Reviewerly/exHarmony huggingface dataset:
User-position aware adaptive display of 3D data without additional stereoscopic hardware

Stereoscopic vision modules have seen limited success in both engineering and consumer world, due to the required additional hardware (image acquisition, Virtual Reality headsets, 3D glasses). In the last years, especially the gaming and education sectors have benefited from such specialized headgear, providing virtual or augmented reality. However, many other industrial and biomedical applications such as e.g. computer aided design (CAD) or tomographic data display, so far have not fully exploited the increased 3D rendering capabilities of present-day computer hardware. We present an approach to use standard desktop PC hardware (monitor and webcam) to display user-position aware projections of 3D data without additional headgear. The user position is detected from webcam images, and the rendered 3D data (i.e. the view) is adjusted to match the corresponding user position, resulting in a quasi virtual reality rendering, albeit without the 3D effect of proper 3D head-gear. The approach has many applications from medical imaging, to construction and CAD, to architecture, to exhibitions, arts and performances. Depending on the user location, i.e. the detected head position, the data is rendered differently to attribute for the user view angle (zoom) and direction. As the user moves his or her head in front of the monitor, different features of the rendered object become visible. As the user moves closer to the screen, the view angle of the rendered data is decreased, resulting in a zoomed-in version of the rendered object.
Re-thinking of student skills to handling basic computer practice in junior high school

This study aimed to evaluate students' skills in basic computer practice in junior high school. We evaluated the student's skills to work with basics of Microsoft office software (Excel and Power Point) according to competency standards and school curriculum. The study involved 173 students who received the computer practice exam on March 2018. Data analysed using Rasch model approach. The results of this study showed that Microsoft Excel and Power Point practice exams are simply to be passed by 57 students. In general, the skills in setting up tables in Microsoft Excel have the highest difficulty level for students. Meanwhile, basic skill in locating information for Power Point objects is the easiest exam material for students. Unfortunately, although the quality as the exam material is good enough, but the material for computer practice exam is insufficient as to separate out the students' skills. Several measurement property assessments also presented to enrich the information from the findings of this study. A further implication from this research is that educators require to reconsider the test material led to students. In addition, it also takes into history the experience of students in operating the computer and perform computer exam procedures.
Intelligent Loading and Unloading System for Signal Processing Based on OBDD

In order to realize the fast dynamic loading and intelligent switching for application tasks in the signal processing platform, this paper analyzes the system of intelligent loading and unloading, and builds the software and hardware resource model for the signal processing platform, situation parameter definition dictionary and intelligent loading and unloading rule base. Then through the intelligent rule decision algorithm based on the ordered binary decision diagram, it is realized that the intelligent loading and unloading for the application task under the minimum manual intervention. Finally, the algorithm proposed in this paper and sequential rule storage algorithm are compared. The results show that the algorithm of the intelligent loading and unloading for signal processing tasks designed in this paper has higher practicability, timeliness and intelligence.
Orientation and Occlusion Aware Multi-Person Pose Estimation using Multi-Task Deep Learning Network

For years, behavior understanding has been a hot topic in the field of computer vision. As an important part of human behavior understanding, pose estimation has attracted lots of interests. Recently, deep learning methods, such as Mask R-CNN, have achieved much better performance for computer vision tasks than that of traditional approaches, as deep neural network can find representative features efficiently. For pose estimation, most of the deep learning approaches mainly focus on the joint feature. However, this feature is not sufficient, especially when the pose is occluded or not intact. In fact, many features other than joint can also contribute to pose estimation, such as body boundary, body orientation and visibility condition. By adopting multi-task strategy, these features can be efficiently combined inside deep learning model for pose estimation. In this paper, we present a multi-task pose estimation approach to deal with human behavior understanding. Our deep learning model is based on Mask-RCNN, of which the output contains 4 tasks: human keypoint prediction, body segmentation, orientation prediction and mutual occlusion detection. Our model is trained on the public dataset COCO, which is further augmented by ground truths of orientation mask and occlusion mask. Experiments show the learning accuracy of the proposed method. Comparisons further illustrate the performance improvement after combining more features by multi-task strategy.
Defect detection method for complex surface based on human visual characteristics and feature extracting

Aimed at the problem of strong background interference introduced in digital image processing from complex surfaces under industrial defect detection, a method for complex surface defect detection based on human visual characteristics and feature extracting is proposed. Inspired by the visual attention mechanism, defect areas can be identified from the background noise conveniently by human eyes. We introduce the improved grayscale adjustment and frequency-tuned saliency algorithm combined with the salient region mask obtained by dilation and differential operation to eliminate the background noise and extract defect areas. Meanwhile the directional feature matching and merging algorithm is applied to enhance directional features and retain details of defects. Testing images are captured by our established detecting system. Experimental results show that our method can retain defect information completely and achieve considerable extracting efficiency and detecting accuracy.
Survey on Various MapReduce Scheduling Algorithms

