Reviewerly/exHarmony huggingface dataset:
Runtime analysis of population-based evolutionary algorithms

tutorial Runtime analysis of population-based evolutionary algorithms: introductory tutorial at GECCO 2020 Share on Authors: Per Kristian Lehrexwid University of Birmingham, Birmingham, UK University of Birmingham, Birmingham, UKView Profile , Pietro S. Oliveto University of Sheffield, Sheffield, UK University of Sheffield, Sheffield, UKView Profile Authors Info & Claims GECCO '20: Proceedings of the 2020 Genetic and Evolutionary Computation Conference CompanionJuly 2020 Pages 458–494https://doi.org/10.1145/3377929.3389890Published:08 July 2020 0citation72DownloadsMetricsTotal Citations0Total Downloads72Last 12 Months30Last 6 weeks6 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
Linearized decomposition codes and finite integer set coverings

In this paper, we study parallel erasure correction (PEC) matrix codes capable of correcting multiple row erasures simultaneously. Our PEC codes are based on linearized decomposition (LD) of polynomials and we show that the LD codes are capable of row erasure correction using small locality sets, even if roughly half the rows are erased. We also study coverings of finite set of integers using predetermined neighbourhoods and use the covering estimate obtained, to bound the rate of LD codes.
Knowledge Extraction from Healthcare Data Using User-Adaptable Keywords-Based Query Language

Nowadays, the volume of the information gathered by any organization increases more and more rapidly. It is essential to be able to use this information efficiently for it to benefit the operation of the organization. There is no point of gathering the information if it is not converted into knowledge. The knowledge extraction process becomes the backbone of any successful organization. Moreover, the extraction of the knowledge must be quick and efficient, so that the newly-obtained knowledge can be put in use at once. The problem addressed in this paper is how to allow the domain expert to extract the knowledge from their information systems themselves without involving the third party in the form of an IT specialist. This goal is of utmost importance for the domain experts, e.g. hospital managers and physicians, because they need to make decisions based on the available knowledge and to do it rapidly and efficiently. We propose a system in this paper that allows formulating queries in the natural language and that also adapts to the specifics of the user. Our experiments show that such kind of querying could provide an improvement in the decision-making process of healthcare professionals.
Applications in Financial Industry: Use-Case for Fraud Management

The actual major issues for application development in various domains, including the financial industry and the associated uses-cases, concern the ways in which big data can be approached in order to meet the real-case constraints. There are already available a lot of data analytics tools that can be successfully applied by the financial organizations in order to perform specific tasks in relationships with their partners and customers, respectively. A challenging use-case of data science in financial industry is represented by the fraud management in which the design solutions are based on supervised and unsupervised learning, in order to avoid the drawbacks of the legacy solutions based on rules. Innovative solutions also include anomaly detection in order to efficiently handle new cases that cannot be actually learned with the supervised predictive modeling-based approaches.
Development of a Test Battery for Cyber Soldiers

There is a great need for people with specialist knowledge about cybersecurity in both the civil and military sector. The Swedish Armed Forces has decided that a basic education for conscript cyber soldiers shall be established. The work presented in this paper describes the development of an aptitude test for use in the selection process of conscript cyber soldiers, including a work analysis, test development, and initial validation. Based on the results from the work analysis and a literature review on other cyber tests, the test battery CyberTest Future Soldiers (CTFS) was developed. The cyber test (CTFS) consists of one skill test (with six subareas), one abilities test (with four subareas), and one survey regarding the test taker’s interests. The results from the initial validation show that the different test sections (skill and ability) measure different aspects important for performance. This lack of correlation makes it possible to select highly skilled people as well as people on a lower skill level, but who should perform well during the education due to their high level of abilities.
Player Needs First: Understanding Player Needs Before Designing a K-pop Themed Mobile Game

The aim of this paper is to present how the Game User Survey (GUR) was used to develop a Minimum Viable Product (MVP) for a game, which mixes Idle mechanics with a K-pop thematic. Thus, we discuss about how User experience (UX) research approaches can be used to drive the definitions of the project scope and bring the perspective of the user (in this case, the player) to the development process. We used Design Thinking’s Double Diamond as a method. In the end, we present the editions learned and deal with this change in the game research paradigm, where the player is called only to validate the decisions made by the development team.
Research on Complexity of Single-Class Object Detection Model

