Reviewerly/exHarmony huggingface dataset:
Using Neural Tensor Networks for Open Ended Short Answer Assessment

In this paper, we present a novel approach to leverage the power of Neural Tensor Networks (NTN) for student answer assessment in intelligent tutoring systems. The approach was evaluated on data collected using a dialogue based intelligent tutoring system (ITS). Particularly, we have experimented with different assessment models that were trained using features generated from knowledge graph embeddings derived with NTN. Our experiments showed that the model trained with the feature vectors generated with NTN, when trained with a combination of domain specific and domain general triplets, performs better than a previously proposed LSTM based approach.
Accurate and fast out-of-plane displacement measurement of flat objects using single-camera based on the sampling moiré method

Abstract Out-of-plane displacement measurement is an important task not only to analyze the microelectronic device’s behavior at the micro-scale but also to evaluate the structural health monitoring (SHM) of infrastructures at the meter-scale. In this study, an accurate and fast out-of-plane displacement measurement method with a single camera is presented, including the theoretical equations, and calibration of phase-gradient to out-of-plane displacement. Since the proposed method requires only a single grating image before and after deformation and analyzes by the sampling moiré method, it yields high accuracy and speed. An experiment of out-of-plane displacement with three different focal length lenses has been conducted to verify the validity of this method. Experimental results indicated that both small and large out-of-plane displacement measurements could be performed accurately. This optical measurement technique will be of high value for SHM of various infrastructures under digital camera or video/cinema camera recording.
Selection-Bias-Corrected Visualization via Dynamic Reweighting

The collection and visual analysis of large-scale data from complex systems, such as electronic health records or clickstream data, has become increasingly common across a wide range of industries. This type of retrospective visual analysis, however, is prone to a variety of selection bias effects, especially for high-dimensional data where only a subset of dimensions is visualized at any given time. The risk of selection bias is even higher when analysts dynamically apply filters or perform grouping operations during ad hoc analyses. These bias effects threatens the validity and generalizability of insights discovered during visual analysis as the basis for decision making. Past work has focused on bias transparency, helping users understand when selection bias may have occurred. However, countering the effects of selection bias via bias mitigation is typically left for the user to accomplish as a separate process. Dynamic reweighting (DR) is a novel computational approach to selection bias mitigation that helps users craft bias-corrected visualizations. This paper describes the DR workflow, introduces key DR visualization designs, and presents statistical methods that support the DR process. Use cases from the medical domain, as well as findings from domain expert user interviews, are also reported.
An Algorithm for Automatic Collation of Vocabulary Decks Based on Word Frequency

This study focuses on computer based foreign language vocabulary learning systems. Our objective is to automatically build vocabulary decks with desired levels of relative difficulty relations. To realize this goal, we exploit the fact that word frequency is a good indicator of vocabulary difficulty. Subsequently, for composing the decks, we pose two requirements as uniformity and diversity. Namely, the difficulty level of the cards in the same deck needs to be uniform enough so that they can be grouped together and difficulty levels of the cards in different decks need to be diverse enough so that they can be grouped in different decks. To assess uniformity and diversity, we use rank-biserial correlation and propose an iterative algorithm, which helps in attaining desired levels of uniformity and diversity based on word frequency in daily use of language. In experiments, we employed a spaced repetition flashcard software and presented users various decks built with the proposed algorithm, which contain cards from different content types. From users' activity logs, we derived several behavioral variables and examined the polyserial correlation between these variables and difficulty levels across different word classes. This analysis confirmed that the decks compiled with the proposed algorithm induce an effect on behavioral variables in line with the expectations. In addition, a series of experiments with decks involving varying content types confirmed that this relation is independent of word class.
BERT+vnKG: Using Deep Learning and Knowledge Graph to Improve Vietnamese Question Answering System

A question answering (QA) system based on natural language processing and deep learning is a prominent area and is being researched widely.The Long Short-Term Memory (LSTM) model that is a variety of Recurrent Neural Network (RNN) used to be popular in machine translation, and question answering system.However, that model still has certainly limited capabilities, so a new model named Bidirectional Encoder Representation from Transformer (BERT) emerged to solve these restrictions.BERT has more advanced features than LSTM and shows state-of-the-art results in many tasks, especially in multilingual question answering system over the past few years.Nevertheless, we tried applying multilingual BERT model for a Vietnamese QA system and found that BERT model still has certainly limitation in term of time and precision to return a Vietnamese answer.The purpose of this study is to propose a method that solved above restriction of multilingual BERT and applied for question answering system about tourism in Vietnam.Our method combined BERT and knowledge graph to enhance accurately and find quickly for an answer.We experimented our crafted QA data about Vietnam tourism on three models such as LSTM, BERT fine-tuned multilingual for QA (BERT for QA), and BERT+vnKG.As a result, our model outperformed two previous models in terms of accuracy and time.This research can also be applied to other fields such as finance, e-commerce, and so on.
Supporting Process Design in the Autonomous Era with New Standards and Guidelines

