Reviewerly/exHarmony huggingface dataset:
Local Indicators of Spatial Autocorrelation (LISA): Application to Blind Noise-Based Perceptual Quality Metric Index for Magnetic Resonance Images

Noise-based quality evaluation of MRI images is highly desired in noise-dominant environments. Current noise-based MRI quality evaluation methods have drawbacks which limit their effective performance. Traditional full-reference methods such as SNR and most of the model-based techniques cannot provide perceptual quality metrics required for accurate diagnosis, treatment and monitoring of diseases. Although techniques based on the Moran coefficients are perceptual quality metrics, they are full-reference methods and will be ineffective in applications where the reference image is not available. Furthermore, the predicted quality scores are difficult to interpret because their quality indices are not standardized. In this paper, we propose a new no-reference perceptual quality evaluation method for grayscale images such as MRI images. Our approach is formulated to mimic how humans perceive an image. It transforms noise level into a standardized perceptual quality score. Global Moran statistics is combined with local indicators of spatial autocorrelation in the form of local Moran statistics. Quality score is predicted from perceptually weighted combination of clustered and random pixels. Performance evaluation, comparative performance evaluation and validation by human observers, shows that the proposed method will be a useful tool in the evaluation of retrospectively acquired MRI images and the evaluation of noise reduction algorithms.
Accelerating Deep Neural Networks by Combining Block-Circulant Matrices and Low-Precision Weights

As a key ingredient of deep neural networks (DNNs), fully-connected (FC) layers are widely used in various artificial intelligence applications. However, there are many parameters in FC layers, so the efficient process of FC layers is restricted by memory bandwidth. In this paper, we propose a compression approach combining block-circulant matrix-based weight representation and power-of-two quantization. Applying block-circulant matrices in FC layers can reduce the storage complexity from O ( k 2 ) to O ( k ) . By quantizing the weights into integer powers of two, the multiplications in the reference can be replaced by shift and add operations. The memory usages of models for MNIST, CIFAR-10 and ImageNet can be compressed by 171 × , 2731 × and 128 × with minimal accuracy loss, respectively. A configurable parallel hardware architecture is then proposed for processing the compressed FC layers efficiently. Without multipliers, a block matrix-vector multiplication module (B-MV) is used as the computing kernel. The architecture is flexible to support FC layers of various compression ratios with small footprint. Simultaneously, the memory access can be significantly reduced by using the configurable architecture. Measurement results show that the accelerator has a processing power of 409.6 GOPS, and achieves 5.3 TOPS/W energy efficiency at 800 MHz.
Secure and efficient access of personal health record: a group-oriented ciphertext-policy attribute-based encryption

The personal health record (PHR) service is a promising model for health data exchange. In practice, however, users’ health data need to be stored in an untrusted cloud server, which requires the design of a mechanism to achieve secure data sharing. Although the traditional attribute-based encryption (ABE) can be employed to facilitate PHR sharing with confidentiality protection, it will become powerless when confronting certain case. For instance, this happens when the health data is encrypted under (‘ophthalmologist’ AND ‘dermatologist’) AND (‘chief physician’). The ciphertext cannot be successfully decrypted in the scenario if there is no doctor who is a specialist in both dermatology and ophthalmology, Motivated by this observation, we propose a group-oriented ciphertext-policy ABE, which classifies users into different groups. Specifically, users in the same group own the same group identifier and different users can combine their attributes to complete the decryption. The decryption operation can be completed successfully when the union of their attributes satisfies the Access Control Policy (ACP) involved in the ciphertext. Additionally, we propose a concrete scheme with a constant ciphertext size, which is independent of the count of attributes in the ACP. Our security analysis shows that the proposed scheme is secure against selective chosen-plaintext attack under the decisional n-BDHE assumption.
Word Representation With Salient Features

Inspired from the idea that the contexts in which a word occurs are of different significance, this paper proposes a novel method, called word representation with Salient Features (SaFe), to represent words using salient features selected from the context words. The SaFe method employs the point-wise mutual information (PMI) method with scaled context window to measure word association between a target word and its context. Then, contexts having word associations will be selected as salient features, where the number of salient features for a given word is decided by the ratio between the number of unique contexts and the total counts of occurrences in the whole corpus. The SaFe method can be used with the positive PMI matrix (PPMI), with each row representing a word, hence the name SaFe-PPMI. Moreover, the SaFe-PPMI model can be further decomposed by using the truncated singular vector decomposition technique to obtain dense vectors. In addition to efficient computation, the new models can achieve remarkable improvements in seven semantic relatedness tasks, and they show superior performance when compared with the state-of-the-art models.
Exploration and Reflection on School-Enterprise Cooperation in Higher Vocational Colleges under the Background of Industry-Education Integration

