Reviewerly/exHarmony huggingface dataset:
Image Retrieval Based on the Combination of Region and Orientation Correlation Descriptors

A large number of growing digital images require retrieval effectively, but the trade-off between accuracy and speed is a tricky problem. This paperwork proposes a lightweight and efficient image retrieval approach by combining region and orientation correlation descriptors (CROCD). The region color correlation pattern and orientation color correlation pattern are extracted by the region descriptor and the orientation descriptor, respectively. The feature vector of the image is extracted from the two correlation patterns. The proposed algorithm has the advantages of statistic and texture description methods, and it can represent the spatial correlation of color and texture. The feature vector has only 80 dimensions for full color images specifically. Therefore, it is very efficient in image retrieving. The proposed algorithm is extensively tested on three datasets in terms of precision and recall. The experimental results demonstrate that the proposed algorithm outperforms other state-of-the-art algorithms.
AN EFFICIENT ALGORITHM FORMINING HIGH UTILITY ASSOCIATION RULES FROM LATTICE

In business, most of companies focus on growing their profits. Besides considering profit from each product, they also focus on the relationship among products in order to support effective decision making, gain more profits and attract their customers, e.g. shelf arrangement, product displays, or product marketing, etc. Some high utility association rules have been proposed, however, they consume much memory and require long time processing. This paper proposes LHAR (Lattice-based for mining High utility Association Rules) algorithm to mine high utility association rules based on a lattice of high utility itemsets. The LHAR algorithm aims to generates high utility association rules during the process of building lattice of high utility itemsets, and thus it needs less memory and runtime
Semantic Text Similarity

Semantics has been, in recent years, an open area of study in the field of information retrieval. Considering supervised classification of text, which is the main focus of this work, semantics is involved in different stages of processing of text: during the indexing stage, during the training phase, and via class prediction step. New foundational similarity measurements from text to text may replace classical similarity measures commonly used with other decision-making classification methods. Here is a brand-new method to test semantic resemblance using the Corpus-based Approach which is sponsored for a text as a brand-new feature that summarizes semantic resemblance between concepts representing the pair-to-pair compared text documents.I.
Multidimensional spatial entanglement transfer through our existing fiber optic network

The global quantum network requires the distribution of entangled states over long distances, with significant advances already demonstrated using polarization, reaching approximately 1200 km in free space and 100 km in optical fiber. While Hilbert spaces with higher dimensionality, e.g., spatial modes of light, allows higher information capacity per photon, such spatial mode entanglement transport requires custom multimode fiber and is limited by decoherence induced mode coupling. Here we circumvent this by transporting multi-dimensional spatial entangled states down conventional single-mode fiber (SMF). We achieve this by entangling the spin-orbit degrees of freedom of a bi-photon pair, passing the polarization (spin) photon down the SMF while accessing multiple orbital angular momentum (orbital) sub-spaces with the other, thereby realizing multi-dimensional spatial entanglement transport. We show high fidelity hybrid entanglement preservation down 250 m of SMF across multiple 2 x 2 dimensions, which we confirm by quantum state tomography and Bell violation measures. This work offers an alternative approach to spatial mode entanglement transport that facilitates deployment in legacy networks across conventional fiber optic links.
Preliminary process in blast cell morphology identification based on image segmentation methods

The diagnosis of blood disorders in developing countries usually uses the diagnostic procedure Complete Blood Count (CBC). This is due to the limitations of existing health facilities so that examinations use standard microscopes as required in CBC examinations. However, the CBC process still poses a problem, namely that the procedure for manually counting blood cells with a microscope requires a lot of energy and time, and is expensive. This paper will discuss alternative uses of image processing technology in blast cell identification by using microscope images. In this paper, we will discuss in detail the morphological measurements which include the diameter, circumference and area of blast cell cells based on watershed segmentation methods and active contour. As a basis for further development, we compare the performance between the uses of both methods. The results show that the active contour method has an error percentage of 5.15% while the watershed method has an error percentage of 8.25%.
I²HRL: Interactive Influence-based Hierarchical Reinforcement Learning

