Reviewerly/exHarmony huggingface dataset:
A GPU Accelerator for Domain Transformation-Based stereo Matching

Stereo Matching, which is a key technique for the depth detection in computer vision, can be widely used in many applications such as SLAM and Auto-driving. High accuracy, high processing speed and mobility of the stereo matching system are required in these ap- plications. Recently, with the development of artificial intelligence technology, a large number of stereo matching algorithms have been proposed, and the accuracy has been constantly improved. However, due to their high computation complexity, most of the current systems require to be run on large-scale hardware platforms. Hence, some researchers join the study of accelerating the stereo matching on the mobile platforms. In this paper, we propose a novel method to accelerate the state-of-the-art stereo matching algorithm Domain Transformation on a small and low power consumption GPU. Our method mainly focuses on improving the efficiency of data transfer between the multi-hierarchy memories of GPU during the cost aggregation step, and it consists of three optimization steps: Weight Generation, Aggregation Cost Penalizing and Wavelength Encoding & Decoding, which can help us solve the bandwidth waste caused by using floating-point data type due to the cost accumulation in Domain Transformation. By using this method, our system achieved 35 fps on Jetson TX2 GPU for 1280x375 pixels images when the maximum disparity is 128.
RootsGLOH2: embedding RootSIFT ‘square rooting’ in sGLOH2

This study introduces an extension of the sGLOH2 local image descriptor inspired by RootSIFT ‘square rooting’ as a way to indirectly alter the matching distance used to compare the descriptor vectors. The extended descriptor, named RootsGLOH2, achieved the best results in terms of matching accuracy and robustness among the latest state‐of‐the‐art non‐deep descriptors in recent evaluation contests dealing with both planar and non‐planar scenes. RootsGLOH2 also achieves a matching accuracy very close to that obtained by the best deep descriptors to date. Beside confirming that ‘square rooting’ has beneficial effects on sGLOH2 as it happens on SIFT, experimental evidence shows that classical norm‐based distances, such as the Euclidean and Manhattan distances, only provide suboptimal solutions to the problem of local image descriptor matching. This suggests matching distance design as a topic to investigate further in the near future.
Requirement ambiguity and fuzziness in large-scale projects: The problem and potential solutions

In large-scale projects, it often occurs that the product the purchaser ends up receiving – possibly from projects extending over many years – differs from what they expected. The provider usually defends its delivered product and may blame the imprecision and ambiguity of the requirements, defined by the purchaser, as the primary reason for misinterpretation of requirements and resulting deficiencies. This letter relies on game theory to explain this problem including both intentional and unintentional misinterpretation of requirements. The letter also highlights the practical and scientific significance of the problem using two real-world cases and suggests potential tools and techniques from soft computing in order to develop decision support systems to address the problem.
Binary Convolutional Neural Network with High Accuracy and Compression Rate

In this paper, we propose an efficient scheme to train a binary convolutional neural network that has high compression rate and classification accuracy. In binary neural networks, weights and activations are binarized to +1 or -1. This brings two benefits: 1)The model size is greatly reduced; 2)Arithmetic operations can be replaced by more efficient bitwise operations based on binary values, resulting in much faster inference speed and lower power consumption. However, binarizing neural networks will result in severe prediction accuracy degradation compared to their counterpart full-precision networks. To solve this problem, we apply three strategies: 1) By summarizing the previous work, we conclude that the large performance loss of binary networks is mainly caused by the binarization of activations, so we propose to apply multiple banalizations to activations. Compared to apply multiple banalizations to weights, it effectively maintains compression rate while improving the accuracy; 2) In order to alleviate gradient mismatches between the forward and backward propagation, We adopt a more precise differentiable approximation when calculating the gradients in backward propagation; 3) In order to further improve the compression rate, we also binarize the last layer with the help of a scale layer.
Deep learning can be used to train naïve, nonprofessional observers to detect diagnostic visual patterns of certain cancers in mammograms: a proof-of-principle study

The scientific, clinical, and pedagogical significance of devising methodologies to train nonprofessional subjects to recognize diagnostic visual patterns in medical images has been broadly recognized. However, systematic approaches to doing so remain poorly established. Using mammography as an exemplar case, we use a series of experiments to demonstrate that deep learning (DL) techniques can, in principle, be used to train naïve subjects to reliably detect certain diagnostic visual patterns of cancer in medical images. In the main experiment, subjects were required to learn to detect statistical visual patterns diagnostic of cancer in mammograms using only the mammograms and feedback provided following the subjects’ response. We found not only that the subjects learned to perform the task at statistically significant levels, but also that their eye movements related to image scrutiny changed in a learning-dependent fashion. Two additional, smaller exploratory experiments suggested that allowing subjects to re-examine the mammogram in light of various items of diagnostic information may help further improve DL of the diagnostic patterns. Finally, a fourth small, exploratory experiment suggested that the image information learned was similar across subjects. Together, these results prove the principle that DL methodologies can be used to train nonprofessional subjects to reliably perform those aspects of medical image perception tasks that depend on visual pattern recognition expertise.
Novel Technique for Isolated Sign Language Based on Fingerspelling Recognition