Higher vocational education is an important part of Vocational Education in China, and deepening school-enterprise cooperation is an important way and method to improve the quality of Higher Vocational education.We must give full play to the role of relevant government departments in promoting the cooperation between Higher Vocational Colleges and enterprises, and do a good job in the top-level design of the cooperation between Higher Vocational Colleges and enterprises.Higher vocational colleges should establish the sense of reform and brand, adopt a variety of school-enterprise cooperation modes according to local conditions, constantly improve the technical service ability of schools, and promote the connotative development of Higher Vocational colleges.Through establishing the guarantee mechanism of school-enterprise cooperation and improving the quality of personnel training, we can constantly improve the long-term operation mechanism of school-enterprise cooperation, constantly improve the quality of education in Higher Vocational colleges, and realize mutual benefit and win-win in the true sense of both sides.
Using Human Attention to Address Human–Robot Motion

Let human-robot motion (HRM) denote the study of how robots should move among people, the work presented herein explores to what extent human attention can be useful to address the HRM. To that end, a computational model of the human visual attention is proposed to estimate how a person's attentional resources are distributed among the elements in their environment. Based on this model, the concept of attention field for a robot is used to define different attentional properties for the robot's motions such as distraction or surprise. The relevance of the attentional properties for HRM is demonstrated on a proof-of-concept acceptable motion planner on various case studies where a robot is assigned different tasks. It is shown how to compute motions that are non-distracting and non-surprising, but also motions that convey the robot's intention to interact with a person.
Application of python programming language in measurements

Application of Python programming language in automation of measurement systems and creating virtual instruments is discussed in this paper. Requirements imposed to the software in order to perform these tasks are listed, and Python modules that support them are presented. Application of proposed techniques are illustrated in seven examples in different application areas. Analysis of software evolution, as well as the evolution of professional education yields conclusion that application of Python in automating measurement systems is promising.
Levels of Political Participation Based on Naive Bayes Classifier

Nowadays, social media is growing rapidly and globally until it finally became an important part of society. During campaign period for the regional head election in Indonesia, the candidates and their supporting parties actively use social media as a campaign tool. Social media like Twitter has been known as a political microblogging media that can provide data about current political event based on users’ tweets. By using Twitter as a data source, this study analyzes public participation during campaign period for 2018 Central Java regional head election. The purpose is to observe how much reaction is given to each candidate who advanced in the election. By using the crawling program, all tweets containing certain candidate names will be downloaded. After going through a series of preprocessing stages, data can be classified using Naive Bayes. Predictor features in classification datasets are the number of replies, retweets, and likes. While the target variable is reaction that is divided into three levels, including high, medium, and low. These levels are determined based on users’ reaction in a tweet. By using these rules, Naive Bayes managed to classify data correctly as much as 76.74% for Ganjar Pranowo and 68.81% for Sudirman Said.
Dynamic Top-K Interesting Subgraph Query on Large-Scale Labeled Graphs

A labeled graph is a special structure with node identification capability, which is often used in information networks, biological networks, and other fields. The subgraph query is widely used as an important means of graph data analysis. As the size of the labeled graph increases and changes dynamically, users tend to focus on the high-match results that are of interest to them, and they want to take advantage of the relationship and number of results to get the results of the query quickly. For this reason, we consider the individual needs of users and propose a dynamic Top-K interesting subgraph query. This method establishes a novel graph topology feature index (GTSF index) including a node topology feature index (NTF index) and an edge feature index (EF index), which can effectively prune and filter the invalid nodes and edges that do not meet the restricted condition. The multi-factor candidate set filtering strategy is proposed based on the GTSF index, which can be further pruned to obtain fewer candidate sets. Then, we propose a dynamic Top-K interesting subgraph query method based on the idea of the sliding window to realize the dynamic modification of the matching results of the subgraph in the dynamic evolution of the label graph, to ensure real-time and accurate results of the query. In addition, considering the factors, such as frequent Input/Output (I/O) and network communication overheads, the optimization mechanism of the graph changes and an incremental maintenance strategy for the index are proposed to reduce the huge cost of redundant operation and global updates. The experimental results show that the proposed method can effectively deal with a dynamic Top-K interesting subgraph query on a large-scale labeled graph, at the same time the optimization mechanism of graph changes and the incremental maintenance strategy of the index can effectively reduce the maintenance overheads.
Multiscale Quantum Harmonic Oscillator Algorithm With Strict Metastability Constraints for Multi-Modal Optimization