It isn’t easy to define a general and clear process in a new system such as an autonomous vehicle. The new technology is complex and lacks experience. In this paper, we reconsider the process used for system development. We assume that the process changes depending on the characteristics of a project [1]. Of course, this is a general agreement. In autonomous vehicles, new technological elements such as AI and ensuring safety are further required. So, it is meaningful to consider how to think about the development process. New standards, guidelines and documents such as UL4600 are emerging for autonomous vehicles. In addition to many standards, dealing with these presents difficulties. We believe that better process design is possible by using the Toulmin model.
Hyperparameter Optimization for Improving Recognition Efficiency of an Adaptive Learning System

Today, several studies have been concretized in the areas of robotics, self-driving cars, intelligent assistance systems, and so on. Developing an increasingly optimal neural network in terms of accuracy and processing speed for resource-limited systems has become a major research trend. Some research orientations include focusing on developing solutions to optimize machine learning models and learning parameters. In this study, we investigated an optimization solution for learning hyperparameters of adaptive learning systems for improving object recognition accuracy. The proposed method was developed from a framework searching a set of learning hyperparameters based on the evaluation of the previous CNN model with the collected dataset during the movement of advanced driver assistance systems (ADAS) equipment. The proposed solution consists of some major steps in a loop of adaptive learning system, such as (1) training an initial recognition model, (2) locating and receiving image data of different cases of the object during ADAS movement based on object tracking process, (3) finding optimal hyperparameters on the found dataset based on the previous recognition model, and (4) using the trained recognition model to update the current recognition model. The experimental results proved that the trained recognition model was capable of being more intelligent and displayed more diverse recognition than the previous model. The updated task for the recognition model was continuously repeated throughout the ADAS life. This approach supports and enables the recognition system to be self-adaptive and more intelligent in real life settings without manually processing.
Saber on ESP32

Saber, a CCA-secure lattice-based post-quantum key encapsulation scheme, is one of the second round candidate algorithms in the post-quantum cryptography standardization process of the US National Institute of Standards and Technology (NIST) in 2019. In this work, we provide an efficient implementation of Saber on ESP32, an embedded microcontroller designed for IoT environment with WiFi and Bluetooth support. RSA coprocessor was used to speed up the polynomial multiplications for Kyber variant in a CHES 2019 paper. We propose an improved implementation utilizing the big integer coprocessor for the polynomial multiplications in Saber, which contains significant lower software overhead and takes a better advantage of the big integer coprocessor on ESP32. By using the fast implementation of polynomial multiplications, our single-core version implementation of Saber takes 1639K, 2123K, 2193K clock cycles on ESP32 for key generation, encapsulation and decapsulation respectively. Benefiting from the dual core feature on ESP32, we speed up the implementation of Saber by rearranging the computing steps and assigning proper tasks to two cores executing in parallel. Our dual-core version implementation takes 1176K, 1625K, 1514K clock cycles for key generation, encapsulation and decapsulation respectively.
LTSA-LE: A local tangent space alignment label enhancement algorithm

According to smoothness assumption, local topological structure can be shared between feature and label manifolds. This study proposes a new algorithm based on Local Tangent Space Alignment (LTSA) to implement the label enhancement process. In general, we first establish a learning model for feature extraction in label space and use a feature extraction method of LTSA to guide the reconstruction of label manifolds. Then, we establish an unconstrained optimization model based on the optimal theory presented in this paper. The model is suitable for solving problems with a large number of sample points. Finally, the experiment results show that the algorithm can effectively improve the training speed and multilabel dataset prediction accuracy.
An unforced megastable chaotic oscillator and its application on protecting electrophysiological signals