Sign language is used by deaf and hard hearing people to exchange information between their own community and with other people. Fingerspelling recognition method from isolate sign language has attracted research interest in computer vision and human-computer interaction based on a novel technique. The essential for real-time recognition of isolate sign language has grown with the emergence of better-capturing devices such as Kinect sensors. The purpose of this paper is to design a user independent framework for automatic recognition of American Sign Language which can recognize several one-handed dynamic isolated signs and interpreting their meaning. We built datasets as a raw data for alphabets (A-Z) or numbers (1-20) by used left-hand the 3D point (X <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">L</sub> , Y <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">L</sub> , Z <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">L</sub> ) or switch by right-hand (X <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R</sub> , Y <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R</sub> , Z <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R</sub> ) centroid as one of contribution. The proposed approach was tested for gestures that involve left-hand or right-hand and was compared with other approach and gave better accuracy. Two machine learning methods are involved like Hidden Conditional Random Field (HCRF), and Random Decision Forest (RDF) for the classification part. The third contribution based on low lighting condition and cluttered background. In this research work is achieved for recognition accuracy over 99.7%.
Tentative diagnosis prediction via deep understanding of patient narratives

A tentative diagnosis is a preliminary suspicion of patient status, which is usually made by physicians according to patient narrative right at admission. It largely depends on the experiences and professional knowledge of physicians. We explored a combination model for automatic tentative diagnosis prediction based on clinical narratives. Text features are extracted in two ways. Firstly, the context semantic features are extracted by attention-based bidirectional long-short term memory (BiLSTM) network. Secondly, the symptom concepts recognized from input texts by Metamap and are vectorized by TF-IDF. Two combination strategies are proposed to utilize both two features for one candidate international classification of diseases (ICD) code recommendation: feature vectors combination and prediction results combination. The experiments performed on MIMIC III dataset. Both of the two combination strategies achieved better performance, comparing with either of the model based on single type feature.
An Adaptive Control Approach for Performance of Big Data Storage Systems

With the explosion of data volume, and technological evolution of Big Data in data management, several data storage platforms are currently available to meet this need, the question often arises is what are the most appropriate storage concepts to support large-scale analytical process. In fact, several competing technologies in this field today, sometimes some better suited to certain types of treatment than others. Each approach has its own strengths and weaknesses. And in general, the use of one does not exclude the other. To effectively deal with this issue, this study presents an approach for ranking the alternative solutions based on ideal values of criteria. For this purpose, a multi criteria decision making (MCDM) model is presented with combination of Analytical Hierarchy Process (AHP) to solve multiple criteria decision-making problems. The proposed model is capable of finding optimal solution for high-dimensional problems with simple and manual calculations.
VLW-Net: A Very Light-Weight Convolutional Neural Network (CNN) for Single Image Dehazing

Camera imaging is one of the most important application areas of computer image and video processing. However, computational cost is usually the main reason preventing many state of the art image processing algorithms from being applied to practical applications including camera imaging. This paper proposes a very light-weight end-to-end CNN network (VLW-Net) for single image haze removal. We proposed a new Inception structure. By combining it with a reformulated atmospheric scattering model, our proposed network is at least 6 times more light-weight than the state-of-the-arts. We conduct the experiments on both synthesized and realistic hazy image dataset, and the results demonstrate our superior performance in terms of network size, PSNR, SSIM and the subjective image quality. Moreover, the proposed network can be seamlessly applied to underwater image enhancement, and we witness obvious improvement by comparing with the state-of-the-arts.
Optimizing Low Energy Pathways in Receptor-Ligand Binding with Motion Planning

