Reviewerly/exHarmony huggingface dataset:
The outlier pixel and sub trees that form segments of image

Abstract Segmentation is a very important step in computer vision. Many segmentation methods are found to get the best results. Every method found always has advantages and disadvantages. Minimum spanning tree is one method used to segment images. Some researchers use thresholds as linear combinations of averages and standard deviations for this method. This study aims to see the effect of outliers on the number of segments formed. This study uses a sample of a 5x5 image with an outlier. Suppose that the distribution of side weights (or the difference in intensity of adjacent pixels) is a normal distribution except for one outlier, and the threshold value is equal to the average. If the weight of the outlier data side is equal to 14 to 16, there are 6 segments formed, for weights equal to 17 to 37, there are 4 segments formed, and for weights equal to 38 or more, there are 2 segments formed. The threshold value (corresponding average) changes significantly by changing the value of one outlier data. This will cause changes in the number of segments in the image that are not good in the segmentation method. So the threshold value should not correspond to statistical values.
Deep Bayesian Data Mining

This tutorial addresses the fundamentals and advances in deep Bayesian mining and learning for natural language with ubiquitous applications ranging from speech recognition to document summarization, text classification, text segmentation, information extraction, image caption generation, sentence generation, dialogue control, sentiment classification, recommendation system, question answering and machine translation, to name a few. Traditionally, "deep learning" is taken to be a learning process where the inference or optimization is based on the real-valued deterministic model. The "semantic structure" in words, sentences, entities, actions and documents drawn from a large vocabulary may not be well expressed or correctly optimized in mathematical logic or computer programs. The "distribution function" in discrete or continuous latent variable model for natural language may not be properly decomposed or estimated. This tutorial addresses the fundamentals of statistical models and neural networks, and focus on a series of advanced Bayesian models and deep models including hierarchical Dirichlet process, Chinese restaurant process, hierarchical Pitman-Yor process, Indian buffet process, recurrent neural network (RNN), long short-term memory, sequence-to-sequence model, variational auto-encoder (VAE), generative adversarial network (GAN), attention mechanism, memory-augmented neural network, skip neural network, temporal difference VAE, stochastic neural network, stochastic temporal convolutional network, predictive state neural network, and policy neural network. Enhancing the prior/posterior representation is addressed. We present how these models are connected and why they work for a variety of applications on symbolic and complex patterns in natural language. The variational inference and sampling method are formulated to tackle the optimization for complicated models. The word and sentence embeddings, clustering and co-clustering are merged with linguistic and semantic constraints. A series of case studies, tasks and applications are presented to tackle different issues in deep Bayesian mining, searching, learning and understanding. At last, we will point out a number of directions and outlooks for future studies. This tutorial serves the objectives to introduce novices to major topics within deep Bayesian learning, motivate and explain a topic of emerging importance for data mining and natural language understanding, and present a novel synthesis combining distinct lines of machine learning work.
An Edge-constrained Iterative Cost Aggregation Method for Stereo Matching

To improve the accuracy of stereo matching in low-textured or depth discontinuous regions of images, a new method using edge-constrained iterative cost aggregation is proposed in this paper. Firstly, a bilateral diffusion is used as the preprocessing to reduce inconsistency in the matching direction, which can make the follow-up matching more reliable. Secondly, a matching cost function that combines both color and edge is adopted, and a two-step iterative cost aggression based on the minimum spanning tree (MST) is then proposed. In the first cost aggregation, to avoid accumulation of small weights happened in low-textured regions, an enhanced weight function with coefficient adjustment is presented. And in the second cost aggregation, to appropriately provide weights to neighbors, an edge constraint is introduced. The edge information used in the second aggregation is produced by the random forest method from the disparity map obtained in the first cost aggregation. Left-right consistency check and epipolar constraint are both adopted to further eliminate false edge points. Finally, the disparity refinement is utilized to optimize the disparity map. The proposed method is conducted on Middlebury v.2 data set. Experimental results demonstrate that the proposed method can achieve higher matching accuracy, compared with other five state-of-art methods.
ECM Factorization with QRT Maps

Quispel–Roberts–Thompson (QRT) maps are a family of birational maps of the plane which provide the simplest discrete analogue of an integrable Hamiltonian system and are associated with elliptic fibrations in terms of biquadratic curves. Each generic orbit of a QRT map corresponds to a sequence of points on an elliptic curve. In this preliminary study, we explore versions of the elliptic curve method (ECM) for integer factorization based on performing scalar multiplication of a point on an elliptic curve by iterating three different QRT maps with particular initial data. Pseudorandom number generation and other possible applications are briefly discussed.
On the (im)possibility of color reconstruction in underwater images

