Reviewerly/exHarmony huggingface dataset:
Fast One-to-Many Reliability Estimation for Uncertain Graphs

Uncertain graphs commonly represent noisy and unreliable real-world datasets in various applications. One fundamental primitive on uncertain graphs is reliability, which evaluates the connection robustness between two specific nodes. Although reliability is helpful to analyze uncertain graphs, it is computationally expensive because (1) the reliability estimation is #P-hard problem and (2) many applications require reliability computations among all possible pairs of nodes. To overcome the aforementioned problems, we present a novel algorithm called Sharing RCSS+, which efficiently computes the reliability among all possible pairs of nodes. Our extensive experiments on both real-world and synthetic uncertain graphs clarified that Sharing RCSS+ achieves a more efficient estimation than the state-of-the-art methods.
A Formalism Unifying Defeasible Logics and Repair Semantics for Existential Rules

Two prominent ways of handling inconsistency provided by the state of the art are repair semantics and Defeasible Logics. In this paper we place ourselves in the setting of inconsistent knowledge bases expressed using existential rules and investigate how these approaches relate to each other. We run an experiment that checks how human intuitions align with those of either repair-based or defeasible methods and propose a new semantics combining both worlds.
Control Flow Invariant Integrity Measurement: Cases Study

In this paper, we present the control flow invariant integrity measurement. Control flow invariant is a certain law followed by running software. In general, the invariant is compromised when the attack subvert the running software. So by checking the control flow invariant, existing attack behavior is exposed. In this paper, we propose five kinds of control flow invariant integrity measurement with the support of the processor debugging feature including BTS (Branch Trance Store), LBR (Late Branch Record) and PMC (Performance Monitoring Counter). We discuss their main challenges and solutions to show the feasibility. Furthermore, we introduce existing methods which is closely related to our present measurement.
Establishment and verification of sub-aperture scanning Fourier transform system for scattered light measurements

We present sub-aperture scanning Fourier transform system, a method for fast Bidirectional reflection distribution function (BRDF) measurements. A single lens is used for Fourier transform, and the scattering light field is measured by sub aperture scanning of the Fourier transform lens. Through Fourier transform, a single measurement can obtain multi-angle scattering data, and sub-aperture scanning further expands the measurement angle range. Due to the full utilization of image detectors, once measurement can get 1944 points’ data inside 10°. Thus the system’s precision can get to 0.005°and the measurement time can shorten ten times. Finally, the method is verified by comparing the experimental data. Experimental results show that the relative error of this method is less than 1%. These results provide theoretical basis and technical support for the application of BRDF in sub aperture scanning Fourier transform system.
Perception of interaction between hand and object

Action knowledge graphs can play a central role in smart cities, smart homes, robot planning, and so on. This is since both of the subject and the object of the actions can add more meaningful information for the higher-level application than the action alone as a predicate. We built a system that generates the action knowledge graphs from video using deep learning. Especially, we propose an algorithm which perceives the interaction between hand and object by measuring the proximity between them with considering the direction of fingers. We showed that this approach achieves the performance of 83% in accuracy using the Stair Lab data.
A nested hybrid filter for parameter estimation and state tracking in homogeneous multi-scale models

Multi-scale problems, where variables of interest evolve in different time-scales and live in different state-spaces, can be found in many fields of science. Here, we introduce a new recursive methodology for Bayesian inference that aims at estimating the static parameters and tracking the dynamic variables of these kind of systems. Although the proposed approach works in rather general multi-scale systems, for clarity we analyze the case of a homogeneous multi-scale model with 3 time-scales (static parameters, slow dynamic state variables and fast dynamic state variables). The proposed scheme, based on nested filtering methology of [1], combines three intertwined layers of filtering techniques that approximate recursively the joint posterior probability distribution of the parameters and both sets of dynamic state variables given a sequence of partial and noisy observations. We explore the use of sequential Monte Carlo schemes in the first and second layers while we use an unscented Kalman filter to obtain a Gaussian approximation of the posterior probability distribution of the fast variables in the third layer. Some numerical results are presented for a stochastic two-scale Lorenz 96 model with unknown parameters.
Detection of Editing Bursts and Extraction of Significant Keyphrases from Wikipedia Edit History

