Reviewerly/exHarmony huggingface dataset:
Development Study on Lecture Evaluation for Synchronous Online Education

Distance learning at universities has been activated since Covid-19. However, for the lecture evaluation, the questionnaire used in the existing face-to-face education or only partially modified questionnaire is being used, even though they are not proper in the context of distance learning. Therefore, this study derived lecture evaluation questionnaires that are proper for distance learning at universities. Based on the previous studies, we derived the basic questionnaire and verified its content validity by experts. First, the ‘class preparation’ area includes questions like providing sufficient learning materials and redesigning customized distance education classes. Second, the ‘class operation’ area are about notice and advance guidance, time distribution, lecture delivery power, interaction and communication methods, syllabus, lecture contents, progress log and technology. Finally, the ‘class evaluation’ includes questions about appropriate evaluation time and the presentation of clear evaluation criteria. This questionnaire is expected to use as self-evaluation criteria for instructors or as a tool for improving the quality of distance learning in terms of university policy. However, only a preliminary survey was conducted only for some students, so a follow-up study is needed to check the statistical verification of the concepts within the questions.
Improved concentration bounds for sums of independent sub-exponential random variables

Known Bernstein-type upper bounds on the tail probabilities for sums of independent zero-mean sub-exponential random variables are improved in several ways at once. The new upper bounds have a certain optimality property.
pyFFS: A Python Library for Fast Fourier Series Computation and Interpolation with GPU Acceleration

Fourier transforms are an often necessary component in many computational tasks, and can be computed efficiently through the fast Fourier transform (FFT) algorithm.However, many applications involve an underlying continuous signal, and a more natural choice would be to work with e.g. the Fourier series (FS) coefficients in order to avoid the additional overhead of translating between the analog and discrete domains.Unfortunately, there exists very little literature and tools for the manipulation of FS coefficients from discrete samples.This paper introduces a Python library called pyFFS for efficient FS coefficient computation, convolution, and interpolation.While the libraries SciPy and NumPy provide efficient routines for discrete Fourier transform coefficients via the FFT algorithm, pyFFS addresses the computation of FS coefficients through what we call the fast Fourier series (FFS).Moreover, pyFFS includes an FS interpolation method based on the chirp Z-transform that can make it more than an order of magnitude faster than the SciPy equivalent when one wishes to perform distortionless bandlimited interpolation.GPU support through CuPy is readily available, and allows for further acceleration: an order of magnitude faster for computing the 2-D FS coefficients of 1000 × 1000 samples and nearly two orders of magnitude faster for 2-D interpolation.As an application, we discuss the use of pyFFS in Fourier optics.pyFFS is available as an open source package at https://github.com/imagingofthings/pyFFS,with documentation at https://pyffs.readthedocs.io.
Anime Scene Generator from Real-World Scenario Using Generative Adversarial Networks

This study illustrates a method for image cartoonization and style transfer, which entails converting a real-world image or video into an aesthetic, anime-style frame. By noticing the animation painting behaviors, this work proposes to independently distinguish three feature maps from pictures: the surface representation that contains smooth color shading characteristic of animation, the construction depiction that resembles flattened global content and clear boundaries in a typical anime frame, and the texture representation that reflects high-frequency surface, forms, and details in animated pictures. All the extracted information will be fed into the generator with the help of a VGG-based discriminator to learn how to cartoonize a real-world photo. This technique’s training objectives are based on each extracted feature map independently, making the model controllable and adjustable. Experiments demonstrate that this method can generate high-quality animation graphics from real-world photos and outperforms many existing methods.
Overview and Computational Analysis of PSO Variants for Solving Systems of Nonlinear Equations

The problem of solving systems of nonlinear equations is one of great difficulty, especially as the scale of these systems grow. Traditional numerical methods rely on selecting good initial estimates for the roots and refine them iteratively, which is not a simple task, especially with large systems of nonlinear equations. Particle swarm optimization (PSO), in turn, is a nature-inspired metaheuristic algorithm for finding the minimum of a function for which multiple improvements and different hybridizations have been proposed. These modifications range from dynamically choosing parameters and topologies to hybridization with other population-based optimization algorithms. All of these modifications have the goal of improving the basic algorithm, and a broader comparison between these proposals is necessary to further understand what might be the path forward in achieving better and more exact results. In this paper, after a brief overview of PSO-based algorithms for nonlinear equation systems, several PSO variants are tested on a number of problems in order to find the solution to non-trivial systems of nonlinear equations. Each variant was tested 100 times on each problem, in order to produce enough samples for a legitimate comparison.
An Analytical Approach for Extracting Entities and Their Emotional Tones in Narrative Scenarios