At present, most of the object detection models are complex, mainly used to deal with large-scale, multi-classes object detection tasks. For medium-sized datasets, the single-class object detection task, it is unnecessary to use complex models. So we studied the complexity of the model. This paper tries two methods to reduce the complexity of the model: compress the model on the backbone network, and replace the original backbone network with a lightweight model, in single-class datasets, found on the detection performance in different complexity of the model presented similar convex function. It is proved that the medium complexity model is the best to solve the single-class object detection task according to the test results. Combined with feature visualization, and sensitivity analysis. It is proved that the medium-complexity model not only reduces the computational cost, but also has good generalization ability. For the datasets of different scales, the model needs to adopt different complexity. This paper provides a good performance model for single-class object detection tasks.
Joking AI via Visual Cues

We propose Artificial Intelligence (AI) based joking framework via visual cues. AI generates or retrieves funny captions relative to images which are shared by AI and humans for facilitating high level interaction between them. We also evaluate how funny the AI created funny captions using a social networking site (SNS).
Human Computer Interfaces Reconsidered: A Conceptual Model for Understanding User Interfaces

The article assesses how human computer interfaces are conceptualized in HCI textbooks and proposes a revised model for understanding user interfaces. In the last decade a variety of novel user interfaces has been introduced. Both new input and output technologies allow users to communicate with digital artifacts in almost limitless ways. However, our understanding of the human computer interface originates from early HCI research, based on the desktop metaphor, including the user, the system, input and output. This article raises the question, how do we conceptualize user interfaces in our own research and for our teaching. The review reveals that there is no consistent understanding of interface concepts in HCI textbooks and that there is a bias towards the desktop metaphor. As a candidate for an updated interface definition, I propose a revised interface model, including 5 concepts: interface paradigm, interaction technique, interaction style, interface platform and interface device. Finally, I report on two examples, how I have used the interface model in the classroom.
Expectation and Reaction as Intention for Conversation System

Intention plays an import role in human daily conversation. Conventionally, human intention exerts influence on conversation contents and atmosphere. Although dialogue systems that involve emotion awareness are popular, implementation of human intention on artificial intelligence does not draw much attention of researchers. The reason is that intention is usually not a spontaneous response of external stimulus, but a self-generated desire and expectation. Moreover, internal intentions are not subjected to external signals that can be observed by third parties. In this research, we experimentally used “reaction” and “expectation” factors to represent intention at a text level and created intentional conversation model based on transformer model. Preliminary results were given to show that applying intention is able to help the a dialogue system address a higher level of engagement in the conversation.
The Human Capital: Economic Analysis of Education [Teoria capitalului uman: analiza economică a educaţiei]

The role of human capital in economic and social development is an old theme, although economists do not yet agree on its exact importance. The paper reveals the cognitive value of this concept, thus responding to the critics who believe that this terminology devalues human dignity, because it assimilates people with carriers of knowledge and skills similar to the parts of a car.
Learning longer-term dependencies via grouped distributor unit

Learning long-term dependencies remains difficult for recurrent neural networks (RNNs) despite their success in sequence modeling recently. In this paper, we propose a novel gated RNN structure, which contains only one gate. Hidden states in the proposed grouped distributor unit (GDU) are partitioned into groups. For each group, the proportion of memory to be overwritten in each state transition is limited to a constant and is adaptively distributed to each group member. In other words, every separate group has a fixed overall update rate, yet all units are allowed to have different paces. Information is therefore forced to be latched in a flexible way, which helps the model to capture long-term dependencies in data. Besides having a simpler structure, GDU is demonstrated experimentally to outperform other models such as LSTM and GRU on both pathological problems and tasks on natural datasets.
Multi-view Visual Question Answering Dataset for Real Environment Applications

In this paper, we propose a novel large scale Visual Question Answering (VQA) dataset, which aims at real environment applications. Existing VQA datasets either require high constructing labor costs or have only limited power for evaluating complicated scene understanding ability involving in VQA tasks. Moreover, most VQA datasets do not tackle scenes containing object occlusion, which could be crucial for real-world applications. In this work, we propose a synthetic multi-view VQA dataset along with a dataset generation process. We build our dataset from three real object model datasets. Each scene is observed from multiple virtual cameras, which often requires a multi-view scene understanding. Our dataset requires relatively low labor cost and in the meantime, have highly complicated visual information. In addition, our dataset can be further adapted to users' requirements by extending the dataset setup. We evaluated two previous multi-view VQA methods on our datasets. The results show that both 3D understanding and appearance understanding is crucial to achieving high performance in our dataset, and there is still room for future methods to improve. Our dataset provides a possible way for bridging the VQA methods aiming at CG dataset with real-world applications, such as robot picking tasks.
Scale prediction based MDNet for infrared target tracking

