Reviewerly/exHarmony huggingface dataset:
Algorithm for Planning, Generating and Simulating Trajectories for a Group of Drones

The methodology of generating trajectories for a group of drones (Unmanned Aircraft System - UAS) was presented in the paper. The software enables flight planning for up to several thousand Unmanned Aerial Vehicles (UAV). The algorithm chooses the i-th UAV's position (Di) in pair with the target position Pj in order to obtain minimum value of the sum of all pair distances DiPj. The system can work in real time even for a large groups of UAV's, which is very difficult using standard gradient methods, especially for low computing resources.
Multi-record Matching Strategy in Assessment of Complex Information System Skills

Complex information system is widely used in modern society, and its skills assessment plays an important role in its practical work. Research on multi-record matching strategy benefits to the quality of assessment results on complex information system skills. This paper takes ERP software as an example and studies the multi-record matching strategy. It firstly designs and implements five record-matching methods, depending on the keywords, the local optimal accuracy, the local optimal scores, the global optimal accuracy and the global optimal scores, and then carries on the comparison to the effect of them. The keywords matching means that all the scoring fields in one record are matched, then the whole record is deemed to be matched. The optimal method refers to selecting the record with the maximum accuracy or scores as the matched record. The local way of matching relies on the order of the records, whereas the global one considers all the possible matches for the determination of the result. When using the accuracy as the optimal selection basis, the number of matching fields in one record must be counted. At the time if the scores are the criteria, the scores of matched fields need to be calculated before working out the final matched records. The paper is aim to help carry out the study in skills assessment and the practical work of complex information system.
The Design of Intelligent Teaching System in Universities Based on Internet of Things and Virtual Reality Technology

The rapid development of network technology provides learners with a variety of platforms, the development of technology to promote the progress of education, education forms and teaching methods have a significant change, effectively promote the education reform. But in the Internet of things and virtual technology’s rapid development today, the traditional digital teaching existing in the informatization construction of the lack of unified planning, distribution system, equipment compatibility is poor, the lack of personalized service and decision support system is increasingly prominent and serious problems influenced the teaching level of ascension, wisdom campus construction based on Internet of things that will become the theme of the current teaching system construction and development. The intelligent classroom learning platform described in this study is a specific solution to the informatization of college education and has certain innovation. The platform is a smart mobile terminal, interactive electronic whiteboard and computer-interactive classroom teaching system, adopting B/S and C/S hybrid architecture model, in order to build a teaching model based on the learning platform, and the implementation of teaching and practice has proved that management platform to help the teacher courses, classroom questioning, realize the interaction between teachers and students, living interaction, is according to the promotion of value.
Resource Provisioning Based Scheduling Framework for Execution of Virtual Machine in Heterogeneous Environment in Cloud Computing

Cloud computing is provides resource allocation which facilitates the cloud resource provider responsible to the cloud consumers. The main objective of resource manager is to assign the dynamic resource to the task in the execution and measures response time, execution cost, resource utilization and system performance. The resource manager is optimizing the resource and measure the completion time for assign resource. The resource manager is also measure to execute the resource in the optimal way to complete the task in minimum completion time. The virtualization is techniques mandatory to allocate the dynamic resource depends on the users need. There are also green computing techniques involved for enhanced the no of server. The skewness is basically used to enhance the quality of service using the various parameters. The proposed algorithms are considered to allocate the cloud resource as per the users requirement. The advantage of proposed algorithm is to view the analysis of cpu utilization and also reduced the memory usage.
Lie transformation on shortcut to adiabaticity in parametric driving quantum systems*

Shortcut to adiabaticity (STA) is a speedway to produce the same final state that would result in an adiabatic, infinitely slow process. Two typical techniques to engineer STA are developed by either introducing auxiliary counterdiabatic fields or finding new Hamiltonians that own dynamical invariants to constraint the system into the adiabatic paths. In this paper, an efficient method is introduced to naturally cover the above two techniques with a unified Lie algebraic framework and neatly remove the design difficulties and loose assumptions in the two techniques. A general STA scheme for different potential expansions concisely achieves with the aid of squeezing transformations.
Application of C4.5 Algorithm for Simulation of Prediction of Victory in Acceptance of Several Prospective Employees