Color is an important cue in object recognition and classification problems. In underwater imagery, colors distort due to light propagation through an absorbing and scattering medium. Distortions depend on a number of complex phenomena, the most important being wavelength-dependent absorption and sensitivity of sensors in trichromatic cameras. Using a simplified model of spectral profile for various colored objects, we show by means of numerical simulation that for fixed hardware and medium properties there exist many spectral profiles that lead to different recorded colors in air (without absorption) but the same color in water (with absorption). The above implies that the restoration of true and unique colors from trichromatic underwater imagery is not possible.
Left ventricular Analysis Method in Cardiac Cine MRI

The advent of non-invasive imaging in sections, has allowed the development of a new morphological exploration and semiology based on the 3D examination of organs. The extraction of myocardial pathologies in cardiac cine-MRI often requires a prior segmentation of the left ventricle (LV). In this paper, a new semi-automatic computer system for left ventricle analysis from short-axis cardiac cine MRI is presented. It is based on first applying a set of image processing and mathematical morphology operators in order to segment the LV. Then the parameters such as end-diastolic volume (EDV), end-systolic volume (ESV) and ejection fraction (EF) are calculated for characterization. The proposed method was tested on the Heart database containing 18 patients. The obtained results are satisfactory and provide good correlation between the proposed segmentation method and that of experts. They confirm the accuracy of the proposed method and its eventual in aid of diagnosis.
About the quantum Fisher information of nearly pure quantum statistical models

We address nearly pure quantum statistical models, i.e. situations where the information about a parameter is encoded in pure states weakly perturbed by the mixing with a parameter independent state, mimicking a weak source of noise. We show that the symmetric logarithmic derivative is left unchanged, and find an approximate analytic expression for the quantum Fisher information (QFI) which provides bounds on how much a weak source of noise may degrade the QFI.
Cost and Energy Aware Dynamic Splitting of Video Traffic in Heterogeneous Networks

The vision towards 5G and beyond is to provide remarkable performance enhancements that enable the launch of new services and markets in different industry verticals. Example scenarios of those services include indoor hotspot, broadband access in a crowd, and dense urban; many of which require very high data rates. Traffic offloading and device-to-device cooperation have been leveraged to expand the system capacity and coverage through using heterogeneous network technologies. In this work, we shed the light on the significant gains incurred when predicted short term network performance is factored into network splitting decisions over multiple wireless interfaces, while guaranteeing a desired quality of experience to end users. Accordingly, we allow dynamic use of multiple network interfaces taking into consideration their energy requirement and price models to deliver premium services and minimize the overall energy consumption and total cost. We develop a novel and efficient real-time traffic splitting approach that makes use of predicted bit rate of each network interface in addition to device-to-device cooperation to decide on the amount of video traffic to be delivered on each interface at every time slot. The proposed approach is validated and evaluated under realistic network conditions. Simulation results demonstrate substantial gains in terms of energy consumption, data cost and quality of user experience as compared to multiple alternative solutions.
Obstacle Avoidance of Welfare Vehicle with Head Mounted Display using Spatial Mapping of Driving Environment

Recently, welfare vehicles are widely used among the aged. However, it is hard for inexperienced beginners to control the welfare vehicle in narrow places such as rooms and corridors. For the problem, the driving assist system for the welfare vehicle which is steered from an objective viewpoint with a transmittable Head Mounted Display (HMD) has been proposed in complex driving areas. Also, the autonomous driving system which can be used in safe areas for the driver has been studied. The advanced function which switches the driving assistance and the autonomous driving is needed because the welfare vehicles drive in the complex driving situations. In this paper, an autonomous driving system is proposed using the wearable MR-device which has collision avoidance function using the spatial mapping. As the system uses the virtual vehicle to avoid the obstacle, this system will be easy to switch the driving assistance and the autonomous driving. The obstacles are modeled by the spatial mapping installed in wearable HMD. Using the virtual vehicle drawn by the hologram, the autonomous system is steered using A* and NavMesh system on Unity. So, the welfare vehicle follows the virtual vehicle with the virtual platoon control constructed on the welfare vehicle. The effectiveness of proposed real-time obstacle avoidance system is evaluated by driving experiments.
Information Source Detection in Social Networks

The rising popularity of online social networks has made information generating and sharing much easier than ever before, due to the ability to publish content to large, targeted audiences. Such networks enable their participants to simultaneously become both consumers and producers of content, shifting the role of information broker from a few dedicated entities to a diverse and distributed group of individuals. While this fundamental change allows information propagating at an unprecedented rate, it also enables unreliable or unverified information spreading among people, such as rumors.
Basic Concepts of the Elective Course on the Hard Computing Problems