Infrared target tracking is increasingly becoming important for various applications in recent years. However, it is still a challenging task as limited information can be obtained from the infrared image. Inspired by the excellent performance of deep tracker, a novel tracker based on MDNet is proposed. As the prior information has great value for target tracking, a modified Back-Propagation network is used for predicting the scale of target during tracking. The result of the prediction is used for generating candidate windows for online learning, which can improve the performance of tracker. To evaluate the proposed tracking algorithm, we performed experiments on the VOT-TIR2016 and AMCOM infrared data. The experimental results demonstrate that our algorithm provides a 1.94% relative gain in accuracy and 21.4% in robustness on VOT-TIR2016 when compared with MDNet.
Optimizing Resource Utilization by Vm Migration Among Virtual Machines of a Cloud Server

In a cloud data center server, there could be many physical machines connected as clients. Each physical machine can hold multiple virtual machines. The effective working of the system is determined majorly on the parameters such as the utilization of the system which depends upon resource utilization by single Virtual Machine and Physical Machine resource utilization in total. To optimize the utilization, it is crucial to fetch the data, take the decision considering the situation, and finally take action by migrating the VM to decide the PM if needed. To automate this process and to make such decisions, it is necessary to appoint a monitor machine which can monitor the VM and PM individually, take and implement the actions needed. However, the monitor machine must implement multi-objective optimization thus considering other factors as well such as Qos, Energy saving, and reduction of time and distance while migrating.
Authority Work, Catalogers, and Contemporary Authors: Results of an Online Survey

The article presents the results of an online survey, which was designed to examine the relationships between catalogers and contemporary authors during the process of establishing authorized access points and creating personal name authority records (NARs) for the purpose of breaking conflicts in the Library of Congress Name Authority File (LC NAF) and/or of obtaining additional identifying attributes. This article may be useful for individuals involved in the creation of personal name authority records.
In-Air Gesture Interaction Using Ultra Wide Camera

We investigate whether the region of hand gesture interaction with AR glasses can be expanded using a regular camera. We measure the accuracy of gesture recognition in the images obtained by two conventional cameras: wide-angle and ultra-wide angle. For both of them, gestures, typically used in AR scenarios such as pinch, palm, pointer, and grab, are recognized with >90% accuracy. The accuracy improves on the periphery of the ultra-wide camera field of view. A usability study confirms that performing gestures in the periphery of the ultra-wide camera view is as convenient, as in its center. Two gestures, zoom and swipe, are distinctively more convenient when made in the peripheral zone. Our findings pave the way for expanding the region of hand gesture interaction with AR glasses using purely computer vision-based techniques.
Use of E-Resources Through Consortia With Special Reference to College Libraries

A consortium is an association of two or more individuals, companies, organizations or governments (or any combination of these entities) with the objective of participating in a common activity or pooling their resources for achieving a common goal. Library consortium gives the freedom for a library having a smaller collection to access any product at a nominal price. The chapter has shown that 58 (31.53%) assistant professors occasionally use it, followed by 44 (23.92%) respondents using it frequently, 39 (21.2%) respondents use it rarely, 26 (14.14%) respondents use it very frequently, and 17 (09.24%) respondents never use it.
Adaptive and efficient high-order rating distance optimization model with slack variable

The classical high-order rating distance model which aims to minimize not only (i) the difference between the estimated and real ratings of the same user–item pair (i.e., the first-order rating distance), but also (ii) the difference between the estimated and real rating difference of the same user across different items (i.e., the second-order rating distance), and use stochastic gradient descent to solve this convex optimization problem in recommender systems, has good performance in prediction accuracy. However, when the manually set parameter for the second-order rating difference is fixed, this model will not converge as the size of dataset increasing, and its performance on efficiency is slow compared with the matrix factorization method. Aiming at improving such model’s adaptability and efficiency, we propose an improved high-order rating distance model with omitting rules based on slack variable, in which the static parameter used to balance the first-order rating distance and the second-order rating distance is replaced by a data-scale sensitive function. We choose Newton method to solve the convex recommendation optimization problem defined in this paper instead of stochastic gradient descent. Our model not only achieves the adaptability by eliminating several static parameters for module balancing, reduces the computation complexity, but also accelerates the optimization function convergence speed. We provide solid theoretical support and conduct comprehensive experiments on four real-world datasets. Experimental results show the proposed model has good performance in terms of prediction accuracy and efficiency.
Introduction to Mobile Robots Navigation, Localization and Mapping

