Reviewerly/exHarmony huggingface dataset:
Development of a Methodology for Protecting a Software Package for Electronic Document Management

This paper discusses the issues of protection of the software complex of mixed electronic document management by biometric authentication. The types of biometric authentication are analyzed and the advantages and disadvantages are shown. The disadvantages of document flow protection systems are described. A technique for searching for incorrect implementations when forming an open string is proposed.
Geometric Analysis of 3D Facial Image Data: A Survey

Background: 3D facial image data has become an important data source in many biometric computing applications due to the increasing availability of 3D surface image collection technologies. Objective: In this survey paper, we aim to review recent advances in 3D geometry-based techniques for facial image analysis and their roles in several critical applications. Method: We first study the 3D facial landmark detection techniques which are often required for many facial data analysis applications. We then review the literatures related to several critical 3D facial image based applications including face recognition, medical diagnosis, and 3D face reconstruction. Conclusion: Our review shows that while 3D facial image data has been widely used as an important biometric data source, critical solutions still need to be developed in applications that require substantial understanding of the underlying anatomic and geometric structures of human faces.
The possibilities of own blocks in the scratch educational environment

In the present study, the main possibilities of own block in Scratch block environment are considered. An example implementation is proposed, which can be used in the curriculum of the discipline "Computer Modeling and Information Technology". Some peculiarities related to the application of own blocks in the creation of animations, algorithms and educational projects in the Scratch block environment are presented.
Verbalizing but Not Just Verbatim Translations of Ontology Axioms

In this paper, we propose an inference-based technique to remove redundancy from natural language (NL) descriptions of Web Ontology Language (OWL) entities. The existing ontology verbalization approaches generate NL text segments that are closer to their counterpart statements in the ontology. Some of these approaches also perform grouping and aggregating of the text segments, aiming at a more fluent and comprehensive representation. However, we observed that the human-understandability of such descriptions is affected by the presence of repetitions and redundancies, and our studies show that such issues can be removed easily at the semantic level than at the NL level. We propose a novel technique called semantic-level refinement (or simply, semantic-refinement) for this purpose. Our approach aims at transforming the knowledge that is represented as a combination of less expressive (and not specific) logic-based expressions into the ones with high expressivity and specificity. This technique utilizes a predefined set of rules which are applied repeatedly on the restrictions associated with the individuals (and the concepts) to obtain a refined set of restrictions, guaranteed to be semantically equivalent to the original representation. Such refined sets of restrictions can then be verbalized to get concise descriptions of the ontology entities. Our experiments on ontologies from two different domains show that the proposed approach could significantly improve the readability of the NL texts when compared to the texts generated without a semantic-level refinement.
A Model-Based Method for Learning Locomotion Skills from Demonstration

While Generative Adversarial Imitation Learning (GAIL) shows remarkable performance in many high dimensional imitation learning tasks, it requires too many sampled transitions, which are infeasible for some real world problems. In this paper, we demonstrate how exploiting the reward function in GAIL can improve sample efficiency. We design our algorithm end-to-end differentiable so that the learned reward function can directly participate in policy updates. End-to-end differentiability can be achieved by introducing a forward model of the environment, enabling direct calculation of the cumulative reward function. However, using a forward model has two significant limitations that it heavily relies on the performance of the forward model and requires multi-step prediction, which causes severe error accumulation. The proposed end-to-end differentiable adversarial imitation learning algorithm alleviates these limitations. Also, we suggest applying several existing regularization techniques for robust training of a forward model. We call our algorithm, integrated with these regularization methods, fully Differentiable Regularized GAIL (DRGAIL), and test DRGAIL on continuous control tasks.
Designing Multiplier-Less JPEG Luminance Quantisation Matrix

A novel approach for generating a multiplier-less approximate JPEG quantisation matrix has been proposed in this paper. It is based on energy distribution of integer DCTs coefficients and rounding to integer power-of-two matrix. No multiplications required during the encode and decode stages using the combination between an integer DCT and the proposed rounded quantisation matrix. An arithmetic operation savings about 44.8% using the proposed quantisation matrix with integer DCT against the JPEG. The proposed quantisation matrix has been successfully evaluated against the conventional JPEG quantisation matrix for all different type test images. The experimental results reveal that JPEG compression scheme base on integer DCT combined with our proposed quantisation matrix can provide significant improvement in PSNR values compared with other quantisation matrices.
Neural-network-based Approach to Detect and Recognize Distorted Text in Images with Complicated Background

