Reviewerly/exHarmony huggingface dataset:
An Augmented Reality Command and Control Sand Table Visualization on Top of an Advanced User Interface Display on Large Touch Table Interface

A prototyping user interface toolkit constitutes a software application for the design and development of futuristic stylized user interfaces for collaborative exploration and validation with regards to decision making and situational awareness. The initial hardware device were large screen touch tables for collaboration. In this work, Augmented Reality (AR) is examined to determine if this technology is useful for future development efforts. This work examines the development and evaluation of an AR-based virtual sand table presentation overlaid over a large touch table display with its own interface. This new way to present the same information allows for a unique collaborative setting to be evaluated as a potential future user interface. This is applied in the context of a group setting where users gather in the collaborative virtual space for decision making and situational awareness.
A Graph-Based Pipe Layout Method Considering Clamp Constraint for Aero-Engine

Pipe layout for aero-engine is generally formulated as seeking the shortest collision-free pipe paths in constrained space while meeting certain engineering constraints. In particular, the constraints of clamp layout need to be considered as well, which makes the problem be more complex. In this paper, a pipe layout method considering clamp constraints for aero-engine is presented. A surface visibility graph routing method is used as a basic routing principle, and a layout strategy considering clamp positions, engineering rules and expert experience is designed. The method aims to pave pipes appropriately in aero-engine constrained space, while meeting clamp constraints. Several routing examples are performed on a simplified aero-engine piping model, which demonstrates the effectiveness of the presented layout method.
Deceptive Path Planning in Dynamic Environment

Deceptive path planning is an important part of military simulation, which is mainly applied to the static environment now. However, it is very necessary to consider how to generate the deceptive path in dynamic environment because elements in the real battlefield change constantly. This paper proposes a phased planning method, which combines static planning results with dynamic data, so that the attacker can eventually find a feasible deceptive path in dynamic environment, which can avoid the moving obstacles. Some simulation experiments prove that the method is really useful for planning deceptive paths in dynamic environment.
Improving Applications of Systems of Systems using Ultra Fast Instance Segmentation

Computer Vision is a valuable tool that can be used to enhance the components of many working systems. In particular, many applications can be improved by incorporating instance segmentation into their designs to help better process visual information in the surrounding environment. However, instance segmentation algorithms have traditionally been too slow to be used by any real-time systems that could benefit from using them. This includes examples such as self-driving vehicles or autonomous drones. In this work we provide an overview of the shortcomings for current instance segmentation algorithms, introduce an ongoing effort to create a new one that achieves ultrafast speeds without sacrificing competitive accuracy, and explain the advantages of employing an ultra-fast real-time version of one as a component in different systems.
Comparative Study of Classifiers for Blurred Images

In this paper, we want to launch a first step for the classification of images according to their degree of blur based on the subjective measurement of DMOS image quality in the Gblur database. For this purpose, we have carried out a comparative study on several classifiers in order to build a robust learning model based on the transformation into a DCT. The class imbalanced has forced us to look for and find appropriate performance evaluation metrics so that the comparison is not biased. It has been found that random forests (RF) give the best overall performance but that other classifiers discriminate better between certain types of images (depending on the degree of blur) than others. Finally, we compared the classification by the proposed model with another classification based on the NIQE quality measurement algorithm. The results of the model proposed by its simplicity are very promising.
On Duplicating of the Elements Representing the Image

The aim of this work is to consider the digital image representation and to find which of their items can be replicated for watermarking. We introduce two schemes for spatial and frequency domain watermarking. We discuss the errors that arise from the round data when a watermarked image is stored in a lossless graphical format for sending to user. The results obtained are illustrated with practical examples of digital watermarking sheet.
Security and Privacy Vulnerabilities in Automated Driving

Driver assistance systems are advancing at a rapid pace, and almost all major companies have started investing in developing autonomous vehicles. However, the security and reliability in this field is still uncertain and debatable. A vehicle compromised by the attackers remotely can be easily used to create chaos of epic proportions. An attacker can control brake, accelerate, and even steering, which can lead to catastrophic consequences. Therefore, an autonomous vehicle can be weaponized extremely easily if proper security protocols are not implemented. This chapter gives a very short and brief overview of some of the possible attacks on autonomous vehicle software and hardware and their potential implications.
Graph Computing Systems for Large-Scale Graph Analysis