This chapter can be considered as an introduction to mobile robotics. This chapter covers a brief mathematical description of mobile robots that consists of kinematic and dynamics with nonholonomic constrains applied to wheeled robots. Then, a terrain representation and mapping survey has been conducted. After that, the path planning study was performed. It includes a planner definition and several approaches to navigation, which are divided by different behaviors and types of world representation. The last part is dedicated to localization approaches and covers the local incremental method and global localization techniques.
Recognition of Objects in the Urban Environment using R-CNN and YOLO Deep Learning Algorithms

Over the course of the last decade, the subfield of artificial intelligence, called deep learning, becomes the main technology that provides breakthroughs in the computer vision area. Likewise, deep learning algorithms made a major impact in the automated driving domain. This research aims to apply and evaluate the performance of two pre-trained deep learning algorithms in order to recognize different street objects. Both RCNN, as well as YOLO algorithms, are used to recognize bikes, cars and pedestrians using the public GRAZ-02 dataset composed of 1476 raw images of street objects. Accuracy greater than 90% is achieved in recognizing all considered objects. The fine-tuning and training of both algorithms is established using databases named ImageNet and COCO, and afterwards, trained models are tried on the test data.
Research on Computational Simulation of Advertising Posters Visual Cognition

Vision, as the main channel for humans to obtain external information, has always been a hot area of research. Especially under the current heat of research on artificial intelligence, a large amount of research has been focused on intersecting research results in psychology and neuroscience with computer science to improve simulation capabilities of computers systems. This article briefly reviews the research progress in human visual cognition processes and their simulation methods in the fields of psychology, neuro-science, and computer science. In addition, eye movement experiments were carried out and models were built based on human cognitive processes to explore the influence of factors such as prior knowledge and emotional experience on model accuracy. Furthermore, the limitations and application prospects of the model were discussed.
Consume Less, Create More – Digital Services in the Context of Sustainability

In 2019 the Coburg University of Applied Sciences offered the course “consume less, create more” to an interdisciplinary group of master students. The course offered a theoretical part including topics like sustainability, maker culture and upcycling as well as a practical part. The practical part was carried out through different project teams using old materials and unused objects from the student’s households to create usable products for the students themselves. The course took place at the maker space of Coburg University, offering classic tools and machines like laser cutters and 3d printers. The challenge was to find different criteria for sustainable DIY projects, produce at least one prototype and to create a tutorial. The main question to be answered is what conclusions can be derived for Digital Services concerning Sustainability from the course. The main methods are discussions with the students accompanied by individual interviews carried out by the authors. Also, observation is used to analyze how Digital Services are used during the creation of the different items. The findings are suggestions for various areas and ideas for Digital Services that concern Sustainability and target an interdisciplinary audience with higher education.
A Recurrent Neural Network Model for Spam Message Detection

This paper presents detection of electronic junk mail called spams using Long Short-Term Memory layers in recurrent neural network. The methods used are applied for successful detection and filtering spam messages. This LSTM method is used to simplify the text into representation of words which will be possible to get the high accuracy rate. To purchase a product the most important factor customer looks into is online reviews. Manufactures and retailers are focused on customer reviews because of the impact created. Spammers usually manipulate the reviews for profit, so detect such malpractices it is important to develop a technique which detects and filters spams. In paper, AI model proposed is effectively ready to consequently recognize spam.
Resuscitating a Dead Rabbit

This paper is about the computer-generated Little Grey Rabbit stories of Professor Eric Mendoza.This (and its sister-work, High Entropy Essays) dates from the early 1960s and is one of the first examples of a fiction-writing computer program.The programming is innovative for its time and still has value as an approach to the challenge of creating literature using a computer.This approach is contrasted with contemporary AI methods of text generation.