Reviewerly/exHarmony huggingface dataset:
The Haemers Bound of Noncommutative Graphs

We continue the study of the quantum channel version of Shannon's zero-error capacity problem. We generalize the celebrated Haemers bound to noncommutative graphs (obtained from quantum channels). We prove basic properties of this bound, such as additivity under the direct sum and submultiplicativity under the tensor product. The Haemers bound upper bounds the Shannon capacity of noncommutative graphs, and we show that it can outperform other known upper bounds, including noncommutative analogues of the Lovász theta function (Duan-Severini-Winter, IEEE Trans. Inform. Theory, 2013 and Boreland-Todorov-Winter, arXiv preprint, 2019).
Seamless Model-Based System Development: Foundations

This is an extended abstract which gives an overview and sufficient references to work on model-based development of cyber-physical systems, both to its formal and scientific foundations as well as to more methodological issues including requirements engineering and architecture. Key concepts are interface behaviour and modular composition to support the modelling of the interaction between systems and their context, between subsystems, and between subservices as part of feature interactions. A logical framework is described for specification, implementation, refinement, and verification.
Design of Relief Materials Storage and Transportation System Based on Blockchain Technology

Blockchain technology has many features such as decentralization, traceability, tamper proof, and transparency. In this paper, a Relief Materials Storage and Transportation System based on blockchain technology is proposed. The blockchain and smart contract is studied to ensure the security of information exchange in the system. The technical architecture of the system is analyzed to be helpful for improving system efficiency and security.
Fog-Assisted Data Security and Privacy in Healthcare

With the advancement in the aging of world’s population and increments of people having chronic diseases, resulted in high demand for expensive medical treatment and care. In this view, the usage of latest technology solutions has been utilized at wide stage in order to improve the health of patient. One of the most prominent solution in this regard is the usage of cloud computing technology for the storage and process of patient health record. The medical data such as CT scan, MRI, X-rays, heart or kidney transplantation videos, and other health information should be available in digital format and such type of huge multimedia big data needs to be kept in the cloud. But, this usage of cloud computing can introduce delay while processing the data which is not tolerable. To deal with this problem, fog computing is used, which allows the data storage and its processing near to the data source. But it also brings with itself many security challenges such as data availability, security, privacy, performance, and interoperability, which requires high consideration. This chapter concentrates on these issues, i.e., how patient data can be retrieved for monitoring while reducing the latency and securing the private data of patient. A pairing-based cryptography technique such as an elliptic curve Diffie–Hellman key agreement protocol and a decoy technique are used to access and store data more securely along with the help of some cryptographic algorithms. In this chapter, we have also exasperated to gather some of the security matters which may stand up in the healthcare sector, and also discuss existing resolutions and emergent threats.
Use of Hadoop for Sentiment Analysis on Twitter’s Big Data

Nowadays, everyone is connected with social media like WhatsApp, Facebook, Instagram, Twitter, etc. Social media is an important platform for sharing ideas, information, opinion, and knowledge among human being. In spite of these basic attributes, we can also analyze the sentiments and emotions. However, handling such big data is a challenging task. Therefore, such type of analysis is efficiently possible only through the Hadoop. In the proposed research, we are going to analyze nature of a particular person on the basis of their behavior on social sites using Hadoop. For analysis purpose in our research, we have taken Twitter data. The sentiment analysis is used as positive, negative, and neutral by using the concept of decision dictionary. The result shows sentiment analysis with good accuracy.
Help ID serial post office burglary suspect accused of stealing more than 100 packages, postal computer

United States Postal Service Inspectors need your help identifying a burglary suspect accused of targeting several post offices in Kitsap County.
Enabling monocular depth perception at the very edge

Depth estimation is crucial in several computer vision applications, and a recent trend aims at inferring such a cue from a single camera through computationally demanding CNNs - precluding their practical deployment in several application contexts characterized by low-power constraints. Purposely, we develop a tiny network tailored to microcontrollers, processing low-resolution images to obtain a coarse depth map of the observed scene. Our solution enables depth perception with minimal power requirements (a few hundreds of mW), accurately enough to pave the way to several high-level applications at-the-edge.
MotionFlow: Time-axis-based Multiple Robots Expressive Motion Programming.