To address the unsatisfactory recognition of distorted text in images with complicated background, a neural-network-based approach was proposed to detect and recognize text. For detection of distorted text, the improved CRAFT model was applied, and deformable convolution was introduced to replace conventional convolution to sufficiently extract the features with irregular background. On this basis, the CRAFT-DCN text detection model was proposed to improve the accuracy of text detection. In order to reduce the interference of distorted text on the recognition model, images of separated texts were tailored according to the coordinates obtained by the detection model. Meanwhile, the Dense-CRNN model was designed, and the dense convolutional layer was introduced in the text recognition model to enhance the reuse of the features, thereby reducing interference of complicated background and recognizing separated distorted text correctly. The experiment results show that, compared with traditional approaches, the improved method introduced in this paper has better detection and recognition rates. And specifically, its text detection accuracy and the text recognition accuracy in actual scenario reach 86.3% and 95.3% respectively.
Judging Medical Q&amp;A Alignments in Multiple Aspects

Question and answer (Q&A) matching is a widely used task, and there have been many works focusing on this. Previous works tend to give an overall label indicating whether the question matches the answer. However, this method mainly relies on detecting identical or similar keywords in Q&A, which is inappropriate for medical text data. Based on a drug, patients’ questions may vary, such as usage, side effects, symptoms, and price. Thus, it is absurd to judge the answer containing the same drug as a matching answer. We argue a better solution is to judge alignments both in entity and intention aspects. To this end, we propose a novel model, which consists of two modules. Specifically, an extractor module gets matching features from text inputs, and then a discriminator module gives alignment labels in both aspects. An adversarial mechanism is designed to disentangle entity matching feature and intention matching feature, which reduces mutual interference. Experimental results show our method outperforms other baselines, including BERT. Further analysis indicates the effectiveness and interpretability of the proposed method.
A Formal and Statistical AI Tool for Complex Human Activity Recognition

Recognition and understanding human activity is a challenging research topic for the last 30 years with significant advances, but with major open problems to be solved. Researchers have proposed a variety of advanced methodologies by approaching this interesting topic from a single direction. By saying a single direction we mean using either formal or statistical approaches. These approaches have their own limitations like the formal approaches cannot scale up beyond a certain level of complexity while the statistical ones don’t work well in low complexities [1]. Thus, a possible solution is the use of a synergistic approach based on the efficient combination of formal and statistical methods. In this chapter, the authors offer a formal-statistical AI tool for the recognition of complex human activities. The formal model is associated with three context-free languages at body, motion, and activity levels. The statistical model is an artificial network that learns the activity of the human body.
Discourse means for maintaining a proper rhetorical flow

In this chapter, we propose dialogue management based on a dialogue discourse tree (DDT) and an extension of a discourse representation for a dialogue that considers additional rhetorical and speech act-based relations between utterances. DDTs’ representation for a dialogue history augmented with the current utterance can be recognized as valid (providing proper rhetorical flow) or invalid, so that a correct utterance can be selected to follow the logic of conversation. Some discourse parsing and reasoning features are obtained in a data-driven setting, and some are obtained in a rule-based framework. We evaluate DDT-based dialogue management in a spectrum of health domains, predicting the last utterance of a patient forum thread. We conclude that by relying on DDT, one can successfully select the utterance maintaining a proper dialogue logic among a set of topically suitable but rhetorically incohesive utterances.
In the Footsteps of Graphicons: Tracing Parameters and Pragmatic Strategies in Graphicon Usage

Abstract The textual intercourse in computer-mediated communication is intertwined with graphicons of various forms, gaining new meanings and functions. They are part of the online culture and, specifically, part of the communicative skills in digital environment. In many cases, graphicons are used not as signs of emotion but rather as indication of the illocutionary force of the textual utterances that they accompany. The current work endeavours to reveal the specific aspect of language use where iconoid objects “take over” and substitute textual utterances. The paper also attempts to trace to what extent pragmatics could be applicable in the analysis of the visual representations (i.e. graphicons) embodied in computer-mediated communication as means of communicative acts. The different graphicon forms and the dynamics in the usage carry additional challenge for the interpretation of the “visual” act. However, it is possible to systematically trace a pattern in the occurrence of the graphicons – their use as a complementary to a written statement, and their use as a single communicative act.
Distributed Sweep Coverage Algorithm Using Workload Memory

