Reviewerly/exHarmony huggingface dataset:
Recurrent Attention Capsule Network for Text Classification

The main advantage of convolutional neural networks is the extraction of key local features with strong expression capabilities, and usually requires a large amount of training data to achieve better performance. However, the convolutional neural network ignores the hierarchical relationship between the local features and the spatial structure, which may result in a lower accuracy of text classification. For this problem, a text classification method based on Recurrent Attention Capsule Network is proposed. The proposed method uses the capsule network to obtain the spatial position relationship of local features, and uses the recurrent structure to obtain the context information of the words, and uses the attention mechanism to further obtain more important key features for classification. Compared with CNN network and RNN network in text classification, the experimental results show that the experimental accuracy of the proposed method is better than six benchmark algorithms.
Forecasting the tuberculosis morbidity rate in Indonesia using temporal convolutional neural network (TCNN)

In this modern era, people become aware and use insurance as part of their long-term financial planning in terms of protecting against unpredictable risk. Health insurance is one type of insurance that can be used to help people in alleviating the costs of treatment of the disease they suffered. Some insurance companies offer insurance products that finance the treatment of Tuberculosis (TB), and one of the references used to calculate premiums is TB morbidity. A model for predicting TB morbidity is needed so that premium calculations can be carried out properly. This study aims to predict the TB morbidity rate in Indonesia using the Temporal Convolutional Neural Network (TCNN) method. The results of the model validation were measured by using the Mean Absolute Percentage Error (MAPE) value, where the model produced in this study has a score below 10 %. The model that obtained in this study is then used to forecast TB morbidity rate in Indonesia from 2019 to 2021.
Computational Linguistics and Mobile Devices for ESL: The Utilization of Linguistics in Intelligent Learning

The presented paper focuses on the potential use of computational linguistics and machine learning in blended learning and m-learning. It suggests that the utilization of these new phenomena, also coined by the term artificial intelligence, could bring many advantages which are still not present in the traditional use of e-learning, blended learning, and m-learning platforms. The modern approach to e-learning should utilize these new phenomena so that it creates a useful platform for educational process. The paper stresses the importance of such an approach as it brings new challenges for the creators and the users which, however, are extremely useful for the modern utilization of smart devices in the e-learning process.
Development Of Mathematical Model Related To Bumps On The Road

Abstract Road bumps are one of the traffic calming tool installed on the roads to control the speed of the vehicles, to avoid accidents and help pedestrians to cross the road. Road bumps should be set up keeping in the mind the various factors such as location type, traffic type, population density, and others. Work was done on the various types of road bumps mainly conical-shaped road bumps and usage of the Fourier series which is effective for periodic shaped bumps. In this paper, we are going to use the Laplace transform which is effective not only on the periodic type of the road bumps but also on the complex structures of road bumps and will study it on the hollow rectangular shaped road bumps. We will find the parameters especially effective distance between the two consecutive road bumps and displacement caused to the vehicle which would help in the designing of the road bumps so that vehicle could move safely over the bumps and least damage could be caused to it and travellers inside it.
A Smart Security System for Accessing Web Services

One of the big issues of security access to web and web services is authentication. Authentication is verifying that the user is who he/she claims to be. Some of the mechanisms set to fight against intrusion are security questions, third-party account login, and (re)CAPTCHA. These solutions are sometimes vulnerable and can be easily exploited by hackers through bots and machine learning programs. We developed a system that enhances web and web services authentication. We tested it against an online OCR software program where it is proven robust. In addition, we conducted a survey about its ease-of-use and the results were promising.
International Exchange of Financial Information on Distributed Ledgers: Outlook and Design Blueprint

The international fiscal system is made by multiple entities that are struggling to establish sound mechanisms for co-operation. Policy initiatives are currently aiming at strengthening the information sharing of financial information for tax purposes across jurisdictions. In this work, I outline the legal landscape for the exchange of information and establish four desirable principles to which information exchange should adhere. Then, I argue that distributed ledger technology appears well-suited to address some of the challenges related to the exchange of financial information. I explore possible designs and lay the foundation for future discussion.
Alongshore Ship Detection Based On Multiscale Feature Fusion of Rotation Region Proposal Networks

Abstract In this paper, we propose an alongshore ship detection method based on multiscale feature fusion of rotation region proposal networks, the method provides an end-to-end ship detection framework, which is divided into three modules: multiscale feature module, rotation region proposal network module and context rotation region pooling module. The multiscale feature module integrates multiscale features, the rotation region proposal module can generate rotating bounding boxes in any direction, the context rotating region pooling module fuses the context information to pool the rotating region and complete the classification and position regression of bounding boxes. Experimental results show that our method can achieve good performance in remote sensing data sets.
A Method of Describing Uncertain Emotions of Facial Expressions

