Reviewerly/exHarmony huggingface dataset:
A Random Walk Tensor Model for Heterogeneous Network Entity Classification

Heterogeneous network entity classification is to predict the labels of entities in a heterogeneous network which consists of multiple types of relations and multiple types of entities. The existing studies have shown that relations information is critical for improving entity classification performance. The relation information is often exploited by clustering the entities into groups based on relation density or constructing relation features by counting the number of different relations. However, these methods only consider the relation information but neglect the importance of different relations in the network. In practice, different relations may have different degrees of importance w.r.t different entities. To this end, we propose a Random walk Tensor Model (RTM) to reveal the relation importance and classify the entities in the heterogeneous network, simultaneously. In RTM, the heterogeneous network is represented as a multi-relational network using a three-way tensor. The tensor is also used to compute the transition probability for the random walk among entities. We build a Markov chain model and use an iterative algorithm to solve the Markov chain equations in the model to obtain the random walk stationary distributions and compute the entity classification and relation ranking results based on such distributions. The theoretical analyses are given to show the rationality and interpretability of the model. The experimental results demonstrate that the RTM can achieve superior classification performance compared with several state-of-the-art methods and obtain a reasonable relation ranking.
Dependency of GPA-ES Algorithm Efficiency on ES Parameters Optimization Strength

In this work, the relation between number of ES iterations and convergence of the whole GPA-ES hybrid algorithm will be studied due to increasing needs to analyze and model large data sets. Evolutionary algorithms are applicable in the areas which are not covered by neural networks and deep learning like search of algebraic model of data. The difference between time and algorithmic complexity will be also mentioned as well as the problems of multitasking implementation of GPA, where external influences complicate increasing of GPA efficiency via Pseudo Random Number Generator (PRNG) choice optimization. Hybrid evolutionary algorithms like GPA-ES uses GPA for solution structure development and Evolutionary Strategy (ES) for parameters identification are controlled by many parameters. The most significant are sizes of GPA population and sizes of ES populations related to each particular individual in GPA population. There is also limit of ES algorithm evolutionary cycles. This limit plays two contradictory roles. On one side bigger number of ES iterations means less chance to omit good solution for wrongly identified parameters, on the opposite side large number of ES iterations significantly increases computational time and thus limits application domain of GPA-ES algorithm.
Image Captioning Based on Automatic Constraint Loss

In recent years, the Encoder-Decoder framework has been widely used in image captioning. In the forecast period, many methods regard the input of the usage model at the previous moment as the output at the moment, which may cause the generated words to get worse. This paper proposes to use the correct rate of the preceding words to constrain the weight of the back words, making the loss weight of the back words increase as the preceding word error rate decreases, namely Automatic Constraint Loss (ACL), reducing the difference in the training and test phase. The experimental results on the MSCOCO dataset show that the addition of the proposed method to the original model, the bleu_1 and bleu_2 scores are greatly improved, and the attention mechanism can more accurately select the image region.
A case Study on the Authentic Learning and Assessments in the University Education

Demands of industry and the professions in the innovative world challenge universities to refine the education structure including learning and assessment methods to enhanced graduate employability, including the development of transferable practical skills. Adopting authentic learning environment and assessment methods university education is the major key to fulfill this demand. The present paper examines the effects of authentic learning and assessments through experimental research approach by means of mixed-method approach. Two comparable groups of university students were selected based on the results of the pretest. Authentic learning environment was created for selected topics in mathematics in Engineering: Projectiles, Circular motion and simple harmonic motion and the Fibonacci sequence with golden ratio. The learning environment includes physical demonstrations with practical, simulations, and examples in industry and real world as entertaining activities. Moreover the authentic assessments were created, capturing the learning outcomes. The results discovered that the differences were significant at 0.01 levels in the attainment of higher order thinking skills. Moreover, learning outcomes subjected to the study of two comparable groups were also significantly different at p<; 0.05 level. The research depicted that the students benefitted by authentic Leaning and significant favor on assessment methods in students' achievements. Further the authentic instruction and assessments bring a positive effect on higher order thinking abilities. Authentic approach provides opportunities to the students to develop the 21st century skills beside the mathematical knowledge and skills. We suggest that the research can be extended to engineering core subjects.
Research on Hard-tipped Calligraphy Classification Based on Deep Learning Method