Hierarchical reinforcement learning (HRL) is a promising approach to solve tasks with long time horizons and sparse rewards. It is often implemented as a high-level policy assigning subgoals to a low-level policy. However, it suffers the high-level non-stationarity problem since the low-level policy is constantly changing. The non-stationarity also leads to the data efficiency problem: policies need more data at non-stationary states to stabilize training. To address these issues, we propose a novel HRL method: Interactive Influence-based Hierarchical Reinforcement Learning (I^2HRL). First, inspired by agent modeling, we enable the interaction between the low-level and high-level policies to stabilize the high-level policy training. The high-level policy makes decisions conditioned on the received low-level policy representation as well as the state of the environment. Second, we furthermore stabilize the high-level policy via an information-theoretic regularization with minimal dependence on the changing low-level policy. Third, we propose the influence-based exploration to more frequently visit the non-stationary states where more transition data is needed. We experimentally validate the effectiveness of the proposed solution in several tasks in MuJoCo domains by demonstrating that our approach can significantly boost the learning performance and accelerate learning compared with state-of-the-art HRL methods.
Construction of Higher Vocational College Curriculum Diagnosis and Improvement Index System Based on AHP

The course diagnosis and the improvement work have the vital significance to the promotion of higher vocational college's personnel training quality. The index system of curriculum diagnosis and improvement in higher vocational colleges is constructed by using AHP method, which provides a certain basis for the work of curriculum diagnosis and improvement.
THE ROLE OF MORPHOLOGICAL AWARENESS AND EXPLICIT MORPHOLOGICAL INSTRUCTIONS IN ELT

As a branch of linguistics, the role of morphology in learning English should not be taken carelessly. As the study of internal structure of words, morphology provides a lot of knowledge necessary to develop students’ English proficiency. However, many English teachers are still not aware of its important role in successful and effective learning process because they tend to be busy searching for media, strategies or techniques which they consider effective in teaching. In relation to the importance of morphology in English language teaching (ELT), this paper aims at explaining the important role of morphological awareness and instructions in teaching English as a foreign language to junior high schools in Indonesia. This is a descriptive study using literature review as the method. The data were secondary data in the form of documents including articles, journals and books. The data were analyzed using a content analysis by reading and reviewing the documents. The results of the data analysis show the significant role of morphological awareness and morphological instructions in facilitating the students at junior high school to learn English more easily, with significantly greater achievement. It is, therefore, concluded that students with morphological awareness or students that are treated by using morphological instruction gain better achievements in their English learning, resulting in their better English proficiency.
Exact yet Efficient Graph Parsing, Bi-directional Locality and the Constructivist Hypothesis

A key problem in processing graph-based meaning representations is graph parsing, i.e. computing all possible derivations of a given graph according to a (competence) grammar.We demonstrate, for the first time, that exact graph parsing can be efficient for large graphs and with large Hyperedge Replacement Grammars (HRGs).The advance is achieved by exploiting locality as terminal edge-adjacency in HRG rules.In particular, we highlight the importance of 1) a terminal edge-first parsing strategy, 2) a categorization of a subclass of HRG, i.e. what we call Weakly Regular Graph Grammar, and 3) distributing argumentstructures to both lexical and phrasal rules.
A Tarot of Things: a supernatural approach to designing for IoT

This paper looks at an alternative approach to design research for IoT, through a practical engagement with philosophy; based on the concept of Carpentry, introduced by design philosopher Ian Bogost.It presents this exploration through the design of a bespoke digital Tarot deck, rooted in Object-Oriented Ontology.This branch of philosophical inquiry withdraws from conventional perceptions of objects and people.Viewing them as equally important 'things', operating with a range of independent and interdependent perspectives; which have been described as "constellations".Through our philosophical carpentry we present a Tarot of Things, which acts as boundary object, for understanding how taking constellation perspectives of networked IoT devices can produce new design approaches.
Design of the Expert System for Edamame Grading Using Forward Chaining Method

Edamame grade is determined based on edamame quality parameters with several parameters that are quite a lot. The process of deciding edamame grade is relatively complicated and challenging. To determine the edamame grade, an experienced expert in the field is needed. With limited grading experts, the design of expert systems for grading edamame can help users or companies to determine edamame grade. This study aims to design an expert system for determining edamame grade, which can determine the quality of edamame accurately. The Design of an expert system in this research uses the forward chaining method. The stages of designing an expert system include problem identification, knowledge acquisition, building a data flow diagram, compile the database, building a decision tree diagram, and developing a user interface. The results showed that the expert system has been verified and validated well. Verification test results show that all the menu buttons on the expert system application are functioning correctly. Then, the validation results also show that the edamame quality output produced by the expert system is compatible with the expert solution, meaning that the expert system's output data is by following with the results of the decision given by the expert. In testing the validation of determining the quality is appropriate. The results of the verification and validation of the edamame grading expert system show that the expert system has produced satisfactory outcomes. Expert system output is compatible with the solution provided by the expert.
Penggunaan Media Papan Flanel untuk Meningkatkan Kemampuan Mengenal Huruf Vokal dan Konsonan pada Anak Kelompok B di TKK Rherhedja 2