Multi-modal optimization is a troublesome problem faced by optimization algorithms.The multiscale quantum harmonic oscillator algorithm (MQHOA) utilizes group statistics strategy to evaluate the state of the population and neglects the individual state.It will lead the particles to be trapped in local optima when addressing multi-modal optimization problems.This paper proposes a modified MQHOA by introducing strict metastability constraints strategy (MQHOA-SMC).The new strategy adopts a joint constraint mechanism to make the particle states mutual constraint with each other.The modified algorithm enhances the ability to find a better quality solution in local areas.To demonstrate the efficiency and effectiveness of the proposed algorithm, simulations are carried out with SPSO2011, ABC, and QPSO on classical benchmark functions and with the newly CEC2013 test suite, respectively.The computational results demonstrate that MQHOA-SMC is a competitive algorithm for multi-modal problems.
Big data: new methods and ideas in geological scientific research

Big data research is a developing field that is bringing change to people’s lives. Big data is an inevitable outcome of the development of science: scientific research has resulted in the accumulat...
PosIX-GAN: Generating Multiple Poses Using GAN for Pose-Invariant Face Recognition

Pose-Invariant Face Recognition (PIFR) has been a serious challenge in the general field of face recognition (FR). The performance of face recognition algorithms deteriorate due to various degradations such as pose, illuminaton, occlusions, blur, noise, aliasing, etc. In this paper, we deal with the problem of 3D pose variation of a face. for that we design and propose PosIX Generative Adversarial Network (PosIX-GAN) that has been trained to generate a set of nice (high quality) face images with 9 different pose variations, when provided with a face image in any arbitrary pose as input. The discriminator of the GAN has also been trained to perform the task of face recognition along with the job of discriminating between real and generated (fake) images. Results when evaluated using two benchmark datasets, reveal the superior performance of PosIX-GAN over state-of-the-art shallow as well as deep learning methods.
Left Atrial Segmentation Combining Multi-atlas Whole Heart Labeling and Shape-Based Atlas Selection

Segmentation of the left atria (LA) from late gadolinium enhanced magnetic resonance imaging (LGE-MRI) is challenging since atrial borders are not easily distinguishable in the images. We propose a method based on multi-atlas whole heart segmentation and shape modeling of the LA. In the training phase we first construct whole heart LGE-MRI atlases and build a principal component analysis (PCA) model able to capture the high variability of the LA shapes. All atlases are clustered according to their LA shape using an unsupervised clustering method which additionally outputs the most representative case in each cluster. All cluster representatives are registered to the target image and ranked using conditional entropy. A small subset of the most similar representatives is used to find LA shapes with similar morphology in the training set that are used to obtain the final LA segmentation. We tested our approach using 80 LGE-MRI data for training and 20 LGE-MRI data for testing obtaining a Dice score of $$0.842 \pm 0.049$$ .
Nonrigid Medical Image Registration Using an Information Theoretic Measure Based on Arimoto Entropy with Gradient Distributions

This paper introduces a new nonrigid registration approach for medical images applying an information theoretic measure based on Arimoto entropy with gradient distributions. A normalized dissimilarity measure based on Arimoto entropy is presented, which is employed to measure the independence between two images. In addition, a regularization term is integrated into the cost function to obtain the smooth elastic deformation. To take the spatial information between voxels into account, the distance of gradient distributions is constructed. The goal of nonrigid alignment is to find the optimal solution of a cost function including a dissimilarity measure, a regularization term, and a distance term between the gradient distributions of two images to be registered, which would achieve a minimum value when two misaligned images are perfectly registered using limited-memory Broyden–Fletcher–Goldfarb–Shanno (L-BFGS) optimization scheme. To evaluate the test results of our presented algorithm in non-rigid medical image registration, experiments on simulated three-dimension (3D) brain magnetic resonance imaging (MR) images, real 3D thoracic computed tomography (CT) volumes and 3D cardiac CT volumes were carried out on elastix package. Comparison studies including mutual information (MI) and the approach without considering spatial information were conducted. These results demonstrate a slight improvement in accuracy of non-rigid registration.
PERGESERAN REALISASI TINDAK KESANTUNAN DIREKTIF MEREDUKSI NILAI KESANTUNAN POSITIF DAN KARAKTER DI KALANGAN PESERTA DIDIK