Cultivating students' ability to write hard-tipped Calligraphy is a very important part of primary and secondary school education. The fair and professional evaluation of hard-tipped calligraphy can effectively promote students to correctly write Chinese characters. All along, the hard-tipped calligraphy written by the students is subjectively scored by the teacher. The difference of teachers' professional level leads to that they cannot give objective professional evaluation of hard-tipped calligraphy just like the experts in the field. Therefore, the method to use intelligent technology to achieve automatic machine evaluation is one of the research hotspots of current intelligent applications. This paper proposes the automatic classification of hard-tipped calligraphy copybooks using the convolutional neural network method based on caffe deep learning framework. Experiments show that the accuracy of single-word neural network modeling evaluation can reach 82.3%, and this method has good classification effect and strong robustness. The research work of this paper also has reference significance for the automatic evaluation of Chinese characters.
Rooted Hypersequent Calculus for Modal Logic S5

We present a rooted hypersequent calculus for modal propositional logic S5. We show that all rules of this calculus are invertible and that the rules of weakening, contraction, and cut are admissible. Soundness and completeness are established as well.
Mediating Open Data Consumption - Identifying Story Patterns for Linked Open Statistical Data

Statistical data account for a very large proportion of data published on open data platforms. This category of data are which are often of high quality, value and public interest; are gradually being published as 5-star linked open statistical data or data cubes (LOSD) for easy integration and cross-border comparability. However, publishing open data as linked data (i.e. graph oriented) significantly increases the technical skill requirements for end-user consumption. We address this problem by mediating the exploration and analysis of LOSD published on open data platforms through the use of data stories. After providing the requisite background information on LOSD, we identified data story patterns from extant literature and show how these patterns can be employed in analysing LOSD. Subsequently, we provide a case study to illustrate the use of these data story patterns as an end-user domain-specific language to explore and analyse LOSD. We argue that using data stories for exploring and analysing on open data platforms has the potential to significantly increase the adoption and use of (linked) open data.
On the Knowledge-Based Dynamic Fuzzy Sets

In 1965, Zadeh [1] introduced fuzzy set as a generalization of crisp set by considering membership degrees of elements. That membership degree is represented gradually as a real number in an interval [0, 1]. Wang et al. [2] generalized the concept of fuzzy set, called dynamic fuzzy sets (DFS). In the DFS, each membership degree of an element is given by a membership function dealing with time variable. Thus, the membership degree of an element in a given dynamic fuzzy set might dynamically change according to the time’s variable. In 2002, Intan and Mukaidono [3] proposed an extended concept of fuzzy set, called knowledge-based fuzzy sets (KFS). In the KFS, the membership degree of an element given a fuzzy set is subjectively determined by a single knowledge. The membership degree of an element with respect to a given fuzzy set may be different provided by different knowledge of persons. This paper combines both concepts, DFS and KFS, called knowledge-based dynamic fuzzy sets (KDFS), by realizing that membership function of a given fuzzy set provided by a certain knowledge may be dynamically changed over time. Three kinds of summary fuzzy sets are proposed and discussed. Some basic operations of KDFS are defined. Their properties are verified and examined.
Multiple-Coil Magnetic Resonance Image Denoising and Deblurring With Nonlocal Total Bounded Variation

One of the complex tasks in image restoration is to restore images under data correlated noise contaminations. In real-time medical imaging scenarios, such as Magnetic Resonance (MR), Ultrasound, Computed Tomography(CT) etc, it is observed that, the data of interest is severely degraded with data dependent noise interventions. A Nonlocal Total Bounded Variation (NLTBV) approach is being proposed in this paper to denoise as well as deblur multiple-coil MR images corrupted by non-central Chi distributed noise and linear Gaussian blur. The energy functional for the restoration model is derived by applying the Maximum A Posteriori (MAP) estimator on the Probability Density Function (PDF) of the non-central Chi distribution. The numerical implementation is performed using the split-Bregman iterative scheme to improve the convergence rate. The proposed model is compared with the other state of the art models in terms of both visual and statistical quantifications to demonstrate it's performance.
Application Research of Neural Network in River Elevation Map

Based on the actual engineering data, BP(Back Propagation) neural network and RBF neural network were used to simulate.Comparing the accuracy of the two networks in the inland river channel elevation fitting, the applicability of these two networks in the river elevation was analyzed. In addition, the influence of network structure parameters on the fitting accuracy is discussed based on establishing the neural network with high adaptability. Because the RBF(Radical Basis Function) network belongs to the local sensing field network, the fitting accuracy of the network with huge input data is not as high as that of the BP network. This paper focuses on the impact factors of the fitting accuracy in two networks through comparing their fitting range.
Using Matrix Decomposition and Frequency Transforms to Detect Forgeries in Digital Images

