Reviewerly/exHarmony huggingface dataset:
Mobile Based Geographic Information System for mapping and data collection Towards 4.0 Industry

Abstract Geographic Information System Technology makes it easier for us to manage geo-referenced information data or mapping the location of an area that is connected to the database. Nahdlatul Wathan is one of the largest organizations in West Nusa Tenggara, where there has been rapid development from year to year, especially in the field of education. The number of Islamic schools (madrasas) under the Nahdlatul Wathan organization is increasing every year. But until now, the location (geographical location) of each school is still not well recorded and mapped. Therefore, we need a geographic information system that can be used for the process of mapping and data collection of schools under the Nahdlatul Wathan organization that can help the management of Nahdlatul Wathan and each Islamic school and community to find out the location. from each school, which can be accessed quickly and precisely.
Risk Analysis for Railway Signaling Safety Data Network Based on Extend Bayesian Attack Graph

Abstract The railway signaling safety data network is a key network to ensure the safety of train operation and improve transportation efficiency. Its information security is closely related to the safe and efficient operation of trains. This paper proposes an extend Bayesian attack graph model for risk analysis of railway signaling safety data network. Based on the traditional Bayesian attack graph, the model introduces the protection node, and describes the causality among network attack, protection and network state through the Bayesian network. First of all, based on the analysis of the vulnerabilities and possible malicious attack paths in the railway signaling safety data network, the extend Bayesian attack graph model is established with the system functional safety accidents as the target nodes. Then calculate the probability of functional safety accidents by Bayesian network, and combine the impact of different accidents to evaluate the risk of system information security. In addition, according to different attacks and the implementation of different protective measures, evidence node state can be set up, and the extend Bayesian attack graph model is able to use Bayesian inference to analyze the system risk.
Modeling Gaze-Guided Narratives for Outdoor Tourism

Many outdoor spaces have hidden stories connected with them that can be used to enrich a tourist’s experience. These stories are often related to environmental features which are far from the user and far apart from each other. Therefore, they are difficult to explore by locomotion, but can be visually explored from a vantage point. Telling a story from a vantage point is challenging since the system must ensure that the user can identify the relevant features in the environment. Gaze-guided narratives are an interaction concept that helps in such situations by telling a story dynamically depending on the user’s current and previous gaze on a panorama. This chapter suggests a formal modeling approach for gaze-guided narratives, based on narrative mediation trees. The approach is illustrated with an example from the Swiss saga around ‘Wilhelm Tell’.
DeepSelfie: Single-Shot Low-Light Enhancement for Selfies

Taking a high-quality selfie photo in a low-light environment is challenging. Because the foreground and background often have different illumination conditions, they suffer heavily from over/under-exposure issues and cannot be treated in the same manner when applying image enhancement algorithms. In this work, we propose DeepSelfie, a learning-based image enhancement framework for low-light selfie photos. We address selfie enhancement as a dual-layer image enhancement problem. The foreground and background are thus separately enhanced and combined together via image fusion. To train the selfie enhancement network, we also introduce a method of synthesizing pairs of noisy and dark raw selfie images and their corresponding well-illuminated images. Through extensive experiments of no-reference image quality assessment as well as human subjective evaluation, we show that DeepSelfie provides better results in comparison to several state-of-the-art methods. The code and datasets can be found at https://sites.google.com/view/deepselfie.
Repulsion-Based Grey Wolf Optimizer

In this paper, a novel algorithm called Repulsion-based Grey Wolf Optimizer (R-GWO) is presented. The proposed algorithm is an improvement of the already existing popular swarm intelligence algorithm, called Grey Wolf Optimizer (GWO). The aim of the proposed work is to enhance the balance between exploitation and exploration in GWO by introducing a repulsion factor. This repulsion factor drifts the population away from the non-promising regions and brings it closer to the optimal solution. To compare the performance of R-GWO and GWO, both the algorithms are run on 16 benchmark fitness functions. The results indicate that R-GWO performs better as compared to the conventional GWO in terms of convergence speed and in discovering the global optimal solution.
A Lightweight Data Sharing Mechanism and Multiparty Computation for CPS

To reduce the complexity and the overhead of the inter-operation in cyber-physical system based on the blockchain, a low-round communication verifiable data sharing mechanism and multi-party computing for collaboration is addressed. Based on the Shamir secret sharing algorithm, the sensing nodes can generate shares of any-degree polynomials of shared numbers in the data sharing mechanism, and the participating witness node is able to validate the shares generated by sensing nodes without interaction. To implement the proposed mechanism, a fully non-interactive secure multiparty computation for cyber-physical system is introduced seamlessly. With simplified communication process, different distributed nodes in the system can jointly obtain different degree negotiated function and each user node is able to validate the replies from the participating witness node. The performance analysis shows that only a low overhead is incurred by the secure collaboration based on the proposed secret sharing mechanism.
Neural Rasch Model: How Do Word Embeddings Adjust Word Difficulty?