Robots have been gradually weaving into the fabric of many areas, including children's education and new media arts. Such combination promotes children's creativity and provides new artistic expression paradigms for artists. However, for robot systems that contain many degrees of freedom, the motion programming can be very complicated. This paper presents a time-axis-based computer aided design tool for multiple robots expressive motion programming, MotionFlow. It allows users who have no prior coding experience to easily add and edit motion clips modules on the time axis. In the meanwhile, it provides a rendered animation preview of the robots' motions, which enables users to evaluate the programming result without repeatedly testing the program on the hardware. In this way, users can perform rapid optimization and adjustment of the motion design. According to the user study, MotionFlow is very easy to use and very efficient for motion editing. It can greatly lower the threshold of multiple robots motion programming and allows users to focus more on creative work.
Ensemble of strategies and perturbation parameter based SOMA for optimal stabilization of chaotic oscillations

In this paper, we are investigating an original ensemble based adaptive strategy for the Self Organizing Migrating Algorithm (SOMA), namely the "Ensemble of Strategies and Perturbation Parameter in SOMA" (ESP-SOMA). The algorithm to which the main attention is focused as well as other state of the art metaheuristic algorithm (SHADE) are utilized in the complex task of time and quality optimal stabilization of chaotic system oscillations. Since there is a growing demand for intelligent and fast problem solutions in engineering, this paper represents an insight into the applicability and effectivity of modern adaptive/state of the art metaheuristic optimization algorithms in the task of constrained and difficult optimization problem. Experiments encompass two different cases of desired behaviour of chaotic system, and two different designs of objective function in terms of complexity. The simple statistical comparison of the results given by four different metaheuristic algorithms is also reported here for the pairs of generic and enhanced versions.
Analyze the Research of Internet of Information Collection Technology Based on Converged Communication

With the in-depth adjustment of our country’s economic structure and the rapid development of various industries, the situation of production safety is severe and complex. There are many types of hidden dangers, the supervision departments have greater risk prevention tasks and higher requirements. The traditional safety supervision method is far from meeting the needs of the current situation. especially in the face of normal conditions for epidemic prevention and control. As an advanced information technology, the Internet of Things (IOT)which support various types of sensor data in front of high-risk industries such as hazardous chemicals, coal mines, non-coal mines (including tailings ponds), fireworks and firecrackers, uses data collection, intelligent analysis, and safe isolation modules to realize the dynamic transmission of real-time monitoring data in batches. It provides reliable and true data support for monitoring and early warning applications, and helps regulatory authorities innovate online supervision and implement the responsibility of corporate safety production.
Estimation of daily global solar irradiance from the air temperature in the state of Paraná, Brazil

ABSTRACT Global solar irradiance (GSI) is a fundamental source of energy on Earth. Despite its importance, sunshine or solar irradiance data are rarely available from weather stations. In the absence of available data, there are empirical methods that can be used to estimate solar irradiance. The objective of this study is to calibrate the parameters and to evaluate the performance of four empirical models of solar irradiance estimation (those of Chen, Hargreaves, Hunt, and Richardson) from air temperature data for eight localities in the state of Paraná, Brazil. Data were obtained from the Meteorological Database for Teaching and Research (BDMEP). For the comparison of means among the models, the Kruskal-Wallis non-parametric test was used. Dunn’s multiple comparison tests were used to analyze which models presented different means from the others. The performance of each model was assessed using the indices Pearson correlation coefficient (r), mean bias error (MBE), root mean square error (RMSE), Wilmott concordance index (d), performance index (c) and the Nash-Sutcliffe efficiency (NSE) coefficient. It was observed that the models proposed by Chen and Hunt presented the best performances in the estimation of GSI for the studied Paraná state localities, given that they yielded results which are closer to the observed historical data.
Improvements to the Robust Transform Based on the Weighted Median Operator Algorithm

The Robust Transform based on the Weighted Median operator algorithm calculates the transform of a signal when it has been exposed to impulsive noise. Since this algorithm demands very long execution time, it is not useful for real time signal processing systems. In this context, this work presents several strategies to improve its performance, such as the reduction of redundant calculations, optimization in the memory access, and a multithreads version of the algorithm. Besides, the original estimation method is modified to decrease even more the average execution time, keeping the quality level of the numeric results. The experimental results show a 30% performance improvement by reducing redundant calculations and optimizing the memory access, without making modifications to the estimation method and without using multi-threaded processing; 93% performance improvement by introducing modifications to the estimation method; and 97% performance improvement by incorporating the multi-threaded processing.
Research on Obstacle Avoidance and Trajectory Planning Strategy of Automatic Parking System

