Reviewerly/exHarmony huggingface dataset:
Inclusive Design of Teaching Aids for Social Science Learning Process for Elementary and Middle School Students based on Creative Mini PC Raspberry Technology

Abstract The purpose of this activity is to educate students and teachers with the inclusive board game invention as a learning tool for memorizing subjects for elementary to high school students. This product answers the problem for the difficulty of students remembering and understanding social science teaching materials. There are two approaches to the Learning Board Game teaching method, the first is visualization of social science teaching materials through interactive videos integrated with images according to subjects and screens and the second is gamification through the elements of challenge and fantasy by answering questions to get rewards cards or marking as a marker gets points. This product was developed with the New Product Development (NPD) Process and uses the Stage-Gate Model. In this model, product design goes through various stages starting from discovery I idea generation to launch with each stage that functions as a quality-gate of product design.
An Approach to Fast Multi-Robot Exploration in Buildings with Inaccessible Spaces

The rapid exploration of unknown environments is a common application of autonomous multi-robot teams. For some types of exploration missions, a mission designer may possess some rudimentary knowledge about the area to be explored. For example, the dimensions of a building may be known, but not its floor layout or the location of furniture and equipment inside. For this type of mission, the Space-Based Potential Field (SBPF) method is an approach to multi-robot exploration which leverages a priori knowledge of area bounds to determine robot motion. Explored areas and obstacles exert a repulsive force, and unexplored areas exert an attractive force. While SBPF has advantages over other methods of robot exploration in terms of simplicity and performance, inaccessible space poses a problem: it exerts a permanent attractive force, pulling robots away from useful exploration elsewhere and creating minima at its boundary. Prior research established a simple method of filling in inaccessible space as a solid obstacle once an enclosing boundary is discovered; however, this method requires the entire enclosing boundary to be discovered before it can be filled. In this paper, we propose a novel combined SBPF and frontier-based method of robot exploration called O-SBPF. Our method adds two new space classifications: open, areas known to be accessible; and occluded, areas which may be inaccessible. We describe a ray-casting approach to designate areas as open or occluded, and incorporate this designation into potential vector calculations. We then show the effectiveness of O-SBPF using ROS/Stage in worlds with inaccessible space. O-SBPF significantly outperforms SBPF in rooms with large obstacles, successfully reaching 95% coverage while SBPF becomes stuck in a minima. In less complex rooms, we show that O-SBPF generally reaches 95% coverage at the same time or before SBPF.
Digital text in the space of modern culture

The digital text within the domain of modern culture can be studied from the standpoint of the cultural-philosophical approach. It is based on M. Bakhtin’s text-centric conception of dialogism and on the post-structuralism practice of text deconstruction in the conceptions by J. Derrida and R. Barthes. The theories of these scientists reveal some basic principles how to understand the digital text phenomenon and what methods to apply in the analyses of these new cultural practices in the space of digital culture. The reduction of cultural information down to a “digital code” determines the dual logic of the text (the logic of culture and digital logic), which should become the subject of focused cultural studies. The notion of hypertextuality plays a special role in the understanding of the digital text, and the deconstructive practice of reading-writing is considered as a kind of the reader’s ‘material intervention’ into the text. It makes the digital text an ongoing autocommunication of the Author-Reader. This process is grounded on the computer interface metalanguage or cultural metalanguage, which organizes the digital text information in a specific way and influences both the functioning of the text as a cultural object and the way it is perceived by the user.
Analysis of software development method selection

Software project failures might indicate the need of software development standard procedures within the company. It applies to a private financial institution chosen as a case of this study. The institution has a significant number of software projects completed behind schedule. According to its Key Performance Indicator (KPI), in the second semester of 2007, there were four projects that were not being implemented. Consequently, it brought domino effects and resulted a financial suffer. To address this problem, a research was conducted to reveal the most suitable software development method for the company. Mixed methods were employed in this investigation. The data were collected using interviews and questionnaires to the key stakeholders of the institution along with over ten-year experiences. The respondents were chosen with purposive sampling, while the interviews were based on structured and non-structured questions. The first interview aimed to gather data for the questionnaires, whilst the second interview purposed to validate the results, conclusion, and suggestion of the research. It was started with the Analytic Hierarchy Process (AHP) to rank software development method alternatives based on relevant criteria. The result shows that Rapid Application Development (RAD) is the most suitable software development method for the company, whilst Scrum and waterfall are in the second and third position. RAD could address the problem of project completion delay in the development and design phases by having those phases to be commerce in parallel for faster completion. Then, it was followed by interviews with the stakeholders to explore feasibility to apply RAD as the standard.
Research on the Application of Decision Support System on Smart Campus