Abstract In order to describe uncertain and fuzzy emotions in recognizing facial expressions, a PAD regressive model based on deep convolutional neural network model is built to quantize the emotions, by which the facial expressions can be mapped to the emotional space of PAD. Then the emotional membership function is proposed to describe the uncertainty and fuzziness of the emotions in the space of PAD. Finally the testing results demonstrate that the regressive model performs well in recognizing facial expressions and the results from describing the emotions quantitatively agree with the human cognition to facial expressions.
A L1 Minimization Optimal Corrective Explanation Procedure for Probabilistic Databases

We propose to use a, recently introduced, efficient L1 distance minimization through mixed-integer linear programming for minimizing the number of valuations to be modified inside an incoherent probabilistic assessment. This is in line with one basic principle of optimal corrective explanation for decision makers. A shrewd use of constraints and of slack variables permit to steer the correction of incoherent assessments towards aimed directions, like e.g. the minimal number of changes. Such corrective explanations can be searched alone, as minimal changes, or jointly with the property of being also inside the L1 distance minimizers (in a bi-optimal point of view). The detection of such bi-optimal solutions can be performed efficiently by profiting from the geometric characterization of the whole set of L1 minimizers and from the properties of L1 topology.
A Secure CBIR Method based on Bag-of-Visual-Words Model under Cloud Environment

Cloud computing platform has powerful computing capacity and nearly infinite resource pool, which provides a strong guarantee for mass data storage and computing. Considering double security threats from malicious external attackers and "honest-but-curious" CSP (cloud service provider), users need to encrypt images to ensure the data security before outsourcing images to cloud. But encryption can have an impact on the necessary data services, such as content based image retrieval (CBIR). A secure CBIR method based on BoVW (Bag of Visual Words) model under cloud environment is proposed in the paper. Images are expressed as frequency histogram by BoVW model, orthogonal decomposition is utilized to divide it into two individual parts of component coefficients thus encryption operation and feature extraction operation can be executed separately, and orthogonal composition is used to fuse the encrypted operation results to construct secure image index. After encrypted image index and encrypted images are outsourced to CSP, distance comparison can be executed by CSP on feature extraction field without violating data privacy. Encrypted images with the closest distance to query trapdoor are returned to users to decrypt and obtain plain images. Any encryption algorithms can be used to encrypt images and search index by using orthogonal transformation, so that the proposed method is practicable. Retrieval precision is improved and better performance are achieved by using BoVW model. The security analysis and experimental results show our scheme has obvious advantages in security and retrieval performance.
Research on object detection technology for human detection

Abstract There are many classical application scenarios in computer vision, and human detection in dense scenarios is a classical problem. Its goal is to find all the people in an image or each frame of an video, as well as their positions and sizes, and frame them in a rectangular box. In recent years, deep learning has been applied to the field of computer vision, laying a solid foundation for human detection based on deep learning. Aiming at the crowdhuman dataset, based on faster-RCNN and feature pyramid network (FPN), this paper studies the training effect. In order to detect small objects, a multi-scale feature fusion network (MFFN) was proposed for feature extraction. According to the serious occlusion of dataset, a double-branch structure was proposed to improve the detection accuracy. These two structures combine to form a new network, we call it MDN, namely multi-feature fusion and double-branch network. Experiments show that these methods are helpful to improve the detection accuracy.
Enhanced Reinforcement Learning with Targeted Dropout

In modern ages, the study on Reinforcement Learning (RL) has driven on Deep Q-Network (DQN) optimization learning prediction and control of Markov decision processes (MDPs). In this paper, the researcher used the Targeted Dropout strategy for RLs DQN that makes straight into learning and would be necessary to deal with MDPs with huge or continuous state and action spaces. Every weight/unit update, the targeted dropout selects a set of elements and to keep only the weights/units of maximum amount, and then apply dropout to the set. It has also a common pruning strategy so focus on fast approximations, such as removing weights with the smallest value or ranking the weights/units by the sensitivity of the network design and even rating by the sensitivity of the task execution with respect to the weights/units and removing the least-sensitive ones. The result shows that the proposed algorithm for enhancing the RL's DQN is more accurate in finding the best action to learn to achieve maximum reward. The simulation presents that in a minimal run of episodes it can achieve the maximum average reward, while without Targeted Dropout it takes more runs to achieve the average reward, and throughout the assessment of the algorithm, the suggested algorithm acquires more learning in finding the large reward value.
Illegal Receipt of Information Containing Trade Secrets (Commercial Espionage) as a Threat to Fair Competition

