Reviewerly/exHarmony huggingface dataset:
Theoretical foundation in designing and developing a mobile app to support ESL learning for STEM learners

Computer-Assisted Language Learning (CALL) has become an emerging trend amid the pandemic alongside the rise of science, technology, engineering, and mathematics (STEM). However, the decline in STEM learners’ pursuing STEM is worrying, and one reason is the decline in English language proficiency. Hence, this paper aims to review theories in design and develop ME4STEM (Mobile English for STEM) to enhance STEM learners’ English vocabulary. This paper adheres to the Design and Development Research (DDR). The design and development of ME4STEM is underpinned by English for specific purposes, mastery learning, cognitivism, social constructivism, problem-based learning, and cognitive theory of multimedia learning theories. Referring closely to the theories, the development of ME4STEM consists of 6 elements: 1) learning videos, 2) practices, 3) reviews, 4) mini-assessments, 5) language exploration, and 6) discussions. The rationale for designing and developing ME4STEM is to encourage learners to learn in a personalized space and at their own pace, especially in this pandemic. ME4STEM is a prototype, contributing to Smart CALL by serving as a bridge between STEM and the English language, leveraging opportunities for interdisciplinary study along with contributing to society by providing sustainable and novel English for STEM supplementary material. ME4STEM caters to all aspects of Smart CALL: personalization, contextualization, and socialization, thus contributing to emerging CALL trends.
Point matching based on affine invariant centroid trees

Abstract Object detection can be formulated as a point matching problem when objects are modeled by point sets. Moments, which have been widely used for point matching, are limited to affine transformations as their support point sets cannot keep invariant. To address this problem, we developed an affine invariant centroid tree (AICT) to obtain a rigorous affine invariant support point set (SPS). The algorithm is constructed by a recursive process: the point set is first divided by the vector from the certain point to the centroid of the point set, and the centroids of subsets are used to generate vectors for renewed partitions. In addition, the centroids of the subsets are stored to form an AICT. The AICT represents the inherent structure of the point set. It is highly tolerant to noise and outliers due to the partitions on the whole point set. More importantly, it is affine invariant owing to the affine invariance of partition. Therefore, we can get rigorous affine invariant descriptors while moments are combined with AICT. The experimental results on synthesized and real data verify that our proposed algorithm outperforms the state-of-the-art point matching methods including shape context, iterative closet point, and the method adopting thin plate spline for rigid robust point matching (TPS-RPM).
Label noise detection under the noise at random model with ensemble filters

Label noise detection has been widely studied in Machine Learning because of its importance in improving training data quality. Satisfactory noise detection has been achieved by adopting ensembles of classifiers. In this approach, an instance is assigned as mislabeled if a high proportion of members in the pool misclassifies it. Previous authors have empirically evaluated this approach; nevertheless, they mostly assumed that label noise is generated completely at random in a dataset. This is a strong assumption since other types of label noise are feasible in practice and can influence noise detection results. This work investigates the performance of ensemble noise detection under two different noise models: the Noisy at Random (NAR), in which the probability of label noise depends on the instance class, in comparison to the Noisy Completely at Random model, in which the probability of label noise is entirely independent. In this setting, we investigate the effect of class distribution on noise detection performance since it changes the total noise level observed in a dataset under the NAR assumption. Further, an evaluation of the ensemble vote threshold is conducted to contrast with the most common approaches in the literature. In many performed experiments, choosing a noise generation model over another can lead to different results when considering aspects such as class imbalance and noise level ratio among different classes.
Learning Visually Grounded Human-Robot Dialog in a Hybrid Neural Architecture

Abstract Conducting a dialog in human-robot interaction (HRI) involves complexities that are hard to reconcile by individual research or engineering works. Towards the development of a robotic dialog agent, we develop a verbal and visual instruction scenario in which a robot needs to enter into a dialog to resolve ambiguities. We propose a novel hybrid neural architecture to learn the robotic part of the interaction. A neural dialog state tracker learns to process the user input depending on visual inputs and dialog instances. It uses variables to allow certain generality to generate the robot’s physical or verbal actions. We train it on a new visual dialog dataset, test different forms of input representations, and validate the robot agent on unseen examples. We evaluate our hybrid neural network approach in handling an HRI conversation scenario that is extendable to a real robot. Furthermore, we demonstrate that the hybrid approach allows generalization to a large range of unseen visual inputs and verbal instructions.
Numerical Analysis and Optimization of English Reading Corpus for Feature Extraction

