Reviewerly/exHarmony huggingface dataset:
Towards Predicting Vehicular Data Consumption

Combining in-car multiple sensors measuring parameters that can be used to improve both safety and efficiency with a plethora of external data sources (e.g., traffic conditions, weather) which, if properly used, can significantly improve the overall trip experience. One source that can help the navigation and provide "context awareness", especially for autonomous driving, are the High Definition (HD) maps, which have recently witnessed a tremendous growth of popularity in vehicular technology and use. As they are limited to a particular geographic area with respect to a given point along a trip, different portions need to be downloaded (and processed) on multiple occasions throughout a given trip, along with the other data from internal and external sources. We take a first step towards formalizing the problem of Predicting Map Data Consumption (PMDC) in the future time instants for a given trip, based on a (time) window from its history, and investigate the use of Long Short-Term Memory (LSTM) networks - a special type of Recurrent Neural Networks (RNN). Significant efforts were focused on generating an appropriate dataset for this study, towards which we fused the information available in multiple heterogeneous data sources. We conducted experimental observations demonstrating the benefits of the proposed approach.
LightPIR: Privacy-Preserving Route Discovery for Payment Channel Networks

Payment channel networks are a promising approach to improve the scalability of cryptocurrencies: they allow to perform transactions in a peer-to-peer fashion, along multihop routes in the network, without requiring consensus on the blockchain. However, during the discovery of cost-efficient routes for the transaction, critical information may be revealed about the transacting entities. This paper initiates the study of privacy-preserving route discovery mechanisms for payment channel networks. In particular, we present LightPIR, an approach which allows a client to learn the shortest (or cheapest in terms of fees) path between two nodes without revealing any information about the endpoints of the transaction to the servers. The two main observations which allow for an efficient solution in LightPIR are that: (1) surprisingly, hub labelling algorithms - which were developed to preprocess “street network like” graphs so one can later efficiently compute shortest paths - also perform well for the graphs underlying payment channel networks, and that (2) hub labelling algorithms can be conveniently combined with private information retrieval. LightPIR relies on a simple hub labeling heuristic on top of existing hub labeling algorithms which leverages the specific topological features of cryptocurrency networks to further minimize storage and bandwidth overheads. In a case study considering the Lightning network, we show that our approach is an order of magnitude more efficient compared to a privacy-preserving baseline based on using private information retrieval on a database that stores all pairs shortest paths.
An Opinion Facilitator for Online News Media

With more and more voices and opinions entering the public domain, a key challenge facing journalists and editors is maximizing the context of the information that is presented on news websites. In this paper, we argue that systems for exposing readers to the many aspects of societal debates should be grounded in methods and tools that can provide a fine-grained understanding of these debates. The present article thereby explores the conceptual transition from opinion observation to opinion facilitation by introducing and discussing the Penelope opinion facilitator: a proof-of-concept reading instrument for online news media that operationalizes emerging methods for the computational analysis of cultural conflict developed in the context of the H2020 ODYCCEUS project. It will be demonstrated how these methods can be combined into an instrument that complements the reading experience of the news website The Guardian by automatically interlinking news articles on the level of semantic frames. In linguistic theory, semantic frames are defined as coherent structures of related concepts. We thereby zoom in on instances of the “causation” frame, such as “climate change causes global warming,” and illustrate how a reading instrument that links articles based on such frames might reconfigure our readings of climate news coverage, with specific attention to the case of global warming controversies. Finally, we relate our findings to the context of the development of computational social science, and discuss pathways for the evaluation of the instrument, as well as for the future upscaling of qualitative analyses and close readings.
Probabilistically Certified Management of Data Centers Using Predictive Control

Data centers are facilities with large number of servers providing cloud services. The increasing number of data centers in the last years has generated environmental concern due to the large amount of energy consumed by them. This also includes some auxiliary services such as the cooling equipment which is known to be very costly. For that reason, efficient data center strategies are needed in order to provide an acceptable quality of service (QoS) and suitable temperature for every server while using the least amount of resources possible. This article presents some strategies to deal with the unified workload and temperature problem that appears in the data center. As the system is modeled as a queue and the control variables have an hybrid nature, some highly parallelizable particle-based optimization algorithms are proposed to solve the optimization problem. Numerical simulations are provided in order to illustrate the effectiveness of the strategy. These simulations also show the improvements obtained from the GPU computing. Finally, a probabilistic evaluation approach is developed in order to provide certificates on the probability of constraint satisfaction without increasing the computational burden of the online problem. Note to Practitioners—This article addresses the problem of deciding in real-time the number of active servers in a data center that is required to meet the quality of service (QoS) demands while keeping energy consumption at a minimum. The temperature set point of the cooling equipment must also be taken into account, as it is advisable to use the minimum cooling that keeps the servers running in safe conditions. The management strategy proposed is based on predictive control. In this way, the number of active servers and temperature set point will be chosen so that the future energy consumption is minimized while guaranteeing that QoS and safety demands are met under different possible operating conditions. Furthermore, the proposed management strategy can be tuned depending on the QoS that it is desirable to provide. The proposed strategy will lead to energy-consumption improvements while having guarantees on the data center performance.
A First Approach for Implementing a Telepresence Robot in an Industrial Environment