A mathematical model is established according to motion characteristics of vehicles during parking, and an inequality algorithm is established according to obstacle avoidance rules. Its feasibility is verified using MATLAB simulation trajectory planning algorithm. During scene setting, a scene of common longitudinal parking with obstacles parked on the parking trajectory is simulated. The vehicle speed is set to a constant low speed, and the algorithm model is used to control the steering wheel angle at different time points to simulate the trajectory. The simulation result proves that the algorithm is real and effective for the scene set here.
Automated algorithm configuration and design

tutorial Automated algorithm configuration and design Share on Authors: Manuel López-Ibáñez University of Manchester, UK University of Manchester, UKView Profile , Thomas Stützle ULB, Brussels, Belgium ULB, Brussels, BelgiumView Profile Authors Info & Claims GECCO '20: Proceedings of the 2020 Genetic and Evolutionary Computation Conference CompanionJuly 2020 Pages 1075–1100https://doi.org/10.1145/3377929.3389875Online:08 July 2020Publication History 0citation115DownloadsMetricsTotal Citations0Total Downloads115Last 12 Months36Last 6 weeks4 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Strongly bounded termination with applications to security and hardware synthesis

Termination checking is a classic static analysis, and, within this focus, there are type-based approaches that formalize termination analysis as type systems (i.e., so that all well-typed programs terminate). But there are situations where a stronger termination property (which we call strongly-bounded termination) must be determined and, accordingly, we explore this property via a variant of the simply-typed λ-calculus called the bounded-time λ-calculus (BTC). This paper presents the BTC and its semantics and metatheory through a Coq formalization. Important examples (e.g., hardware synthesis from functional languages and detection of covert timing channels) motivating strongly-bounded termination and BTC are described as well.
Information system security outsourcing and information sharing level decision

Recent information systems investment have focused on outsourcing IS security and supply chain integration.MSSPs are the most efficient way to provide IS security by leveraging the economies-of-scale in providing a critical mass of human Internet security expertise. While information sharing facilitate optimization of security functions, they also exacerbate the information security risk. This paper focus on game problems between enterprise and MSSP. we use differential game to study interaction effect between the MSSPs' input and supply chain integration level, then two cases were analyzed.
Soft-Sensor Model of Mine Water Inrush Sources based on PCA-CRHJ Network

The soft-sensor model for identifying the source of mine water inrush and its performance are studied experimentally. In this novel framework, principal component analysis (PCA) has been used to decompose the original water quality historical data into several sub principal component data which can effectively extract the characteristics of multivariate time series. After that, cycle reservoir with hierarchical jumps (CRHJ) model is established by training and verifying the principal component data. Through the simulation and verification of the historical water inrush data of two measured mines, the experimental results show that the PCA-CRHJ model shows the best performance compared with echo state network (ESN) and cycle reservoir with jumps (CRJ) models generally. The hybrid model, with accurate and stable results, is highly effective for mine water inrush source discrimination and can flexibly be applied in other mine regions.
DFS <sup>3</sup>

Distributed file systems present distinctive forensic challenges in comparison to traditional locally mounted file system volume. Storage device media can number in the thousands, and forensic investigations in this setting necessitate a tailored approach to data collection. The Hadoop Distributed File System (HFDS) produces and maintains partially persistent metadata that is pursuant with a logical volume, a file system, and file addresses on the centralized server. Hence, this research investigates the viability of using a residual central server digital artifact to generate a history model of the distributed file system. The history model affords an investigator a high-level perspective of low-level events to narrow investigative process obligations. The model is generated through set-theoretic relations of the file system essential data structure. Graph-theoretic ordering is applied to the events to provide a history model. The research contribution is a rapid reconstruction of the HDFS storage state transitions generating timelines for system events to forensically assess HDFS properties with conceptual similarity to traditional low-level file system forensic tool output. The results of this research provide a prototype tool, DFS3, for rapid and noninvasive data storage state timeline reconstruction in a big data distributed file system.
Exploring Chapel Productivity Using Some Graph Algorithms

A broad set of data science and engineering questions may be organized as graphs, providing a powerful means for describing relational data. Although experts now routinely compute graph algorithms on huge, unstructured graphs using high performance computing (HPC) or cloud resources, this practice hasn't yet broken into the mainstream. Such computations require great expertise, yet users often need rapid prototyping and development to quickly customize existing code. Toward that end, we are exploring the use of the Chapel programming language as a means of making some important graph analytics more accessible, examining the breadth of characteristics that would make for a productive programming environment, one that is expressive, performant, portable, and robust.
Retrieval of Videos of Flowers Using Deep Features