Analyzing novels or stories helps us to review or evaluate certain aspects related to the plot, characters or the theme of the story content. But story analysis done manually is an extremely time-consuming task. The introduction of machine learning algorithms has made analysis of text relatively easier. Relation extraction is a Natural Language Processing (NLP) task that helps us to extract relations of people, places and other entities in a text. Identifying the characters and their intent in stories is an important part for understanding the meaning of the story. The characters are firstly identified, and sentences related to them are selected. Relation extraction is performed by extracting the Subject-Verb-Object (SVO) triples in the selected sentences, and the relations of the extracted entities are represented using a knowledge graph. Performing sentiment analysis on the extracted information of the entities will enable us to understand the emotions regarding the characters or plots involved in the story. Finally, a summary of the story is presented using the latent semantic analysis (LSA) method. These would constitute a good story analyzer that could help us understand the essence of the story represented through a shorter format.
Gray Scale Image Enhancement with CPSO Algorithm for Medical Applications

In an image processing system, image enhancement plays a crucial role. Improvement of image quality by maximizing the information in the given input image is the main aim of this paper. Adaptive histogram equalization and histogram equalization are most popular non-heuristic or classical techniques for image enhancement by preserving main features of the input image. These techniques failed in achieving good quality enhancement. Optimization techniques have been proposed for enhancement of image problem. The quality is enhanced by selecting the optimal parameters based on objective function formulated during optimization process. The formulation of objective function plays an important role in optimization problem. This paper offers an effective objective approach for image enhancement using constriction factor-based particle swarm optimization (CPSO) algorithm. The proposed algorithm has been tested on medical images like knee cyst, spine MRI, and liver tumor. The proposed algorithm is successful in improving the quality of these images.
Alerting the Impact of Adversarial Attacks and How to Detect it Effectively via Machine Learning Approach: With Financial and ESG Data

ESG is short for “environmental, social, and governance” and it has been widely used as an indicator for investment. Investment firms asserted that they would incorporate the ESG indicator into their portfolio. Therefore, various AI approaches were applied to analyze the relationship between ESG and the benefit of the company. However, the adversarial attacks on AI models were prominent these days, and they could badly affect financial performance. This research aims to alert the danger of the attack and how to detect the anomaly data. The experiment involves two stages and focuses on classification performance and detecting noise data, respectively. In the first stage, it revealed that the accuracy of the classification on the noise dataset could drop almost 15% compared to the ordinary dataset. In the second stage, local outlier factors and isolation forest algorithms were applied to detect the noise data and they revealed 95.156% and 84.1% for detecting, respectively. These experiments yield that even tiny values of noise could influence the result significantly, and suggest a way to detect them. The limitation of this research is that it only conducts uncomplicated binary classification, and could not propose a way to defend the attack or filter the noise. Further research should be conducted to apply these algorithms to more sophisticated classification or regression. However, it is still worthwhile that this research could alert the risk of the adversarial attack on AI models, and suggest a probability of applying the proposed model to other fields of research.
Cross-Project Defect Prediction by Using Optimized Light Gradient Boosting Machine Algorithm

Cross-project defect prediction (CPDP) is a process that trains the prediction model on the source project and predicts the defect of the target project. We present an amended LightGBM algorithm as a defect prediction model for the CPDP process. The hyperparameters of the LightGBM algorithm are optimized by using the Bayesian optimization process to maximize the area under the curve. The proposed model uses the values of optimized hyperparameters to train the data and then classify the faulty and non-faulty instances on test data. To deal with class imbalance issue, the borderline-SMOTE technique is used. Since the dataset used is class imbalanced, so accuracy and sensitivity can mislead the performance results. Therefore for performance evaluation, the false-positive ratio and false-negative ratio should be minimized. The proposed model is further compared with XGBoost and random forest algorithm, which are also optimized in the same way. The proposed model achieves an accuracy of 99.12%.The efficacy of the proposed model is further verified with ROC curves.
Natural Language Inference on Imbalanced Datasets

Reasoning ability along with the skill of arriving at logical inferences is central to artificial intelligence and a key requirement to get one step closer toward natural language understanding. Once such task which involves learning this skill is natural language inference (NLI) which involves the task of categorizing a given premise and hypothesis into one of three classes—neutral, entailment or contradiction. The availability of datasets such as the Stanford Natural Language Inference (SNLI) dataset [1] has allowed the use of deep learning networks to tackle this problem. The paper presents the results of two models: (1) LSTM model and (2) LSTM with self-attention where both models are trained on different subsets of the SNLI dataset. The paper tests and compares the performance of both models on different subsets of the dataset containing varying proportions of entailment, contradiction and neutral labels and provides an analysis to explain possible reasons for incorrect predictions.
An Interleaving Approach to Control Mobile Device and Elements via Screen Buffer and Audio Streaming

