Reviewerly/exHarmony huggingface dataset:
Tangible ideation : How to design for and with children?

In design, collaborative notions and design practices have expanded the type of users from adults to the younger generations. In line with this trajectory, children have become actively engaged as social actors and partners in research and design practices. There are two approaches which typify this; Participatory Design has involved children in environmental planning and design, whereas the Child-Computer Interaction community focuses on the domain of scientific research on the interaction between children as well as computational and communication technologies. Grounded particularly in Human-Computer Interaction research, Cooperative Inquiry has been introduced, applied and expanded in practice as a method of designing a technology working with both adults and children. Although children’s participation and involvement have been acknowledged in research and design, the development has leaned toward environmental planning and design as well as computational and communicational technologies, rather than other domains. Moreover, design practitioners have continued to encounter difficulties in practices designing for and designing with children, since working with children requires a thorough understanding and knowledge of children’s cognitive and physical development (i.e. dexterity, strength, motor skills and all developing significantly throughout childhood), as well as an adaptation of methodological matters. Furthermore, the imbalance of power relationships among stakeholders (children, educators, parents, experts in child development, designers and researchers) is considered.The purpose of this research is to the communication and collaboration methods of adult designers (design practitioners) with children in the design process. This research has been framed by experimental design research. Through its own experiments (design practices), the research has developed research questions and responses through an analysis built upon the findings from each practice. The practices have focused on furniture and spatial design; however, the key findings and recommendations of this research have been inclusively engineered for general design practitioners and researchers working with children. During the empirical practices with children, I have attempted to explore the children’s position, the approaches and procedures, as well as the manners and tools supporting interaction and communication with children in the design process when designing for and with children. Based on these research questions, the objectives of this study are to understand the value of children’s participation in design, to position the designer’s role in designing for and with children, and to provide practical guidance for adult design practitioners and researchers working with children. This study ultimately aims to enhance the well-being of children through their participation and collaboration in both design and education.I examine prominent research and design techniques associated with children in the literature review section. In…
Marcel Danesi. 2017. The semiotics of emoji: The rise of visual language in the age of the internet. London: Bloomsbury Academic. Pp. 208. US $22.95 (softcover).

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Investigation of directional and directionally-unbiased devices in linear optics for quantum walk applications

The optical beam splitter is one of the main building blocks in photonics-based quantum information processing. Traditionally, beam splitters are used in feed-forward configurations that reflect their natural directional-bias, meaning a photon cannot leave the scattering element through the entrance port. However, this directional-bias constraint can be circumvented by designing new linear-optical configurations that include mirrors in addition to the beam splitter. This directionality removal restores a full symmetry in the scattering element and allows the input photon to leave the system also from the input port. Such a system can be seen as a scattering center, enabling execution of quantum walks on rather complex optical graph networks with great savings in hardware resources when compared with existing approaches utilizing directionally-biased devices. The directionallyunbiased configuration can be realized using different optical systems. Analysis of some originally directional optical devices and the basic principles of their conversion into directionally-unbiased systems form the base of this paper. Several quantum walk procedures executed on graph networks constructed using directionallyunbiased nodes are discussed.
Safety Valuation of Military Aviation Control System Based on Cloud Model

Abstract At present, the flight training, army operation increase constantly, the army ATC faces rigorous challenges. The safety evaluation on ATC can enhance the control effect and guarantee fly safety. In this paper, an evaluation index system is established based on ‘human factor, machine factor, environment factor and management factor’. On the basis of AHP and cloud model, the safety evaluation model is built. To validate the model, an experiment is operated, the result is accord with the real situation, which proves the scientifically and feasibility of this model.
Primary Equipment Fault Diagnosis Strategy for Box Substation Based on PSO-BP Neural Network

Box type substations are widely used in industrial, commercial and urban transmission and distributed system, which play an important role in power systems, and has considerable meaning in box type substation’s fault diagnosis. Through the study of the box type substation’s internal structure, working principle and the analysis of fault and fault characteristics. A fault diagnosis network model based on PSO-BP neural network is proposed to integrate the system data. To avoid the drawbacks of BP neural network algorithm learning and make the model have a good convergence and adaptability, the optimization of BP neural network is carried out by using the optimal characteristics of PSO. The simulation model results that the network has a good recognition effect, and it’s very promising in the fault prediction of box type substation.
Automatically Configuring Parallelism for Hybrid Layouts