With the continuous development and advancement of Internet technology, building an information management mechanism and a smart campus control model is an inevitable trend in keeping with the times. Among them, the decision support system can provide a more convenient management path for construction work and take advantage of Internet of Things technology and RFID radio frequency technology to ensure that big data management is more convenient and effective. This paper analyzes the characteristics of the smart campus and the overall framework of the decision support system, while focusing on the design and implementation process of the smart campus decision support system for reference only.
Medical Image Enhancement Using Deep Learning

This chapter aims to introduce medical image enhancement technology using 2-dimentional and 3-dimentional deep learning. The article starts from basic methods about convolutional layer, deconvolution layer, loss function and evaluation functions for beginners to easily understand. Then, typical state-of-the-art super-resolution methods using 2D or 3D convolution neural networks will be introduced. From the experimental results of the network introduced in this chapter, readers can not only make a comparison about the network structure but also have a general understanding about network performance.
Influence of Control Parameters and the Size of Biomedical Image Datasets on the Success of Adversarial Attacks

In this paper, we study dependence of the success rate of adversarial attacks to the Deep Neural Networks on the biomedical image type, control parameters, and image dataset size. With this work we are going to contribute towards accumulation of experimental results on adversarial attacks for the community dealing with biomedical images. The white-box Projected Gradient Descent attacks were examined based on 8 classification tasks and 13 image datasets containing a total of 605,080 chest X-ray and 317,000 histology images of malignant tumors. We concluded that: (1) An increase of the amplitude of perturbation in generating malicious adversarial images leads to a growth of the fraction of successful attacks for the majority of image types examined in this study. (2) Histology images tend to be less sensitive to the growth of amplitude of adversarial perturbations. (3) Percentage of successful attacks is growing with an increase of the number of iterations of the algorithm of generating adversarial perturbations with an asymptotic stabilization. (4) It was found that the success of attacks dropping dramatically when the original confidence of predicting image class exceeds 0.95. (5) The expected dependence of percentage of successful attacks on the size of image training set was not confirmed.
Application of artificial neural networks for fusion of data from radar and depth sensors applied for persons’ monitoring

Abstract The research reported in this paper is related to the fusion of measurement data from the impulse-radar sensors and infrared depth sensors applied in a system for unobtrusive monitoring of elderly persons. Three methods for data fusion, based on the artificial neural networks – one trained on real-world data, and two trained on synthetic data generated on the basis of two different models of the data – are compared with respect to their capacity of decreasing the uncertainty of position estimation in a series of experiments which involved the tracking of a moving person.
Energy-Aware Dynamic Pricing Model for Cloud Environments

Energy consumption is a critical operational cost for Cloud providers. However, as commercial providers typically use fixed pricing schemes that are oblivious about the energy costs of running virtual machines, clients are not charged according to their actual energy impact. Some works have proposed energy-aware cost models that are able to capture each client's real energy usage. However, those models cannot be naturally used for pricing Cloud services, as the energy cost is calculated after the termination of the service, and it depends on decisions taken by the provider, such as the actual placement of the client's virtual machines. For those reasons, a client cannot estimate in advance how much it will pay. This paper presents a pricing model for virtualized Cloud providers that dynamically derives the energy costs per allocation unit and per work unit for each time period. They account for the energy costs of the provider's static and dynamic energy consumption by sharing out them according to the virtual resource allocation and the real resource usage of running virtual machines for the corresponding time period. Newly arrived clients during that period can use these costs as a baseline to calculate their expenses in advance as a function of the number of requested allocation and work units. Our results show that providers can get comparable revenue to traditional pricing schemes, while offering to the clients more proportional prices than fixed-price models.
Universal algorithms for multinomial logistic regression under Kullback–Leibler game

We consider the framework of competitive prediction, where one provides guarantees compared to other predictive models that are called experts. We propose a universal algorithm predicting finite-dimensional distributions, i.e. points from a simplex, under Kullback–Leibler game. In the standard framework for prediction with expert advice, the performance of the learner is measured by means of the cumulative loss. In this paper we consider a generalisation of this setting and discount losses with time. A natural choice of predictors for the probability games is a class of multinomial logistic regression functions as they output a distribution that lies inside a probability simplex. We consider the class of multinomial logistic regressions to be our experts. We provide a strategy that allows us to ‘track the best expert’ of this type and derive the theoretical bound on the discounted loss of the strategy. We provide the kernelized version of our algorithm, which competes with a wider set of experts from Reproducing Kernel Hilbert Space (RKHS) and prove a theoretical guarantee for the kernelized strategy. We carry out experiments on three data sets and compare the cumulative losses of our algorithm and multinomial logistic regression.
Numerical Assessment of Orthographic Neighbourhood Size Fluctuation in Writing Using Fractal Dimension Analysis