In order to solve the problem of comparative analysis ability of English reading corpus, meet the needs of the construction of language feature analysis system of English reading corpus, make up for the shortcomings of English reading corpus, and improve the wide application of English, the construction method of English reading corpus is proposed. For a long time, English has been an important communication tool in terms of national politics and final economic communication. Especially with the in-depth development of economic globalization, English plays a vital role in international communication. Therefore, the study of the semantics and features of English texts can provide important information materials for English learning and communication in nonnative English speaking countries. Taking this as the starting point, this paper focuses on the numerical analysis and optimization of English reading corpus based on feature extraction and puts forward a language feature analysis system of English reading corpus. At the same time, feature extraction and text classification are used to improve the comparative analysis ability of English reading corpus. The research shows that the feature extraction oriented English reading corpus can solve the problem of English reading learning and improve the ability of reading and analyzing English articles.
Multi-level Proposal Relations Aggregation for Video Object Detection

Video information often deteriorates in certain frames, which is a great challenge for object detection. It is difficult to identify the object in this frame by just utilizing the information of one frame. Recently, plenty of studies have shown that context aggregating information through the self-attention mechanism can enhance the features in key frames. However, these methods only exploit some of inter-video and intra-video global-local information, not all of it. Global semantic and local localization information in the same video can assist object classification and regression. The intra-proposal relation among different videos can provide important cues to distinguish confusing objects. All of this information is able to enhance the performance of video object detection. In this paper, we design a Multi-Level Proposal Relations Aggregation network to mine inter-video and intra-video global-local pro-posal relations. For intra-video, we effectively aggregate global and local information to augments the proposal features of key frames. For inter-video, we aggregate the inter-video key frame features to the target video under the constraint of relation regularization. We flexibly utilize the relation module to aggregate the proposals from different frames. Experiments on ImageNet VID dataset demonstrate the effectiveness of our method.
Scalable Off-chain Blockchain for Vehicular Network

One way of improving the security of a vehicular network is by incorporating blockchain technology into the network. However, under certain conditions, blockchain-based networks do pose several challenges, such as processing latency and limited throughput. Scalability, that is, the ability of the network to perform as expected with increasing loads, is also a concern. The current work aims to show that the deficiencies of the blockchain-based vehicular network can be reduced by using an off-chain approach where the major computing overhead of the main blockchain is channeled off-chain to speed up processing and provide scalability. Simulations are carried out to measure the performance and scalability of the blockchain network in terms of processing latency and data throughput. Simulations of operation under different conditions (varying number of nodes and members) are carried out using computers to represent roadside units and Rasberry Pis to represent the vehicles. The Ethereum blockchain is used as the main blockchain in the simulation with Raiden Network acting as the off-chain scaling solution for the network. The simulation results of 80 nodes network showed that the off-chain assisted blockchain network performs and scales better than the conventional blockchain network with improving the throughput by 17 times while lowering the latency by 99%.
An Automated System for Fingerprinting User Privacy in Web Applications

It is not a secret that user’s sensitive information can be leaked by side-channel analysis. Through analysing web traffic, e.g., the size and the length of web traffic, a user’s privacy can be inferred. In this paper, an automated system is developed to analyse leaks of user sensitive information in web applications. Compared with previous works, this work takes on a new perspective. Instead of examining transitions directly interacting with sensitive information, this work focuses on leaks of user information which are disclosed by transitions indirectly interacting with sensitive information. And we find that these indirect transitions can leak more user privacy than transitions directly interacting with sensitive information.
Isolation Graph Kernel

A recent Wasserstein Weisfeiler-Lehman (WWL) Graph Kernel has a distinctive feature: Representing the distribution of Weisfeiler-Lehman (WL)-embedded node vectors of a graph in a histogram that enables a dissimilarity measurement of two graphs using Wasserstein distance. It has been shown to produce better classification accuracy than other graph kernels which do not employ such distribution and Wasserstein distance. This paper introduces an alternative called Isolation Graph Kernel (IGK) that measures the similarity between two attributed graphs. IGK is unique in two aspects among existing graph kernels. First, it is the first graph kernel which employs a distributional kernel in the framework of kernel mean embedding. This avoids the need to use the computationally expensive Wasserstein distance. Second, it is the first graph kernel that incorporates the distribution of attributed nodes (ignoring the edges) in a dataset of graphs. We reveal that this distributional information, extracted in the form of a feature map of Isolation Kernel, is crucial in building an efficient and effective graph kernel. We show that IGK is better than WWL in terms of classification accuracy, and it runs orders of magnitude faster in large datasets when used in the context of SVM classification.
Visualized Intelligent Warehouse Safety Control System Using Target Detection