Employees as individual who work for employer, Based on agreement and work agrements both written and unwritten, to carry out a job in a certainposition or activityby obtaining compensation paid based on a certain period, Job adjustments or other Provisions applied by the employer. Including individuals who do woork in public office,and it can be assumed that employeses are all residents who can do work and get a salary every mount, Both at school and company and school also require several new employees who will be placed as teachers in the field of sport. Therefore we need method or method to indentify prospective new employees in the field of sport at school. This was done to get the creteria needed by the school. C4.5 Algorithm can be used to make predictions or clafication of prospective new employees who want to join ABC school using the concept of entropy and gain.
PDKE: An Efficient Distributed Embedding Framework for Large Knowledge Graphs

Knowledge Representation Learning (KRL) methods produce unsupervised node features from knowledge graphs that can be used for a variety of machine learning tasks. However, two main issues in KRL embedding techniques have not been addressed yet. One is that real-world knowledge graphs contain millions of nodes and billions of edges, which exceeds the capability of existing KRL embedding systems; the other issue is the lack of a unified framework to integrate the current KRL models to facilitate the realization of embeddings for various applications. To address the issues, we propose PDKE, which is a distributed KRL training framework that can incorporate different translation-based KRL models using a unified algorithm template. In PDKE, a set of functions is implemented by various knowledge embedding models to form a unified algorithm template for distributed KRL. PDKE implements training arbitrarily large embeddings in a distributed environment. The effeciency and scalability of our framework have been verified by extensive experiments on both synthetic and real-world knowledge graphs, which shows that our approach outperforms the existing ones by a large margin.
Algorithms for Multi-Functional Complex Network Model

Different from previous complex network models based on graph theory, multi-functional complex network model (MFCN) only uses nodes to represent, and the content of graph theory is no longer applicable in it. In order to better apply MFCN, the storage structure, construction algorithm and traversal algorithm are defined and verified in the paper. The advantages of MFCN over the previous complex network models in constructing network are discussed.
Analysis of Association Rules of Investment Risk and Accounting Based on Artificial Intelligence Model

In recent years, with the rapid development of artificial intelligence technology, data mining technology has been used in various fields to analyze massive data and obtain valuable information. In the financial field, investment risk exists all the time. In order to better analyze the relationship between investment risk and accounting, this paper proposes an association rule analysis method based on artificial intelligence model. Aiming at the fact that the traditional AI model is disturbed by inaccuracy factors and the association rule analysis method cannot deal with financial data well, this paper studies the association rule analysis method in depth, improves the pruning process based on IUA algorithm, and proposes an interactive association rule mining algorithm HIUA based on Hash structure. By comparing and testing the performance of each algorithm, it is proved that the proposed algorithm greatly improves the mining efficiency.
Research on Motion Trajectory Tracking Control Method Based on Mobile Robot Servo System

Mobile robots have unparalleled advantages over other types of robots. They can be easily integrated into the daily life and working environment of human beings to help human beings accomplish certain tasks. Everyone in the group has independent decisions and behaviors, but the group has extensive altruistic cooperative behaviors. In the complex multi degree of freedom system, the robot needs to use its own multisensor information to identify the changes of external environment and its own state, and adjust its own motion actuator. In different task requirements, the shape of distributed control and the strictness of maintaining the distribution are different, so the requirements of environment are different. Based on automation technology, this paper analyzes the tracking control mode of mobile robot servo system, aiming at the characteristics of many degrees of freedom, high real-time and reliability requirements.
Identifying Suggestions in Airline-User Tweets Using Natural Language Processing and Machine Learning

Social networks have moved from online sites to interact with your friends to a platform where people, artists, brands, and even presidents interact with crowds of people daily. Airlines are some of the companies that use social networks such as Twitter to communicate with their clients through messages with offers, travel recommendations, videos of collaborations with YouTubers, and surveys. Among the many responses to airline tweets, there are users' suggestions on how to improve their services or processes. These recommendations are essential since the success of many companies is based on offering what the client wants or needs. A database of tweets was created using user tweets sent to airline accounts on Twitter between July 30 (2019) and August 8 (2019). Natural language processing techniques were used on the database to preprocess its data. The latest classification results using Naive Bayes show an accuracy of 72.44%.
Leveraging Statistic and Semantic Features for Similar Question Detection Using Fusion XGBoost