Since Google introduced the first distributed graph computing system Pregel, many similar systems are proposed. The distributed graph computing systems become a standard platform for large-scale graph analysis. Compared to the previous graph processing libraries, the new systems have the advantages of scalability, usability, and flexibility. In this chapter, we briefly review the basic concepts of the distributed graph computing systems, including the architecture, execution flow, and programming abstraction and computation models (e.g., vertex-centric, edge-centric, subgraph-centric, etc.). In this book, we concentrate on the vertex-centric computation model and then describe two excellent and popular programming abstractions—vertex programming abstraction and gather–apply–scatter (GAS) programming abstraction. Finally, we introduce the workload-aware cost model which classifies the factors influencing the performance into two types—workload source and workload distribution. The model helps to estimate the workload for a distributed graph computing system and guides us to optimize the systems and algorithms smartly.
Nested Sparse Classification Method for Hierarchical Information Extraction

Use of sparse representation method for classification of visual data has proved its efficiency for challenges encountered in unconstrained face identification. The main identified constrains which heavily affect classification accuracy are variation in expression, pose and lighting. In this paper, a novel classification method is developed as nested sparse classification (NSC) method which incorporates the advantages of sparse representation manifold. In NSC method, sparse representation-based classification is implemented in a nested manner which allows the extraction of hierarchical relationship information between test and training samples. The hierarchical relationship information helps the classifier to be discriminative to inter-personal face changes while robust to intra-personal variations. The implementation not only improves the classification performance but also makes the system capable of being scaled. The improved accuracy of proposed NSC method is demonstrated by experiments carried out on two standard databases (ORL database and YALE database). The performance is analysed with an improvement of more than 2% in terms of classification accuracy.
An Adaptive Genetic Algorithm Approach for Optimizing Feature Weights in Multimodal Clustering

Social Media is a popular channel of communication, where people exchange different types of high volume and multimodal data. Cluster analysis is used to categorize this data to extract useful information. However, the variation of features that can be used in clustering makes the clustering process difficult, since some features may be more important than others, and some may be irrelevant or redundant. An alternative to traditional feature selection techniques, especially with the absence of domain knowledge, is to assign feature weights that depend on their importance in the clustering process. In this paper, we introduce a multimodal adaptive genetic clustering (MAGC) algorithm that clusters data according to multiple features. This is done by adding feature weights as an extension to the clustering solution. In other words, feature weights evolve and improve alongside the original clustering solution. In addition, the number of clusters is not determined a priori, but it is adapted and optimized during the evolutionary process as well. Our approach was tested on a large collection of Flickr images metadata and was found to perform better than a non-adaptive genetic algorithm clustering approach and to produce semantically related clusters.
IndoAcro: An Indonesian Acronym and Expansion Repository with Data Auto-Update Implementation

Abstract IndoAcro is an Indonesian acronym and expansion repository created using machine learning and big data technology. The repository can be publicly accessed from www.indoacro.cs.unsyiah.ac.id. Six important steps of IndoAcro have been developed and implemented, which consists of (1) data crawling, (2) data cleaning, (3) generating candidate pairs of acronym and expansion, (4) generating numerical features, (5) classifying the candidate pairs, and (6) filtering the classification results. In this study, we introduce and analyze the implementation of data auto-update for IndoAcro. Since it was developed, IndoAcro has 2,232 pairs of acronym and expansion, collected from more than 50 thousand online news articles. Because no auto-update approach has been implemented previously, the number of acronym and expansion pairs in the database is monotonous, dull, and static. In this study, we introduce and analyze the implementation of data auto-update for IndoAcro. We have analyzed and evaluated the data auto-update process for 180 days, each process consists of 2 days interval. We found that the data auto-update approach has successfully implemented and updated the data for IndoAcro. We collected 1,639 pairs of acronym and expansion in the first run, 343 and 224 pairs in the second and third runs.
Autocorrelation values of quaternary cyclotomic sequence of length 2pm

We completely determine the autocorrelations of the quaternary cyclotomic sequences over [Formula: see text] of length [Formula: see text] presented in [P. Ke and S. Zhang, New classes of quaternary cyclotomic sequence of length [Formula: see text] with high linear complexity, Inf. Process. Lett. 112 (2012) 646–650] in general without the restrictions about [Formula: see text].
ANTARES - ANonymous Transfer of vehicle Access Rights from External cloud Services

