Share-VDE, and Linked Data for Production Phase 2 (LD4P2) for work on Bibliographic Framework (BIBFRAME), and PCC institutions have been very active in the exploration of MARC to BIBFRAME conversion processes. This article will review the involvement of PCC in the development of BIBFRAME and examine the work of LC, Share-VDE, and LD4P2 on MARC to BIBFRAME conversion. It will conclude with a discussion of areas for further exploration by the PCC leading up to the creation of PCC conversion specifications and PCC BIBFRAME data.
Modeling Emotion Oriented Approach through Agent-Oriented Approach

eLearning is introduced to promote self-learning anytime and anyway. To date, various components have been added into an eLearning platform to produce an interactive and engaging eLearning platform. Among an interest, component is embedded emotion into the eLearning system. While emotion is paying much attention nowadays, there is lacking a systematic way to model emotion-based eLearning. Without the systematic approach, it is hard to debug, design and develop an emotion-based e-learning system. This paper introduces emotion goals through the agent-oriented approach. Besides, we demonstrate how to design an emotion-based quiz master as an embedded emotion element for the eLearning system through the proposed modeling approach. This thesis suggests a standardized way to model an emotion-oriented application through Agent-Oriented Modelling (AOM). The AOM is extended to guide the elicitation, analysis, design and implementation of an emotion-oriented application, a kind of interactive application. With the emotion model, it can serve as a guide to design, redesign, discuss the emotional elements among the software development team. This is important for better debugging and project management, especially for emotion, led applications.
Task-scheduling Algorithm based on Improved Genetic Algorithm in Cloud Computing Environment

Background: Min-min and max-min algorithms were combined on the basis of the traditional genetic algorithm to make up for its shortcomings. Methods: In this paper, a new cloud computing task-scheduling algorithm that introduces min-min and max-min algorithms to generate initialization population, selects task completion time and load balancing as double fitness functions, and improves the quality of initialization population, algorithm searchability and convergence speed, was proposed. Results: The simulation results proved that the cloud computing task-scheduling algorithm was superior to and more effective than the traditional genetic algorithm. Conclusion: The paper proposes the possibility of the fusion of the two quadratively improved algorithms and completes the preliminary fusion of the algorithm, but the simulation results of the new algorithm are not ideal and need to be further studied.
Drone proximity detection via air disturbance analysis

The use of unmanned aerial vehicles (drones) is expanding to commercial, scientific, and agriculture applications, including surveillance, product deliveries and aerial photography. One challenge for applications of drones is detecting obstacles and avoiding collisions. A typical solution to this issue is the use of camera sensors or ultrasonic sensors for obstacle detection or sometimes just manual control (teleoperation). However, these solutions have costs in battery lifetime, payload, operator skill. We note that there will be an air disturbance in the vicinity of the drone when it’s moving close to obstacles or other drones. Our objective is to detect obstacles from monitoring the aforementioned air disturbance, by analyzing the data from the drone’s gyroscope and accelerometer. Results from three experiments using the Crazyflie 2 micro drone are reported here. We show that it is possible to reliably detect when a drone is passing under another using by using data mining algorithms to recognize the air disturbance caused by the other drone.
Multiple Face Recognition Surveillance System with Real-Time Alert Notification using 3D Recognition Pattern

This paper presents a surveillance system that can detect multiple persons and provides a notification once a banned person is detected. A person is banned once the system matches its face from the faces registered in the database. The system uses Haar Cascade to detect faces from the surveillance and Iterative Closest Point (ICP) to match the detected faces from the database. Position Map Regression Network is also used to construct the 3D models of the detected faces. The researchers tested the system for the process time of matching, the maximum angle the system can process, detection of faces given different conditions of occlusions, and detection of faces given different distances. The matching accuracy of the surveillance system is 90% and the fastest process time is 1 second.
An Application of Quantum Annealing Computing to Seismic Inversion

Quantum computing, along with quantum metrology and quantum communication, are disruptive technologies that promise, in the near future, to impact different sectors of academic research and industry. Among the computational challenges with great interest in science and industry are the inversion problems. These kinds of numerical procedures can be described as the process of determining the cause of an event from measurements of its effects. In this paper, we apply a recursive quantum algorithm to a D-Wave quantum annealer to solve a small scale seismic inversions problem. We compare the obtained results from the quantum computer to those derived from a classical algorithm. The accuracy achieved by the quantum computer is at least as good as that of the classical computer.
Are Blockchain-based Systems the Future of Project Management? A Preliminary Exploration

Blockchain technologies have introduced a platform for a new wave of project management systems, providing managers with a range of characteristics, capabilities, and feature sets to aid their practice as they engage in increasingly complex processes and projects.This paper presents an explorative case-study in which open-ended interviews were conducted with practicing project managers.The interviews are analysed to understand currently deployed project management tools, technologies, and methods and to contextualise how blockchain-based systems may allow for improvements.Five constructs emerge: transparency, control, dynamic status updating, incentives, and trust.Feedback suggests blockchain-based alternatives could offer significantly better performance within each of these constructs, and thus should be explored as the technological backbone to the next generation of project management systems.