This paper presents an algorithmic model for the retrieval of natural flower videos using query by frame mechanism. To overcome the drawback of traditional algorithms, we propose an automated system using a deep convolutional neural network as a feature extractor for the retrieval of videos of flowers. Initially, each flower video is represented by a set of keyframes, then features are extracted from keyframes. For a given query frame, the system extracts deep features and retrieves similar videos from the database using k-nearest neighbor and multiclass support vector machine classifiers. Experiments have been conducted on our own dataset consisting of 1919 videos of flowers belonging to 20 different species of flowers. It can be observed that the proposed system outperforms the traditional flower video retrieval system.
Learning Engineering @ Scale

Scaled learning requires a novel set of practices on the part of professionals developing and delivering systems of scaled learning. IEEE's Industry Connections Industry Consortium for Learning Engineering (ICICLE) defines learning engineering as "a process and practice that applies the learning sciences, using human-centered engineering design methodologies, and data-informed decision-making to support learners and their development." This event will bring together learning engineering experts and other interested conference participants to further define the discipline and strategies to establish learning engineering at scale. It will also serve as a gathering place for attendees with shared interests in learning engineering to build community around the advancement of learning engineering as a professional practice and academic field of study.
A Method for Determining Web News Suitable as Teaching Materials of Regional Study in Elementary Schools

Newspaper in Education (NIE) is an educational approach to utilize the newspaper as a teaching material. The purpose of our research is to support elementary school teachers by recommending Web news suitable for regional study in NIE for elementary schools. This paper analyzes news articles within NIE worksheets and proposes a method to determine news articles suitable as teaching materials for regional study using Support Vector Machine (SVM) based on features of the contents of news articles, regionality and readability of articles. The effectiveness of the proposed method was evaluated by the experiments. From the experimental results, we confirmed that the proposed method using Word2Vec, Learned Kanji and Local Characteristic Words as the features obtained the highest precision of 87.6%. We also confirmed that there is a possibility that the proposed method can be applied to news articles in any region.
Natural Language Processing in Online Reviews

The analysis of the online data posted on various e-commerce sites is required to improve consumer experience and thus enhance global business. The increase in the volume of social media content in the recent years led to the problem of overfitting in review classification. Thus, there arises a need to select relevant features to reduce computational cost and improve classifier performance. This chapter investigates various statistical feature selection methods that are time efficient but result in selection of few redundant features. To overcome this issue, wrapper methods such as sequential feature selection (SFS) and recursive feature elimination (RFE) are employed for selection of optimal feature set. The empirical analysis was conducted on movie review dataset using three different classifiers and the results depict that SVM could achieve f-measure of 96% with only 8% selected features using RFE method.
Adaptive Artificial Bee Colony (AABC)-Based Malignancy Pre-Diagnosis

Lung cancer is one of the leading causes of death. The survival rate of the patients diagnosed with lung cancer depends on the stage of the detection and the timely prognosis. Hence, early detection of anomalous malignant cells is needed for pre-diagnosis of lung cancer as it plays a major role in the prognosis and treatment. In this work, an innovative pre-diagnosis approach is suggested, wherein the size of the dataset comprising risk factors and symptoms is considerably decreased and optimized by means of an Adaptive Artificial Bee Colony (AABC) algorithm. Subsequently, the optimized dataset is fed to the Feed-Forward Back-Propagation Neural Network (FFBNN) to perform the training task. For the testing, supplementary data is furnished to well-guided FFBNN-AABC to authenticate whether the supplied investigational data is competent to effectively forecast the lung disorder or not. The results obtained show a considerable improvement in the classification performance compared to other approaches like Genetic Algorithm (GA) and Particle Swarm Optimization (PSO).
Automatic Monitoring of Grid Operation Risks Based on Data Mining

On the basis of recognizing importance of safe operation in power grid, more comprehensive evaluation index system is built in this paper for power grid engineering operations. Moreover, interval analytic hierarchy process is used to determine weight of evaluation index, which greatly reduces influence of subjective factors and makes evaluation index more scientific and reasonable. Besides, concept of interval number is introduced to minimize influence of subjective factors in determining weight of evaluation indicators. In addition, power grid operation is divided into four subsystems structure, equipment, management, and environment so that comprehensive evaluation and research on grid operation risks can be performed. What's more, factors affecting power grid engineering are divided into static influence factors in which structure of power grid and operating status of equipment are concluded and dynamic influence factors where external environment and internal management are involved. Additionally, according to characteristics and requirements of power grid engineering, matter-element extension method is used to construct evaluation model and calculate its correlation degree. Besides it, combined with examples to prove applicability of model, it is pertinently pointed out that operation and maintenance management of equipment need to be strengthened so that professionalism and skills of management personnel can be improved, and safe and stable operation of power grid will be guaranteed.