Distributed processing frameworks process data in parallel by dividing it into multiple partitions and each partition is processed in a separate task. The number of tasks is always created based on the total file size. However, this can lead to launch more tasks than needed in the case of hybrid layouts, because they help to read less data for certain operations (i.e., projection, selection). The over-provisioning of tasks may increase the job execution time and induce significant waste of computing resources. The latter due to the fact that each task introduces extra overhead (e.g., initialization, garbage collection, etc.). To allow a more efficient use of resources and reduce the job execution time, we propose a cost-based approach that decides the number of tasks based on the data being read. The proposed cost-model can be utilized in a multi-objective approach to decide both the number of tasks and number of machines for execution.
Discrete fourier transform based Vowel Onset Point Detection Using Spectral Peaks Energy

In the speech signal, vowel onset point (VOP) represents the moment at which the vowel portion starts in a consonant vowel (CV) utterance. Energy content in the excitation source, energy levels of the vowels and the epoch location in a speech signal are the key points of VOP identification. In general, VOP plays an important role in speaker recognition, language identification and several speech processing tasks. In this paper I proposed a method which is stated as VOP detection using the energy content in the spectral peaks.
The Influence of Age and Gender in the Interaction with Touch Screens

Touch screens are nowadays one of the major interfaces in the interaction between humans and technology, mostly due to the significant growth in the use of smartphones and tablets in the last years. This broad use, that reaches people from all strata of society, makes touch screens a relevant tool to study the mechanisms that influence the way we interact with electronic devices. In this paper we collect data regarding the interaction patterns of different users with mobile devices. We present a way to formalize these interaction patterns and analyze how aspects such as age and gender influence them. The results of this research may be relevant for developing mobile applications that identify and adapt to the users or their characteristics, including impairments in fine motor skills or in cognitive function.
Indowordnet’s help in Indian language machine translation

Languages with insufficient digitally available resources, such as, Indian–Indian and English–Indian language Machine Translation (MT) system developments, faces the difficulty to translate various lexical phenomena. In this paper, we present our work on a comparative study of 440 phrase-based statistical trained models for 110 language pairs across 11 Indian languages. We have developed 110 baseline statistical machine translation systems. Then, we have augmented the training corpus with Indowordnet synset word entries of lexical database and further trained 110 models on top of the baseline system. We have done a detailed performance comparison using various evaluation metrics such as BLEU score, METEOR, and TER. We observed significant improvement in evaluations of translation quality across all the 440 models after using the Indowordnet. These experiments give a detailed insight in two ways: (1) usage of lexical database with synset mapping for resource poor languages and (2) efficient usage of Indowordnet synset mapping. Moreover, synset mapped lexical entries helped the SMT system to handle the ambiguity to a great extent during the translation.
Propagation of quantization error in performing intra-prediction with deep learning

Standard video compression algorithms use multiple “Modes”, which are various linear combinations of pixels for prediction of their neighbors within image Macro-Blocks (MBs). In this research, we are using Deep Neural Networks (DNN) with supervised learning to predict block pixels. Using DNNs and employing intra-block pixel values’ calculations that penetrate into the block, we manage to obtain improved predictions that yield up to 200% reduction of residual block errors. However, using intra-block pixels for predictions brings upon interesting tradeoffs between prediction errors and quantization errors. We explore and explain these tradeoffs for two different DNN types. We further discovered that it is possible to achieve a larger dynamic range of quantization parameter (Qp) and thus reach lower bit-rates than standard modes, which already saturate at these Qp levels. We explore this phenomenon and explain its reasoning.
A Prior Knowledge Based Neural Attention Model for Opioid Topic Identification

The opioid epidemic has become a serious public health crisis in the United States. Social media sources such as Reddit containing user-generated content may be a valuable safety surveillance platform to evaluate discussions discerning opioid use. This paper proposes a prior knowledge based neural attention model for opioid topics identification, which considers prior knowledge with attention mechanism. Experimental results on a real-world dataset show that our model can extract coherent topics, the identified less discussed but important topics provide more comprehensive information for opioid safety surveillance.
The Impact of Cloud Computing on Decision Support System in Adopting Knowledge Management in Saudi Organizations