As car sharing becomes an increasingly common task, mediating user access rights from external servers comes with threats regarding user’s privacy. Clearly, users can be tracked by service mediators, e.g., cloud providers, that manage vehicle fleets, etc. In this work we design and test a simple solution based on oblivious transfer, a well-known and secure cryptographic block, that allows to preserve user’s privacy when gaining access to the vehicle. We test the feasibility of deploying such a solution on Android capable smartphones but also account for potential in-vehicle components, e.g., car head units, that may be soon put to such tasks. We use Microsoft Azure as cloud service provider and deploy a Java implementation, based on the Bouncy Castle cryptographic library, on the server side. Our experimental results show that Android based units are capable of handling the required cryptographic operations and the implementation of the employed protocol can be done by existing open-source support.
Finding Needles in a Haystack

Abstract Convex polygons are distinguishable among the piecewise $$C^\infty $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mi>C</mml:mi> <mml:mi>∞</mml:mi> </mml:msup> </mml:math> convex domains by comparing their visual angle functions on any surrounding circle. This is a consequence of our main result, that every segment in a $$C^\infty $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mi>C</mml:mi> <mml:mi>∞</mml:mi> </mml:msup> </mml:math> multicurve can be reconstructed from the masking function of the multicurve given on any surrounding circle.
re|Traces of Search

The software art installation 're|Traces of Search' probes human-software relationships by examining in detail a specific and situated action: searching on the web. This work exposes the inner workings of this human/non-human connection; the software - the keystrokes, the scripts and function calls occurring during the exchange - are revealed as a tangible artifact and interactive sonification. We invite guests to explore this non-human representation and touch the software, as it touches us back. As well as highlighting a hidden complexity, this work touches upon questions of transparency and privacy of search engines, and to what extent our relationship with software is rooted in control, in contrast to care.
Methodological Aspects of the Problems of Modern School Management

The materials reflecting the authors' study on the problems of improving the activities of the modern school are presented. Based on the analysis of the emerging new labor structure of society, a generalized system of modern education is proposed. The key place allocated to it in the system of general education is emphasized. The main problems of the modern secondary school in general and the management of its activities in particular are considered. The main attention is paid to the formation of the ranking of schools, which includes indicators of diverse characteristics. It is proposed to consider the educational institution as a social system, within the framework of which the students develop skills of behavior, relationships in the team and with the external environment. It is noted that in each educational institution, an internal organizational and psychological culture is formed, which leaves its mark on almost all the processes taking place there, in particular, it creates restrictions on the possibility of making certain decisions and the likelihood of their implementation. This position necessitates the formation of a school-specific system of consistent indicators, including a number of qualitative indicators. The use of such indicators in school management processes necessitates the development of specialized methods for assessing the effectiveness of its activities. The article proposes a method for assessing qualitative characteristics, providing an increase in their level of objectivity.
Digital environment of vocational education in the Russian Federation

Abstract The article presents the formation of a digital educational environment for vocational education in the Russian Federation. Particular attention is paid to pedagogical approaches, the architecture of the digital educational environment, the experience of the St. Petersburg State University of Industrial Technologies and Design in the field of its use for training forest industry.
Development of m-learning for basic education subject in the teacher training institute

Abstract In recent decades, ownership of mobile devices has increased. The increasing number of people who own and use mobile devices opens up opportunities for the use of mobile technology in the world of education, one of which is the utilization of mobile learning (m-learning) devices. M-learning is intended to complement existing learning and provide opportunities for students to re-learn material that is not mastered anywhere and anytime. This certainly can provide a different experience in the learning process for students, development of mobile learning as an alternative learning media that is most appropriate and effective today. Development of m-learning using the prototyping method so that developers and users can interact with each other during the process of making an application, and users can evaluate the app directly. This mobile learning will be used to support basic education subjects in Teacher Training Institute.
LDA Based Approach for Topic Description from Spoken Audio Content

Language is an important way of communication for people and is expressed in both written and speech form. The idea of this paper is on computing the framework for speech recognition wherever information required is to be made available in short time span. The input taken is in spoken audio form and the output derived is text description. In this system analysis of audio content is performed and then classified into text description. It has two sections, Training and Testing, Where in training section, the process of feature extraction of audio content is performed and are stored in database, in the testing section matches the features and provides the text description of the audio content. Further by applying topic modelling method and the summary of audio file is derived. This system uses database of 50 domain audio samples, with each domain contains 5 audio samples. The performance of the system is measured in terms of processing time and recognition. This system is considered to be high accuracy rate as compared to conventional method. Hence we derived accuracy rate of the system is up to 89.12%.
Chinese Explanatory Opinion Relationship Recognition Based on Improved Target Attention Mechanism

