Reviewerly/exHarmony huggingface dataset:
Kernel density estimation-based sampling for neural network classification

Imbalanced data occurs in a wide range of scenarios. The skewed distribution of the target variable elicits bias in machine learning algorithms. One of the popular methods to combat imbalanced data is to artificially balance the data through resampling. In this paper, we compare the efficacy of a recently proposed kernel density estimation (KDE) sampling technique in the context of artificial neural networks. We benchmark the KDE sampling method against two base sampling techniques and perform comparative experiments using 8 datasets and 3 neural networks architectures. The results show that KDE sampling produces the best performance on 6 out of 8 datasets. However, it must be used with caution on image datasets. We conclude that KDE sampling is capable of significantly improving the performance of neural networks.
Outlier-robust kalman filter in the presence of correlated measurements

We consider the robust filtering problem for a state-space model with outliers in correlated measurements. We propose a new robust filtering framework to further improve the robustness of conventional robust filters. Specifically, the measurement fitting error is processed separately during the reweighting procedure, which differs from existing solutions where a jointly processed scheme is involved. Simulation results reveal that, under the same setup, the proposed method outperforms the existing robust filter when the outlier-contaminated measurements are correlated, while it has the same performance as the existing one in the presence of uncorrelated measurements since these two types of robust filters are equivalent under such a circumstance.
The position of the sun based on a simplified model

A simplified model for the position of the sun at any location on earth is shown to produce fairly accurate results when compared to values calculated in a spreadsheet from the United States National Oceanic and Atmospheric Administration (NOAA). A straightforward derivation of the model is presented, in which all terms and parameters have clear physical meaning. In the first version of the model, the only parameters are the tilt of the earth's axis and the date and time of the nearest northern hemisphere winter solstice. The differences between the elevation and azimuth calculated using the model and the NOAA results are typically a few degrees or less, and it is seen that the primary cause of these differences is the eccentricity of the earth's orbit. A simple correction is then added to the model, in which the only parameters are the earth's orbital eccentricity and the date and time of the perihelion nearest the solstice already in the model. This removes most of the differences between the model and the NOAA results. The model is shown to work in both the northern and southern hemispheres.
Research on UAV Control Method Based on Eye Tracking

To a certain extent, eye movement behavior can reflect people's psychological cognitive activities on things, and can be used for auxiliary control of drones. This paper designed an architecture and function of the manipulation intention detection module. The eye movement data was processed and transformed to the command of drones, by detection of the different eye movement pattern of operators. The re experimental results showed that the eye movement behavior pattern detection module can realize the rapid and accurate recognition of the operator's eye movement behavior patter, which can be used to control the external devices alike drones.
Where and Why is My Bot Failing? A Visual Analytics Approach for Investigating Failures in Chatbot Conversation Flows

The ongoing coronavirus pandemic has accelerated the adoption of AI-powered task-oriented chatbots by businesses and healthcare organizations. Despite advancements in chatbot platforms, implementing a successful and effective bot is still challenging and requires a lot of manual work. There is a strong need for tools to help conversation analysts quickly identify problem areas and, consequently, introduce changes to chatbot design. We present a visual analytics approach and tool for conversation analysts to identify and assess common patterns of failure in conversation flows. We focus on two key capabilities: path flow analysis and root cause analysis. Interim evaluation results from applying our tool in real-world customer production projects are presented.
Exploring Neural Network Layers for Knowledge Discovery