The advent of social networking and internet of things (IoT) has resulted in exponential growth of data in the last few years. This, in turn, has increased the need to process and analyze such data for optimal decision making. In order to achieve better results, there is an emergence of newly-built architectures for parallel processing. Hadoop MapReduce (MR) is a programming model that is considered as one of the most powerful computation tools for processing the data on a given cluster of commodity nodes. However, the management of clusters along with various quality requirements necessitates the use of efficient MR scheduling. The chapter discusses the classification of MR scheduling algorithms based on their applicability with required parameters of quality of service (QoS). After classification, a detailed study of MR schedulers has been presented along with their comparison on various parameters.
CRITIQUE OF PURE KNOWLEDGE

The reason that this material carries the name of the critique of “pure knowledge” is the fact that a problem is being considered here, for the solution of which neither scientific, nor philosophical approach can be applied, based on the experience of sensations and thinking of the thinking being, that is, the educated person on ideas about the knowledge of classical philosophy and science. The critique of “pure knowledge” is based on the method of transcendental materialism. It is designed to create the conditions for a clear statement of the fact that it is not acceptable for a thinking being to conceive a certain “true knowledge”, besides that knowledge, which it can comprehend in its judgment about observable signs of regular changes in states and properties of objects interacting with it, in the experience of sensations and thinking. Therefore, the subject of “pure knowledge” is all knowledge about the interaction of signs of changes in the states and properties of objects of concrete existence, accessible to observation in the experience of sensations and thinking of the “object of the living”. Moreover, in the thinking of the thinking being, “pure knowledge” can be transformed into “scientific knowledge” or “ideological knowledge”. Therefore, all other ideas about “knowledge”, the signs of which can’t be observed in the experience of sensations and thinking of the thinking being, can be defined as “imaginary knowledge”. These ideas can be based on the varying degree of diversion of thought from experience in its ability of imagination and fantasy. As a result of an extended period of “accumulation” by memorizing and recalling in experience information containing signs of “pure knowledge” in the thinking of a thinking being, signs of some kind of “new” understanding of the stored features of “pure knowledge” can appear. This phenomenon can create conditions for the formation in thinking of a creature of a thinking illusory opinion about the possibility of formulating the concepts of “pure knowledge” a priori, to the experience of sensations and thinking.
MVP: Finding the Most Valuable Posts in Financial Social Networks

With the availability of the Internet, financial social networks such as StockTwits and SeekingAlpha are emerging to provide opportunities for investors around the world to gather and share their experiences and opinions on the stock market. When these social networks become more and more popular, millions of users join and post huge amount of posts every single day to discuss all kinds of topics related to the market, e.g., the news and events about companies, when to buy and sell shares, which stocks to buy. If a user has a question related his/her investment, he/she needs to browse all the posts returned by searching the stock symbols, however due to the high volume of the posts he/she will drown in the information and hardly to find the answers he/she wants to know. In this paper, we propose to apply a language model with dynamic pseudo relevance feedback to obtain relevant posts in StockTwits, so that people can quickly and easily grasp the ongoing events of their interested financial news. Experiments and a case study on the StockTwits dataset demostrate the effectiveness of the dynamic pseudo relevance feedback.
The Semantic Information Method Compatible with Shannon, Popper, Fisher, and Zadeh’s Thoughts

Popper and Fisher’s hypothesis testing thoughts are very important. However, Shannon’s information theory does not consider hypothesis testing. The combination of information theory and likelihood method is attracting more and more researchers’ attention, especially when they solve Maximum Mutual Information (MMI) and Maximum Likelihood (ML). This paper introduces how we combine Shannon’s information theory, likelihood method, and fuzzy sets theory to obtain the Semantic Information Method (SIM) for optimizing hypothesis testing better. First, we use the membership functions of fuzzy sets proposed by Zadeh as the truth functions of hypotheses; then, we use the truth functions to produce likelihood functions, and bring such likelihood functions into Kullback-Leibler and Shannon’s information formulas to obtain the semantic information formulas. Conversely, the semantic information measure may be used to optimize the membership functions. The maximum semantic information criterion is equivalent to the ML criterion; however, it is compatible with Bayesian prediction, and hence can be used in cases where the prior probability distribution is changed. Letting the semantic channel and the Shannon channel mutually match and iterate, we can achieve MMI and ML for tests, estimations, and mixture models. This iterative algorithm is called Channels’ Matching (CM) algorithm. Theoretical analyses and several examples show that the CM algorithm has fast speed, clear convergence reason, and wild potential applications. The further studies of the SIM related to the factor space and information value are discussed.
A formal and automated approach to exploiting multi-stage attacks of web applications