The research aims to: (1) know how to use the media on the flannel board in learning the recognition of vowels and consonants in group B TKK Rherhedja children. (2) Improve the ability of children in group B TKK Rherhedja 2 in recognizing vowels and consonants through the media flannel board. This research is a class action research (CAR), which is implemented in 2 cycles. The data collection techniques used are observation and documentation. The subject of this study was the children of group B at TTK Rherhedja 2 with a total of 18 people. The results of the research proved, (1) Media Flannel board already used well in learning to children group B in TTK Rherhedja 2. On cycle I, the percentage of applicability rate is 75% in good category and on cycle II increased to a very good category with a percentage of 95%. (2) Ability to recognize letters in group B Children in TKK Rherhedja 2 of the 18 children on the pre cycle, newly developed children were 6 peoples or 33.33%, children who started to grow were 9 peoples or 50%, children develop as expected were 3 peoples or 16.67% and very good child was 0%. After using the flannel board media, there was an increase in cycle I: children who were just developing 0%, children who were starting to develop were 6 peoples or 33.33%, children developing as expected were 12 peoples or 66,67%. In Cycle II there was an increase in the ability to recognize letters as follows: the percentage of children newly developing was 0%, the percentage of children starting to develop was 0%, the percentage of children developing as expected was 44.44% or 8 peoples, and very well developed children was 55.56% or 10 peoples. Thus, the Flannel Board Media can improve the ability of group B children in recognizing vowels and consonants in TKK Rherhedja
Standard models for exploration and development complex copper-porphyry deposits in the Far East of the Russian Federation

The article deals with the prospects of development of copper-porphyry deposits discovered within the largest ore occurrences and prospecting areas of the Russian Far East. Revaluation of large reserves of copper with associated elements account (gold, silver, molybdenum) on geological data based increases the economic justification for the development of already discovered deposits, as well as the continuation of exploration work on the identified ore occurrences. Geological exploration in a certain sequence necessitates the preparation of complex prospecting, appraisal and multifactor models for the development of large copperporphyry deposits in the Far Eastern region of the Russian Federation. Complex models of geological objects for prospecting and searches of complex copper-porphyry deposits are formed in relation to each geological and industrial type. In the article, authors offer exploration typical models to develop of complex copper-porphyry deposits at different stages of geological survey. The models show the features of the deep structure of the geological objects of the simulated class and the main features of their ore-metasomatic and geochemical zoning, as well as trends in the composition and physical properties of ore-controlling geological bodies with depth. Authors present the theoretical basis of prospecting and appraisal features, reflecting the patterns of manifestation of the studied ore-bearing areas in the Far East, Russia, leads the analysis the complex of geological and geophysical studies of the deep structure of copper-porphyry deposits, formulates methodical approaches for develop modelling of copper-porphyry type fields to increase an efficiency of a geological and economic assessment of these geological objects. These approaches make it possible to comprehensively assess the parameters of exploration and development of deposits, taking into account the export-oriented orientation and investment attractiveness of the Far Eastern region of Russia.
Distant Transfer Learning via Deep Random Walk

Transfer learning, which is to improve the learning performance in the target domain by leveraging useful knowledge from the source domain, often requires that those two domains are very close, which limits its application scope. Recently, distant transfer learning has been studied to transfer knowledge between two distant or even totally unrelated domains via unlabeled auxiliary domains that act as a bridge in the spirit of human transitive inference that two completely unrelated concepts can be connected through gradual knowledge transfer. In this paper, we study distant transfer learning by proposing a DeEp Random Walk basEd distaNt Transfer (DERWENT) method. Different from existing distant transfer learning models that implicitly identify the path of knowledge transfer between the source and target instances through auxiliary instances, the proposed DERWENT model can explicitly learn such paths via the deep random walk technique. Specifically, based on sequences identified by the random walk technique on a data graph where source and target data have no direct connection, the proposed DERWENT model enforces adjacent data points in a sequence to be similar, makes the ending data point be represented by other data points in the same sequence, and considers weighted classification losses of source data. Empirical studies on several benchmark datasets demonstrate that the proposed DERWENT algorithm yields the state-of-the-art performance.
Pengembangan Buku Saku Sebagai Bahan Ajar Kearsipan Kelas X OTKP SMK Negeri 1 Jombang