We consider the material of the elective course for the young students, and briefly describe both so-called hard problems and some methods necessary to develop programs for their implementation on the computer. For this, we are considering several real problems of discrete optimization. For each of them we consider both “greedy” algorithms and more complex approaches. The latter are, first of all, are considered in the description of concepts, understandable to “advanced” young students and necessary for the subsequent program implementation of the branches and bounds method and some associated heuristic algorithms. According to the authors, all this “within reasonable limits” is available for “advanced” young students of 14–15 years. Thus, we present our view on the consideration of difficult problems and possible approaches to their algorithmization – at a level “somewhat higher than the popular science”, but “somewhat less than scientific”. And for this, the paper formulates the starting concepts which allows one of such “complications” to be carried out within the next half-year.
A Survey of Calibration Methods for Traditional Cameras Based on Line Structure Light

Abstract The line structure light three-dimensional reconstruction system is a kind of three-dimensional non-contact measurement system, which has the advantages of high precision, high speed, small damage to objects and strong adaptability. Camera calibration is a major factor that constrains the accuracy of 3D measurement systems. The camera calibration is based on the pinhole imaging model, and through a series of complex calculations, the camera’s internal parameters (focal length, distortion coefficient) and external parameters (rotation matrix and translation vector). The different calibration methods use different calibration targets, which can be divided into 3D calibration targets, 2D calibration targets, and one-dimensional calibration targets according to the characteristics of the calibration targets. This paper mainly discusses: calibration content and significance, calibration methods for different targets and evaluation methods for calibration of different targets. Firstly, the content and significance of calibration are expounded. Then, according to different calibration targets, the calibration algorithm is analyzed. Finally, the calibration algorithm is analyzed and summarized, and the development trends, advantages and disadvantages of different calibration methods are pointed out.
Capturing Contextual Influence in Context Aware Recommender Systems

In the present evolving phase of information technology, Recommender systems (RSs) have been established as widely accepted platform for handling & managing the information overload problem. In order to facilitate an individual user in decision making for selection of any product or service, user preferences are first captured implicitly or explicitly and a predictive model is built to derive personalized recommendations. Data Sparsity, malicious / biased data by users, diversity in recommendations, temporal dynamics, etc are few of the key challenges experienced by new age recommendation systems. Dimensionality reduction techniques help to decompose the rating matrix in the form of latent factors with lower ranks and attempts to solve the data sparsity problem. Matrix Factorization (MF) is the well-accepted technique in this aspect. In order to generate more accurate and meaningful recommendations, Context aware RS (CARS) is the new emerged technique in recommender systems. Tensor Factorization or generalized matrix factorization facilitates the most suitable and generic way of integrating contextual information into RSs. The relevant contextual information will always improvise the performance of the recommender system but irrelevant contextual information could degrade it drastically. The work presented here, provides the discussion over comparison of Recommender System's performance at various scenarios such as generating recommendations without considering any contextual information and with considering the contextual information. There are few techniques available to find out the context relevancy in CARS. Their impact on the performance of RS is also discussed.
Governance of IS Security in a Cloud Computing Ecosystem: A Longitudinal Approach

This research proposes an analysis of IS governance in three phases: pre-adoption, the adoption and post-adoption of cloud computing. This approach aims to highlight the different artefacts and key concepts of security governance in a cloud computing ecosystem. It is necessary to ask the question: Which solution to choose? It is also important to differentiate between three key artefacts: data as an asset, the cloud solution as a system, and the service provider as an actor to understand their relationship with the enterprise.
Selection of Lifting Scheme based Wavelet Filters for Image Compression in Resource Constrained Wireless Multimedia Sensor Networks

Sensor nodes in wireless multimedia sensor networks have very limited resources like low memory capacity, low processing capability, and low data rate. Most of the applications in WMSN require image data which is naturally large in size and it has to be compressed before transmission in WMSN with very small bandwidth. Lifting scheme for computing discrete wavelet transform and SPIHT image coding algorithm are suitable to implement in wireless sensor nodes which have some limitations in power consumption, processing capability, memory and bandwidth. This paper addresses selection of suitable wavelet filter type for image compression using SPIHT coding algorithm. In this paper we have implemented SPIHT algorithm based on the lifting scheme of different wavelets (Haar, Daubechies 4 (DB4), Daubechies 6 (DB6), Cohen-Daubechies-Feveau wavelet (CDF 9/7), and Cubic B-splines) in MATLAB. The experimental subjective and objective results show the lifting CDF 9/7 wavelet filter gives excellent results for bit rates 0.1 bpp, 0. 2 bpp, 0.5 bpp, 0. 8 bpp, and 1 bpp. CDF 9/7 wavelet filter gives an acceptable image quality even for very low bit rates which makes it peferrable for implementation in low data rate applications of WMSN.
Deep Learning Approach to Video Compression