Nowadays mobile devices are powered with huge features (voice enabled, OnDevice AI, health, IoT) which requires device access and validation methods should be in smarter and efficient way. In this paper, we proposed a unified and efficient framework design to access remote devices via screen buffer and live audio streaming concepts which can benefit multiple projects. Our framework has 5 novel aspects: (1) streaming your devices screen buffer in real time using decoding raw H264 buffers (2) streaming your audio input from external PC in real time to access and operate your device in remote conditions (3) dynamic sharing of device screen and elements help to create automated scripts in real time (4) framework is platform and language independent to support Windows and Linux platform (5) faster device screen element detection using json RPC client server architecture. The proposed framework integrated into different projects (Bixby, Internet of Things, OnDevice AI) to benefit with core features like Remote (device access anywhere, live audio streaming), benchmark solution across devices in single frame and real-time device sharing between users and played major role in differentiating our quality products to market in short span of time.
KGAN: A Generative Adversarial Network Augmented Convolution Neural Network Model for Recognizing Kannada Language Digits

Kannada is a south Indian language with a history of two thousand years and spoken by more than sixty million people. Kannada language has its own script for alphabets and digit representations. So there is a need for convolution neural network (CNN) model to recognize Kannada language scripts. This paper presents a design of a CNN model to recognize Kannada digits. One of the challenges faced while designing a CNN model is data over fitting. Data over fitting is a phenomenon where the trained model arrives at parameter values such that they can classify only the instances provided during training resulting in reduction of accuracy for a new unseen test instance. To overcome this problem, datasets are split into train and test sets. The detriment of this system is lesser number of instances to train the CNN. Increasing the number of training instances is a good approach, but the complexity in data collection is to be answered. In this paper, we explore generative adversarial network (GAN) as an additional data generator and its suitability. Results of analysis on the experiment revealed the following advantages; first, the data augmentation has a positive impact on CNN, next, GAN-generated data meets qualitative requirement as train and test dataset and last, epoch value for training CNN has influence on data under fitting and data over fitting phenomenon.
Mixed Reality Applications in Business Contexts

Mixed reality is becoming increasingly relevant in business. In the corporate environment, such as logistics or maintenance, the use of data glasses allows extensive possibilities for process optimization and quality assurance. In the area of construction, virtual models either as augmentation of reality or mapped in virtual reality offer new approaches to experience ability. The goal of this paper is to show the manifold possibilities of mixed reality in the enterprise environment. For this purpose, selected application scenarios with corresponding realization stages will be shown and analyzed regarding their added value.
Where Divergent Ideas Converge: Answers to AUT Found on Short List of Word Co-Occurrences Terms

Divergent thinking (DT) is a fundamental part of creative ideation. Understanding its role in cognition and its attainment through language technology can provide the scaffolding to enhance creative endeavors. This study is a proof of concept on the automatic generation of keyword responses as found on the AUT (Alternative Uses Task), a test commonly used to measure DT. Within a psychometric perspective, we propose a frequency-based simple word co-occurrence method called Co-OBM (co-occurrence-based method). Drawing from Natural Language Processing (NLP) we choose the adequate parameter settings for this task, including part-of-speech tagging (POS), word form, association measure, minimum occurrence in corpus and window size. Through our experiments, we show how most of the popular responses to AUT can be identified in a short word co-occurrence list, together with some of the least frequent, and usually more creative, responses, how this outcome is not random but based on linguistic patterns (Experiment 1); and how Co-OBM output can enhance the performance of subjects’ AUT responses (Experiment 2). This is relevant since word co-occurrence is at the core of current language models. Our work aims to reflect and provide technical and empirical foundations for the development of distributional language models for creative purposes.
Analysis of the FO Transformation in the Lattice-Based Post-Quantum Algorithms