Background/Objectives: Due to enormous increase in the volume of the information within and outside of the organizations, traditional business and information models are unable to cater to the needs of the modern business needs. Methods/Findings: In this context, Cloud Computing (CC) is gaining rapid popularity amongst businesses due to its ability to handle technical and organisational facets. Cloud Computing has evolved as a reliable technology, offering enhanced Decision Support Systems (DSS) at a low cost for the organizations. CC has displayed its capability of handling large volumes of data and providing seamless services to the companies. This research explored the impact of CC on DSS and Knowledge Management (KM) on Saudi organizations. The major achievement of this research was to fill the gap in knowledge by researching the impact of integration of CC, DSS and KM for Saudi organizations. The key finding of this research is that there is a significant positive impact of CC on DSS and KM in Saudi organizations. Based on this extensive research, we strongly believe that by taking recommended precautionary measures, security concerns about CC could be overcome resulting in benefits outweighing the risks for Saudi organizations. Application: Based on the literature and qualitative research, we recommend that Saudi organizations should consider migrating from traditional business models to Cloud Computing to stay competitive in this globalised world. Keywords: Cloud Computing, Decision Support Systems, Knowledge Management
Poetics of bibliographical thinking

Review of the book by V. P. Leonov. The bibliographical thinking. On the way to the unified book world [In Russian]. – St. Petersburg: Library of the Academy of Sciences, 2018. – 118 p. – ISBN 978-5-336-00241-6. The new book by the V. P. Leonov, science advisor of the Russian Academy of Sciences Library, examines relevant theoretical problems: The role and place of psychology in studying bibliographical processes, specific features of bibliographical thinking, logics and intuition in text bibliographical analysis and search for information. The reviewer emphasizes the complexity of the topic due to its multifacet character and multiple factors that impact thinking, and unavailability of thinking components for direct observation. The reviewer also demonstrates the fundamental approach toward the phenomenon of bibliographical thinking: In his study, V. Leonov builds on the fundamental classical works by leading psychologists and physiologists. The reviewer shares the author’s attitude toward the balance of the bibliographical and creative thinking. She also emphasizes that V. Leonov analyzes various aspects of bibliographical thinking: from the structure to its development factors. The philosophical and culturological review of the world bibliographical heritage in the book’s second part is of great value, too. The reviewer suggests that Leonov’s ideas will put impetus on further studies to make bibliographical thinking a scientific term rather than a metaphorical one.
Disparity Image Analysis for 3D Characterization of Surface Anomalies

The detection of internal defects in composite materials, due to anomalies during the production processes, is a big issue especially for the production of large structures in aeronautic contexts. The costs of the repair processes can weigh upon the total costs, and sometimes the repair itself can be unfeasible and cause the material to be rejected. The early detection of anomalies during the production phase can interrupt the production chain and the solution to the detected problems can be soon found. In this paper we propose the use of an appropriate sensorial setup, based on vision and laser, to monitor the production line and of a pipeline of signal processing methodologies to detect anomalies in the stratification of composite materials. Gaps and overlaps between adjacent stripes that are beyond or below the allowed ranges are soon detected and automatically highlighted to the human operators.
Trajectory Planning of UAV in Unknown Dynamic Environment with Deep Reinforcement Learning

Providing a collision-free, safe and efficient optimal trajectory for unmanned aerial vehicles (UAVs) in an unknown dynamic environment is one of the most important issues for researchers. In this paper, a trajectory planning approach for UAV in unknown dynamic environment based on deep reinforcement learning (DRL) is proposed. This study models trajectory planning of UAV as a discrete-time, discrete-action problem, and then proposes an improved deep Q network (IDQN) algorithm to solve it. The IDQN algorithm adds the track angle information of UAV to the reward function to speed up the learning process, furthermore, it also improves the action selection strategy and learning rate setting. Besides, in simulation, the paper considers the trajectory constraints of UAV in order to make the obtained trajectory have better practical availability. Simulation results demonstrate the effectiveness of the IDQN algorithm to implement UAV trajectory planning with constraints in unknown dynamic environments. Meanwhile, comparison with the classical DQN (CDQN) algorithm is conducted to further explore the advantage of the method.
THE POTENTIAL AND CHALLENGES IN THE CONSERVATION OF HAJJ HISTORY AND HERITAGE IN MALAYSIA