The Rasch model is a probabilistic model for analyzing the psychological test response data such as second language tests; it is especially useful to automatically obtain a common scale of difficulty for various types of test questions by fitting the model to the response data of the test takers obtained from various methods such as reading comprehension questions and vocabulary questions. Because a test-taker can answer only some hundreds of vocabulary questions from tens of thousands of words in second language vocabulary, there exists a strong need to estimate the difficulty of the words that were not used during the test. For this purpose, the word frequency in a large corpus was previously used as a major clue. Although recent advancements in natural language processing enable us to obtain considerable semantic information about a word in the form of word embeddings, the manner in which such embeddings can be utilized while adjusting the word difficulty estimation has not been considerably investigated. Herein, we investigate how to effectively leverage word embeddings for adjusting the word difficulty estimates. We propose a novel neural model to fit the test response data. Further, we use the trained weights of our neural model to estimate the difficulty of the words that were not tested. The quantitative and qualitative experimental results denote that our model effectively leverages word embeddings to adjust simple frequency-based word difficulty estimates.
Scalable Heterogeneous Multiagent Learning from Demonstration

We present a method of supervised learning from demonstration for real-time, online training of complex heterogenous multiagent behaviors which scale to large numbers of agents in operation. Our learning method is applicable in domains where coordinated behaviors must be created quickly in unexplored environments. Examples of such problem domains includes disaster relief, search and rescue, and gaming environments. We demonstrate this training method in an adversarial mining scenario which coordinates four types of individual agents to perform six distinct roles in a mining task.
Data Mining Methods for Optimizing Feature Extraction and Model Selection

How can we carry out on-the-fly data mining on massive amounts of data, to make relevant predictions, based on data for similar observations to the one currently under consideration? In this paper we show the benefit of using large numbers of computationally efficient analyses to tune the feature extraction, and prediction, steps in data mining, using cross-validated prediction accuracy as the evaluative criterion. Different feature extraction strategies are also compared in terms of their predictive effectiveness in this context. While the research reported here focused on clinical prediction of healthcare outcomes, the results should have broader implications for large scale data mining in general.
An investigation on methods for axis detection of high-density generic axially symmetric mechanical surfaces for automatic geometric inspection

The detection of the symmetry axis from discrete axially symmetric surfaces is an interesting topic, which is transversal to various fields: from geometric inspection to reverse engineering, archeology, etc. In the literature, several approaches have been proposed for estimating the axis from high-density triangular models of surfaces acquired by three-dimensional (3D) scanning. The axis evaluation from discrete models is, in fact, a very complex task to accomplish, due to several factors that inevitably influence the quality of the estimation and the accuracy of the measurements and evaluations depending on it. The underlying principle of each one of these approaches takes advantage of a specific property of axially symmetric surfaces. No investigations, however, have been carried out so far in order to support in the selection of the most suitable algorithms for applications aimed at automatic geometric inspection. In this regard, ISO standards currently do not provide indications on how to perform the axis detection in the case of generic axially symmetric surfaces, limiting themselves to addressing the issue only in the case of cylindrical or conical surfaces. This paper first provides an overview of the approaches that can be used for geometric inspection purposes; then, it applies them to various case studies involving one or more generic axially symmetric surfaces, functionally important and for which the axis must be detected since necessary for geometric inspection. The aim is to compare, therefore, the performances of the various methodologies by trying to highlight the circumstances in which these ones may fail. Since this investigation requires a reference (i.e. the knowledge of the true axis), the methodologies have been applied to discrete models suitably extracted from CAD surfaces.
Modeling and development of a multi-agent space for the secondary school

One of the guiding principles of the Fourth Industrial Revolution is the need for lifelong learning. This determines the growing role of intelligent educational systems to provide the necessary learning resources and services to users at any time and any place. This article presents the modeling and development of an intelligent multi-agent learning environment for the secondary school, developed by a team of the DeLC laboratory at the University of Plovdiv “Paisii Hilendarski”, Bulgaria. The learners are placed at the focus of the environment by personal assistants supporting work with the environment.
Four Flavors of Entailment