We propose a formal and automated approach that allows one to (i) reason about vulnerabilities of web applications and (ii) combine multiple vulnerabilities for the identification of complex, multi-stage attacks. We have developed WAFEx, an automatic tool that implements our approach and we show its efficiency by applying it to real-world case studies. WAFEx was able to generate, and exploit, previously unknown attacks.
Þráinn Hjálmarsson - Þráinn Hjálmarsson: <i>Influence of Buildings on Musical Tone</i>. CAPUT ensemble, Nordic Affect, Ensemble Adapter, Icelandic Flute Ensemble and Kristin Thora Haraldsdottir. Carrier Records.

Þráinn Hjálmarsson - Þráinn Hjálmarsson: Influence of Buildings on Musical Tone. CAPUT ensemble, Nordic Affect, Ensemble Adapter, Icelandic Flute Ensemble and Kristin Thora Haraldsdottir. Carrier Records. - Volume 73 Issue 288
Secure Transmission of Data through Electronic Devices using ECC Algorithm

In recent days securing the data while transferring through electronic devices from one end to the other has became a challenging task to both sender and the receiver. During the transmission of private data over the electronic devices may be hacked some times by the hackers. The data can be secured by using the cryptographic concept. This paper is about how the data are protected while transferring the data from one electronics devices to another using the ECC algorithm. Cryptographic algorithms plays an important role in securing the data against malicious attacks. The main goal of cryptography is not only to secure data from being hacked or attacked also it can be used for authentication of users. There are two types of cryptographic algorithms namely Symmetric key cryptographic algorithms and Asymmetric key cryptographic algorithms. Symmetric key cryptographic algorithm uses the only one key for both encryption and decryption process, where as Asymmetric cryptographic algorithm uses two different keys for encrypting and decrypting the messages. The public key is made publicly available and can be used to encrypt messages. The private key is kept secret and can be used to decrypt the received messages. Nowadays, many electronic devices like electronic phones, tablets, personal computers are in the workplace for transferring the data. Elliptical curve cryptography (ECC) is a public key encryption technique based on elliptic curve theory that can be used to create privacy, integrity and confidentiality, faster, smaller, and more efficient cryptographic keys.
Towards immersive stereo vision from a mobile platform

In this paper, we propose a novel method for the 3D reconstruction of urban scenes using a front-facing stereo camera in a vehicle. Our point-based approach warps an active region of the reconstructed point cloud to the next frame and uses an extended information filter for the temporal fusion of clustered disparity estimates in pixel bins. We splat the information of projected pixels according to subpixel weights and discard uncertain points. This method allows us to remove redundant points required for the reconstruction and at the same time presents a significantly denser model than competing approaches with improved disparity estimates. Our approach avoids common visual artifacts like spurious objects in the reconstruction. This results in a reconstruction with higher visual fidelity compared to other approaches, which is important for immersive applications. We compare our proposed system to other approaches in a quantitative and qualitative evaluation on the KITTI odometry dataset.
Research of wide swath image mosaic technology based on area-array detector adopting whiskbroom scanning mode

A new method of splicing infrared thermal Unmanned aerial vehicle(UAV) image with special acquisition mode is presented. We used the bi-directional whiskbroom scanning frame infrared UAV (300,000 pixels) developed by the Shanghai Institute of Technical Physics,Chinese Academy of Sciences(SITP) to obtain the image of an area in haiyan,Zhejiang province.In order to get more accurate temperature data in a wide range, correction and optimization of the mosaic strategy of this infrared image are needed.In this paper, an orthographical correction model based on drone position and orientation system (POS) parameters was established, By optimizing scale invariant feature transform (SIFT) matching parameters and Random Sampling Consensus (RANSAC) algorithm, more reliable matching results were obtained.After rough calculation of adjacent images by image location to calculate whether it is an adjacent image, this can reduce the operation time of the splicing algorithm.The overlapping area images fused with the multi-resolution pyramid algorithm. Finally, the large area image and temperature inversion map of the study area were obtained. Inversion results showed that the error of temperature inversion less than 0.2 degree by comparing with the original temperature before unspliced.It can meet the subsequent application requirements of the UAV infrared image.
IdentiCyte: Simple red blood cell identification software

