Reviewerly/exHarmony huggingface dataset:
Applying Distributed Ledger Concepts to a Swiss Regional Label Ecosystem

Improving current supply chains by using distributed ledger technology (DLT) has been a highly researched topic during the last years. Currently, there are numerous articles elaborating on how such technologies can theoretically improve supply chains. However, case studies of such concepts and their economic value are scarce. In order to bridge this gap, we collaborated with a regional label company to clarify how a distributed ledger technology would benefit their ecosystem. This work answers the question of how such a prototype would look and whether it adds value. By following design science research practices, we design two artifacts based on requirements gathered in 14 interviews and discuss the artifacts’ elements within an evaluation panel. Our findings show that a distributed ledger application for the regional label ecosystem should have an open and decentralized architecture giving all participants full access to the shared data while still providing security and privacy for sensitive data. Additionally, data capturing should be simple. However, such an application does not add sufficient economic value and is currently of no practical interest in the regional label ecosystem as the expenditure likely exceeds the benefit.
Perfect Hash Families: The Generalization to Higher Indices

Perfect hash families are often represented as combinatorial arrays encoding partitions of k items into v classes, so that every t or fewer of the items are completely separated by at least a specified number of chosen partitions. This specified number is the index of the hash family. The case when each t-set must be separated at least once has been extensively researched; they arise in diverse applications, both directly and as fundamental ingredients in a column replacement strategy for a variety of combinatorial arrays. In this paper, construction techniques and algorithmic methods for constructing perfect hash families are surveyed, in order to explore extensions to the situation when each t-set must be separated by more than one partition.
Analysis of Heat Loss Control Method for Building Heating Transmission and Distribution based on Big Data Analysis

The phenomenon of leakage is serious and the heat loss is large in the heating network of North China. A heat loss control method for building heating transmission and distribution based on big data analysis is proposed. The adaptive coverage and network structure model of the information acquisition node of heat energy loss control for building heating transmission and distribution are constructed. The data of heat energy loss of building heating transmission and distribution collected by building heating energy sensor are processed by information fusion. The big data processing algorithm for heat energy loss control of building heating transmission and distribution is constructed, and the data control of heat energy loss in building heating transmission and distribution is realized by the method of information feedback adjustment. The embedded integrated control technology is used to optimize the heat loss control system of building heating transmission and distribution. The test results show that the system has good reliability, intelligence and big data analysis ability.
A Blockchain Solution for Operational Parameters Monitoring Platform for DC Microgrids

Large penetration of renewable distributed energy resources, as well as the effective integration of storage systems and electric vehicles, are some of the required strategies to reduce the impact of the energy sector on greenhouse gas emissions. Microgrids offer an efficient platform to facilitate the integration of these systems, however, the increased complexity this strategy comes with, requires the use of sophisticated, reliable, and secure monitoring and control systems. The outbreak of cryptocurrency technologies has drawn particular attention, especially, the underlying Blockchain technology. The decentralized structure of this technology enables implementing control strategies, parameters monitoring, and business processes in microgrids. The aim of this work is to present the development of a framework to monitor operational parameters from an islanded microgrid. To build the Blockchain network, the Hyperledger-Fabric development platform was used. To simulate the behavior of the microgrid, an object-oriented open-source library using the Modelica modeling language was used to model a DC microgrid. The performance of the architecture was evaluated through operational parameters such as the difficulty and the number of nodes. Results suggest that changes in these factors can affect significantly the transaction time. The proposed architecture can be used as a basis to study Blockchain communication effects in smart grids and their development.
Efficient Robust Parameter Identification in Generalized Kalman Smoothing Models

Dynamic inference problems in autoregressive (AR/ARMA/ARIMA), exponential smoothing, and navigation are often formulated and solved using state-space models (SSMs), which allow a range of statistical distributions to inform innovations and errors. In many applications, the main goal is to identify not only the hidden state, but also additional unknown model parameters (e.g., AR coefficients or unknown dynamics). We show how to efficiently optimize over model parameters in SSM that use smooth process and measurement losses. Our approach is to project out state variables, obtaining a value function that only depends on the parameters of interest, and derive analytical formulas for first and second derivatives that can be used by many types of optimization methods. We illustrate this by implementing Newton, Gauss–Newton, and quasi-Newton algorithms on a numerical example. The approach can be used with smooth robust penalties such as Hybrid and the Student's T, in addition to classic least squares. We use the approach to estimate robust AR models and long-run unemployment rates with sudden changes.
Federated Soft Gradient Boosting Machine for Streaming Data