Question text similarity calculation is a fundamental and essential research problem for community question answering services. Different question text collections have various characteristics. Some frequently answered questions may have distinct statistical patterns, while some questions are syntactically different but semantically similar. To measure question similarity more adaptively to different kinds of question text, this paper proposes a method for identifying similar question utilizing the combination of both statistic and semantic features based on XGBoost. The method extracts semantic and statistical features from question text. After that, a feature set generation method is proposed, along with a model fusion strategy. Based on the standard Yahoo! dataset containing 25,569 questions with answers, three experiments have been conducted to evaluate the performance of the method. Results show that it achieves a precision of 88.65% and a recall of 71.85% outperforming a list of baseline methods.
Machine Fault Detection under the Background of Strong Interference Based on Line Learning Music Algorithm

Abstract First, this article introduces the importance of fault diagnosis in social production, and analyzes many existing machine fault detection methods. Considering the use conditions of the algorithm, these methods may be complicated and may not be suitable for online applications. Especially for the background of strong interference, this phenomenon occurs in the early stage of machine failure, and the traditional weak signal detection method loses its function. The characteristics of random noise modulation are given, and then an improved online learning evolutionary MUSIC algorithm is designed. First, it constructs a data array based on the sampling sequence, and then analyzes the signal spectrum under the condition that the number of signals is underestimated. Because traditional analysis methods are limited to current data, it is difficult to obtain stable analysis results under strong interference. Finally, some historical analysis data is weighted to obtain the true spectrum. The effectiveness of the algorithm can be seen through simulation. For weak fault signals under strong interference background, this method has better detection performance than FFT algorithm.
Extended Abstract: Tactical Translation Solutions for Component-based Content Management

Localization teams working in component-based content management systems (CCMS) respond to the challenges of CCMS and agile workflows with creative tactics. Through interviews, I shed light on the invisible work of localization teams and translators so that other technical communicators can become better advocates and CCMS architects.
Interpretability of Neural Network Binary Classification with Part Analysis

In this paper, we describe searching for an explanation of some particular decision of shallow convolutional neural network classification result. The method is the division of the initial input image, followed by the hiding and classification of separate parts. As a result of local interpretation search, we get a pair of images, each of which represents hidden parts of the initial image but classified differently. We tested the proposed approach for a shallow convolutional neural network model of different training quality using the Food-5K dataset. The average quantity of images that are used for explanations is estimated. We compared the analysis of the division of the images with known hiding of the superpixels method and showed that the success explanation rate is better for the proposed method.
A Framework to Improve Performance and Energy Efficiency of Embedded Intelligence Service Systems

Due to the high real-time requirement and limited resources, the computing performance and power consumption are critical to embedded intelligent service systems. The thread group scheduling strategy, as a common method to optimize multi-core processor’s performance, performs poorly in power optimization. However, with data volume continues proliferating, the power consumption is still surging. Correspondingly, many power optimization methods, if not scheduled well, will swap between cache states frequently, thus degrading device performance. In this paper, we propose a framework that combines the thread scheduling strategies with dynamic power mode control strategies together to make better trade-off between system performance and power consumption. Experimental results show that compared to using only scheduling policies, the systems combining Dynamic Power Management (DPM) and Bank Usage Table (BUT) policies have 14.8% and 9.5% performance growth as well as 9.9% and 13.2% power consumption reduction, respectively. The energy delay product (EDP) is decreased by 20.1% and 22.5%, respectively.
45‐1: <i>Invited Paper:</i> Multi‐Spliced LCDs for Foldable Mobile Device ‐ Seamless Technology and Applications

This article presents the use of spliced LCD displays on foldable mobile devices. In order to achieve a perfect spliced display, we need to use a four‐sided narrow border design LCD and visual seamless technology. The article introduces two new techniques for ultra narrow borders and uses these two techniques to overcome the rainbow pattern and textual continuity of the seam edges between two adjacent LCD displays.
Toward Model-Based Integration Specifications to Secure the Extended Enterprise

Abstract With the growing digitalization of manufacturing and the associated reliance on digital data, manufacturers, their supply chains, and their customers are exposed to many and varied digital threats. One such digital threat is compromised integrity of the data produced and consumed, the result of data tampering, either intentional (i.e., cyberattacks) or unintentional (e.g., human or system error). Because of the reliance on digital data, data tampering can lead to serious damage in the physical world, such as structurally weaker or functionally different parts. In the cybersecurity world, a key factor in the cost of a digital threat is the mean time to identify and contain that threat. Because of the complexity of today’s goods and the distributed nature of supply chains, designing, manufacturing, and distributing a product involves a significant number of heterogeneous information systems operating on both business and engineering data. These systems are integrated to consume data from each other in a high-volume and fast-paced environment where tampered data can be quickly propagated across many systems and organizations. To promptly identify the “infected” systems and contain the threat, an efficient data traceability strategy is required. Although we have previously developed traceability methods for file-based and agent-based integrations, this paper discusses the need for and benefits of traceability at the data field level, supported by model-based integration specifications (or data mappings). We then present a state of the art of existing formal integration definition methods. Finally, we conclude with an evaluation of these methods.
Logic-Based Multi-agent Path Finding with Continuous Movements and the Sum of Costs Objective

