Reviewerly/exHarmony huggingface dataset:
Localization of Text in Photorealistic Images

Detection and localization of text in photorealistic images is a difficult, and not yet completely solved, problem. We propose the approach to solving this problem based on the method of semantic image segmentation. In this interpretation, text characters are treated as objects to be segmented. In this paper proposes the network architecture for text localization, describes the procedure for the formation of the training set, and considers the algorithm for pre-processing images, reducing the amount of processed data and simplifying the segmentation of the object “background”. The network architecture is a modification of well-known DeepLabv3 network and takes into account the specifics of images of text characters. The proposed method is able to determine the location of text characters in the images with acceptable accuracy. Experimental results of assessing the quality of text localization by the IoU criterion (Intersection over Union) showed that the obtained accuracy is sufficient for further text recognition.
Coherence Regularization for Neural Topic Models

Neural topic models aim to predict the words of a document given the document itself. In such models perplexity is used as a training criterion, whereas the final quality measure is topic coherence. In this work we introduce a coherence regularization loss that penalizes incoherent topics during training of the model. We analyze our approach using coherence and an additional metric - exclusivity, responsible for the uniqueness of the terms in topics. We argue that this combination of metrics is an adequate indicator of the model quality. Our results indicate the effectiveness of our loss and the potential to be used in the future neural topic models.
Improved Wall-following Driving for Robot Operating System-based High-speed Autonomous Mobile Robot

This study demonstrates an -based high-speed autonomous mobile robot using an improved wall-following driving algorithm. The right triangular method is a popular wall-following technique. However, it cannot provide a sufficiently fast response for the abrupt change in wall direction at a corner, resulting in crashes. Therefore, a mixed-mode method is proposed by using the triangle bisection method at a corner to achieve high-speed cornering. The autonomous robot detects a corner using a corner recognition algorithm. The implemented robot employs ROS for system operations and LIDAR sensor to scan wall faces. The maximum measured speed around corners is 2.8 ㎧ (10.1 ㎞/h), which is 75% higher than that of the triangular method. The robot passes through a right angle corner without crashing at a maximum speed of 1.8 ㎧ (6.5 ㎞/h). The implemented robot platform and algorithm can be applied and expanded for the development of high-speed autonomous mobile robots.
Extract, shoehorn, and load

Data doesn't always fit nicely into a new home.
A Solar Intensive Approach for Smart Environment Planning in Gandhinagar, Gujarat

With time and technological development, human race is gradually elevating its dependence on machinery. The incessant usage of energy is therefore increasing manifolds. However, the realisation of reducing reliance on non-renewable energy sources was felt long back, no concrete steps are taken in day to day life. Solar Energy is considered to be one of the most abundant and sustainable forms of energy in a tropical country like India. It can thus be used to fulfil the demands risen due to dearth of urbanization. Gandhinagar was chosen to be one of the first Model Solar City after the Solar City Master Plan was prepared for the city. Considering the growing demand and need, the city was assessed using Remote Sensing (RS) and Geographic Information System (GIS) technique for its building footprints and solar rooftop potential was evaluated using standard insolation models. It was felt that geospatial techniques play a pivotal role in mapping the potential zones useful for resource tapping. It is expected that only 30% of rooftop area can reduce the dependence on non-renewable energy by 200% in residential sector. Planned cities and all planned developments within the urbanized regions can adhere to use technology to calculate the effect of usable rooftop area, height of the structure or building and insolation falling on the surface so as to reduce their energy demand. Such technologically sound and low cost solutions can help in driving developing nations’ need of reliance on non-renewables. The study is an attempt to understand how efficiently geospatial technology can play a role in determining the solar potential and help in converting the city into a sustainable and green city by using the tool of “Smart Energy”.
Interactive Music on the Web

The web browser provides a novel platform for interactive music with a number of unique advantages: easy access, online collaboration, platform independence and abundant online resources. The goals of this chapter are (1) to help readers comprehend the benefits and challenges involved in developing and using browser-based interactive music applications; (2) to provide a basic technical foundation for writing a Web Audio program in the context of creating interactive music applications; and (3) to explore the opportunities that interactive music on the web offers, by identifying the five emerging trends of current music applications. Ultimately, we wish to encourage readers to create applications that permit novel musical expression and collaboration through the use of Web Audio.
On efficient network similarity measures