Federated learning has received wide attention in both academic and industrial communities recently. Designing federated learning models applicable on the streaming data has received growing interests since the data stored within each participant may often vary from time to time. Based on recent advancements on soft gradient boosting machine, in this work, we propose the federated soft gradient boosting machine framework applicable on the streaming data. Compared with traditional gradient boosting methods, where base learners are trained sequentially, each base learner in the proposed framework can be efficiently trained in a parallel and distributed fashion. Experiments validated the effectiveness of the proposed method in terms of accuracy and efficiency, compared with other federated ensemble methods as well as its corresponding centralized versions when facing the streaming data.
Taller de visitas de obra, modo virtual por suspensión de docencia presencial

La pandemia provocada por la enfermedad del covid-19 trajo como consecuencia un confinamiento que impidió seguir con las visitas a obra de la asignatura Taller de visitas a obra de 4º curso del Grado en Fundamentos de la Arquitectura. Para solucionarlo se implementaron 3 metodologías diferentes de carácter virtual. La primera consistió en la elaboración de la memoria de construcción de un hospital basándose en el seguimiento de la obra de libre acceso a través de internet. La segunda y tercera consistieron en la adaptación de herramientas desarrolladas por 2 Proyectos de Innovación Educativa, el proyecto “Architect-Visitas de obra” donde se desarrollaron vídeos de sistemas constructivos y de defectos constructivos, y el proyecto “Buildings 360º” donde se desarrolló una plataforma virtual para la visita de obras. Para evaluar la adaptación de la asignatura se realizaron encuestas a los alumnos que mostraron un Índice de satisfacción muy elevado. Pandemic caused by the covid-19 disease brought as a consequence a confinement that prevented the continuation of visits to building sites of the 4th year of the Grade in Foundations of Architecture. To solve this problem, three different virtual methodologies were implemented. The first one consisted in elaboration of construction specifications of a hospital based on the monitoring of work shown on a web site with periodic photographs of its progress. The second and third consisted of adaptation of tools developed by 2 Educational Innovation Projects, the Architect-Worksites project where videos of construction systems and construction defects were developed, and the Buildings 360º project where a virtual platform for worksites visit was developed. In order to evaluate the adaptation of the subject, surveys were carried out with students, which showed a very high satisfaction index.
Grammar Compression by Induced Suffix Sorting

A grammar compression algorithm, called GCIS, is introduced in this work. GCIS is based on the induced suffix sorting algorithm SAIS, presented by Nong et al. in 2009. The proposed solution builds on the factorization performed by SAIS during suffix sorting. A context-free grammar is used to replace factors by non-terminals. The algorithm is then recursively applied on the shorter sequence of non-terminals. The resulting grammar is encoded by exploiting some redundancies, such as common prefixes between right-hands of rules, sorted according to SAIS. GCIS excels for its low space and time required for compression while obtaining competitive compression ratios. Our experiments on regular and repetitive, moderate and very large texts, show that GCIS stands as a very convenient choice compared to well-known compressors such as Gzip 7-Zip; and RePair the gold standard in grammar compression; and recent compressors such as SOLCA, LZRR, and LZD. In exchange, GCIS is slow at decompressing. Yet, grammar compressors are more convenient than Lempel-Ziv compressors in that one can access text substrings directly in compressed form without ever decompressing the text. We demonstrate that GCIS is an excellent candidate for this scenario, because it shows to be competitive among its RePair based alternatives. We also show that the relation with SAIS makes GCIS a good intermediate structure to build the suffix array and the LCP array during decompression of the text.
Setup of a local P2P electric energy market based on a smart contract blockchain technology