Video streaming requirement has increased exponentially and video currently consumes 75% of the internet traffic. Due to which video streaming and storage is a huge challenge for service providers. Image and video compression algorithms rely on codecs which are encoders and decoders that lack adaptability. Due to the advent and advances in Deep Learning these issues can be solved. This paper proposes a method for video compression using neural networks that outperforms the H.264/AVC video coding standard as measured using Multi-Scale - Structural Similarity Index (MS-SSIM).The neural network model proposed is a multi-layer architecture consisting of two parts i) Encoder and ii) Decoder. The training of the two parts of the model happens together and during test time the encoder and decoder are separated to be used as just like any another compression encoding and decoding modules. The entire model's purpose was to try and capitalize on the temporal and spatial dependencies between frames of a video.
Detection of Distracted State Based on Head Posture and Facial Expression

Research and development of automatic driving have been progressing actively. Level 3 autonomous driving requires shifting of driving activity between the system and a driver. Such shifting poses high risks of a severe traffic accident if a driver is in a distracted state. Nevertheless, no distracted state detection method has been established. For our earlier study, research was conducted to extract characteristic driving behavior patterns in distracted states by quantifying eye movement. Results indicated a challenging task for further development: detecting gaze information with high accuracy. For this study, time-series changes of head posture and facial expressions in a driving concentration state and distracted state are quantified using hierarchical growth type recurrent SOM and a U-matrix. We assess the possibility of detecting driving behavior patterns that involve head posture and facial expressions and which characterize a distracted driver state.
Privacy-Preserving Decentralised Singular Value Decomposition

With the proliferation of data and emerging data-driven applications, how to perform data analytical operations while respecting privacy concerns has become a very interesting research topic. With the advancement of communication and computing technologies, e.g. the FoG computing concept and its associated Edge computing technologies, it is now appealing to deploy decentralized data-driven applications. Following this trend, in this paper, we investigate privacy-preserving singular value decomposition (SVD) solutions tailored for these new computing environments. We first analyse a privacy-preserving SVD solution by Chen et al., which is based on the Paillier encryption scheme and some heuristic randomization method. We show that (1) their solution leaks statistical information to an individual player in the system; (2) their solution leaks much more information when more than one players collude. Based on the analysis, we present a new solution, which distributes the SVD results into two different players in a privacy-preserving manner. In comparison, our solution minimizes the information leakage to both individual player and colluded ones, via randomization and threshold homomorphic encryption techniques.
Hybrid automatic speech recognition model for speech-to-text application in smartphones

The development of speech recognition systems has been widely carried out in various fields. Its development is often used in smart device technology to facilitate users in giving instructions to a system through sound. Development of identical speech recognition with development to improve speech reading accuracy and fast response time processing. This paper aims to try an approach through hybrid methods by combining client-side and server-side services to benefit from both services. The smartphone device was chosen as a speech recognition computing device because it is a technological device that has become a necessity of everyday life in searching for information. In the paper we will propose a hybrid automatic speech recognition model, how the model works and the framework of the application.
PENGARUH MODEL QUANTUM LEARNING TERHADAP KETERAMPILAN MENULIS PUISI SISWA KELAS X AKUNTANSI SMK NEGERI 2 KOTA JAMBI

This research aims to describe the impact of of quantum learning model towards students ability in writing poetry at class X of accounting major SMK Negeri 2 Jambi City school year 2018/2019. The design of this research uses quasi experiment with a quantitative approach. Sampling was carried out using the purposive sampling technique of the X-grade students of SMK Negeri 2 Jambi City. It is obtained that class X of accounting 2 as the experimental class and class X of accounting 3 as the control class. The research results show that the average score of the experimental is 80.42 and the average score of control class is 74. After testing the normality test, the experimental class sig score is 0.170 &gt; 0.05 and the control class sig score is 178 &gt; 0.05 (significance level). Thus it can be concluded that the data of the two classes is fall intro category of normal distribution. From the homogeneity test, it is obtained that sig score is 0.462 &gt; of a significant level (α = 0.05) Thus it can be concluded that both classes have the same or homogeneous variants. Hypothesis testing uses SPSS 20 applications. T test of two independent samples shows that SIG T count 0,043 &lt; 0.05 then Ho is rejected Ha is received. So, the results of students ability in writing poetry at class X of Accounting at SMK Negeri 2 Jambi city that use quantum learning model is better than the learning outcomes of students who use conventional learning.