In an online collaboration system such as Wikipedia, edit history is stored as revisions. Topics of articles or categories grow and fade over time, and evolutionary information is retained in its edit history. We consider that a great amount of information that is related to real life events is embedded in such edit history of documents. This paper focuses on a particular temporal text mining task: effectively extracting keyphrases from burst periods in the edit history of Wikipedia articles or category. We first combine the ARIMA model with a decay function to find typical edit burst periods, then perform keyphrase extraction on burst periods to reveal topics of bursts. However, keyphrase extraction methods, such as TextRank, do not consider temporal trends in text stream. In this paper, we propose TextRank_nfidf which reflects temporal trends into phrase node weights, by computing smoothed difference of editing frequency between revisions. We confirm that detected bursts and keyphrases are matching well with events along the timeline.
Dynamically Jointing character and word embedding for Chinese text Classification

Chinese text classification is drawing attention in these few years. Different from English texts, there is no natural separator between Chinese words. With the development of deep learning, many character-level only models have been proposed for Chinese text classification to tackle this problem, which have achieved more success than word-level models. But the word information is also important for Chinese text representation, especially for short texts with less information. However, most of neural network models either just concatenate character-level representation and word-level representation, or use massive external knowledge to represent the whole text, which is complex and time-consuming. For better and easier representing the Chinese text without any external knowledge and using as much character and word information as possible, we propose a simple model jointed character and word embedding dynamically, called DJCW. Firstly, the character-level and word-level BiLSTM Model is introduced to extract features of texts with indefinite lengths. Secondly, the char and word are weightedly combined and the weights are changed dynamically. Finally, experiments conducted on five open-source text datasets show our model can handle the texts with different lengths and has achieved good stability results.
Case Study - Clothing Design with Castelo Branco Embroidery Focusing on the Symbology of Embroidery

The Castelo Branco Embroidery is full of decorative grammar, it is endowed with symbolism, both in design and in what concerns the application of embroidery stitches and the colour palette. Here the typical embroidery of Castelo Branco is applied to a garment and studied from the design of the piece to the symbolism and history of the typical embroidery. A non-interventionist and interventionist action research methodology was used, and the following hypothesis was formulated: Is it possible to create new pieces of clothing respecting the symbolic load attributed to the embroideries of Castelo Branco? This investigation reinforces the importance of preserving and identifying the tradition of embroidery, giving it an innovative character, focusing on examples of pieces already created where respect for traditional materials and symbols allowed for the development of innovative garments. The reality is that in real terms the Embroidery never actually became extinct. A further note is made to recent uses of the embroidery in clothing and accessories which demonstrate its versatility in enhancing the creativity of designers.
Специфика гипертекста в художественной литературе (на примере романа Дж. С. Фоера «Дерево кодов»)

Kashkan T. A. Specific Features of Hypertext in Fiction (Illustrated with “Tree of Codes” by Jonathan Safran Foer). The article deals with the phenomenon of hypertext. Special attention is paid to its concept and features. Moreover, the same set of features is revealed in the novel Tree of Codes - by Jonathan Safran Foer.
Theory and Practice of Second-Order Rewriting: Foundation, Evolution, and SOL

We give an overview of the theory and practice of second-order rewriting. Second-order rewriting methods have been demonstrated as useful that is applicable to important notions of programming languages such as logic programming, algebraic effects, quantum computation, and cyclic computation. We explain foundation and evolution of second-order rewriting by presenting the framework of second-order computation systems. We also demonstrate our system SOL of second-order laboratory through various programming language examples.
A physical knowledge-based extreme learning machine approach to fault diagnosis of rolling element bearing from small datasets

The learning-based methods have been widely applied to design a fault diagnosis model for rolling element bearing. However, the mainstream methods can only deal with the large training dataset, which is always violated in practical application. In this paper, we propose a physical knowledge-based hierarchical extreme learning machine(H-ELM) approach to adapt the problem of fault diagnosis for bearing with the small and imbalanced dataset. First, the proposed method uses the simple feature extraction algorithm to build a knowledge base for sample selection from the historical database, and the given training dataset is augmented with knowledge base. Second, a modified H-ELM algorithm is developed to identify fault location and recognize fault severity ranking based on the augmented dataset. Third, we design a self-optimizing module to optimize the sample selection and improve the performance of the H-ELM network. To evaluate the effectiveness of the proposed approach, the H-ELM without knowledge base and data augmentation-based support vector machine(SVM), back propagation neuron networks(BPNN) and deep belief networks(DBN) are tested in the numerical experiments to present a comprehensive comparison. The experimental results demonstrate that our approach outperforms in accuracy than other counterparts when dealing with the small and imbalanced datasets.
Despeckling of ultrasound images based on the multiresolution approach and Gaussianization transform