While after months of virtual-only meetings, the benefits of face-to-face meetings have been clearly missed, the alternatives are not only remote and on-site. An interesting path to explore is the usage of telepresence robots (TPR). A TPR is a video conference system with remote control movement capabilities. It allows individuals to work remotely from one place (e.g., home) while at the same time experiencing another place (e.g., production plant). Despite having been used to promote social interaction in office, academic, and medical environments, TPRs have not been implemented in an industrial context. Similarly, formal research for TPR application in industrial environments is amiss. This paper provides an overview of a series of preliminary tests and interviews with technical users and industry experts to determine suitable implementation scenarios and requirements for a TPR in an industrial environment. As a conclusion, an initial set of requirements and four possible application scenarios are presented.
Engaging Children in Remotely Ideating and Programming Smart Things

Recent research tried involving different end-users in the design of smart things, such as smart watches that sense high temperature, process it, and react by vibrating. Designing new smart things is a lengthy process, which requires exploring what smart things are made of, ideating them, as well as programming them. Engaging children across an entire design process is thereby demanding, e.g., it requires sustained attention and logical reasoning. Engaging children in design at a distance is even more complicated, e.g., there are no reference models. This paper reports on the design of an online workshop, which aimed at engaging children across the entire design process of smart things, of increasing complexity, and at a distance. Children were different for age and gender. An engagement questionnaire was administered to understand whether children were engaged in smart-thing design at a distance. This paper offers a descriptive statistical analysis of the results of the questionnaire, which are discussed to distil lessons for future engaging smart-thing design workshops at a distance.
Transforming OPC UA Information Models into Domain-Specific Ontologies

Semantics interoperability is important for cyber-physical systems to enable complex data processing and facilitating interworking. OPC Unified Architecture (OPC UA) provides an extensible information model but lacks formal semantics. Ontologies based on the Web Ontology Language (OWL) can provide such formal semantics. Thus, we present a transformation approach that converts OPC UA information models (or only parts of them) into domain-specific ontologies. The transformation process consists of two steps. The first step is a mapping from OPC UA to OWL Full. The second step performs a graph transformation, based on SPARQL rules, into the domain-specified target ontology. The adaption of this transformation to the source information model and the target ontology can be accomplished by only adapting these transformation rules. The presented approach is evaluated for a use case of an industrial heating process to show its flexibility.
Promo Abuse Modeling in E-Commerce Using Machine Learning Approach

To increase the attractiveness of new customers and customer engagement, e-store makes various promos. However, as faced by XYZ, an e-commerce company based in Indonesia, some of the promos are prone to be misused by some users. A well-known type of abuse is the reduplication of accounts by the same user to get more coupons or promotions fraudulently. This abusive act can bring huge losses to the company, as the promotional campaign will miss the intended targets. The purpose of this study is to examine whether some data mining techniques such as the J48 Algorithm and Random Forest Algorithm can be deployed to detect promo misuse based on available customer's profile. The study uses a dataset from the transaction history recorded by XYZ company during the years 2018 and 2019. The results show that both algorithms can accurately model the FRAUD on the promos where the Random Forest Algorithm obtain a significantly higher level of accuracy.
The history of creation and development of automated control and design systems on the “Kamaz” automobile plant