The orthographic size of a targeted word, the number of new words that can be generated from a targeted word by exchanging a single letter, offers a research window where words can be transformed into numerical values. The CLEARPOND technology from Northwestern University was used for the transformation. A writing can then be modelled as a time series where the fluctuation can be further described using fractal dimension analysis. This project used the Higuchi fractal method for the computation of the fractal dimensions of time series. The proof of concept was conducted using writing examples which include Astronomy writing and English writing, the responses of Trump and Clinton in a Presidential election debate, and song lyrics. The results suggested that a high fractal dimension has an association with a high-demand cognitive task. The use of fractal dimension analysis as a writing assessment tool is discussed with relationship to the current lexical diversity computation technology.
Online Multi-object Tracking Using Single Object Tracker and Markov Clustering

In this paper, we address the challenging problem of online Multi-Object Tracking (MOT). We find for those targets that are occluded or too small, the detectors usually fail to locate them. But an SOT tracker always provides a prediction for each target in the next frame. Therefore, we propose to use Single Object Tracking (SOT) predictions as complementary to detections. Also, we solve the data association problem via a new clustering method based on the Markov Clustering Algorithm (MCL). We first build a graph based on the targets, detections and SOT predictions, and then separate different identities by clustering. Experimental results on the MOT17 benchmark shows that our proposed method outperforms previous state-of-the-art methods w.r.t. MOTA and also reduces the number of false negatives and fragments significantly.
Hierarchical Image Inpainting by a Deep Context Encoder Exploiting Structural Similarity and Saliency Criteria

The purpose of this paper is to present a context learning algorithm for inpainting missing regions using visual features. This encoder learns physical structure and semantic information from the image and this representation differentiates it from simple auto encoders. Such properties are crucial for tasks like image in-painting, classification and detection. Training was performed by patch-wise reconstruction loss using Structural Similarity (SSIM) jointly with an adversarial loss. The reconstruction loss is also augmented using spatially varying saliency maps that increase the error penalty on distinctive regions and thus promote image sharpness. Furthermore, in order to improve image continuity on the boundary of the missing region, distance functions with increasing importance towards the center of the inpainting region are also used either independently or in conjunction with the saliency maps. We also show that our choice of reconstruction loss outperforms conventional criteria such as the L2 norm. This means giving more weight to pixels closer to the border of the missing image parts and also giving more important to salience parts of the image to guide the reconstruction, thus producing more realistic images.
Should researchers use data from security breaches?

Evaluating the arguments for and against using digital data derived from security breaches.
Spatial-Temporal Bottom-Up Top-Down Attention Model for Action Recognition

Driven by the importance of capturing non-local information in video understanding, we propose Spatial-temporal Bottom-up Top-down Attention Module (STBTA). Features are processed across in multiple scales and then combined to best capture the spatial relationships associated with the region of interest and the surrounding environment in a complicated scene. Attention maps are used for adaptive feature refinement. STBTA can be plugged into any feedforward network architectures and is end-to-end trainable along with CNN. Extensive experiments on UCF101, HMDB51, Kinetics-400 datasets demonstrate that the proposed method can improve the performance for action recognition.
Modern Approaches to the Language Data Analysis. Using Language Analysis Methods for Management and Planning Tasks

The article discusses promising directions for the use of modern automatic methods for analyzing natural language data for solving a wide range of practical problems. The technology of creating electronic corpora (collections) of texts is considered as a tool for the transition from model linguistics to tagged data linguistics. The principles of the creation of marked corpora of texts, the possibilities and limitations of their use are considered. Creation of a marked corpus of texts in which language data that is downloaded from the Internet is processed sequentially before issuing the results to users is described. The conveyor consists of the following steps: uploading data from the Internet; definition of the language in which the text is written; unloading metadata; splitting the texts into paragraphs and sentences; deduplication; tokenization; automatic language markup; uploading cleared and marked data to the network. The prospects for the development of language data analysis systems are presented. Requirements for the creation of corpora for solving problems of public administration and strategic planning are developed. Properties that should have such bodies are considered. Those include: corpus format, corpus volume, the degree of the linguistic analysis depth, corpus-manager structure. A description of the marked corpora of texts developed at the Artificial Intelligence Research Center (AIReC) of Ailamazyan Program Systems Institute of the Russian Academy of Sciences, with a reference to the tasks of extracting information about persons, events and situations from the texts of news reports is presented. A retrospective review of the development of systems for automatic processing of natural language texts in the areas of machine translation and human-machine interaction is given.
Co-design and Innovation: Tools, Methods and Opportunities for the Generation of Innovation Through User Involvement