This chapter addresses the sweep coverage problem of MAS in the uncertain environment. A novel formulation of distributed sweep coverage is proposed for multiple agents to cooperatively complete the workload in the coverage region. To save the sweep time, each agent takes part in partitioning the whole region using its partition bar while sweeping its own subregion at a constant rate. The trajectories of partition bars form the boundaries between adjacent subregions. Essentially, the partition operation is carried out by means of workload memory in order to balance the workload in each subregion. In particular, it is proved that the dynamics of MAS is input-to-state stable. Theoretical analysis is conducted to obtain the upper bound of the error between the actual sweep time and the optimal sweep time. Moreover, a sufficient condition is provided to avoid the collision of partition bars during the partition. Finally, numerical simulations demonstrate the effectiveness of the proposed approach.
A Method to Predict Semantic Relations on Artificial Intelligence Papers

Predicting the emergence of links in large evolving networks is a difficult task with many practical applications. Recently, the Science4cast competition has illustrated this challenge presenting a network of 64.000 AI concepts and asking the participants to predict which topics are going to be researched together in the future. In this paper, we present a solution to this problem based on a new family of deep learning approaches, namely Graph Neural Networks.The results of the challenge show that our solution is competitive even if we had to impose severe restrictions to obtain a computationally efficient and parsimonious model: ignoring the intrinsic dynamics of the graph and using only a small subset of the nodes surrounding a target link. Preliminary experiments presented in this paper suggest the model is learning two related, but different patterns: the absorption of a node by a sub-graph and union of more dense sub-graphs. The model seems to excel at recognizing the first type of pattern.
Average Mean Functions Based EM Algorithm for Mixtures of Gaussian Processes

The mixture of Gaussian process functional regressions (mix-GPFR) utilizes a linear combination of certain B-spline functions to fit the mean functions of Gaussian processes. Although mix-GPFR makes the mean functions active in the mixture of Gaussian processes, there are two limitations: (1). This linear combination approximation introduces many parameters and thus a heavy cost of computation is paid for learning these mean functions. (2). It is implicitly assumed that the mean functions of different components share the same degree of smoothness because there is a parameter controlling the smoothness globally. In order to get rid of these limitations, we propose a new kind of EM algorithm for mixtures of Gaussian processes with average mean functions from time-aligned batch trajectory or temporal data. In this way, the mean functions are iteratively updated according to the distributed trajectory data of the estimated Gaussian processes at each iteration of the EM algorithm and the effectiveness of this estimation is also theoretically analyzed. It is demonstrated by the experimental results on both synthetic and real-world datasets that the proposed method performs well.
Attractiveness of Instructional Media Based on Android for Learning in Elementary School

The use of technology in the field of education is increasingly being used, but the use of Android-based learning media is still not widely used. Android-based learning media has advantages and disadvantages that are interesting to study further about the attractiveness of this media. This study aims to determine the attractiveness of android-based learning media in learning in elementary schools. This research was developed with quantitative descriptive as the method. The research subjects consisted 26 students of fifth grade SDN Krisik 03 Kabupaten Blitar. The research result were carried out on students of fifth grade SDN Krisik 03 Blitar Regency obtained a percentage of 85%, therefore Android-based learning media on learning in elementary school is very attractive. So it can be said that the use of android-based learning media can attract students' interest to learn.
Design of External Wall Painting Robot for High-Rise Buildings

The external walls of buildings have traditionally been painted by human beings being suspended down from the terraces using gondolas. This is a job which has high risk of fatality. The objective of the project is to reduce this risk and increase productivity by developing an automated programmable and/ or an autonomous external wall painting robot for high-rise buildings which can paint and clean the external walls of buildings without the need of any human intervention. The robot consists of a crane, which is located on the terrace of the building. The crane supports the load of the painting robot via suspended cables and also controls its motion along the vertical wall, while vacuum pads are deployed as an anti-swing mechanism on the wall to prevent the robot from oscillating due to high winds. An arm equipped with a nozzle is programmed to perform painting as it moves along the coordinate axes. This project uses design and simulation software Solidworks, Autodesk Fusion 360 and ANSYS to test the functioning and safety of the machine and provides the customers with a range of product options based on various working conditions which might potentially be present on different working sites.
Search Engine Optimization by Re-Ranking the Product Search Result Based on User Click Data

Blibli.com provides a search engine for its customers. It used Solr search engine with only plain BM25 similarity function which is based on probability. In order to improve search engine performance, this research tried to implement an algorithm that involves user click data to re-rank the search results of Solr search engine. We used 1386 product data given by Blibli.com and worked with 5 judges to label the data. The implementation of clickthrough data algorithm which was built into a plugin in Solr search engine gave a promising result, that there is an increased of the mean average precision as much as 21%. Hence, we concluded that the clickthrough data algorithm has increased the rank quality of Blibli.com product search result in Solr search engine. However, there are still many irrelevant products retrieved in the Solr search engine, although its ranking position were below the relevant products.
Encrypted All-reduce on Multi-core Clusters