Nowadays, preservation of clinical data in medical images is an important research issue. Ultrasound imaging has a vast application in medical science in disease detection and diagnosis. Suppression of speckle noise called despeckling is greatly required in ultrasound images for enhancing and improving texture analysis and edge preservation. Gaussianization transform, which Gaussianizes the dataset, is a powerful tool for sharp estimation of density and increases the capability of restoring the original dataset. This concept is implemented in this chapter for the removal of speckle noise from ultrasound images. The proposed method is an improvement over the statistically approached denoising method. At first, an appropriate probability density function is chosen for modeling ultrasound image sub-band wavelet coefficients. Then, the sub-band coefficients are Gaussianized through a Gaussianization transform. Further Gaussian distribution parameters for each sub-band are estimated. The wavelet coefficients are restored by implementing the Bayesian minimum mean square error estimator. Both true and synthetic images are taken for simulation, and different qualities and performance parameters are used to prove the efficacy of the proposed method. The projected method improves state-of-the-art methods remarkably.
Digital Design of Aids for Activities of Daily Living

People with physical disabilities and people with moderate, severe to profound intellectual disabilities often need aids for activities of daily living (AADLs). Currently almost all of them are provided with prefabricated aids which often do not fit well or insufficiently. A bespoke aid would be an added-value here. In this paper, approaches to design such bespoke AADLs with different technical complexities are elaborated. As an example the implementation and workflow of two cases are covered: a bespoke joystick handle for an electric wheelchair and a plate-raiser. For both cases, an overview of the practical implementation is given, including the technical considerations. Also an implementation is provided using a 3D scan of the end-user or object on which the AADL is fitted, and one that is usable without 3D scan. The choice of implementation strategy is based on the needs of the end-user, and the preferences of the health care professional who designs the aid. By providing different options for health care professionals to design (and produce) bespoke AADLs, this study aims to lower the threshold to provide end-users with bespoke AADLs that are better tailored to their needs, more specifically when the bespoke AADLs would benefit them better than the prefabricated ones. Hereby, this study aims to contribute to the quality of life and participation of the end-users.
Improved Collaborative Filtering Recommendation Algorithm Based on Weibo Content

Taking Weibo content as the research object and aiming at the disadvantages of only popular recommendation for Weibo, we propose personalized recommendation scheme. The improved user-based collaborative filtering algorithm is used to give a recommendation list. In this paper, the keywords extraction technology is used to extract the keywords of Weibo content as the project, and the results of the sentiment analysis algorithm are used as the scores to form the project score matrix. Then, the similarity of the item and the score are calculated by the cosine similarity and the Jaccard coefficient, respectively, the final similarity result is obtained by weighted fusion. The experimental results show that the improved algorithm proposed in this paper can effectively solve the problem that the microblog content has no scoring system, which helps to improve the performance of the recommendation system and obtain better recommendation results.
Opto-Radio Noise based True Random Number Generator

This work describes the design and implementation of a true random numbers using event and radio noise-based approach. The proposed mixed signal design generates random numbers in cadence 180nm CMOS technology with the combination of acquisition, frontend circuit, LFSR, atmospheric noise block while FPGA is used for voltage generation. The acquisition block captures the event while the frontend circuitry is used to digitalize the event. To add entropy in the system, XOR gate and LFSR provides one of the inputs while atmospheric noise, recorded from surroundings and converted into appropriate voltage by FPGA acts as another input in the phase based arbiter-TDC block. The proposed design achieved a speed of 30Mbps for the detection window of 200ns and dissipates 300μW of power when designed at 1.8V. The obtained results were validated by NIST 2.1.2 suite. The proposed novel design is highly suitable for true random number generation.
Robust and Efficient Image Alignment Method Using the Student-t Distribution

Pixel-based image alignment has always struggled to simultaneously reject outliers, avoid local minima and run quickly. There are many robust cost functions that perform well in terms of rejecting outliers, but they can yield unstable results during long image sequences as a result of their inability to adjust to changes in image content. In this paper, we propose a parameterised student-t cost function that can interpolate between two cost functions that are amongst the most widely used ones in image alignment problems, the L2 norm (quadratic function) and the Cauchy-Lorentzian function. We also propose a parameter estimation method that helps to find optimal parameters for the proposed cost function for a video. Experiments prove that the proposed approach can estimate the alignment variable accurately relative to the existing cost functions without demanding a higher computational cost.
Homomorphic transform-based dual image watermarking using IWT-SVD for secure e-healthcare applications