In an era of the widespread introduction of information technology, the broadest possibilities are opening up for the implementation of additional services for prosumers in the electricity market. The traditional electric power system of Russia allows customers to receive electricity only from large power plants, which leads to centralization and monopoly. Currently, the concept of distributed generation is gaining popularity. The idea is that small electric power generators are distributed throughout the local power network. The introduction of such an electrical network will improve the reliability of the system. However, such a network needs effective management and an electricity metering system. This paper proposes the use of distributed network blockchain technology as a solution to these problems. The proposed double-chain blockchain model will provide for the electric energy trading within the local electricity market, without third-party intermediaries, with a sufficient level of security and automation for all participants.
Exploring the research themes and their relationships of LIS in China from 2013 to 2018 using co-word analysis

This study aims to examine the research themes and their relationships of library and information science (LIS) in mainland China during the period of 2013–2018. Data were collected from the China Academic Journal Network Publishing Database (CAJD) and co-word analysis method was employed to measure correlation among the extracted key words. Seventeen theme-clusters, network characteristics (centre and density), clustering strategy diagram, and correlation network are revealed through cluster analysis, multidimensional scaling (MDS), and social network analysis. The results show that in recent years research on LIS in China, several important research themes like Electronic Government, Reading Promotion, and Social Media have emerged with high relevance, which can be considered as concentrated, mature and well-developed. Some research topics such as Ontology, Big data, and Cloud Computing have developed on a considerable scale, while others remain isolated and undeveloped like Knowledge Sharing and Virtual Community. In addition, the current research has also identified a few of emerging research topics with great potential for development like Mobile Library, Subject Services, and User Experience. The study is beneficial to understand the overall picture of research themes and their relationships of LIS in China in recent years to provide support for follow-up research.
A Novel Collective Crossover Operator for Genetic Algorithms

Crossover is the main genetic operator which influences the power of evolutionary algorithms. Among a variety of crossover operators, there has been a growing interest in multi-parent crossover operators in evolutionary computation. The main motivation of those schemes is establishing comprehensive collective collaboration of more than two chromosomes in the population to generate a new offspring. In this paper, a novel all-parent crossover operator called collective crossover for genetic algorithm is proposed. In this method, all individuals in the current population are involved in recombination part and one offspring is generated. The contribution of each individuals is defined based on its quality in terms of fitness value. The performance of the collective crossover operator is tested on CEC2017 benchmark functions. The results revealed that the proposed crossover operator performs better when compared to well-known two-parent crossover operators including one-point and two-point crossovers. In addition, the differences between collective crossover and the other crossover operators are statistically significant for the most cases.
Smart Donations: Event-Driven Conditional Donations Using Smart Contracts On The Blockchain

Recent work has questioned the largely unconditional nature of charitable donations and explored the value of conditional giving with contemporary donors. In this paper, we extend this work by exploring how to operationalise features of conditionality in charitable giving, situated in the context of large international non-governmental organisations (NGOs). Building on prior engagements with international aid organisations, we present design considerations and a conceptual architecture supporting real-time, conditional giving for individual and institutional donations. Our architecture leverages properties of distributed-ledger technologies (DLT) to empower donors to (i) attach conditions to their donation, (ii) store funds in a secure, decentralised escrow and (iii) automatically release funds once conditions are met. Unlike prior work that envisions radical disintermediation and the removal of intermediate NGOs using DLT, our work recognises the expertise of NGOs in tackling complex global problems and instead investigates compelling new way for charities to increase transparency and accountability by introducing dynamic pledge controls.
A Comparative Study of Indexing Techniques Effect in Big Data System Storage Optimization

The growth of the information and networking technologies such as mobile devices, social media, and cloud applications required an efficient management big data system. Moreover, especially storage system because of its great importance in terms of the cost of big data analysis to provide techniques which can search, access, process, retrieve, and then distribute data item in speed manner. So, the most important technique for dealing with big data storage in the cloud is Indexing and distribution. The continuous increase in big data requires improved indexing and distribution systems, which motivate new optimized indexing schemes. In general, most of these techniques categories the indexing techniques of big data to three general categories: Artificial Intelligent indexing (AI), Non-Artificial Intelligent indexing (NAI), and Collaborative Artificial Intelligent (CAI) indexing. The goal of this paper is to provide readers with a systematic understanding of insights the emerging techniques. This article provides a thorough survey of the storage in a big data system regarding the two most important terms in the storage (Indexing and Distribution) to gain in-depth knowledge for the existing hashing techniques with clarification of the main characteristics and how can use hash indexing. It also provides an overview of Indexing in the Hadoop framework, especially in HDFS, for future development.
Artificial Intelligence And Digital Forensics