Language politeness, especially politeness of directive actions among elementary school students is the main foundation for instilling the character building of children. This directive politeness action has decreased over time in line with the century of global communication and computing. This study aims to describe the shift in the realization of directive politeness act (DPA) among elementary students with a Javanese cultural background. This research method takes the form of qualitative with a dual case study strategy. Sources of research data include all elementary school students and teachers in Surakarta and Yogyakarta. Data collection techniques are carried out by observing techniques actively involved, observing free involvement, and reinforced by FGD techniques. The research data in the form of speech from DPA elementary school students in conducting language activities in a school environment both in formal and non-formal settings. Data analysis was carried out using Leech model of politeness scale technique which was strengthened by politeness analysis with the principle of social, cultural harmony, the harmony of the Gunarwan and Prayitno models. The results of the study abstract that: (a) the realization of the DPA students experience a shift in line with the shifting of the time and context of the wearer, (b) the realization of DPA can reduce the positive politeness values and psychological characteristics of the students' character, (c) the realization of the student's DPA This digital trend tends to use direct and literal methods in line with the influence of the context of communication patterns and centuries of practical computing, (d) the taxonomic shift in the dominant DPA in the sub-TKD requires, (e) the DPA taxonomy begs parallel with its realization in the sub-DPA pleading, (f) the taxonomic shift in DPA invites the dominant in the sub-DPA to persuade, (g) the taxonomic shift in DPA advises the dominant sub-DPA to remind, (h) the taxonomic shift in DPA reprimands the dominant sub-DPA satire, (i) the taxonomy of DPA prohibits parallel with the realization of the sub-DPA prohibits, (j) the character of the DPA used by elementary students with a Javanese cultural background shifts in line with shifting understanding the values of language politeness and understanding of character among the wearer.
Estimation of Global Solar Radiation from Temperature Extremes: A Case Study of Hebron City, Palestine

Solar radiation is the main energy source for mankind and an accurate data of solar radiation levels for a particular location is vital for the optimum operation of solar energy transducers such as photovoltaic cells and solar thermal collectors.This study aimed to calibrate some of the existing models in the literature for estimating daily global solar radiation parameter using available measured records of air temperature extremes and new models were developed based on maximum and minimum air temperatures.Applicability of the Hargreaves model, Allen model, Bristow-Campbell model and Chen et al. model were evaluated for computing the global solar radiation for Hebron city in Palestine.Estimated values were compared with measured values in terms of the coefficient of determination (R 2 ) and root mean square error (RMSE).All models provide good estimates when compared to the accurate values with R 2 0.9226 (Bristow-Campbell model) to 0.9547 (Chen et al. model), while the proposed model provides a value of 0.9632.The RMSE value ranges from 0.7632 for Chen et al. model to 0.9211 for Bristow-Campbell model, however a lower value (0.7118) for the proposed model.This study found that the proposed model estimates global solar radiation at the location of study better than the other models.
One Monad to Prove Them All

One Monad to Prove Them All is a modern fairy tale about curiosity and perseverance, two important properties of a successful PhD student. We follow the PhD student Mona on her adventure of proving properties about Haskell programs in the proof assistant Coq. On the one hand, as a PhD student in computer science Mona observes an increasing demand for correct software products. In particular, because of the large amount of existing software, verifying existing software products becomes more important. Verifying programs in the functional programming language Haskell is no exception. On the other hand, Mona is delighted to see that communities in the area of theorem proving are becoming popular. Thus, Mona sets out to learn more about the interactive theorem prover Coq and verifying Haskell programs in Coq. To prove properties about a Haskell function in Coq, Mona has to translate the function into Coq code. As Coq programs have to be total and Haskell programs are often not, Mona has to model partiality explicitly in Coq. In her quest for a solution Mona finds an ancient manuscript that explains how properties about Haskell functions can be proven in the proof assistant Agda by translating Haskell programs into monadic Agda programs. By instantiating the monadic program with a concrete monad instance the proof can be performed in either a total or a partial setting. Mona discovers that the proposed transformation does not work in Coq due to a restriction in the termination checker. In fact the transformation does not work in Agda anymore as well, as the termination checker in Agda has been improved. We follow Mona on an educational journey through the land of functional programming where she learns about concepts like free monads and containers as well as basics and restrictions of proof assistants like Coq. These concepts are well-known individually, but their interplay gives rise to a solution for Mona's problem based on the originally proposed monadic tranformation that has not been presented before. When Mona starts to test her approach by proving a statement about simple Haskell functions, she realizes that her approach has an additional advantage over the original idea in Agda. Mona's final solution not only works for a specific monad instance but even allows her to prove monad-generic properties. Instead of proving properties over and over again for specific monad instances she is able to prove properties that hold for all monads representable by a container-based instance of the free monad. In order to strengthen her confidence in the practicability of her approach, Mona evaluates her approach in a case study that compares two implementations for queues. In order to share the results with other functional programmers the fairy tale is available as a literate Coq file. If you are a citizen of the land of functional programming or are at least familiar with its customs, had a journey that involved reasoning about functional programs of your own, or are just a curious soul looking for the next story about monads and proofs, then this tale is for you.