Abstract In this paper, we introduce a novel 3D chaotic oscillator which shows megastability without any external excitation. Some important dynamical properties of the proposed novel system were derived and investigated. Data protection application and its security analysis were realized for electrophysiological signals such as ECG, EEG and EMG on a microcomputer. This paper includes both encryption and data hiding processes for high security. Also a user interface was developed. For the encryption process, random numbers were generated by the megastable chaotic oscillator. These random numbers were tested with NIST-800-22 test which is the most widely accepted statistical test suite. The encrypted electrophysiological signals were analyzed by entropy, differential attacks, histogram, correlation, initial condition sensitivity, etc. The results of the analysis have shown that the proposed two level security method can be used in many fields as mobile. The most important feature of this paper is that both encryption and data hiding processes were implemented for electrophysiological signals. The experimental results verify that the proposed method has high security and is suitable for the protection of vital electrophysiological signals.
The higher topological complexity in digital images

Y. Rudyak develops the concept of the topological complexity TC(X) defined by M. Farber. We study this notion in digital images by using the fundamental properties of the digital homotopy. These properties can also be useful for the future works in some applications of algebraic topology besides topological robotics. Moreover, we show that the cohomological lower bounds for the digital topological complexity TC(X,κ) do not hold.
Using public and free platform-as-a-service (PaaS) based lightweight projects for software architecture education

Background: Software architecture is a crucial while significantly challenging topic in the computer science curriculum. Although "learning by doing" is widely advocated to address this topic's abstract and fuzzy nature, educators are still facing various difficulties in practice, especially including students' vicious circle of inexperience and the mental model dilemma in experiential learning.
Weight asynchronous update: Improving the diversity of filters in a deep convolutional network

Abstract Deep convolutional networks have obtained remarkable achievements on various visual tasks due to their strong ability to learn a variety of features. A well-trained deep convolutional network can be compressed to 20%–40% of its original size by removing filters that make little contribution, as many overlapping features are generated by redundant filters. Model compression can reduce the number of unnecessary filters but does not take advantage of redundant filters since the training phase is not affected. Modern networks with residual, dense connections and inception blocks are considered to be able to mitigate the overlap in convolutional filters, but do not necessarily overcome the issue. To do so, we propose a new training strategy, weight asynchronous update, which helps to significantly increase the diversity of filters and enhance the representation ability of the network. The proposed method can be widely applied to different convolutional networks without changing the network topology. Our experiments show that the stochastic subset of filters updated in different iterations can significantly reduce filter overlap in convolutional networks. Extensive experiments show that our method yields noteworthy improvements in neural network performance.
Wearable Sensors Corresponding to Various Applications in Medical/Healthcare Field

This paper describes the TDK wristband type wearable sensor “Silmee ™ W22”. Intended use is mainly in Medical/Healthcare services to monitor lifestyle-related diseases, such as dementia, cerebral infarction and frailty. This paper will discuss three research studies related to serious conditions requiring care. Moreover, this paper will show that TDK wearables have been used in a wide range of fields such as fleet management and productivity improvement, etc.
User Preference and Reviews Analysis with Neural Networks for Travel Recommender Systems

These days' people like to explore numerous places all over the world predominantly which are highly recommended.For instance, people who are new to a particular location regularly choose places to visit manually by typing some of them wished by the user working on search engine applications.It becomes difficult whenever to search manually and plan accordingly which might be not accurate.To overcome the above issues, the travel recommendation system applies sentiment analysis comparing user preferences, number of days, number of people and delivers the top recommended places to the user considering the pre-existing reviews of the places which are recommended.While processing it calculates the similarity between user preferences/inclinations and reviews using sentimental attributes (positive and negative) to match the similarity.Travel recommendation system takes sentiment polarity and similarity value as parameters on the whole, also the days to be visited and suggests esteem places.
Low-light color image enhancement based on Retinex

In view of the problems of low image brightness, obvious noise, poor contrast, and difficulty in obtaining detailed information in dark areas under low light environment, we propose to combine the improved particle swarm optimization algorithm with a single-scale Retinex algorithm. We convert the original RGB image to the HSI color space,and each pixel of the low light image is classified separately. The adjacent pixels are calculated with the same kernel function value. The pixels with different H values are use different filter templates to complete the image enhancement. And solve the problem of image halo effect and color distortion caused by the real-time operation of the Retinex algorithm spatial filtering. Experimental results show that the proposed algorithm performs well in brightness, contrast, and color restoration.
Statistical moments of power spectrum: a fast tool for the classification of seismic events recorded on volcanoes