The paper describes the history of the creation and development of automated control and design systems at the KAMAZ machine-building enterprise. A brief overview of the KAMAZ machine-building enterprise is given. KAMAZ is the main manufacturer of heavy trucks, the company is a fully integrated manufacturer of the trucks, from model development, to the products distribution and service support. The hardware used, the tasks solved during the period 1974 - 1991 years, modern hardware and information technology are described. Data acquisition and its processing were carried out using CM 1420 computers, then the data was transferred to the KAMAZ Main Computing Center. At the beginning of 1990, a network of registration devices was deployed, a numerical accounting of engines and power units was organized throughout its production cycle. Basic production and management services were chosen as the main direction of automation. In the 80s, the software for solving new tasks was improved, the tasks functions were refined for their computer implementation. With the advent of personal computers, a local computer network was created for tools accounting. The development of modern machine-building production management complexes is described: digital product, digital production, digital services. In the future information technologies development, the tasks are set to create supercomputer technologies for the automotive industry, to develop a digital service for product development with suppliers, to organize an access for technological services to a single information space from low-performance devices, and to create a secure local area network for technological equipment.
Saliency Computation for Virtual Cinematography in 360° Videos

Recent advances in virtual reality cameras have contributed to a phenomenal growth of 360$^{\circ }$∘ videos. Estimating regions likely to attract user attention is critical for efficiently streaming and rendering 360$^{\circ }$∘ videos. In this article, we present a simple, novel, GPU-driven pipeline for saliency computation and virtual cinematography in 360$^{\circ }$∘ videos using spherical harmonics (SH). We efficiently compute the 360$^{\circ }$∘ video saliency through the spectral residual of the SH coefficients between multiple bands at over 60FPS for 4K resolution videos. Further, our interactive computation of spherical saliency can be used for saliency-guided virtual cinematography in 360$^{\circ }$∘ videos.
Applying Deep Learning to Combat Mass Robocalls

Mass robocalls affect millions of people on a daily basis. Unfortunately, most current defenses against robocalls rely on phone blocklists and are ineffective against caller ID spoofing. To enable detection and blocking of spoofed robocalls, we propose a deep learning based virtual assistant application for smartphones to automatically vet incoming calls. Similar to a human assistant, the virtual assistant can pick up an incoming call and screen it with deep learning modules without user interruption to determine if the call is unwanted. We show that our system can block unwanted mass robocalls and preserve the user experience of a phone call.
Improving estimation of distribution genetic programming with novelty initialization

Estimation of distribution genetic programming (EDA-GP) replaces the standard variation operations of genetic programming (GP) by learning and sampling from a probabilistic model. Unfortunately, many EDA-GP approaches suffer from a rapidly decreasing population diversity which often leads to premature convergence. However, novelty search, an approach that searches for novel solutions to cover sparse areas of the search space, can be used for generating diverse initial populations. In this work, we propose novelty initialization and test this new method on a generalization of the royal tree problem and compare its performance to ramped half-and-half (RHH) using a recent EDA-GP approach. We find that novelty initialization provides a higher diversity than RHH and the EDA-GP also achieves a better average fitness using novelty initialization.
Analysis of the Application of Steganography Applied in the Field of Cybersecurity

The image files have data areas that are not very important for the visualization of the corresponding image and, if these areas are changed or altered, the image does not have a perceptible visual change, therefore they can be used to hide information, having almost certainty that the image has not been altered. The least significant bit (LSB) method consists in replacing the last bit of each byte and can do it in several bytes at the same time and the image will not have a noticeable change. Each substituted bit is part of a letter of the message that you want to hide in the given image. In general, images with PGM extension are very useful to apply the technique (LSB) since they are images whose content is given without following some algorithm of compression; This means that the content of the file is more readable and easy to work since it is in the ascci format (US standard code for information exchange). The content of an image with PGM extension, can be visualized using a simple text editor, so that the ASCII information can be seen as a representation of whole numbers, the value of these numbers will represent the intensity of color, 255 is white and 0 is black. If each row of the matrix represents an image data and every last digit, which is the least significant bit, is altered, then the eight bits corresponding to each altered byte would represent a letter belonging to a hidden message word. If this sequence is repeated several times you can hide more information such as phrases or sentences. Steganography is a useful tool in the field of computer security where it is required to verify that private information is correctly used by the user determined using the encryption in the personal key. There are already several software products that are responsible for carrying out this process, one of these cases is OpenStego, which encrypts the information using steganography and through passwords ensures that only the bearer of it will be able to visualize said information. We will show that the use of the Fortran, MatLab programming language presents great advantages for steganography since access to memory spaces through pointers allows freeing up memory at runtime, which provides a high degree of efficiency in the execution of a Program. Fortran, being a programming language oriented to mathematical calculation in general, has tools that facilitate its application and expressly the matrix calculation used in steganography.
Tellus-Onto: uma ontologia para classificação e inferência de solos na agricultura de precisão