Starting from the role of Design as a strategic factor of innovation in the production and social field, the chapter deals with the main methods of participatory design. In particular: Design for product and service innovation; Participatory design and co-design; Co-design tools for design and development of a project; Cultural probes; the co-design workshop: from conception to prototyping.
GWMA Algorithm for Host Overloading Detection in Cloud Computing Environment

Energy consumption and Service Level Agreement (SLA) in Cloud computing environment are important issues in cloud management. Dynamic consolidation of the Virtual Machines (VMs) need effective and efficient distribution for VMs migration to hosts in data center. The process of VMs migration needs to evaluate host capability, VM placement and reallocation, which satisfy SLA criterions under a flexible service plan. Therefore, the plan is to select effective resource allocation to achieve cost minimization, reduce energy consumption and avoid SLA violation. We propose Generally Weighted Moving Average (GWMA) algorithm to detect overloaded hosts, which deals with dynamic consolidation of VMs based on an analysis of historical data of the resource usage by VMs. It increases the accuracy in calculation of the upper threshold for host overloading and consequently increases accuracy in identification to deal with VMs migration issue.
Scaffolding Learning for the Novice Players of Go

From DeepBlue to AlphaGo, computer game is the drosophila of Artificial Intelligence. For the AI services of assisting human learning, we believe computer board games can also play the role of the drosophila. From the viewpoint of social development, after the rise of AI, human need more ability of logical thinking and judgement than before. Advocating computer games is an excellent tool for the training of logical concepts and hence produces positive impact in our society. Since human Go players have a different process of reasoning compared to Go programs today, we need to develop learning methods that more closely match how humans think. Deep Learning takes inspiration from human cognitive processes and is similar to human intuition. As a result, Go programs developed with Deep Learning generate plays that feel more human. We use Deep Learning and Reinforcement learning to develop scaffolding learning system for Go. The system contains human-like Go programs with various strengths, which allows novice players to learn the game progressively. We also introduced a simplified variant of Go, named Jungo. The game could help the beginners to learning the game of Go.
A quantitative study on average number of spins of two-player Dreidel

We give a high precision approximation of the average number of spins in a simplified version of a two-player version of the game Dreidel. We also make a conjecture on the average number of spins of the full version of the game.
Square Puzzle Solving Using Border Compatibility Matching

This paper we proposed a vision-based square puzzle reconstruction method using the color compatibility feature. Using similarity equation via Hausdorff-based border compatibility, the best piece of a puzzle can be obtained to form the initial combination by calculating the similarity of adjacent pieces and completing the puzzles after three combinations. Based on the experimental results tested on 25 images where classified into 35-piece and 70-piece puzzles, the average success rate reached 87.1% and 17 of the 35-piece puzzles were solved with 100% accuracy. For the class of 70 pieces, 77.8% was obtained on average, and 13 of them were solved with 100% accuracy.
An Automatic Location and Recognition Method for Bank Card Number

The Optical Character Recognition (OCR) technology is widely used in intelligent identification of bank cards, since it can improve the work efficiency and user experience in mobile payment. Conventional methods have the problems of low recognition rate and location accuracy. Therefore, an automatic location and recognition method for bank card number is proposed. Firstly, novel Connected Text Proposal Network (CTPN) algorithm is improved to locate the bank card number. Then, the Convolutional Recurrent Neural Networks (CRNN) algorithm is optimized to identify the card number. Some experimental results show that the method has a high positioning accuracy and recognition rate.
Edge Computing based Smart Meeting Room Controller

In this study, we address the high latency challenge of classical centralized cloud based smart environments. To this end, we propose an edge computing solution for real time meeting room attendance control system in which user tracking and identification is required. In the layout of our architecture, Bluetooth devices associated with users publish their unique MAC addresses to a service agent operating at the edge layer. We established a scalable emulation test bed to understand the behavior of the proposed system and analyzed the performance of the functional components in two different scenarios; first scenario is built with the proposed edge solution and the second one is a classical cloud based approach. We present the early encouraging results obtained for data transmission overhead that reveal the relevance of the edge based design.