Digital images are very popular, but they are quite easy to manipulate. This may have bad consequences, especially when the editing changes important content in the images. Therefore, detecting traces of modifications in digital images is an urgent need. There are various techniques have been proposed, mainly based on finding specific features on the spatial domain or on the frequency domain of the images. In this paper, we study some matrix decomposition methods, which apply for detection of image manipulations. Next, we employ QR matrix factorization for design a new image forgery detection scheme. We also compare the proposed scheme with some other widely-used schemes based on frequency transforms and matrix decomposition methods. The experimental results on hundreds of images in different types show that the matrix decomposition based schemes accurately detect the copy-paste forgeries with rather low false positive rates. The schemes are robust against some common attacks. Moreover, the QR based scheme is much faster than the others. This characteristic is very useful when working with a large scale image dataset in practice.
Expert System in Determining the Quality of Superior Gourami Seed Using Forward Chaining-Based Websites

Gourami (Osphronemusgouramy) is the original fish of Indonesian waters that has spread to Southeast Asia and China. Taxonomically, based on The fish labirinth, it is including the family Osphronemidae. Gourami fish is one of many commodities cultivated by the farmers, this is because the market demand is high enough. This fish is one of well-known consumption fish species and has many demands in Indonesia. This is because Gourami has superiorities, there are good taste of meat, easy maintenance and relatively stable prices. This fish has long been known and has been widely cultivated. To determine the type of gourami seed we used forward chaining method, so we easily know the quality of the seed of the Gourami by using this application.
The Historical Review and Current Trends in Speech Synthesis by Bibliometric Approach

To build awareness of the development of Speech Synthesis, this study clarifies the citation and bibliometric analysis of research publications of Speech Synthesis during 1992–2017. This study analysed 17,161 citations from a total of 1256 articles dealing with Speech Synthesis published in 333 journals based on the databases of SCIE, SSCI and AH&CI, retrieved via the Web of Science (WOS). Bradford Law and Lotka’s Law, respectively, examined the distribution of journal articles and author productivity. Furthermore, This study determines the citation impact of Speech Synthesis using parameters such as extent of citation received in terms of number of citations per study, distribution of citations over time, distribution of citations among domains, citation of authors, citation of institutions, highly cited papers and citing journals and impact factor of 17,161 citations. This study can help researchers to better understand the history, current status and trends of Speech Synthesis in the advanced study of it.
Towards a learning-algorithm agnostic generative policy model for coalitions

Autonomous systems are expected to have a major impact in future coalition operations. These systems are enabled by a variety of Artificial Intelligence (AI) learning algorithms that contextualize and adapt in varying, possibly unforeseen situations to assist humans in achieving complex tasks. Moreover, these systems will be required to operate in dynamic and challenging environments that impose certain constraints such as task formation and collaboration, ad-hoc resource availability, rapidly changing environmental conditions and the requirement to abide by mission objectives. Therefore, such systems require the capability to adapt and evolve so that they can behave autonomously at the edge of the network in new situations. Crucially, autonomous systems have to understand the bounds in which they can operate based on their own capability and the constraints of the environment. Policies are typically used by systems to define their behavior and constraints and often these policies are manually configured and managed by humans. AI-enabled systems will require novel approaches to rapidly learn, create, augment, and model emerging policies to support real-time decision making. Recent work has shown that such policy model generations are possible through symbolic learning to shallow and deep learning approaches for different classes of problems. Motivated by this observation, in this paper, we propose to apply recent advances in explainable-AI to develop an approach which is agnostic to the learning algorithm, thus enabling seamless policy generation in the coalition environment.
Weighted KNN Algorithm Based on Random Forests

In this paper, we proposed a weighted KNN algorithm based on random forests. The proposed algorithm fully measures the differences in the importance of each feature, and overcomes the shortcoming of k-nearest neighbor (KNN) algorithm in classifying unbalanced data sets and data sets of different feature importance. The classification accuracy of the KNN algorithm is effectively improved, and the performance of the proposed algorithm is verified through experiments.
Multi-agent Healthcare Information System on Hadoop

The healthcare industry generates a large amount of data by keeping patients’ medical history and due to the diversity of clinical medical equipment. In this paper we address problems that exist in the organ transplantation medical field. Some matching algorithms (based on centralized data) were already presented but expanding the system when it comes to Big Data was not considered before. Problems that may occur in the context of Hadoop are related to uneven data distribution and MapReduce processing. The question that arise is whether a centralized graph algorithm can be adapted to MapReduce in order that the results to be equivalent to centralized processing and efficiency to grow through parallel processing. Our solution uses intelligent agents to collect and process medical data. In case of the matching algorithm, the distributed version can approximate a solution obtained by using the centralized application, but it is more effective as a response time in the Big Data context.
Clothing Identification based on Fused Key Points