Opinion relationship recognition is an important part of the opinion mining task. Its main purpose is to extract the opinion element tuple from the user comment data and identify the relationship between them, such as evaluation object, evaluation content, opinion explanation, opinion object. Because the comments of the network having are characterized by randomness, diversity of opinions and different formats, it will become more difficult for the opinion mining task. If we can extract the interrelationships between the various explanatory opinion elements, it not only makes subsequent tasks easier but also applies its extracted results to other related tasks. For example, applying the opinion seven-tuple from the opinion extraction task to the text summary generation task can greatly improve the effectiveness of the text summary generation task. In this paper, we have improved on the traditional LSTM-Attention model and proposed an opinion relationship recognition framework based on improved Target Attention Mechanism. Also, we conducted experiments in two different domains, and the experimental results show that the performance has been effectively improved in two domains. We also explored two different pre-training strategies, Word2vec and Elmo, to further analyze the impact of pre-training on this experiment.
APPLICATIONS OF CONSTRUCTION AN EOG TELEVISION CONTROL MODEL

Speaking and writing are two common easiest mediums of communication for human. However, they are not applicable when it comes to handicapped who has hearing or speaking inability. Handicapped often encounter a hard time in expressing their feelings and thoughts to others. Furthermore, there are abundant of product in the market, which uses eye movement, light reflection theory and electrooculogram (EOG) as the input of their control system. These implication aims to ease the disabled to use modern devices. Therefore, a research is done on constructing an EOG Television Control Model to help the disabled to have remote on the television. By using the eye’s electrical signal obtained via vertical and horizontal motion, user can easily control television by rolling or blinking their eyes.&#x0D; Motivation/Background: The main motivation for this research is to look for an alternative solution to help patients especially those with physical disabilities to control television using eyes motions without difficulties. Moreover, selection for the best television monitoring method among all possible solution is also one of the research progress.&#x0D; Method: The most important method being used in this research is by using EOG to detect eyes motion and convert each and every unique motion into a specific command for television monitoring action. Wireless communication device is an additional to allow long range television control using EOG signal.&#x0D; Results: It is shown that the EOG signal for left and right and up and down motion of eyeballs differ with each other. Using the motion and by setting a threshold value to quantized the signal, a better quality television monitoring command is generated.&#x0D; Conclusions: EOG signal allow user especially those with physical disabilities to monitor television with ease by performing unique eye rolling motion.
Making Things Meaningful

In this chapter, building on the previous discussion of knowledge/knowing, I turn to consider the themes of meaning and communication in design research. To open, I explore of the work of three key theorists—Roberto Verganti, Klaus Krippendorff and Nathan Crilly—who have each explored these themes in detail. From this, I turn once again to Dewey’s work, looking in particular at his handling of the subjects of language, the imagination and the ‘work of art’. With these positions set out, I then move to consider some of the possible implications for design research, focusing in particular on the ‘work of art’ concept. Ultimately, emphasis is placed on how Dewey’s insights can support the theoretical articulation of meaningfulness in design as well as the communicative value of artifacts in academic design research.
An intelligent ordering control system based on hand gesture recognition

In this paper, an intelligent ordering system based on gesture recognition and PYNQ_Z2 is proposed, in which gesture segmentation is realized in new color space YCgCr by skin detection, background subtraction and face removal, and then the gesture model created by palm center and fingertip localization is compared with a defined gesture dictionary to implement gesture recognition. Experimental results show that the system has small delay, high accuracy and high performance of human-computer interaction.
Robust and Resilient Authentication on MapReduce Model

This paper has anticipated a new distributed and parallel computing paradigm commonly deployed for an authentication solution in MapReduce model used by Hadoop to process BigData. This paper describes the Pseudo Identity based Authentication Framework (PIAF) which includes PRID generation, Group Session Key (GSK) generation and novel authentication mechanism of Client can be done by using Lagrange interpolation based on ZKP so that the data transferred is not understandable for the third-party who observers the channel. Resource manager is authenticated by comparing its own polynomial with the polynomial generated by the client. The polynomial or key at which both the polynomials are mapped is the secret key used to encrypt or decrypt the data at Resource manager and client respectively. This paper provides a new approach which uses MR model in deployment and security designs of Hadoop. The protocols are executed between a Client in a cloud (Prover) and a Resource manager (Verifier).