Based on the fact that modern business is moving away from the practice of industrial espionage, and towards a civilized way of obtaining information - competitive intelligence, which uses the information obtained entirely from official sources, it can be recognized as a strategy that actively strengthens its position in the structure of modern companies around the world. Based on the analysis of scientific literature, as well as standards of international and national law, the article specifies signs of unfair competition, including the specific actions of illegal competition taken by economic cooperators, which violate the law, trade practices, requirements of integrity, reason and justice. The authors specify the manifestations (forms) of “economic war”, economic (industrial) espionage and illegal intelligence methods that violate the Criminal Code of the Russian Federation. The article describes the relationship between sources of confidential information and actions that lead to the acquisition of this information by perpetrators. In conclusion, it is proposed that the overwhelming amount of information that is necessary for competitors can now be obtained by using legal methods - through publications that are openly available; public presentations of employees; exhibitions; data from commodity and stock exchanges. The list of data that must be officially published by the organization is constantly expanding.
Remote Treatment System of Phantom Limb Pain by Displaying Body Movement in Shared VR Space

The phenomenon that amputees feel pain at the position of their lost limb is called phantom limb pain. Although the cause of phantom limb pain has not yet been medically elucidated, several hypotheses have been established from past studies, and some treatment systems of phantom limb pain have been developed based on these hypotheses. In these treatments, the instructions of the therapist who is familiar with phantom limb pain are essential. However, there is a problem that the number of therapists is insufficient and that existing treatment systems require the therapist to be next to the patient. In order to solve this problem, realizing remote treatment is expected. Therefore, in this research, we propose a phantom limb pain remote treatment system in which a therapist can give instructions by presenting not only voice information but also body movements to a remote patient in a shared VR space. In this study, we conducted a user study to investigate the difference between the case where the therapist gives instructions to the patient in the same place and the case where the therapist gives the patient instructions remotely by using our system. As a result, it was suggested that our proposed system would improve the efficiency of movement transmission. This study is thought to solve the problem of insufficiency of therapists for phantom limb pain.
“Product-Based” Master Program at ASCREEN Interactive Center

Innovative training technologies, based on comprehensive modeling of professional activities in the educational setting, are of great interest in the context of the integration of professional standards in higher professional education. The competency-based approach and contextual training provide the methodological basis for the process implementation while professional and educational tasks, along with collaborative learning and new interactive formats of work in various training centers, provide powerful tools of putting it into practice. Since 2017, a model of a “product-based” master program has been tested as a pilot project at the Sevastopol State University on the basis of the Institute of National Technological Initiative. The program’s key principle is the market readiness of students’ qualification work as the product of the master’s course and its evaluation by the professional community. The methodology and technology for implementation of the “product-based” master program in the ASCREEN Interactive Center are presented in this paper using the example of the “Innovative Shipbuilding” master program of the Sevastopol State University.
An Investigation of Morphological Productivity of Nominal and Verbal Compounds in Legal Discourse

Abstract Compounding is one of the most productive word-formation processes in contemporary Standard English. Hence, new patterns occur regularly. Productivity is one of the characteristic features of human language which implies the ability to create and understand new word forms by the speaker of a language. This was the starting point and motivation of this paper. The main aim of this paper was to investigate the morphological productivity of nominal and verb compounds in English as a foreign language in terms of the most productive and the least productive patterns of compounds in a written English corpus (consisted of 60073 words) by 75 students enrolled in undergraduate studies at the Law Faculty at SEEU. The quantitative measure used for the evaluation of the productivity of compounding patterns was according to hapax legomenon [P = n 1 / N]. Findings from the empirical approach show that the most productive compounds in the analyzed corpus (professional /Legal English context) are verb compounds, followed by special noun compounds, whereas the least productive were noun compounds. The analysis of the corpus showed that morphological productivity in compounding increased in the writing of students with higher degree of competence and proficiency of English.
Cooking Recipe Analysis based on Sequences of Distributed Representation on Procedure Texts and Associated Images