This paper presents novel graph similarity measures which can be applied to simple directed and undirected networks. To define the graph similarity measures, we first map graphs to real numbers by utilizing structural graph measures. Then, we define measures of similarity between real numbers and prove that they can be used as proxies for graph similarity. Numerical results are derived to show the domain coverage of these measures as well as their clustering ability. The latter relates to the efficient grouping of graphs according to certain structural properties. Our numerical results are sensitive to these properties and offer insights useful for designing effective graph similarity measures.
DESHADOWING OF HIGH SPATIAL RESOLUTION IMAGERY APPLIED TO URBAN AREA DETECTION

Different built-up structures usually lead to large regions covered by shadows, causing partial or total loss of information present in urban environments.In order to mitigate the presence of shadows while improving the urban target discrimination in multispectral images, this paper proposes an automated methodology for both detection and recovery of shadows.First, the image bands are preprocessed in order to highlight their most relevant parts.Secondly, a shadow detection procedure is performed by using morphological filtering so that a shadow mask is obtained.Finally, the reconstruction of shadow-occluded areas is accomplished by an image inpainting strategy.The experimental evaluation of our methodology was carried out in four study areas acquired from a WorldView-2 (WV-2) satellite scene over the urban area of São Paulo city.The experiments have demonstrated a high performance of the proposed shadow detection scheme, with an average overall accuracy up to 92%.Considering the results obtained by our shadow removal strategy, the pre-selected shadows were substantially recovered, as verified by visual inspections.Comparisons involving both VrNIR-BI and VgNIR-BI spectral indices computed from original and shadow-free images also attest the substantial gain in recovering anthropic targets such as streets, roofs and buildings initially damaged by shadows.
Composition: A Fresh Look at an Old Topic

Composing separate components is a fundamental design principle for concurrent systems. Composition $$A_1\cdot A_2$$ of two components $$A_1$$ and $$A_2$$ is mostly modeled by “gluing” according elements of the interfaces of $$A_1$$ and $$A_2$$ . Composition of many components is usually assumed to be associative (i.e. $$(A_1 \cdot A_2) \cdot A_3 = A_1 \cdot (A_2 \cdot A_3)$$ ). In this paper we suggest such a composition operator for any kind of graph based structures. The central and new idea exploits the observation that in a composed system $$A_1 \cdot ... \cdot A_n$$ , every component $$A_i$$ $$(2 \le i \le n-1)$$ has a left partner $$A_{i-1}$$ and a right partner $$A_{i+1}$$ . The interface of $$A_i$$ hence canonically partitions into the left and the right port of $$A_i$$ . To gain $$A_1 \cdot A_2$$ , elements of the right port of $$A_1$$ are glued with corresponding elements of the left port of $$A_2$$ . We present two instantiations of this framework, modeling synchronous and asynchronous composition, respectively, of components with local states.
Telemetry-Aware Add-on Recommendation for Web Browser Customization

Web Extensions (add-ons) allow clients to customize their Web browsing experience through the addition of auxiliary features to their browsers. The add-on ecosystem is a market differentiator for the Firefox browser, offering contributions from both commercial entities and community developers. In this paper, we present the Telemetry-Aware Add-on Recommender (TAAR), a system for recommending add-ons to Firefox users by leveraging separate models trained to three main sources of user data: the set of add-ons a user already has installed; usage and interaction data (browser Telemetry); and the language setting of the user's browser (locale). We build individual recommendation models for each of these data sources, and combine the recommendations they generate using a linear stacking ensemble method. Our method employs a novel penalty function for tuning weight parameters, which is adapted from the log likelihood ratio cost function, allowing us to scale the penalty of both correct and incorrect recommendations using the confidence weights associated with the individual component model recommendations. This modular approach provides a way to offer relevant personalized recommendations while respecting Firefox's granular privacy preferences and adhering to Mozilla's lean data collection policy. To evaluate our recommender system, we ran a large-scale randomized experiment that was deployed to 350,000 Firefox users and localized to 11 languages. We found that, overall, users were 4.4% more likely to install add-ons recommended by our ensemble method compared to a curated list. Furthermore, the magnitude of the increase varies significantly across locales, achieving over 8% improvement among German-language users.
Augmented reality based learning media for virtual instruction of LPG cylinder assembly

In Indonesia, Liquefied Petroleum Gas (LPG) is the major fuel used for cooking by households. A fuel conversion program from kerosene to LPG implemented since 2007 has been reduced the fuel subsidy budget until 39.17 trillion rupiah per year. Unfortunately, the application of LPG to the new users has evoked a serious hazardous behavior that may cause an explosion because of inappropriate cylinder installation and non-standardized daily usages. LPG explosion can lead to injuries, material damages, and casualties. Nevertheless, most of LPG users learned how to use the equipment by themselves without reading any manual instructions—which actually have been provided by Pertamina. The present research attempted to develop an augmented reality based learning media to present the LPG installation and risk mitigation procedures. This application software named LPG Guide that was developed by defining systematic assembly process, exploring the user voices and anticipating the operational risk. Systematic marker and 3D animated object were designed by applying AR technology. Application and usability testing were conducted to evaluate and explore the opportunity for further improvement.
Collaborative Framework for Fuzzy Co-clustering