Purpose of the study: The study aims to identify the prospects and challenges in preservation of hajj history and heritage in Malaysia.&#x0D; Methodology: This paper reports the analysis of documents and data collection on hajj conducted through library research.&#x0D; Principal Findings and Novelty: The results of this study show that the principal challenges in preservation of hajj history and heritage in Malaysia are limited literature, difficulties in obtaining information pertaining to Hajj in pre-Malaysia days, poor documentation of the primary sources as well as individual narratives on hajj exercise and unrecorded stories and memories of individuals. This study shows that the history and heritage of Hajj are seen to have tremendous potential to assist in strengthening the tourism industry based on heritage in Malaysia.&#x0D; Applications of this study: Appropriate measures are to be taken by the government to prevent the loss of history and heritage of the Hajj operations in Malaysia. Efforts must also be made to document the memories of different parties who were directly involved in the Hajj exercises for future widespread.
Edge Detection Algorithm Based on Texture Blocks

Traditional edge detection algorithms affect the performance of the edge detection results based on the setting of the threshold value. Setting the threshold value too high may cause a loss of edge texture with a smaller gradient value. Setting the threshold value too low may cause excessive noise retention. To effectively overcome the problems caused by an inappropriate threshold setting, this paper summarizes and classifies a series of textural changes by observing the changes in the edge texture, and uses block mode to assemble the edges of the construction objects. Experimental results show that, compared with a traditional edge detection method, the proposed method not only excludes the possible problems caused by the threshold setting, it can also detect more abundant edge information.
Robot Indoor Text Contents Recognition Based on Visual SLAM

Abstract Although research on indoor mobile robots has evolved over the years and has made good progress in many areas, few researchers have noticed their combination in text/character recognition and navigation applications. In fact, robots navigate and move with obstacles and various visual disturbances without prior knowledge, and people and objects are possible obstacles. Indoor robots may need to recognize text/characters to help them determine their location and move on. In order to enable mobile robots to navigate and locate more accurately, we improved the visual text content recognition system based on Faster RCNN. This method uses the attention mechanism to replace the original re-sharp method in Faster RCNN, and the visual attention mechanism can be adopted for the anchor. Reduce the number and error, and reduce the interference of non-target information on the identification system. This method can reduce the problem of large system consumption time. Experiments on the indoor robot dataset show that the method has certain advantages in accuracy and cost time.
Combination of Scheduling and Dynamic Data Replication for Cloud Computing Workflows

Cloud computing is a powerful and high-capacity system, because it can satisfy various demands and share resources for users. It also to benefits from a capacity for treatment and unlimited storage. However, it is burdensome for the providers of internet services that the user demands are increasing as computer capacity is growing stronger and stronger. Therefore, the techniques of workflow scheduling and data replication are used to decrease the costs of the data intensive application. Unfortunately, these two approaches, which are very complementary, are used separately. In this article, a combination of workflow scheduling based on the clustering of data and dynamic data replication strategies has been introduced together. A Cloud simulator, Cloudsim, is used to evaluate the performance of the proposed algorithm. Simulation results show the effectiveness of the proposed algorithm in comparison with well-known algorithms such as random data placement and the Build Time algorithm.
Emerging Technology and Its Applications Require New Standards

Let’s put aside the question whether Blockchain is a real technology or a passing fad – a hype. The ups and downs of your fortunes in Bitcoin and similar cryptocurrencies in the last couple of years may have clouded your views. Short-term or long-term success, or failure, of cryptocurrencies may depend on the success, or failure, of Blockchain, but it is important to recognize that Blockchain and the Distributed Ledger Technology (DLT) have far greater uses in wide-ranging applications. One such use of Blockchain is as a key enabling technology of 5G applications. Prof. Chaudhry and Dr. Asad present their research to propose a Blockchain-based Network Slice and Resource Brokerage system to build an open, transparent, and fair 5G ecosystem. They explain the challenges of addressing the massive cooperation required among 5G devices and potential solutions using Blockchain.
Visual Pose Estimation Based on the DenseNet Network

An end-to-end neural network model based on DenseNet was designed to estimate the pose of the camera in this paper. The picture frame captured by the camera and the camera position (3-dimensional space coordinates) and pose (quaternion) corresponding to the picture frame are the inputs to the network model. Through the neural network model, the spatial structure information and the higher-layer features in the image are trained and learned, so that the network model finally outputs the 7-dimensional vector representing the camera position (3-dimensional space coordinates) and the pose (quaternion). Due to the pose estimation constraint of the network, the training effect of the model is guaranteed, and the pose estimation ability of the network is improved. The trained model is validated on the StMarysChurch Dataset. The experimental results show that the network model has good performances in accuracy and shorter training time.