Nowadays, online community sites on cooking and eating activities are recognized as an indispensable infrastructure for daily life.In order to accurately respond to increasingly sophisticated user requirements, it is necessary to extract the characteristics of each cooking recipe and to clarify the relationship among them. Since mapping each recipe into a vector space by using representation learning is one of the most promising ways for the cooking recipe analyses, a wide variety of distributed representations of recipes have been proposed.In this paper, to provide a precise representation of cooking recipes from a different perspective, we propose to represent each recipe using two sequences of distributed representation.One sequence is obtained from cooking steps written in recipe text using BERT, and the other one is derived from sequences of associated images during cooking by the VGG16 convolutional neural network.To assess the effectiveness of the proposal, we perform cluster analysis for recipes on four dishes based on the standard DTW distance among sequential distributed representations.
A Comparison of Explanatory Measures in Abductive Inference

Computer simulations have been carried out to investigate the performance of two measures for abductive inference, Maximum Likelihood (ML), and Product Coherence Measure (PCM), by comparing them with a third approach, Most Probable Explanation (MPE). These have been realized through experiments that compare outcomes from a specified model (the correct model) with those from incorrect models which assume that the hypotheses are mutually exclusive or independent. The results show that PCM tracks the results of MPE more closely than ML when the degree of competition is greater than 0 and hence is able to infer explanations that are more likely to be true under such a condition. Experiments on the robustness of the measures with respect to incorrect model assumptions show that ML is more robust in general, but that MPE and PCM are more robust when the degree of competition is positive. The results also show that in general it is more reasonable to assume the hypotheses in question are independent than to assume they are mutually exclusive.
Federated Cubature Particle Filter and its Application in SINS/GPS/DR Integrated Navigation System

A federated filter mixture of cubature particle filter and Kalman filter (FCPF) is proposed for integrated navigation system. In allusion to nonlinear sub-filtering model of navigation system based on multi-information sources, cubature particle filter (CPF) is blend into federal structure framework. Compared with EKF (Extended Kalman Filter), CPF can effectively avoid linearization error and obtain higher precision. Moreover, federal structure can improve the reliability of the navigation system. By taking land vehicle based upon SINS(Strap-down Inertial Navigation System), GPS(Global Positioning System) and DR(Dead Reckoning) as the research object, the application of FCPF could get better navigation results than FEKF.
SaneNet: Training a Fully Convolutional Neural Network Using Synthetic Data for Hand Detection

We propose a specific formation of synthetic hand images, to train a fully convolutional neural network, for detecting hands in real scenarios. Our methodology aims to achieve this by further exploiting the invariancy concept of these networks. Thus the conventional, and expensive, techniques of domain randomization are not employed to create realistic synthetic images. We train the networks using two types of training images: 1. purely synthetic, and 2. with a combination of a few (100) real images. The results suggest our simplistic and sorely cheap approach of generating synthetic hand could successfully be utilized to detect hands in challenging scenarios.
RACoN: a robot activity recognition approach using a convolutional neural network for the RoboCup Standard Platform League Penalty Shot Challenge

In this paper, we present the early stage of the development of a multi-agent system. Embodied inside a Nao robot that will perform in the RoboCup Standard Platform League Penalty Shot Challenge, called RACoN (Robot Activity Convolution Nao). With this approach, the Nao robot will be able to interact with their environment and be aware of the activities of the other players. By mapping cameras, sensors, and actuators as individual agents across the ROS environment. Allows us to integrate then across the RaCoN Architecture, transforming it into a Pandemonium Artificial Cognitive Systems. In this kind of workspace, the robots will detect and recognize other robot activities using a ConvNet (Convolutional Neural Networks). These convolutional neural networks are trained to discriminate between teammates, opponents, archery, the ball, and the field, allowing the kicker and the goalkeeper to perform well complying with the RoboCup standard league rules.
Data Hiding Technique Based on NFC-Enabled Smartphones

The progressive advancement of smartphone which offers precise customized configuration and varied capacity abilities has dramatically reshaped the way we use technology. Many applications in many fields have been provided by integration of Near Field Communication (NFC) into smartphones (for example electronic ticketing and access control frameworks). This paper presents a data hiding technique by creating a highly secured connection based on NFC-Enabled Android Smartphones which is called NFC-Stego. The proposed technique solved one of the most challenging data hiding open problems which is restoring deleted files (cover-files) which may help in extracting the payload from the stego-file (Stego-analysis). The experiments conducted showed that that the NFC-Stego is a powerfully secured user friendly system which enables highly capacity data to be hidden along with high imperceptibility.
The Reception of Education Reforms through the Blogosphere

Teachers and other Edu-professionals in the United Kingdom are a professional body charged with educating each generation of children from birth to age 18, and beyond to Higher Education (HE). Over the last 10 years or so, they have formed a significant community in the blogosphere.