Privacy preserving data mining is a fundamental approach for utilizing multiple databases including personal or sensitive information without fear of information leaks. In this chapter, a framework of securely applying fuzzy co-clustering to multiple cooccurrence information, which is stored in multiple organizations, is reviewed with illustrative examples.
Witness Encryption with (Weak) Unique Decryption and Message Indistinguishability: Constructions and Applications

In this paper, we investigate WE scheme with the unique decryption and message indistinguishability, as well as its compelling applications. Our contributions are three-fold: (i) we first propose the notion of WE with MI and weak unique decryption, and give a construction based on public-coin differing-inputs obfuscation (diO), pseudorandom generator, and the Goldreich-Levin hard-core predicate; (ii) We show that our WE with MI and weak unique decryption can be used to construct a 4-round non-black-box honest-verifier zero-knowledge argument protocol; and (iii) We present a WE scheme with unique decryption and MI based on public-coin diO and weak auxiliary input multi-bit output point obfuscation (AIMPO). Moreover, we show that using our WE with unique decryption, we can get rid of the limitation of honest-verifier zero-knowledge property, thus yielding a 4-round non-black-box zero-knowledge argument.
What’s in a Game? The Effect of Game Complexity on Deep Reinforcement Learning

Deep Reinforcement Learning (DRL) combines deep neural networks with reinforcement learning. These methods, unlike their predecessors, learn end-to-end by extracting high-dimensional representations from raw sensory data to directly predict the actions. DRL methods were shown to master most of the ATARI games, beating humans in a good number of them, using the same algorithm, network architecture and hyper-parameters. However, why DRL works on some games better than others has not been fully investigated. In this paper, we propose that the complexity of each game is defined by a number of factors (the size of the search space, existence/absence of enemies, existence/absence of intermediate reward, and so on) and we posit that how fast and well a game is learned by DRL depends on these factors. Towards this aim, we use simplified Maze and Pacman environments and we conduct experiments to see the effect of such factors on the convergence of DRL. Our results provide a first step in a better understanding of how DRL works and as such will be informative in the future in determining scenarios where DRL can be applied effectively e.g., outside of games.
Vision-Based Hand Detection in Various Environments

People use their hands the most to interact with computers. However, there are many inherent problems with the methods used for hand detection. Various shapes of hands, complex backgrounds and illumination can induce much misdetection. To use hands to interact with a computer, it is very important to have a robust hand area. Therefore, this paper proposes a method of acquiring a robust hand area in various environments. The proposed system operates in real-time and has good performance in environments of various illuminations and complex backgrounds.
Informative and formal description of structure dynamics control task of cyber-physical systems

The authors propose the informative and formal description of Structure Dynamics Control Task of Cyber-physical Systems (CPS).The main advantage of proposed poly-model multicriterion approach for the structure dynamics control tasks decision is that, in comparison with previously developed control models oriented on CPS simulation, proposed models allow to increase the effi ciency, validity and, in general, the quality of the SD control CPS due to the original combination of their static and dynamic models.
Using deep learning to improve recommendation with direct and indirect social trust

Deep learning is advancement in machine learning with many hidden layers. It can provide better results even with less number of features. In deep learning, large scale data automated learning is possible with less feature engineering. It is of high significance for big data because of large scale of data available for training. Many applications areas such as image processing, speech recognition and social network analysis have proven to provide better results with the use of deep learning. Large scale of data is generated due to social networking sites, sensor networks and business transactions. It is very difficult for user to select any product or topic of interest, Social recommendation is active research area due to its significance in reducing information overload. It is intelligent systems which are used to provide suggestions to users for any product or topic. In this article, deep learning is applied on social recommendation technique with our proposed approach based on transitive trust. Cold start and sparsity are main limitations in social recommendation. Experiment analysis proves that these issues are resolved by using our proposed approach incorporated with deep learning.
An Improved Approach to Background Removal Using Haar-Based Preprocessing for Phase Features

The paper proposes moving object detection in Haar preprocessed images by removing background initialization constraint like a change in illumination. The fundamental objective of the proposed method is to increase the processing speed. In this work, background subtraction algorithm using phase as a feature is used to model background and distance transform is used for foreground extraction. Operation is performed with and without Haar compressed images. Experiment on real-time video streams with Haar-based phase feature implementation shows that the speedup in object detection is achieved compared to baseline method. Performance improvement of about 79% is observed in processing speed of frames with only 25% additional frame memory.
A Method Based on S-transform and Hybrid Kernel Extreme Learning Machine for Complex Power Quality Disturbances Classification

