Reviewerly/exHarmony huggingface dataset:
Configurable Heuristic Adaptation for Improving Best First Search in AI Planning

Automated planning is one of the most prominent AI challenges. In the last few decades, there has been a great deal of activity in designing planning techniques and planning engines, with a focus on forward state-space search. Despite the ubiquitous use of heuristics in AI planning, these techniques are susceptible to being easily trapped by undetected dead ends and huge search plateaus. In this paper we introduce a highly configurable heuristic adaptation process based on the idea of dynamically penalising unpromising actions when an inconsistency in the heuristic evaluation is detected; its aim is to reduce the bias affecting specific actions, thereby encouraging exploration by the search process and adding diversity in the neighbourhood selection process. Our extensive experimental analysis demonstrates that the proposed heuristic can be configured to improve significantly the performance of best first search planning on a range of benchmark domains.
SMM-Promotion of Additional Education Services in the Aspect of Vocational Guidance for Schoolchildren

The article shows a number of problems of modern Russian education and outlines ways to solve them through additional education, which allows you to implement the professional orientation of schoolchildren. To select additional education courses, the younger generation turns, first of all, to Internet resources, often to social networks. SMM promotion technology can be applied in the field of education to popularize scientific knowledge, attract and inform consumers of educational services. The purpose of the article is to describe the effective experience of SMM-promotion of additional education services in the aspect of professional orientation for schoolchildren on the VKontakte social network. The functionality of this social network allows you to create conversations, thematic groups and place informational messages and educational media content in them. The article describes the community "Physico-mathematical school" Quantum"", representing the school of the same name, operating on the basis of physical and mathematical faculty of the federal state budgetary educational institution of higher education "Mordovian State Pedagogical Institute named after M. Ye Evseveva".
A Hybrid Scheme for Spatio-Textual Recommender System

Location Based Social Networks (LBSNs) enable their user to share their check-ins and post reviews about them. The availability of spatial and textual information in LBSNs offers an opportunity to explore user's history and preferences to find the locations that the user might be interested in. Point-Of-Interests (POIs) spatial features are one of the most important data available on LBSNs as it has a huge impact on user's choice of new location to visit. Users' reviews and POIs' categories are another valuable resources of information in LBSNs which help infer users' interest and POIs' features. Recent researches attempt to improve the performance of POI recommendation models by making use of different information sources available in social network. In this paper, we examine the impact of using this information on the accuracy of recommendation task. Our major contribution is proposing the model which use heterogeneous context information in the form of a weighted linear combination. We argue that the weights of this combination should be learned for each user separately instead of using the same set of weights for all users. We provide an algorithm for learning the weights for each user such that recommendation accuracy is improved. In addition, it is enable to incorporate extra information source to our proposed model without requirement of changing the model completely or adding extra complexity to it. Experiments conducted on two large datasets of real world, Yelp and Foursquare, shows the effectiveness of the proposed method.
WHAT IS A RULE OF INFERENCE?

Abstract We explore the problems that confront any attempt to explain or explicate exactly what a primitive logical rule of inference is , or consists in . We arrive at a proposed solution that places a surprisingly heavy load on the prospect of being able to understand and deal with specifications of rules that are essentially self-referring . That is, any rule $\rho $ is to be understood via a specification that involves, embedded within it, reference to rule $\rho $ itself. Just how we arrive at this position is explained by reference to familiar rules as well as less familiar ones with unusual features. An inquiry of this kind is surprisingly absent from the foundations of inferentialism—the view that meanings of expressions (especially logical ones) are to be characterized by the rules of inference that govern them.
A Comparative Study of Web Design for Reengineering Pharmacokinetics Application

The issues found in stand-alone pharmacokinetics Java application, iKinet, has indicated a platform change. The availability of the application is limited due to how it is distributed, and only those with a copy can use it. This is also the case in terms of its accessibility because it has a software dependency that only has supports for few operating systems. This study aims to propose a more suitable platform that can expand the availability and accessibility of the iKinet application to users. This study also aims to propose a suitable approach in developing the application that is to be deployed in the proposed platform. We have proposed the web to be used as the platform for the application. As for the approach in developing the web application, we have proposed to utilize the Responsive Web Design (RWD) approach. Based on the results obtained through the experiments, it is of the authors’ opinion that the limitations of the iKinet desktop applications were addressed in the web application. As compared to the previous application, the application is highly available and accessible to users, requiring only a device that has a web browser for access. However, unlike the previous application, the user would require Internet access to access the application. A solution to this has been suggested.
A Comparative Study of Supervised Learning Algorithms for Symmetric Positive Definite Features