Artificial intelligence (AI) is a well-established branch of computer science concerned with making machines smart enough to perform computationally large or complex tasks that normally require human intelligence; furthermore, it comprises a combination of technologies that can obtain insights and patterns from a massive amount of data which is a crucial element of forensic analysis. This chapter focuses on AI and its subfields: machine learning and deep learning—in general—and also details AI and data mining techniques pertaining to digital forensics. In highlighting the current shortcomings of prevailing approaches, we propose a new approach to offer a clearer insight into potential data, and/or detect variables of interest, as well as assess the future of digital forensics in the concluding section.
How Do I Understand Them? Integrating Empathy into Course Design through Personas

Literature from neuroscience and educational psychology are continuously underscoring the importance of emotion in learning, which brings considerable implications for education. While some areas, such as design thinking, have embraced empathy as a technique for better connecting design with user need, empathy as a construct still remains ambiguous. In addition, many course design theories and frameworks focus heavily, if not exclusively, on cognitive processes; apprenticeships of the head and hands. Educators hoping to intentionally and systematically engage in empathetic strategies, or apprenticeships of the heart, have little guidance from traditional course design models for doing so. The special session will introduce one tangible method to engage in and integrate empathy into course design.
Emulating Centralized Control in Multi-Agent Pathfinding Using Decentralized Swarm of Reflex-Based Robots

Multi-agent pathfinding (MAPF) represents a core problem in robotics. In its abstract form, the task is to navigate agents in an undirected graph to individual goal vertices so that conflicts between agents do not occur. Many algorithms for finding feasible or optimal solutions have been devised. We focus on the execution of MAPF solutions with a swarm of simple physical robots. Such execution is important for understanding how abstract plans can be transferred into reality and vital for educational demonstrations. We show how to use a swarm of reflex-based Ozobot Evo robots for MAPF execution. We emulate centralized control of the robots using their reflex-based behavior by putting them on a screen's surface, where control curves are drawn in real-time during the execution. We identify critical challenges and ways to address them to execute plans successfully with the swarm. The MAPF execution was evaluated experimentally on various benchmarks.
Deep Learning-Based Logging Recommendation Using Merged Code Representation

When developing a large scale software product, it is essential to share a common set of structural coding guidelines and standards among the project team members. In this paper, we propose MergeLogging, a deep learning-based merged network using various code representations for automated logging decisions or other tasks. MergeLogging archives the enhanced recommendation ability that utilizes orthogonal code features from code representations. Our case study with three open-source project datasets demonstrates that logging accuracy can reach as high as 93%.
Architectural Guidance in Automotive for Privacy and Security: Survey and Classification

Industrial domains, such as Internet of Things, Industry 4.0, and automotive, rapidly move towards autonomous connected systems. Security and privacy of these even-more connected systems demand particular attention. The whole value network serving original equipment manufacturers shall assist in addressing these concerns. System architects responsible for designing (parts) of such future systems can benefit from architectural guidance documents. For example, reference designs can help to resolve recurring architectural questions and assist in making high-level design decisions. A shared understanding of what guidance forms exist and how they differ is desired. Unfortunately, it is currently lacking.This paper surveys the state-of-the-art and state-of-practice of architectural guidance items in the automotive domain. We analyze what practitioners and researches perceive as guidance useful for addressing security and privacy concerns. To collect inputs, we conducted a survey within SECREDAS- a project comprised of 69 organizations from both industry and research domains. Based on the content of these inputs, we identified and cross-compared the following types of architectural guidance items: reference architectures, architecture frameworks, standards, protocols, and interfaces.The resulting overview and classification show practitioners which architectural guidance documents are actually used and which are relevant to consider. This paper also aims to inform discussions on architecting autonomous connected systems in general, as the need to address security and privacy concerns has a broad scope.