Determination of ligand binding pathways is an important factor to predict drug efficacy in drug discovery. Ligand-receptor binding involves the motion of many degrees of freedom, which can make binding pathways difficult to discover with traditional methods. Interactive molecular docking tools can allow users to explore the high dimensional energy landscape of the ligand-receptor system with rigid molecular models to determine low energy ligand states and pathways to binding. To introduce the effect of ligand flexibility in molecular docking with rigid body models, we use ensembles of distinct ligand conformation states that can be swapped during exploration. Our method emulates ligand flexibility effects in rigid body docking at no extra computational cost. Our automated method simulates user search performance with a path optimization algorithm. We find that allowing the algorithm to include different ligand conformations in its search for states of lower energy can result in optimized low energy pathways with reduced search times in difficult areas near energy barriers. This method can be adapted to include molecular flexibility effects in interactive rigid body molecular docking running in commodity hardware, such as molecular docking games.
Mobile Robot Path Planning Algorithm Based on Rapidly-Exploring Random Tree

A path planning algorithm is proposed for the problems of high node randomness and repeatability using the Rapidly-exploring Random Tree (RRT) algorithm in the path planning. Firstly, the method of region division centered on newly generated nodes is proposed to reduce randomness, the generation rules of temporary target nodes are guided to reduce repetitiveness, then improve the efficiency of path planning through adaptive step size strategy, smooth the planned path to improve the length of the path in the end. Simulation results show that the improved RRT algorithm can effectively plan the path for mobile robots.
Augmented Reality and Mobile Consumers

In this chapter, consumer perceptions of augmented reality mobile applications will be emphasized and the analysis will be carried out through the mobile application markets of two different countries. In the research, the top 20 applications were selected from the UK and USA mobile application markets and the last consumer evaluations regarding these applications were obtained. In accordance with the purpose of the research, text mining methods were used to evaluate the expressions of consumers, since data mining methodologies can contribute to a better understanding of unstructured data. In the research, top words, bigram, and trigram are used in consumer comments. Then sentiment analysis method is employed to determine the emotions in consumer comments. Authors conclude that both markets have positive polarities. While the study provides a theoretical contribution in terms of consumer evaluations and new product perception, it also contributes to the sector in terms of expressions and evaluations used by consumers.
The User Experience Analysis of Computer Graphics Educational Comics (GRAFMIC) based on Markerless Augmented Reality

This study aims to analyze the user experience of computer graphics educational comics (GRAFMIC) based on markerless augmented reality to ensure the sustainability of the product. The main product consists of educational comics that are integrated with markerless AR which can display learning details, where users can interact directly with the animation shown. The ADDIE model was used to develop the product that consists of 5 steps, namely Analysis, Design, Development, Implementation and Evaluation. When GRAFMIC and the AR application were developed, 4 experts and 4 lecturers engaged to validate the products to ensure that the product is ready to be implemented in the learning activity. Having been validated by the experts and lecturers, the product was implemented in the learning process by engaging 40 undergraduate students of Informatics Education. In the evaluation stage, the students asked to investigate and evaluate the user experience (UX) of this learning innovation product by answering several questions provided in the questionnaire. User Experience Questionnaire (UEQ) is used to analyze and also benchmark the product. The results of the UX analysis and the benchmark confirm that the attractiveness and the pragmatic quality of the product are good. On the other hand, the results also gave us valuable information for enhancing the hedonic quality of the product.
A Conceptual Multi-Dimensional Model for Smart Learning Environments

Internet of things (IoT) and the availability of personal devices give many choices for people to learn everything, anytime, and anywhere by connecting to the Internet. This condition changes all aspects of learning environment: spaces, contents, processes, assessment, and skills. It brings a vision of Smart Learning Environments (SLE) as the concept of applying smart environment to expand the learning environment, which can enhance learning processes by integrating smart technology and pedagogy aspects. This research uses qualitative synthesis by classifying SLE research topics to obtain SLE dimensions. Our SLE's conceptual model consists of multi-dimension of SLE, both internal and external factors of learning. The multi-dimensional model in SLE includes smart learning (SmL), smart pedagogy (SmP), and smart supporting system (SmS). This result is essential to design a learning environment that can be arranged by learners. They can utilize all conditions and devices according to personal needs, anywhere, anytime, and anyhow by considering aspects of pedagogy.
An efficient ASIC Implementation of QARMA Lightweight Algorithm

Lightweight cryptographic algorithms play an important role in data security of applications in Internet-Of-Things(IOT). To provide a cipher suitable for the IOT devices, we investigate the performance of QARMA block cipher. In this paper, we proposed a part-iterative architecture for QARMA which integrates encryption and decryption operations. We implemented it on ASIC in CMOS 55 nm technology, where a max frequency of 666.67MHz is obtained. The results show that our design has achieved 54% area reduced and simultaneously 25 times max frequency increased, and the throughput increases by 1.56 times compared with the fully unrolled implementation.
A link between two classes of permutation polynomials