In recent years, the use of Riemannian geometry has reportedly shown an increased performance for machine learning problems whose features lie in the symmetric positive definite (SPD) manifold. The present paper aims at reviewing several approaches based on this paradigm and provide a reproducible comparison of their output on a classic learning task of pedestrian detection. Notably, the robustness of these approaches to corrupted data will be assessed.
Aspetti funzionali e implementativi del Museo epigrafico digitale EpiCUM

This work describes the main development activities of the EpiCUM project with particular reference to the implementation and functional aspects of the results achieved within it. EpiCUM aims to present and make available with a single digital museum the whole epigraphic corpus of the civic museum Castello Ursino di Catania, codified in EpiDoc.The project stems from the collaboration between the Institute of Cognitive Sciences and Technologies of the CNR and the Municipality of Catania and has gained the interest of the I.Sicily project and the involvement of the M.M. Lazarus of Catania with school-work alternation activities. The first phase of the project was mainly dedicated to the recognition of the epigraphs and to the preparation of the exhibition Voci di Pietra which proposes a selection of epigraphs according to innovative exposure methods through the intelligent use of technology and digital. The next phase focused on the analysis and coding in the open EpiDoc format of all the inscriptions of the civic museum and on the realization of the digital museum.All information on epigraphs are encoded in EpiDoc within XML files and are easily accessible through the various sections of the digital museum in which they are presented in a simple and intuitive way, and visualized within epigraphic cards. The epigraphic cards provide access to the XML files containing the EpiDoc encodings. The epigraphic information can be appropriately queried through a section of the digital museum dedicated to searches. With this method of organizing epigraphic information in the digital museum, the whole epigraphic heritage of the civic museum of Catania can be used for various purposes, both for scholars in the sector, but also for all other types of possible users.Notice that in order to facilitate the navigation through epigraphic information, it was decided to separately include also information about the religious sphere, the collections of provenance and the copy status of the epigraphs, encoding them within the XML files by means of a specially selected set of EpiDoc markup elements.
Web based food combination system for diabetes mellitus type 2 with genetic algorithm

Abstract As the age of a person makes it easily infected with a disease such as diabetes. In this case, the food of people suffering from the disease must eat foods that are in accordance with the nutrients needed to reduce the patient’s blood sugar levels. Based on this problem, this study aims to develop a food combination system to help diabetics manage food according to patient criteria. This system requires criteria for producing food combinations and using genetic algorithm methods to produce the best alternative food combinations for diabetics. The combination of food resulted is taken based on the efficiency of chromosomes from genetic algorithms. The genetic algorithm accuracy calculation method reaches 100% by comparison with manual calculations from 5 test cases that have been made. From these results it can be concluded that the application of genetic algorithm methods is sufficient to assist diabetics in making food combinations that fit the patient’s criteria.
Debate on the potential mood: textual and traductological analysis of the expression of hypothesis and the future in middle Italian and middle Spanish

The potential mood has been a matter of long-standing controversy.In Italian Studies, conditional verb forms have traditionally belonged to this category, frequently called the conditional mood, even when Alarcos Llorach includes future tenses in it, too.Following the functionalist principles of Hispanic linguistics, Alarcos defends the position that the future should be included within the conditional mood.According to this innovative claim, in line with the ideas advocated by the linguist Pavao Tekavčić, Emilio Alarcos' proposal has proved to be perfectly applicable to Italian.This is the reason why a contrastive perspective has been adopted.In this article, a close scrutiny of the first translation into Spanish of The Decameron aims to show the similarities and differences between the Spanish edition and the original Italian text.With the focus on the detailed study of the hypothetical meaning of some verbal periphrases and modal adverbs, as well as on the values of verb forms conjugated in the potential (both future and conditional tenses), the grammatical analysis will reveal the way in which Spanish and Italian, specifically 14th-century Tuscan, have, since the Middle Ages, shared a number of morphosyntactic characteristics, many of which have survived until today.A conclusion can be drawn from all of this: Italian and Spanish behave in the same way, except for one case relating to the use of the conditional.It must be noted that the conditional perfect in Italian is used in certain contexts to refer to facts that in Spanish are expressed in the conditional simple, since they are considered non-finite and, thus, aspectually non-perfect.
Under-Sample Binary Data Using CURE for Classification