In response to the phenomenon of weak warehouse security and low defensiveness, we have built an intelligent risk identification security management and control system to help the company's warehouse security comprehensively improve. By combining multi-dimensional video, infrared monitoring, image recognition, trajectory motion mathematical model, multi-dimensional information fusion and other related technologies, the system recognizes and monitors the movement trajectory of warehouse personnel, work clothes, and safety helmets, and conducts multi-dimensional safety management and control. Realize the identification of unsafe clothing and real-time warning and prompt of illegal intrusion of staff in the warehouse, minimize the occurrence and prevention of safety accidents, and improve the company's intelligent warehouse safety management and control level.
A Study on the Response to Cybercrime Threatening K-Webtoon

As K-webtoons receive worldwide attention, Korea's status is increasing, and the Korean webtoon industry is also developing. Along with that, however, cybercrime attacking K-webtoons is also on the rise. Until now, the issue of K-webtoon infringement has been dealt with only for copyright infringement. However, from now on, it should be dealt with in terms of economic crime and cybercrime. The problem is that Korean criminal justice has limitations in responding to cybercrime. This is because the current legal system was built on the basis of analog society. Therefore, in this paper, we examined the current status and type of cybercrime attacking K-webtoons, limitations of current laws, and countermeasures. As a result, the K-Webtoon industry should continuously monitor crime damage and calculate the scale of damage more accurately. The government should strengthen public relations and monitor related crimes. In particular, investigative agencies should strengthen international investigation cooperation and strengthen punishment by treating the crime as an organized crime. The legislature should come up with a law that can deal with the problem of illegal webtoon servers as economic crimes and introduce legal grounds to recover criminal profits. And measures should be legislated to allow investigative agencies to respond appropriately to cybercrime, such as online search.&#x0D; Korea stands tall as a mainstream of world culture. One of them is the K-Webtoon. However, cybercrime is threatening the K-Webtoon. Therefore, it is necessary to find a way to respond appropriately to the cybercrime. Citizens should continue to pay attention to the threat posed by K-webtoons. In addition, countermeasures against cybercrime should be discussed in the public debate. This is not just a problem for K-Webtoon writers or their industries. The legislature, the government, and all the Korean people should solve it together.
Unusual Event Detection from Surveillance

Detecting the unusual events is being difficult in intelligent video research. Though the technology is developed enormously in recent years, there is still lack of some concepts which makes easy and reduce manpower to do the things those require huge amount of manpower and time. Regarding this issue, there is a huge challenge in detecting the unusual events, if a person drives on a road it is normal event, but if the same person drives on pedestrian road it is not a normal event, there are many aspects to consider it as abnormal event, when it comes to normal surveillance it is impossible to understand which is abnormal and normal, only the video can be recorded but cannot classify the event, thus unusual event detection from surveillance is designed to classify the normal and abnormal events through anomaly detection process. Convolutional layers have showed potential in new applications of object detection and recognition mostly in images. Convolutional neural networks, on the other hand, are supervised and required labels to learn signals. Spatiotemporal recurrent neural network is used to learn the model by combining two primary components: spatial feature representation and temporal feature evolution. According to practical results on the avenue subway, the detection correctness of our system is equivalent to state-of-the-art methods at a significant speed of up to 45 frames per second.
Mirrors and Memory in Quantum Automata

In this paper we start from the simplest form of Quantum Finite Automata (QFAs), namely Measure-Once QFAs with cut-point. First we elaborate on a variant of their semantics that can be obtained through a shift from the Schrödinger to the Heisenberg picture of Quantum Mechanics. In the Schrödinger picture states evolve in time while observables remain constant, while in the Heisenberg one states are constant and observables evolve. Interestingly, in the case of a QFA such shift reverts time-evolution. However, the equivalence of the two pictures over the class of QFAs holds thanks to the closure of the class with respect to language mirroring. Since the expressive power of such class of automata remains limited to infinite languages, we then consider their extension with bounded (multi-letter QFAs) and unbounded memory. Unfortunately, while bounded memory enhances the expressive power, the unbounded memory approach does not behave as one would expect.
Assessment of Creditworthiness Models Privacy-Preserving Training with Synthetic Data