Soil analysis laboratories demand large volumes of data used in precision agriculture. Among them, parameters that represent soil fertility such as texture and organic matter guide the fertilization process. However, this process can take time, thus limiting its usefulness. Therefore, this article proposes an ontology called Tellus-Onto that extends the state of the art in the classification of Brazilian soils according to the organic and textural composition. A series of axioms and semantic rules provided consultations and inferences about their instantiated basis. In order to test the ontology, we added 98 soil sample results and their classifications were inferred precisely and automatically.
Sparse regression over clusters: SparClur

Prediction tasks in personalized medicine require models that combine accuracy and interpretability. We propose an integer optimization approach for building sparse regression models with enforced coordination, using data partitioned among leaves in a prediction tree. We show that the method recovers the true underlying relationship between observations and target variables in large-scale synthetic data in seconds. We apply our method to several real-world medical prediction problems and observe that the additional structure imposed provides a substantial gain in interpretability, at a low cost to accuracy.
On chordal phylogeny graphs

An acyclic digraph in which every vertex has indegree at most i and outdegree at most j is called an (i,j) digraph for some positive integers i and j. Lee et al. (2017) studied the phylogeny graphs of (2,2) digraphs and gave sufficient conditions and necessary conditions for a (2,2) digraph to have a chordal phylogeny graph. Their work was motivated by problems related to evidence propagation in a Bayesian network for which it is useful to know which acyclic digraphs have chordal moral graphs (phylogeny graphs are called moral graphs in Bayesian network theory). In this paper, we extend their work. We completely characterize the phylogeny graph of a (1,i) digraph and an (i,1) digraph, respectively, for any positive integer i. Then, we study phylogeny graphs of (2,j) digraphs to show that the phylogeny graph of a (2,j) digraph D is chordal if the underlying graph of D is chordal for any positive integer j. Especially, we show that as long as the underlying graph of a (2,2) digraph is chordal, its phylogeny graph is not only chordal but also planar.
Negative learning ant colony optimization for the minimum positive influence dominating set problem

Recent research has shown that adding negative learning to ant colony optimization, in addition to the traditional positive learning mechanism, may improve the algorithms' performance significantly. In this paper we consider the application of this novel ant colony optimization variant to an NP-hard combinatorial optimization problem known as the minimum positive influence dominating set problem. This problem has applications especially in the context of social networks. Our results show, first, that the negative learning variant significantly improves over the standard ant colony optimization variant. Second, the obtained results show that our algorithm outperforms all competitors from the literature.
Adopting lexicase selection for michigan-style learning classifier systems with continuous-valued inputs

In this work, two variants of lexicase selection are examined for their performance improvement potential in the context of XCS variants allowing for continuous-valued inputs. We furthermore propose a niche-specific mode of operation for lexicase selection when adopted for utilization in XCS, implemented by a dedicated classifier experience storage. To evaluate the impact of lexicase selection on XCS' classification and regression capabilities, our proposed modified variants are tested across various continuous-valued single-step tasks, including both typical toy problems and real-world datasets related to the agricultural domain as well as regression problems.
Extended Dijkstra Algorithm in Path Planning for Vision Based Patrol Robot

In the recent years, Global positioning system (GPS) has worked with Global Navigation Satellite System (GNSS) to provide higher accuracy quality of locating a device. However, this localization service doesn't reach indoor facility. For an indoor type vision based patrol robot, the problem would encounter in path planning that allows the patrol robot to find the optimum path to reach the destination and return to home position. In this paper, an extended Dijkstra algorithm is proposed for path planning to the vision based patrol robot for surveillance purposes. This design used visual type sensor, range sensor and IMU system to instantaneously update the map data according to the current path of the vision robot and apply the path planning feature to perform obstacle avoidance and re-routing process based on the obstacle type faced by the robot. The result shown by this approach indeed capable to complete multiple cycles of testing with positive result.
Application of 5G Wireless Communication Technology in Hazardous Medical Waste Treatment

With the rapid development of economy in China, medical industry has made great progress nowadays, but there are still many problems during the very developing process. To be specific, medical waste is increasing year by year, and has already brought great pressure on the hazardous waste treatment industry. At present, there are still a large number of hospitals without standardized and efficient hazardous medical waste treatment process due to imperfect supervision process and logistics network. It is of great significance to study the effective treatment of hazardous medical waste. In this paper, a point-to-point (P2P) goods information document system has been established based on the fifth generation (5G) broadband wireless communication technology and big data platform so as to supervise the whole hazardous medical waste treatment process, and by employing such system, wide coverage, low delay and high security of hazardous medical waste treatment process can thus be realized.