Classification is a major break-through in the field of research. The performance of a classifier is highly dependent on the preprocessing. Drawback with most of the classifiers is its performance. It always focuses on the class having a high number of samples and ignores the class having fewer numbers of samples. This problem is identified through state-of-the-art evaluation metrics. To overcome this problem, the data in imbalanced form are converted into balanced form before the classification process. In the proposed work, instead of balancing, samples are re-sampled with the help of cluster based technique CURE. It performs under-sampling by reducing the majority samples but not balancing with minority samples. The experimental results show that the data re-sampled through CURE performs better.
Commentary on Professor Austin Bradford Hill's Alfred Watson Memorial Lecture

Abstract As availability of health care data for research opens up new frontiers in medical statistics, keeping a focus on the science behind the data is more important than ever to promote sound research and protect the validity of research results. Though the electronic databases currently amassed for research far exceed in scale and scope the observational research Professor Hill likely conceived of, his guidance to statisticians to ground our work in the biological and medical processes behind the data remains salient across the decades.
Margin Guidance Network for Arbitrary-shaped Scene Text Detection

Segmentation-based scene text detection approaches have been adopted to arbitrary-shaped texts and have achieved a great progress. However, false detection always easily exist when the arbitrary-shaped texts are close to each other. In this paper, we propose the Margin Guidance Network (MGN) that mainly based on the margin constraint residual module (MCRM) to address aforementioned problem. The MCRM considers the margins between multiple text instance masks to guide the training of network and improve the performance on text detection. The MCRM contains two prediction branch, the one can generate the multiple different scale of masks for a text instance and the other branch is used to generate multiple margins between the above masks. Experimental results on three public benchmarks including ICDAR2015, CTW1500 and Total-Text have demonstrated that the proposed MGN achieves the state-of-the-art results.
English-Japanese Machine Translation for Lecture Subtitles using Back-Translation and Transfer Learning

In this study, we report our efforts to improve the performance of English-Japanese machine translation on the “Technology, Entertainment, Design (TED)” lecture data. We experimented with two methods to improve English-Japanese translation focusing on data augmentation. The first method is a data augmentation using Japanese monolingual corpus through back translation. The second method is transfer learning, in which a large out-of-domain English-Japanese parallel corpus was employed. We also investigated the combination of the two methods. The results indicated that the two methods produced better result than the baseline, which involved only the in-domain parallel corpus. There was an improvement in the BLEU score of 0.33 for the back-translation method and 1.37 for the transfer learning and 1.33 for the combined methods.
Image Salient Object Detection Based on K-means and Level Set Superpixel Segmentation

Abstract In order to detect and segment the salient objects in digital images to solve complex machine visual problems, this paper proposes an image salient object detection algorithm based on K-means and level set superpixel segmentation. The algorithm segments a given target image into multiple superpixel regions with similar features to abstract unnecessary details in the images, and reduce the number of colors in all superpixels by Histogram acceleration to increase computational efficiency. The saliency map obtained by calculating the distance of all superpixel in the Lab color space, and optimizes the detection effect by background prior and multi-scale spatial fusion. A large number of experiments show that the saliency detection method proposed in this paper is superior to algorithms such as SR, AC, FT, MSS, LC, CA, SF, HC and RC in accuracy, recall rate, AUC value and average absolute error.
Similarity and Agreement Measures and Their Application in Medical Diagnostic Prediction System

Due to importance of measuring the degree of resemblance, the similarity measure is widely adopted in various areas of the information systems (e.g., medical informatics and information retrieval) and in several applications like medical diagnostic, image processing, and pattern recognition. However, most of the existing similarity measures focus mainly on the degree of similarity without consulting expert(s) about the results. In this paper, an efficient tool for measuring similarity and agreement of objects that embeds experts’ opinions is proposed to assess similarity among features and agreement of opinions among experts. To obtain such robust measuring tool, three construction steps were followed. Firstly, adapting soft expert set as a general structure that consists of four components: objects, attributes, experts, and experts’ opinions. Secondly, representing the soft expert set, without losing stored information, in such a way as to fit the proposed similarity-agreement measure and make it simpler and more meaningful than the similar existing measures. Thirdly, axiomatizing the similarity-agreement measure for the case of two experts to simplify the model. Further, a diagnostic prediction application and its algorithm is discussed in this context, along with analysis of the experimental results. Analysis of performance of the proposed similarity-agreement measure revealed that it has high accuracy, sensitivity, and value of the F-measure and that it has better performance than existing state-of-the-art tools.
Concluding a CRM Session