Newer variants of the Fujisaki–Okamoto transformation are used in most candidates of the third round of the NIST Post-Quantum Cryptography standardization call in the category of public key encryption schemes. These transformations are applied to obtain a highly secure key encapsulation mechanism from a less secure public key encryption scheme. Furthermore, there are five candidates (three finalists and two alternatives) that passed to the third round of the process and whose security is based in lattice problems. This work analyzes the different ways in which the lattice-based candidates of the NIST call apply the Fujisaki–Okamoto transformation and the particularities of each application. The study of such differences and their repercussion in the design of the proposals will allow a better understanding of the algorithms. Moreover, we propose a modification of the Kyber algorithm—the only public key encryption candidate established as a PQC standard by NIST in its more recent publication—in order to avoid the re-encryption in the decapsulation algorithm and, in this way, to reduce the side channel attacks vulnerability.
Head pose healthiness prediction using a novel image quality based stacked autoencoder

This paper introduces an approach aiming to determine head pose healthiness of computer users. The main contributions of this paper are: 1) Image Quality Assessment (IQA) based Stacked Autoencoder (referred to as IQASAE) which adjusts the value of learning rate based on the quality of images; 2) Head Pose Healthiness Prediction (HPHP) framework which leverages the proposed IQASAE algorithm in combination with image processing operations; 3) A set of features suitable for face analysis applications; 4) Ontology-driven semantic framework which enables further exploiting pose estimation results within applications in synergy with healthcare expert domain knowledge about pose healthiness. Our framework was evaluated on both offline (BIWI and AFLW) and online (our own, collected using Arduino) datasets. Furthermore, it was compared to several state-of-art methods, including Multi-Layer Perceptron (MLP), CART, Random Forest, Convolutional Neural Networks (CNN), Temporal Deep Learning Model (TDLM), hybrid CNN with Support Vector Machine (SVM), Quatnet and Trinet. According to the achieved experimental results, it reaches accuracy up to 79.63% outperforming all of them, except Quatnet and Trinet. However, the main advantages of IQASAE compared to state-of-art methods are: 1) it does not require selection of features, so the processing time is reduced, 2) utilizing angle between chin and mouth reduces training time for SAE, 3) leveraging vector-based feature set to create training data resulted in a significant improvement, especially in offline facial images.
Human-Centric Machine Learning for Temporal Knowledge Graphs: Towards Understanding the European Alternative Fuels Market

Machines&#x2019; ability to learn the behavior of complex real world systems has been the main research focus in temporal knowledge graphs (TKG). However, combining the human&#x2019;s input - as part of a real-world TKG - into the modeling process has not yet been investigated. To fill this gap, we propose a novel human-centric machine learning (HCML) framework for TKG link prediction. The main goal is to demonstrate the value of a human-machine online optimization coupled with the self-attention mechanism. We argue that the joint development of a human-machine TKG model can detect low-signal information about the evolution of the graph that can have a significant impact on the dynamics. Finally, our proposed HCML framework is discussed on the basis of the European alternative fuels market as an exemplary use case with the outlook of the approach.
SIL-LAND: Segmentation Incremental Learning in Aerial Imagery via LAbel Number Distribution Consistency

Segmentation incremental learning has received a lot of attention in recent years due to the ability to overcome the problem of catastrophic forgetting. Our study found that differences in label number distribution affect the performance of segmentation incremental learning. Because the labels for pixels of the old category are marked as background when the model is trained on the new tasks, the label number distribution is inconsistent with static learning that is considered to be the upper bound on incremental learning, which hinders the mitigation of the catastrophic forgetting problem. In response to the above problems, we propose an incremental learning method named SIL-LAND, which improves the accuracy by making the label number distribution of our method close to that of static learning. From the perspective of high-level semantic labels, we propose the prototype update mechanism for the problem that non-adaptive representative prototypes ignore the sample diversity of semantic categories in remote sensing images. By compensating for the difference in label number distribution at the feature level, the distance between the prototype and the actual class center is reduced; Aiming at the lack of semantic consistency between feature vectors and prototypes, we propose a similarity measure module to increase the intra-class similarity between the prototype and corresponding feature vectors. From the perspective of one-hot labels, we propose label reconstruction, including foreground screening and background padding to make the number distribution of one-hot labels as close as possible to that of static learning. A series of experimental results demonstrate the effectiveness of our method.
Comparison of PRESENT and KLEIN Ciphers Using Block RAMs of FPGA

In order to tackle the problem of data security in resource constrained devices, many lightweight ciphers have been introduced. The PRESENT and KLEIN ciphers are popular lightweight block ciphers of SPN architecture. The implementation of the ciphers has been tried on an FPGA, mainly due to its flexible nature and the presence of block RAMs which can be used to store the intermediate states of the cipher, leaving behind more slices for other applications that maybe running in parallel. In this paper, the comparison results of PRESENT-80 and KLEIN-80, implemented using just slices/registers and block RAMs, have been presented.