Counting and identifying red blood cells (RBCs) by shape is an important step in diagnosing several diseases and disorders. This is traditionally done by hand (a tedious and time consuming process) or with specialized equipment. There exist several tools to count and classify cells in an image automatically; however, they often sacrifice simplicity in favour of having a wide range of applications. In this paper, we present IdentiCyte, a program specifically designed to quickly count and identify RBCs from a series of microscope images as simply as possible.
Acceleration-Based Road Terrain Classification

Road-type classification is the process of categorizing road terrain into different types such as asphalt, concrete, grass and gravel. Intuitively, the amount of vibration that is caused by a vehicle navigating on a particular road type is a valuable source of information. Therefore, collecting a vehicle’s vibration information to obtain specific characteristics of different road terrains is of high interest. For this reason, an accelerometer is mounted on the suspension to measure the vertical component of the vibration of the vehicle.
Comparison of stereo matching algorithms for obstacle detection and collision avoidance

In this paper the authors compared the accuracy of several stereo matching algorithms using problem-oriented metrics developed by the authors earlier for obstacle detection. For comparison we have chosen the most computationally effective open-source algorithms, suitable for using in autonomous systems with limited processor capacities. The quality of the algorithms was compared on the public dataset KITTI Stereo Evaluation 2015. The hypothesis that the problemoriented metric of the stereo matching quality will lead to a different ranking than the universal metric, was not confirmed. At the same time, our measurements of the algorithms execution time showed results significantly different from those stated on KITTI portal.
Real-time 3D shape measurement by fringe projection and GPU parallel computing

When fringe projection profilometry is applied for real-time 3D shape measurement, several problems remain to be solved such as multi-wavelength heterodyne phase unwrapping is sensitive to motion and the computation cost is high. In this paper, a real-time 3D shape measurement method with optimized multi-wavelength heterodyne phase unwrapping and GPU parallel computing is proposed. Experimental results demonstrate that the proposed method can acquire 3D shape at 40 fps. Dynamic object with discontinuities can be measured and the phase unwrapping mistakes are eliminated by smoothing the phase of beat frequency during multi-wavelength heterodyne phase unwrapping.
Application of Hybrid-Ihacres models for water availability in Siak River

Watershed management with potential water resources greater than or equal to 20% of the potential of water resources in the province requires a device capable of addressing those needs. The Siak river area is a potential source of water resources greater than 20%. Until now, the Siak river area does not yet have an integrated water resource information system; thus information on the potency of water absorption cannot be adequately recorded. Prediction of water availability in watersheds has significance for the management of a watershed. The research aims to develop a hydrological model to strengthen the water availability information to complete the water availability information. The built model is a combination of a conceptual model with wavelet (hybrid model) that is wavelet-ihacres. The wavelet transform method has the advantage of decomposing and reconstructing the data to produce better predictions. The results showed that the combined wavelet-ihacres have a coefficient correlation between observation data and output model of 0.737. The value is classified as a strong correlation.
Quantum entanglement in spin chains with the XY Hamiltonian at the quantum state transfer

In the present work the generation of entanglement between the receiver and sender is investigated in the process of the quantum state transfer in the homogeneous one-dimensional chain of spins 1/2 with the XY -Hamiltonian in the approximation of the nearest neighbour interactions. Fidelity is obtained for a quantum state transfer for various numbers of spins and temperatures and values of initial polarisation of the sender’s spin. The transmitted pure state is encoded in the state of first spin (sender) in the initial time moment. The other spins are in the thermodynamic equilibrium state. The reduced density matrix for the receiver and the sender is obtained for an investigation of entanglement in the “sender-receiver” system. The effect of temperature and polarization of the transmitted state on the generation of entanglement in the system is also investigated.
Research Hotspots Evolving Action Detection based on Time Sequence Journal Topic Model

Since the research hotspot development in academic fields is mainly reflected through academic journal contents, how to analyze the evolving action of academic journal related topics is a huge factor for researchers in grasping the tendency of research hotspots. This paper considered and combined two characteristics of academic journals: 1) topic property and 2) time-sequence feature to realize journalsâ€™ time-sequence topic extraction, which also puts forward the TS-JTM (Time Sequence Journal Topic Model) at the same time. On the basis of TS-JTM, we developed topic-snapshot journal hotspot evolution model based on time sequence, and proposed a method which could detect the continuing, emerging, splitting, amalgamating or disappearing between two neighbor topic-snapshots, with adopting topic similarity measurement based on Kullback-Leibler (KL) Divergence. Our experiments show that the proposed method could realize evolving analysis of journalsâ€™ research hotspots effectively.