Multi-agent path finding with continuous movements and time (denoted MAPF\(^\mathcal {R}\)) is addressed. The task is to navigate agents that move smoothly between predefined positions to their individual goals so that they do not collide. Recently a novel solving approach for obtaining makespan optimal solutions called SMT-CBS\(^\mathcal {R}\) based on satisfiability modulo theories (SMT) has been introduced. We extend the approach further towards the sum-of-costs objective which is a more challenging case in the yes/no SMT environment due to more complex calculation of the objective. The new algorithm combines collision resolution known from conflict-based search (CBS) with previous generation of incomplete propositional encodings on top of a novel scheme for selecting decision variables in a potentially uncountable search space. We experimentally compare SMT-CBS\(^\mathcal {R}\) and previous CCBS algorithm for MAPF\(^\mathcal {R}\).
Cognitive Computing and Its Applications

Cognitive computing is a combination of cognitive science and computer science. Cognitive science is study of the human brain and its functionality whereas computer science seems to have severe impacts in our personal lives, healthcare, etc. Use of massive unstructured data in past few years have led to invention of cognitive systems. Programmable computers focused on fast calculations of large amounts of data whereas cognitive systems are intended towards exploring data, finding new correlations, and context in data in order to come up with new solutions. The goal of cognitive computing is to increase boundaries of human perception instead of replacing the way human thinks. A new industrial revolution in the form of cognitive computing is responsible for job automation healthcare, transportation, home automation, and many more. This chapter includes a brief history of cognitive computing and also the eras of computing in order to understand the growth of cognitive computing in future and also the applications based on cognitive technology.
Combinatorial Manifold learning Based on Wolf Pack Algorithm with Gaussian Parameters

Isometric mapping algorithm and stochastic neighbor embedding algorithm are both important nonlinear dimension reduction algorithms in manifold learning algorithm, which are used to reduce the dimension of data from the perspective of popular distance and probability respectively, both of these algorithms will eventually come down to a nonlinear optimization problem, but usually the optimization methods we use, gradient descent and Stochastic gradient descent, can easily lead to the trap of local MINIMA, therefore, in order to obtain better global convergence and faster convergence speed, we need a better algorithm to replace it, that is, the combined manifold wolves algorithm introduced in this paper.
Format and Usage Model of Security Patterns in Ontology-Driven Threat Modelling

To provide security for modern computer systems (i.e. identify threats and employ countermeasures) threat modelling is used on early stages of life cycle (requirements, design). Security patterns can be applied as security design decisions. However there are some challenges, related to management of security patterns, in particular, lack of methods to identify the necessity of security patterns and weak integration with security risk-based models. To overcome these restrictions we have developed an ontological format (schema), which allows a) creating security pattern catalogs, and b) defining context labels to map patterns with design decisions and security problems. We have proposed a usage model of security pattern catalogs. The usage model enables creation of domain-specific threat models, used for ontology-driven threat modelling. Also, OWL ontology and a free toolset (Java, OWL API) have been developed to manage security pattern catalogs and motivate development of high-level software tools for maintenance of security pattern catalogs.
Criminal Punishment Prediction Based on Fuzzy Document Vector Model

Abstract The drug addict problem is more and more critical to the world during current periods, analysing the criminal process and predicting the final punishment from judgments report is an interesting and important task. Existing studies on text analysis and language model supply methods based on special feature selection and ontology models generation which need external knowledge by human experts. In this paper, however, we creatively leveraged such text data onto prediction in the public judgments without human business. We propose a combined framework to capture the prediction problem by considering both valued based rules and fuzzy document models. This framework contains the complete process as: information extraction, term fuzzy and document vector regression. We setup an experiment on a real-world dataset and compare our model with traditional classification and regression methods. The results show that our model outperforms than others by both RMSE and R squared measures.