We present a novel approach for enumerating partial models of a propositional formula, inspired by how theory solvers and the SAT solver interact in lazy SMT. Using various forms of dual reasoning allows our CDCL-based algorithm to enumerate partial models with no need for exploring and shrinking full models. Our focus is on model enumeration without repetition, with potential applications in weighted model counting and weighted model integration for probabilistic inference over Boolean and hybrid domains. Chronological backtracking renders the use of blocking clauses obsolete. We provide a formalization and examples. We further discuss important design choices for a future implementation related to the strength of dual reasoning, including unit propagation, using SAT or QBF oracles.
Chinese Speech Recognition System based on Deep Learning

Abstract This paper builds a complete Chinese speech recognition system, including acoustic model and linguistic model, which can recognize the input audio signal into Chinese characters. The system realizes the modeling of acoustic model and linguistic model in speech recognition based on deep framework, of which the acoustic model is CNN-CTC and linguistic model is transformer. The data set uses THCHS-30, which refers to 30-hour Chinese speech database of Tsinghua University. The experimental results show that the Chinese speech recognition system based on deep learning achieves 90% accuracy on the test set and has an excellent effect on Mandarin speech recognition in quiet environment.
Demonstration for Weaving a Second Skin

Weaving as a craft possesses the structural, textural, aesthetic, and cultural expressiveness for creating a diversity of soft, wearable forms that are capable of technological integration. In this paper, we extend the woven practice for crafting on-skin interfaces, exploring the potential to "weave a second skin." Weaving incorporates circuitry in the textile structure, which, when extended to on-skin interface fabrication, allows for electrical connections between layers while maintaining a slim form. Weaving also supports multi-materials integration in the structure itself, offering richer materiality for on-skin devices. Using a fabrication approach which leverages skin-friendly PVA film for on-skin adherence, we present a series of case studies illustrating the functional and design potential of adapting weaving for crafting on-skin interfaces. This is the demonstration abstract accompanying the full paper.
AI-Enabled Real-Time Sign Language Translator

Even in recent times with the advancement in technology, there exists a hindrance in seamless communication with the hearing and speech-impaired section of the society. Inclusive communication is instrumental for a society to function as a whole. It is not only essential for exchanging ideas, but also for progress and innovation. A lack of means for spontaneous communication should not stand in the way of socializing, employment or productivity. We propose an android application that interprets American Sign Language into English language using convolutional neural network with an aim to provide real-time translation to facilitate seamless communication. Although there is a presence of computer-based translation application for sign language recognition, the availability of such applications over an android platform is relatively few in number. The proposed sign language translator finds its applicability in gesture-controlled applications like human–computer interaction, providing control action for various home appliances and electronic gadgets that are triggered by gestures when given as an input. The proposed work is aimed to transform hearing and speech abnormality to normality, thus eliminating their dependencies.
A Comprehensive Survey and Analysis on Path Planning Algorithms and Heuristic Functions

This paper explores the performance of four commonly used path planning algorithms of A*, D*, LPA* and D* Lite in both static and dynamic environments. To assess the effect of heuristic functions for path planning algorithms, four commonly used heuristic functions of Manhattan distance, diagonal distance, Euclidean distance and squared Euclidean distance are used based on three simulation environments with different map size and complexity. Experimental results show that the heuristic function significantly affects the computing performance and the final path of path planning algorithms, and Manhattan distance is the most efficiency heuristic function for all the four path planning algorithms. We apply A*, D*, LPA* and D* Lite algorithms to static and dynamic environments, respectively with different map size and complexity. We find that A* has the best performance in relatively simple static environments. When the map size and complexity of the static environment is highly increased, D* Lite is the best choice. In contrast, D* Lite has the best performance for path planning in dynamic environments. However, we also find that A* also has an excellent performance in dynamic environments when the map size and complexity are decreased.
An Exploratory Analysis of Technical Issues in Remote Education Between International Medical Institutions