This study aims to 1) the results of the development of pocket books as class X archival teaching materials OTKP SMK Negeri 1 Jombang through a scientific approach according to K13 revised 2017, 2) the feasibility of developing pocket books as teaching materials archival class X OTKP SMK Negeri 1 Jombang through a scientific approach according to K13 revised 2017, and 3) the responses of students in class X OTKP SMK Negeri 1 Jombang through a scientific approach according to K13 revised 2017. The development model used in this study is the 4D model from Thiagarajan (Define-Design-Develop-Dessiminate). Disseminate stage was not carried out because this research only reached the development stage. This research was conducted on 20 students of class X OTKP 1 at SMK Negeri 1 Jombang. Instruments used in this study included validation sheets of material, language, graphics, and student response sheets using data analysis techniques on the results of the percentage of the feasibility of a pocket book. With the results of the validation of material experts by 90%, language experts by 80%, and graphic experts by 94%. The total validity of the whole validation was 89.75% with very proper interpretation. Based on students' responses got 96.4% results with very good interpretation. It can be concluded that the research into the development of pocket books as archival teaching materials with very strong interpretation criteria is stated to be well used as supporting teaching materials.
Metamorphization of society: The factor of ‘side effects’ and globalization of nothing

Today physical, biological and social worlds develop increasingly quicker and in a more complex way that includes the phenomena of metamorphoses. Traditionally, they were considered as determined mainly by external factors, i.e. the forces of nature. Contemporary metamorphoses seem to become of a complex man-made nature. Compared to traditional metamorphoses with rigid and predictable results, contemporary metamorphoses of societies can produce both negative and positive consequences, which proves the non-linear dynamic picture of the world. There is also a traumatic tendency - when something is metamorphosed into nothing. Due to digitalization, nothing becomes more complex and pure from cultural and humane characteristics, thus, revealing new expressions of the death of the social: humans are metamorphosed into digital beings. Metamorphization of society can produce common goods as a side effect of the bad. The author argues that the formal-rational, pragmatic transformations of society and nature, like the scientific and technological innovations of mercantile type, deform and dehumanize life-worlds. The global traumatization in the form of liquid catastrophes permanently changes the living and non-living nature, structure of soil, water and air, desocializes human relations, facilitates transformations of something into nothing, people into non-people, places into non-places, things into non-things. However, people as reflexive actors can turn metamorphoses into things-for-man. To start this process, it is necessary to change the pragmatic monodisciplinary principles of science by the interdisciplinarity ones to ensure a humanistic turn in science and technologies.
Identifying Principals and Accessories in a Complex Case based on the Comprehension of Fact Description

In this paper, we study the problem of identifying the principals and accessories from the fact description with multiple defendants in a criminal case.We treat the fact descriptions as narrative texts and the defendants as roles over the narrative story.We propose to model the defendants with behavioral semantic information and statistical characteristics, then learning the importances of defendants within a learning-to-rank framework.Experimental results on a real-world dataset demonstrate the behavior analysis can effectively model the defendants' impacts in a complex case.
A Chinese-English Machine Translation Model Based on Deep Neural Network

In this paper, the neuro-machine translation model is studied. Firstly, the encoder-decoder framework used in neuro-machine translation is introduced. Then, the neuro-machine translation models based on RNN, LSTM and GRU are constructed respectively according to different network structures. Aiming at the problem of dealing with long-distance dependence, attention mechanism is integrated into translation model, so the preprocessing module, encoder-decoder framework and attention module of the system are also adopted. Furthermore, a translation model based on bidirectional GRU is proposed to improve the translation performance by enhancing the source language context information. A comparative analysis of the above translation models shows that the proposed translation model is effective.
Learning Illumination from a Limited Field-of-View Image

Illumination estimation is a crucial part of augmented reality since it can make the virtual object look more realistic. However, single image-based lighting estimation is challenging due to the limited information. Here we combine deep learning with the spherical harmonic (SH) lighting which is widely used in precomputed radiance transfer. Specifically, a convolutional neural network that predicts SH coefficients from an image is designed, trained and tested. Moreover, we construct a new dataset for training SH coefficients based on the existing panorama dataset. The method in this work can finally predict realistic lighting from a single, limited field-of-view image, and it presents better results in some cases compared with previous research.
On the Decidability of Intuitionistic Tense Logic without Disjunction

Implicative semi-lattices (also known as Brouwerian semi-lattices) are a generalization of Heyting algebras, and have been already well studied both from a logical and an algebraic perspective. In this paper, we consider the variety ISt of the expansions of implicative semi-lattices with tense modal operators, which are algebraic models of the disjunction-free fragment of intuitionistic tense logic. Using methods from algebraic proof theory, we show that the logic of tense implicative semi-lattices has the finite model property. Combining with the finite axiomatizability of the logic, it follows that the logic is decidable.