The study of permutation polynomials over finite fields has attracted many scholars' attentions due to their wide applications and there are several different forms of permutations over finite fields. However, there is little literature on the relation between different forms of permutations. In this paper, we find an equivalent relation between permutation polynomials of the form f(x)=xh(x2n−1) over F22n and permutations of the form F(x)=H1(Δ)H1(Δ)+H2(Δ)+x on F2n, where Δ=1x2+x+δ, δ∈F2n with Tr2n(δ)=1 and H1,H2 are the x-coordinate and 1-coordinate of h (see Definition 2.4), respectively. A special case with H1+H2=1, which means F(x)=H1(1x2+x+δ)+x, has been already studied [8], [16], [21], [22]. We summarize the known permutations and then construct 8 new classes of permutations of such form, from which one can derive some permutation polynomials of the form xh(x2n−1) over F22n directly. Finally, we give an asymptotic result about (1x2+x+δ)2k+x to be a permutation over F2n using the Hasse-Weil bound.
Privacy Concerns in the VR and AR Applications in Creative Cultural Industries

Creative culture industry refers to industries that combine the creation, distribution, production, and commercialization of creative contents in advertising, design, film &amp; video, interactive leisure software, music, publishing, and television and radio. This chapter focuses on the museum sub-sector of the creative cultural industries. These sub-sectors have eagerly embraced digital technologies to promote cultural heritage by transforming their archaeological finds into more immersive creative and cultural contents. However, the pervasive nature of mobile technologies and the heavy reliance on consumers' locational information have caused rising privacy concerns among consumers. This chapter employed a text mining study to explore how potential privacy infringement, as one of the most observable social impacts of mobile-based and location-sensitive AR and VR technologies, is discussed in major media outlets around the world. Discussions and implications conclude the chapter.
Adult Content Classification on Indonesian Tweets using LSTM Neural Network

In the last decade, social media networking sites have become an inseparable part of people’s life. However, not all of content in social media contain beneficial and necessary information. This can be seen from the existing of negative and harmful content in social media, such as adult or pornographic content. Therefore, this study aims to build a model for adult content classification by using Long Short Term Memory (LSTM) Neural Network to classify adult content and non-adult content. We also compared our LSTM methods with Multinomial Naive Bayes, Logistic Regression, and Support Vector Classification. According to our experiments, the best model was obtained from the LSTM model with two LSTM layers and dropout reached the accuracy of 98,39% and the loss value of 5,08&.
Optimization of Genetic Algorithms on Backpropagation Neural Network to Predict National Rice Production Levels

Rice is the staple food for Indonesian people that cannot be separated from anything. Every community needs rice for their survival. Annual Indonesia rice production is fluctuating. It needs rice production information to anticipate the rice quota which meets the demands. For this reason, this study purposes to predict rice production in Indonesia. The method used in this study is a Backpropagation Neural Network (BPNN) with genetic algorithm (GA) optimization for time series forecasting. Using Genetic Algorithms, BPNN was optimized then used as weighting input for better predictions, then the prediction results will be used as a benchmark for the next rice production so Indonesian rice demands will be fulfilled. We use the data of rice production published from 1970 until 2017. The results demonstrate that the BPNN model optimized using Genetic Algorithms has a very high performance with Mean Absolute Percentage Error (MAPE) values 1.4084% for training data and 2.3394% for testing data. It is more accurate than the BPNN model without GA optimization, it yields MAPE values 1.4797% for training data and 2.6944% for testing data. Based on the best model, we deliver the prediction of the rice production in the next four years after 2017.
An Effective Image-Adaptive Hybrid Watermarking Scheme with Transform Coefficients

Robust and invisible watermarking provides a feasible solution to prove the ownership of the genuine content owner. Different watermarking algorithms have been presented by the researchers in the past but no algorithm could be termed as perfect. Proposed work puts forward a novel image-adaptive method of embedding a binary watermark in the image in a transparent manner. Discrete wavelet transform (DWT), singular value decomposition (SVD) and discrete cosine transform (DCT) are used together in the proposed hybrid watermarking scheme. Image-adaptive nature of the scheme is reflected in the usage of only high entropy [Formula: see text] blocks for the watermark embedding. Binary watermark is embedded in the DCT coefficients using a flexible strength derived from the means of the DCT coefficients. This flexible strength factor (SF) has different value for the DCT coefficients originated from different [Formula: see text] blocks. Any desired level of visual quality could be obtained by varying the adjusting parameter of the flexible SF. Side information generated in the watermark embedding is used in the detection of watermark. The presented watermarking technique shows better robustness in comparison to the three contemporary watermarking techniques.