International education programs that use videoconferencing (VC) are common now and many are being implemented in the medical field. Irrespective of the value of the content, the learning process can be disrupted when technical quality of VC is compromised. In this study, a participatory design (PD) was used with focus group discussion between technical support staff to determine and classify the causes of technical issues in international remote medical education by VC. As a result, a quality of experience (QoE) -based framework was developed based on the technical issues raised. User behavior and three factors (context, human and system factors) influence technical errors, either preventing or creating difficulties, and affecting each other. For future prototyping of the PD process, it is suggested to have a flexible and specific operational procedure which considers variables of engineers’ background.
Deep Learning Based Diversity Combining for Generic Noise and Interference

In this work, we develop a deep learning (DL) based robust data detection algorithm for a receive-diversity system, where the data is corrupted by Gaussian and different non-Gaussian noise and interference in different diversity branches. A fully-connected deep neural network (DNN) is designed for this purpose and is trained with different noisy datasets offline. The developed DNN is then applied in real-time for data detection process. We emphasize that the detector does not require the noise distribution to be known for its operation. Furthermore, by the use of simulations, we show that the proposed DL-based detector performs much better than the conventional maximal ratio combining (MRC) detector for non-Gaussian noise. Moreover, simulation results point out that the proposed detector is robust enough to operate in time-varying noisy environment.
Two-dimensional multi-scale perceptive context for scene text recognition

Inspired by speech recognition, most of the recent state-of-the-art works convert scene text recognition into sequence prediction. Like most speech recognition problems, context modeling is considered as a critical component in these methods for achieving better performance. However, they usually only consider using a holistic or single-scale local sequence context, in a single dimension. Actually, scene texts or sequence contexts may span arbitrarily across a two-dimensional (2-D) space and in any style, not limited to only horizontal. Moreover, contexts of various scales may synthetically contribute to text recognition, in particular for irregular text recognition. In our method, we consider the context in a 2-D manner, and simultaneously consider context reasoning at various scales, from local to global. Based on this, we propose a new Two-Dimensional Multi-Scale Perceptive Context (TDMSPC) module, which performs multi-scale context learning, along both the horizontal and vertical directions, and then merges them. This can generate shape and layout-dependent feature maps for scene text recognition. This proposed module can be handily inserted into existing sequence-based frameworks to replace their context learning mechanism. Furthermore, a new scene text recognition network, called TDMSPC-Net, is built, by using the TDMSPC module as a building block for the encoder, and adopting an attention-based LSTM as the decoder. Experiments on benchmark datasets show that the TDMSPC module can substantially boost the performance of existing sequence-based scene text recognizers, irrespective of the decoder or backbone network being used. The proposed TDMSPC-Net achieves state-of-the-art accuracy on all the benchmark datasets.
A Heuristic for Efficient Reduction in Hidden Layer Combinations for Feedforward Neural Networks

In this paper, we describe the hyperparameter search problem in the field of machine learning and present a heuristic approach in an attempt to tackle it. In most learning algorithms, a set of hyperparameters must be determined before training commences. The choice of hyperparameters can affect the final model’s performance significantly, but yet determining a good choice of hyperparameters is in most cases complex and consumes large amount of computing resources. In this paper, we show the differences between an exhaustive search of hyperparameters and a heuristic search, and show that there is a significant reduction in time taken to obtain the resulting model with marginal differences in evaluation metrics when compared to the benchmark case.
Use of Silicon-based Sensors for System Reliability Prediction

More sensors mean better visibility and subsequently increased systems reliability… up to a point. Additional relevant silicon-based sensors require additional software, test cases, and product costs. In this paper we argue that the cost/benefit balance has shifted towards having more sensors with the advent of telemetry mechanisms to carry the sensor readings and machine learning facilities to low-overhead anomaly detection models and resources. The choice of the sensors to use and their association to high-level function-related sensors has demonstrated that silicon-based sensors in system components add important value on top of high-level function-related sensors, such as root cause analysis; early failure prediction, and system reliability prediction.
Matching Prediction to Communication and Computing for Proactive VR Video Streaming

Proactive tile-based video streaming can avoid motion-to-photon latency of wireless virtual reality (VR) by computing and delivering the predicted tiles in a segment to be requested before playback. All existing works either focus on tile prediction or on tile computing and delivering, overlooking the facts that these three tasks have to share the same duration and the quality of experience (QoE) depends on the worst performance of them. In this paper, we jointly optimize the duration of the observation window for prediction and the durations used for computing and communication to maximize the QoE of watching a VR video. We find the global optimal solution by decomposing the original problem equivalently into subproblems, with which we find prediction-limited or resource-limited region. Simulation results demonstrate the gain of the optimized durations by using two existing prediction methods with a real dataset.