We address the issue of how to conclude a CRM session in a comprehensive manner, to satisfy a user with the detailed extended answer with exhaustive information. For a question-answering session, the goal is to enable a user with thorough knowledge related to her initial question, from a simple fact to a comprehensive explanation. In many cases, a lengthy answer text, including multimedia content compiled from multiple sources, is the best. Whereas comprehensive, detailed answer is useful most of the times, in some cases, such an answer needs to defeat a customer claim or demand when it is unreasonable, unfair or is originated from a bad mood. We formulate a problem of finding a defeating reply for a chatbot to force completion of a chatbot session. Defeating a reply is expected to attack the user claims concerning product usability and interaction with customer support and provide an authoritative conclusive answer in an attempt to satisfy this user. We develop a technique to build a representation of a logical argument from discourse structure and to reason about it to confirm or reject this argument. Our evaluation also involves a machine learning approach and confirms that a hybrid system assures the best performance finding a defeating answer from a set of search result candidates.
Object-Oriented Motion Estimation using Edge-Based Image Registration

Video data storage and transmission cost can be reduced by minimizing the temporally redundant information among frames using an appropriate motion-compensated prediction technique. In the current video coding standard, the neighbouring frames are exploited to predict the motion of the current frame using global motion estimation-based approaches. However, the global motion estimation of a frame may not produce the actual motion of individual objects in the frame as each of the objects in a frame usually has its own motion. In this paper, an edge-based motion estimation technique is presented that finds the motion of each object in the frame rather than finding the global motion of that frame. In the proposed method, edge position difference (EPD) similarity measure-based image registration between the two frames is applied to register each object in the frame. A superpixel search is then applied to segment the registered object. Finally, the proposed edge-based image registration technique and Demons algorithm are applied to predict the objects in the current frame. Our experimental analysis demonstrates that the proposed algorithm can estimate the motions of individual objects in the current frame accurately compared to the existing global motion estimation-based approaches.
Asymmetric Cryptosystem on Matrix Algebra over a Chain Ring

The revolutionary idea of asymmetric cryptography brings a fundamental change to our modern communication system. However, advances in quantum computers endanger the security of many asymmetric cryptosystems based on the hardness of factoring and discrete logarithm, while the complexity of the quantum algorithm makes it hard to implement in many applications. In this respect, novel asymmetric cryptosystems based on matrices over residue rings are in practice. In this article, a novel approach is introduced. Despite the matrix algebra M(k,ℤn), the matrix algebra M(k,Rn′), Rn′ = ℤ2[w]⟨wn−1⟩ as the chain ring is considered. In this technique, instead of exponentiation, the inner product automorphisms the use for key generation. The chain ring provides computational complexity to its algorithm, which improves the strength of the cryptosystem. However, the residue ring endangers the security of the original cryptosystem, while it is hard to break using Rn′. The structure of the chain ring deals with the binary field ℤ2, which simplifies its calculation and makes it capable of efficient execution in various applications.
Developing the Android Application Based Learning Media of Light Vehicle Engine Maintenance Course

This research aimed at (1) Developing the Android Application based learning media of Light Vehicle Engine Maintenance (PMKR) course (2) evaluating the feasibility of the developed learning media, based on media experts and material expert's review, also questionnaire feedback from users. This is an R&D (Research and Development) research, adopting the four-D model (define, design, develop, disseminate). The study was conducted at the Teknologi Kendaraan Ringan (TKR) class of SMK Negeri 3 Yogyakarta, in the school academic year of 2019/2020. Data collected by observation, interview and questionnaires. Descriptive technique is used for the development stage data analysis, while the learning media feasibility is perceived by translating quantitative data into certain levels of qualitative data. The research results showed that the developed learning media is very feasible, as given score 3.59 by expert judgments and an average score of 3.45 from the users' responses.
Limit Action Space to Enhance Drone Control with Deep Reinforcement Learning

Although many research progresses on deep reinforcement learning, it is not yet perfect. It may take too much time or even fail to solve the problem. Therefore, simplifying the problem by intentionally limiting the agent's action space should help train the agent efficiently and effectively. To verify that, in this paper, we analyze the performances of various action space designs for controlling a drone with deep reinforcement learning. We have designed six different action spaces according to the degree of freedom to analyze the effect of limiting the agent's action space on performance metrics such as travel distance and time, goal rate, and total reward. We show that by limiting the degree of freedom, the agent learns to reach the goal faster with less travel distance and achieve a higher goal rate and reward.
Boosting Binary Neural Networks for FPGA

In this work, we propose an efficient method to execute neural networks on edge devices using FPGA. When executing a neural net on an edge device, quantization is performed to compress the model, which reduces the execution memory and shortens the execution time, but if the expressive power is reduced by quantization, the accuracy of the model is reduced. In this work, we propose a method to partition the network in order to execute the quantized network on FPGA as fast as possible without loss of accuracy.