In order to solve the problem of low accuracy of multi-category clothing recognition, an algorithm combining global and local features of clothing is proposed, which is aimed at solving the decrease of clothing recognition rate caused by changes of perspective and attitude and complexity of images.Based on the deep convolutional neural network, the algorithm locates the key points of clothing, excavates the key points image blocks, fuses the low-level visual information of the key points image blocks and the high-level semantic information of the whole image for clothing classification.This paper conducts experiments on the Deepfashion dataset, uses keras for data enhancement, and compares it with HOG+SVM image recognition algorithm and deep learning algorithms such as Alexnet and FashionNet. The experimental results show that the proposed algorithm can effectively improve the recognition rate of clothing.
Modifications of the Givens Training Algorithm for Artificial Neural Networks

The Givens algorithm is a supervised training method for neural networks. This paper presents several optimization techniques that could be applied on the top of the Givens algorithm. First, the classic variant of the Givens method is briefly described. The main section of the article contains a detailed description of the proposed retry worst samples, skip best samples, and the Givens epoch update optimization techniques. The paper concludes with the simulation results and an overall summary.
Evaluating Post-quantum Signatures for IoT Devices

With the advent of quantum computing, traditional digital signatures schemes such as RSA, DSA, and ECDSA will become insecure thanks to Shor’s algorithm which will break them in polynomial time. Thus, cryptosystems resilient to quantum computing must be considered, especially for devices in the Internet of Things which limitations may hinder the use of secure cryptosystems. An alternative is the use of hash-based signatures which are post-quantum and provide a flexible trade-off between performance (both storage and processing time) and security level adaptable to a device’s restrictions. This paper presents a straightforward background on basic hash-based signatures, an implementation of hash-based signatures schemes, specifically Winternitz and Merkle Signature Scheme, on an Arduino Due platform, and a performance evaluation. Our results show that hash-based signatures are feasible for IoT-like devices.
Structuring Element-counting Approach for Morphological Pattern Spectrum-based Image Manipulation Detection

The use of digital images has become quite widespread in legal, medical, and private contexts. However, anyone can easily edit or manipulate any digital image on a computer. Thus, an effective method for detecting digital image manipulation is required for retaining authenticity. Image-manipulation detection is applied in investigations of crimes and photographic evidence. In this paper, we describe a technique for detecting a manipulated image by morphological pattern spectrum. Before we have researched morphological pattern spectrum detected manipulation. We propose the new technique which counts a number of the same scale as the structuring element scale by morphological pattern spectrum in an image. So it can judge a manipulated image in detail because the new technique can detect the number of a manipulated scale. Thus, even if the previous technique judge a large different in the pattern spectrum between the original image and the manipulated image, the new technique can judge to be actually small manipulation.
Machine learning of symbolic compositional rules with genetic programming: dissonance treatment in Palestrina

We describe a method for automatically extracting symbolic compositional rules from music corpora. Resulting rules are expressed by a combination of logic and numeric relations, and they can therefore be studied by humans. These rules can also be used for algorithmic composition, where they can be combined with each other and with manually programmed rules. We chose genetic programming (GP) as our machine learning technique, because it is capable of learning formulas consisting of both logic and numeric relations. GP was never used for this purpose to our knowledge. We therefore investigate a well understood case in this study: dissonance treatment in Palestrina's music. We label dissonances with a custom algorithm, automatically cluster melodic fragments with labelled dissonances into different dissonance categories (passing tone, suspension etc.) with the DBSCAN algorithm, and then learn rules describing the dissonance treatment of each category with GP. Learning is based on the requirement that rules must be broad enough to cover positive examples, but narrow enough to exclude negative examples. Dissonances from a given category are used as positive examples, while dissonances from other categories, melodic fragments without dissonances, purely random melodic fragments, and slight random transformations of positive examples, are used as negative examples.
A Sequent-Type Calculus for Three-Valued Default Logic, Or: Tweety Meets Quartum Non Datur

Sequent-type proof systems constitute an important and widely-used class of calculi well-suited for analysing proof search. In this paper, we introduce a sequent-type calculus for a variant of default logic employing Łukasiewicz’s three-valued logic as the underlying base logic. This version of default logic has been introduced by Radzikowska addressing some representational shortcomings of standard default logic. More specifically, our calculus axiomatises brave reasoning for this version of default logic, following the sequent method first introduced in the context of nonmonotonic reasoning by Bonatti, which employs a complementary calculus for axiomatising invalid formulas, taking care of expressing the consistency condition of defaults.