Complex power quality disturbances (CPQDs) classification can be regarded as a typical application of multi-label (ML) learning. In this study, we propose a new recognition method for CPQDs based on S-transform (ST) and a hybrid kernel function-based extreme learning machine (ELM) for ML learning (HKEML). The signal processing techniques S-transform is utilized to extract the distinctive features of the CPQDs. A novel ML classifier called HKEML is constructed by combining hybrid kernel function-based multiclass ELM and a thresholding learning method-based kernel ELM. Finally, a test study was conducted using Matlab synthetic signals and real signals sampled from a three-phase standard source under different noise conditions. Compared with several recent state-of-the-art ML learning algorithms, HKEML achieved better classification performance but with greatly superior computational speed.
Learning Explainable Control Strategies Demonstrated on the Pole-and-Cart System

The classical problem of balancing an inverted pendulum is commonly used to evaluate control learning techniques. Traditional learning methods aim to improve the performance of the learned controller, often disregarding comprehensibility of the learned control policies. Recently, Explainable AI (XAI) has become of great interest in the areas where humans can benefit from insights discovered by AI, or need to check whether AI's decisions make sense. Learning qualitative models allows formulation of learned hypotheses in a comprehensible way, closer to human intuition than traditional numerical learning. In this paper, we use a qualitative approach to learning control strategies, which we demonstrate on the problem of balancing an inverted pendulum. We use qualitative induction to learn a qualitative model from experimentally collected numerical traces, and qualitative simulation to search for possible qualitative control strategies, which are tested through reactive execution. Successful behaviors provide a clear explanation of the learned control strategy.
Can the Expansion of Prediction Errors be Counterbalanced in Reversible Data Hiding?

Recent pixel value ordering (PVO) based development of reversible data hiding (RDH) schemes highly relies on an effective prediction error expansion (PEE). A prediction error is usually expanded either by embedding a bit in it or by shifting it by a “suitable” value. With PVO, this expansion is made directional, ı.e., left-ward, right-ward or both centering a reference pixel. This PVO property leads us to investigate that once a set of original pixels are expanded upon their predicted errors in one level of embedding, can that expansion be counterbalanced in an additional level of embedding? In this paper, we attempt to address this question in developing a new RDH scheme with PVO and classic PEE. An input image is partitioned into a set of non-overlapping blocks of size \(1\times 3\). Data-bits are embedded into the blocks in two phases of embedding. Our early results have demonstrated that the counterbalance of expanded errors is possible to offer better embedding rate-distortion performance than the original baseline scheme. Future endeavor on generalizing the proposed scheme and its information theoretic analysis may create a new avenue in data hiding research.
Interactional Aesthetics of Blockchain Technology

Blockchain technology holds the potential of facilitating fundamentally novel ways for users to relate to systems of finance, governance, and identity management. At the same time, the inherent complexity of blockchain systems makes designing for and interacting with the technology profoundly challenging. Furthermore, the question of how social interactions will change under the influence of Blockchain systems remains an open one. In this paper we examine how concepts and procedures adopted from interaction aesthetics can serve as an intellectual and methodological lens to creatively and critically examine the emerging technology of the Blockchain.
Software Effort Estimation Using an Optimal Trees Ensemble: An Empirical Comparative Study

Since information systems have become the heartbeat of many organizations, the investment in software is growing rapidly and consuming then a significant portion of the company budget. In this context, both the software engineering practitioners and researchers are more interested than ever about accurately estimating the effort and the quality of software product under development. Accurate estimates are desirable but no technique has demonstrated to be successful at effectively and reliably estimating software development effort. In this paper, we propose the use of an optimal trees ensemble (OTE) to predict the software development effort. The ensemble employed is built by combining only the top ranked trees, one by one, from a set of random forests. Each included tree must decrease the unexplained variance of the ensemble for software development effort estimation (SDEE). The effectiveness of the OTE model is compared with other techniques such as regression trees, random forest, RBF neural networks, support vector regression and multiple linear regression in terms of the mean magnitude relative error (MMRE), MdMRE and Pred(l) obtained on five well known datasets namely: ISBSG R8, COCOMO, Tukutuku, Desharnais and Albrecht. According to the results obtained from the experiments, it is shown that the proposed ensemble of optimal trees outperformed almost all the other techniques. Also, OTE model outperformed statistically the other techniques at least in one dataset.