We consider the encrypted all-reduce operation on multi-core clusters. We derive performance bounds for the encrypted all-reduce operation and develop efficient algorithms that are theoretically optimal in that they asymptotically achieve the performance bounds. We empirically evaluate our encrypted all-reduce algorithms on production clusters. The results show that with the right algorithm, encryption can be incorporated in the all-reduce operation on large messages without significant overheads on modern multi-core clusters whose compute node has a large number of cores.
Deep Learning-Based Transaction Prediction in Ethereum

In recent years, deep learning has been applied to data analysis in the financial field. One of the important applications is time series prediction. Meanwhile, with the advent of blockchain technology, cryptocurrencies have attracted attention in the financial field and the public. Therefore, there has been a lot of researches done on the time series prediction of cryptocurrencies. However, most of these studies are about predicting the prices of various cryptocurrencies, lacking in predicting the transaction amount. As platforms for trading cryptocurrencies, cryptocurrency exchanges play an important role in the crypto market. In this paper, we collect the transaction data of 15 exchange addresses on Ethereum which is an open-source public blockchain platform with smart contract functions. By modeling the prediction of the transaction value as a time series prediction problem, we conduct experiments using deep learning-based methods to make predictions. Experimental results show that deep learning is more effective in predicting transaction value compared with traditional methods.
Applications of Open Source Intelligence in Crisis Analysis—A COVID-19 Case Study

The ongoing Coronavirus pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), also known as COVID-19, was first identified in the city of Wuhan in late December 2019. It was declared a pandemic during march 2020, as it caused widespread infections and deaths. COVID-19 has caused millions of deaths across the world, making it one of the deadliest pandemics that has ever occurred. The great pandemic affected billions of lives economically and emotionally. Traditionally, in-person surveys were organised in order to perform crisis analysis to identify and create statistics of people affected during such pandemic outbreaks or disasters. Being an extensive process by itself, the amount of time these surveys consume and the requirement of a large workforce to collect such statistics do not favour the government or the governing authorities in coming up with solutions and timely services that would upbring the affected set of people. Apart from taking into account the physical damage caused, there is no mechanism that gives importance to the mental state of the masses in such situations. This study aims at performing crisis analysis based on the emotions/reactions exhibited by the people on the Internet. The collection of the entire dataset is done with the help of open-source intelligence tools. A monthly sentiment analysis is performed to compare the emotions, followed by performing sentiment analysis on the dataset collected from March 2020 to March 2021 to analyse the emotions shown by people during COVID-19 and sentiment prediction with projected accuracies on the collected dataset that is done using six machine learning classification algorithms.
Multi-DIP: A General Framework for Unsupervised Multi-degraded Image Restoration

Most existing image restoration algorithms only perform a single task. But in the real world, the degradation pattern could be much more complex, such as blurred images that have been smudged or images with haze that have been blurred, and we call it multi-degradation. Many of these degenerations are coupled with each other, making it impossible to restore images by merely stacking the algorithms. In this paper, we propose Multi-DIP that uses DIP networks to solve the multi-degradation problem. We integrate multiple image restoration tasks into a unified framework. However, multi-degradation can cause difficulties for DIP networks to extract image priors. To alleviate this problem, we design a multi-scale structure to stabilize and improve the quality of generated images. We implement two image restoration tasks with the proposed DIP framework: deblur + inpainting and dehaze + deblur. Extensive experiments show that our proposed method achieves promising results for restoring multi-degraded images.
Traded and combined cooperative control of a smart wheelchair

Abstract This article deals with a human–machine cooperative system for the control of a smart wheelchair for people with motor disabilities. The choice of a traded control mode is first argued. The paper then pursues two objectives. The first is to describe the design of the cooperative system by focusing on the dialogue and the interaction between the pilot and the robot. The second objective is to introduce a new cooperative mode. In this one, three features are proposed: two semi-autonomous features, a wall following and a doorway crossing, during which the user can intervene punctually to rectify a trajectory or a path, and an assisted mode where, conversely, the machine intervenes in a manual control to avoid obstacles. This mode of intervention of an entity, human or machine, supervising a movement controlled by the other is referred as “combined control.” Examples of scenarios exploiting the cooperative capabilities of the system are presented and discussed.