Confidentiality of medical images and patient records needs to be preserved in a secure manner. So, in this chapter an efficient dual image watermarking technique is discussed that uses fusion of homomorphic transform (HT), integer wavelet transform (IWT), singular value decomposition, and Arnold transform (AT) for e-healthcare applications. In this work two image watermarks are simultaneously embedded inside a cover medical image. The first watermark is an image, while the second watermark consists of encrypted patient record data. HT decomposes a cover medical image into its illumination and reflectance components. Further embedding of both watermarks takes place in this reflectance component, which enhances robustness and imperceptibility concurrently, whereas security of the proposed approach is attained by encrypting the patient record through AT. The proposed scheme efficiently uses the merit of IWT such as direct mapping of integers to integers, which facilitates improved extraction of the watermark image as compared to conventional wavelet transform. Hence, the proposed scheme exhibits excellent robustness and imperceptibility, which is further enhanced by embedding watermarks in desired sub-bands of the reflectance component. Simulation results clearly illustrate the remarkable robustness and perceptual invisibility of the proposed scheme subjected to various nongeometric and geometric attacks. Moreover, the results clearly highlight improvement in robustness of the proposed technique over other recently reported schemes.
Efficient Semantic Segmentation Using Multi-Path Decoder

Benefiting from the booming of deep learning, the state-of-the-art models achieved great progress. But they are huge in terms of parameters and floating point operations, which makes it hard to apply them to real-time applications. In this paper, we propose a novel deep neural network architecture, named MPDNet, for fast and efficient semantic segmentation under resource constraints. First, we use a light-weight classification model pretrained on ImageNet as the encoder. Second, we use a cost-effective upsampling datapath to restore prediction resolution and convert features for classification into features for segmentation. Finally, we propose to use a multi-path decoder to extract different types of features, which are not ideal to process inside only one convolutional neural network. The experimental results of our model outperform other models aiming at real-time semantic segmentation on Cityscapes. Based on our proposed MPDNet, we achieve 76.7% mean IoU on Cityscapes test set with only 118.84GFLOPs and achieves 37.6 Hz on 768 × 1536 images on a standard GPU.
A new type of RMB counting device

Abstract In order to measure the quantity of paper with high speed and accuracy, a new measuring technology based on the principle of capacitance sensing is proposed and a measuring device is made. Put two metal plates of the same size at a certain distance with a piece of A4 paper in the middle. When the thickness of the paper changes, the capacitance on the metal plate will change. Use the capacitance sensor fdc2214 to sense the change. After modeling and analysis, determine the functional relationship between the thickness of the paper and the measured value, and calculate the paper quantity. The test results show that the device can count the number of paper accurately and quickly.
Depth Perception Tendencies in the 3-D Environment of Virtual Reality

The human brain is not able to process the vast amount of visual information that originates in the environment around us. Therefore, a complex process of human visual attention based on the principles of selectivity and prioritization helps us to choose only the most important parts of the scene for further analysis. These principles are driven by the visual saliency derived from certain aspects of the scene parts. In this paper, we focus on the objects’ depth salience which undoubtedly plays its role in processing visual information and has still not been thoroughly studied until now. The aim of our work is to investigate depth perception tendencies using an advanced experimental methodology in the environment of virtual reality. Based on the state-of-the-art of the attention modelling in the 3-D environment, we designed and carried out an extensive eye-tracking experimental study in the virtual reality with 37 participants observing artificial scenes designed for exploring trends in the depth perception in the virtual 3-D environment. We analyzed the acquired data and discuss the revealed depth perception tendencies in virtual reality alongside future possible applications.
A Sequential $L_{p}$-norm Filter for Robust Estimation

A novel robust sequential Lp filter is developed in this paper by leveraging the maximum a posteriori estimation theory and using generalised normal distributions to represent both state prediction errors and measurement residuals. The formulation leads to the flexibility of choosing the parameter p for two different types of aforementioned error sources. Numerical simulations are given for a nonlinear ground tracking scenario, with measurements corrupted with outliers. Results indicate the new Lp -norm filter presents robustness to filter initialisation errors and measurement outliers and outperforms a standard unscented Kalman filters and the Huber unscented Kalman filter in terms of error statistics.