Credit scoring models are the primary instrument used by financial institutions to manage credit risk. The scarcity of research on behavioral scoring is due to the difficult data access. Financial institutions have to maintain the privacy and security of borrowers’ information refrain them from collaborating in research initiatives. In this work, we present a methodology that allows us to evaluate the performance of models trained with synthetic data when they are applied to real-world data. Our results show that synthetic data quality is increasingly poor when the number of attributes increases. However, creditworthiness assessment models trained with synthetic data show a reduction of 3% of AUC and 6% of KS when compared with models trained with real data. These results have a significant impact since they encourage credit risk investigation from synthetic data, making it possible to maintain borrowers’ privacy and to address problems that until now have been hampered by the availability of information.
Application Specific Integrated Circuit Implementation of Discrete Fractional Fourier Transform

Asian Journal of Organic & Medicinal Chemistry includes communications, full papers (articles), short papers, green chemistry, reviews, and perspectives. Specifically, these include synthesis, synthetic methods and regents, mechanism of reactions, biologically active compounds with their possible mechanism of action, natural products, in vitro and in vivo tests of compounds, computational chemistry, physical-analytical-inorganic chemistry, nanotechnology, drug carriers, proteomics and genomics. Rapid synthesis of new compounds following eco-friendly methods and their functions as medicinally active agents is certainly the central part of chemical and medicinal research in modern times. On the basis of this fact, significant emphasis is placed to rapid publication and disseminates original discoveries in this new journal.
Conjecture on Characterisation of Bijective 3D Digitized Reflections and Rotations

Bijectivity of digitized linear transformations is crucial when transforming 2D/3D objects in computer graphics and computer vision. Although characterisation of bijective digitized rotations in 2D is well known, the extension to 3D is still an open problem. A certification algorithm exists that allows to verify that a digitized 3D rotation defined by a quaternion is bijective. In this paper, we use geometric algebra to represent a bijective digitized rotation as a pair of bijective digitized reflections. Visualization of bijective digitized reflections in 3D using geometric algebra leads to a conjectured characterization of 3D bijective digitized reflections and, thus, rotations. So far, any known quaternion that defines a bijective digitized rotation verifies the conjecture. An approximation method of any digitized reflection by a conjectured bijective one is also proposed.
Prototype of a system based on voice assistants for cognitive stimulation of the elderly

The population of Europe is getting older and factors such as labor mobility and the low birth rate have increased single-person households. In addition, many older people want to stay at home. For this to be possible, it is necessary to promote active aging policies that slow cognitive decline and help maintain good mental health. In this article we describe a system based on voice assistants (VAs) to improve the physical and mental health of the elderly. The solution proposes different activities to the user and gathers information about the status of the user. Voice assistants are an ideal tool to promote the autonomy of the elderly thanks to their low cost, the multiple possibilities they offer to interact with the user and their ease of use. A preliminary study of the system has been conducted with six users aged between 60 and 93 years. The results show a good predisposition to integrate the solution into their daylife. It has also been verified that regardless of age, all the participants were able to interact with the system and complete the proposed activities.
Evolutionary Instance Selection Based on Preservation of the Data Probability Density Function

The generation of massive amounts of data has motivated the use of machine learning models to perform predictive analysis. However, the computational complexity of these algorithms depends mainly on the number of training samples. Thus, training predictive models with high generalization performance within a reasonable computing time is a challenging problem. Instance selection (IS) can be applied to remove unnecessary points based on a specific criterion to reduce the training time of predictive models. This paper introduces an evolutionary IS algorithm that employs a novel fitness function to maximize the similarity of the probability density function (PDF) between the original dataset and the selected subset, and to minimize the number of samples chosen. This method is compared against six other IS algorithms using four performance measures relating to the accuracy, reduction rate, PDF preservation, and efficiency (which combines the first three indices using a geometric mean). Experiments with 40 datasets show that the proposed approach outperforms its counterparts. The selected instances are also used to train seven classifiers, in order to evaluate the generalization and reusability of this approach. Finally, the accuracy results show that the proposed approach is competitive with other methods and that the selected instances have adequate capabilities for reuse in different classifiers.
Computer Vision Based Unistroke Keyboards

In this paper we present a unistroke keyboard based on computer vision. The keyboard can be made of paper containing an image of the keyboard which has an upside down U-shape. Each character is represented by a nonoverlapping rectangular region. The user enters a character to the computer by covering the character region with a stylus. The actions of the user are captured by a camera and the covered key is recognized. During the text entry process the user need not have to raise the stylus from the keyboard and this leads to faster data entry rates. In a companion system the user imitates writing on a surface using a pointer or a stylus. In this case the trace of the pointer is analyzed and the characters are recognized. The character set of the continuous hand writing system is based on the Graffiti alphabet to achieve very high recognition rates.