Abstract. Spectral analysis has been applied to almost thousand seismic events recorded at Vesuvius volcano (Naples, southern Italy) in 2018 with the aim to test a new tool for a fast event classification. We computed two spectral parameters, central frequency and shape factor, from the spectral moments of order 0, 1, and 2, for each event at seven seismic stations taking the mean among the three components of ground motion. The analyzed events consist of volcano-tectonic earthquakes, low frequency events and unclassified events (landslides, rockfall, thunders, quarry blasts, etc.). Most of them are of low magnitude, and/or low maximum signal amplitude, therefore the signal to noise ratio is very different between the low noise summit stations and the higher noise stations installed at low elevation around the volcano. The results of our analysis show that volcano-tectonic earthquakes and low frequency events are easily distinguishable through the spectral moments values, particularly at seismic stations closer to the epicenter. On the contrary, unclassified events show the spectral parameters values distributed in a broad range which overlap both the volcano-tectonic earthquakes and the low frequency events. Since the computation of spectral parameters is extremely easy and fast for a detected event, it may become an effective tool for event classification in observatory practice.
A BLOCKCHAIN-BASED APPROACH TO PREVENT HIDDEN CONTAGION OF COVID-19

The unprecedented outbreak of COVID-19 has become a grave concern worldwide because of its highly infectious nature. Besides extensive research on developing vaccine or medicine to prevent this virus attack, many technology-based solutions are also getting more importance as no one knows till date how to put a full stop on it. Being first conceptualized in 2008, blockchain technology gained enormous popularity in the cryptocurrency domain. However, nowadays, blockchain has also become popular in the healthcare domain as a privacy-preserving and data authenticity technique. The primary goal of our experiment is to develop a framework which can utilize the features of permissioned blockchain and maintain the fully controlled sharing of confidential health record to exhibit the health status and COVID-19 history of a patient using a mobile QR code based solution. So, the administrative team of any public place can be aware of the health condition of any people who are using this platform to prevent the unaware and hidden contagion of COVID-19. This article is the first of its kind to propose such a method to aid in solving this crisis. In our experiment, we have shown that Hyperledger Fabric as a base technology along with a robust user registration algorithm and data accessibility can solve the problem under consideration with a minimal health record of the patient. The proposed architecture also offers the patient more control over the health record, where the healthcare service provider assures the authenticity of the data, and the intrinsic feature of the blockchain technology makes it immutable.
G2GML: Graph to Graph Mapping Language for Bridging RDF and Property Graphs

How can we maximize the value of accumulated RDF data? Whereas the RDF data can be queried using the SPARQL language, even the SPARQL-based operation has a limitation in implementing traversal or analytical algorithms. Recently, a variety of database implementations dedicated to analyses on the property graph (PG) model have emerged. Importing RDF datasets into these graph analysis engines provides access to the accumulated datasets through various application interfaces. However, the RDF model and the PG model are not interoperable. Here, we developed a framework based on the Graph to Graph Mapping Language (G2GML) for mapping RDF graphs to PGs to make the most of accumulated RDF data. Using this framework, accumulated graph data described in the RDF model can be converted to the PG model, which can then be loaded to graph database engines for further analysis. For supporting different graph database implementations, we redefined the PG model and proposed its exchangeable serialization formats. We demonstrate several use cases, where publicly available RDF data are extracted and converted to PGs. This study bridges RDF and PGs and contributes to interoperable management of knowledge graphs, thereby expanding the use cases of accumulated RDF data.
Data mining classification algorithms: An overview

Data mining is also defined as the process of analyzing a quantity of data (usually a large amount) to find a logical relationship that summarizes the data in a new way that is understandable and useful to the owner of the data. This paper examines the various types of classification algorithms in Data Mining, their applications and categorically states the strengths and limitations of each type. The weaknesses found in each algorithm demonstrate how tasks cannot be performed well when only one type of algorithm is applied. For this reason, it is the view of the writer that further research needs to be carried out to explore the potential of combining several of these algorithms to solve machine learning problems.
Approach for Modeling Search Web-Services Based on Color Petri Nets

The article discusses issues related to the construction and implementation of search web services. Algebra for modeling and building web services is presented. As the basic operations for algebra are used: sequence, alternative selection and cycle. In addition, combined operations are determined based on the basic ones. The operations were chosen to provide a general and extended set of operations for the implementation of web search services. After defining each construction, the formal semantics of the operator is given in terms of Petri nets. An example of modeling and development of a real estate web-service in the CPN Tools. The indexes of efficiency of the developed service.