Research quest to explore artificial neural network weights to understand the underlying patterns has been a point of interest since later 1990s. Recent advances in artificial neural networks particularly with deep neural networks provided an opportunity to explore and expose weights in the individual layers for discovery, extraction and transfer of knowledge. Several experiments with transfer of weights from a particular layer to other layers are conducted to analyse how the classification is affected by moving weights into particular layer in a particular position. This paper, for the first time tries to investigate the importance of individual layers in a neural network through systematic experimental evaluation. Experiments are carried out using feed-forward deep neural network with different topologies. Three data sets namely MNIST, IRIS and a synthetic hierarchical data set are used for the experiments. Knowledge extraction and transfer of knowledge (through weights) experiments are performed with multiple strategies. The results indicate that the middle layer weights possess some underlying representation and has highest impact on classification accuracy. When the middle layer weights are transferred to an untrained deep neural network, irrespective of topology or type of data set (from the three data sets used), there is a significant improvement in classification accuracy.
PENGEMBANGAN APLIKASI ANDROID BERBANTU SOFTWARE ISPRING SUITE SEBAGAI MEDIA PEMBELAJARAN EKONOMI MADRASAH ALIYAH MA’ARIF 9 KOTAGAJAH TAHUN PELAJARAN 2018/2019

&#x0D; Learning media is one of the needs that must be fulfilled in the learning process. The results of the pre-survey of learning media used in the Aliyah Ma'arif 9 Kotagajah Madrasah, researchers found several problems, namely the use of learning media that were less innovative in learning and the limitation of images in teaching books, worksheets and powerpoints so that students were not attracting attention in the learning process . So that learning media are needed that can attract the attention of students such as android applications, android application software assisted ispring suite as an economic learning media arranged concisely, clearly, and made in a practical form so that students are more interested and enthusiastic in learning and can understand the material. The development model used in this development is the ADDIE development model which consists of several stages, namely (Analysis, Design, Development, Implementation, and Evaluation). Based on the results of the development of android applications assisted by software ispring suite as an economic learning media it is known the results of recapitulation of data that have been carried out by learning media material experts on android applications with an average score of 86% included in the very feasible category and declared valid, the results of recapitulation from media experts with scores on average 87% fall into the category of very decent and declared valid. Furthermore, the practicality test by IPS X grade 1 students of Aliyah Ma'arif 9 Kotagajah gets an average score of 92.2% and falls into the very practical category so that it is declared practical. research shows that economic learning media using this android application is suitable for use in learning. Thus it can be concluded that the learning media of android applications are learning media that are valid and practical to be used in the learning process.
Multi-Qubit Size-Hopping Deutsch-Jozsa Algorithm with Qubit Reordering for Secure Quantum Key Distribution

As a classic quantum computing implementation, the Deustch-Jozsa (DJ) algorithm is taught in many courses pertaining to quantum information science and technology (QIST). We exploit the DJ framework as an educational testbed, illustrating fundamental qubit concepts while identifying associated algorithmic challenges. In this work, we present a self-contained exploration which may be beneficial in educating the future quantum workforce. Quantum Key Distribution (QKD), an improvement over the classical Public Key Infrastructure (PKI), allows two parties, Alice and Bob, to share a secret key by using the quantum physical properties. For QKD the DJ-packets, consisting of the input qubits and the target qubit for the DJ algorithm, carry the secret information between Alice and Bob. Previous research from Nagata and Nakamura discovered in 2015 that the DJ algorithm for QKD allows an attacker to successfully intercept and remain undetected. Improving upon the past research we increased the entropy of DJ-packets through: (i) size hopping (H), where the number of qubits in consecutive DJ-packets keeps on changing and (ii) reordering (R) the qubits within the DJ-packets. These concepts together illustrate the multiple scales where entropy may increase in a DJ algorithm to make for a more robust QKD framework, and therefore significantly decrease Eve’s chance of success. The proof of concept of the new schemes is tested on Google’s Cirq quantum simulator, and detailed python simulations show that attacker’s interception success rate can be drastically reduced.
The Arithmetic Optimization Algorithm Based-on Particle Energy and Particle Diffusion

Arithmetic Optimization Algorithm (AOA) is a new-proposed meta-heuristic algorithm motivated by the distribution behavior of the leading arithmetic operators. It is proved to perform well in optimization processes of complex engineering design problems. AOA has a slow convergence rate and tends to fall into local minima in multimodal optimization problems. This paper proposes an Arithmetic Optimization Algorithm based on particle energy and particle diffusion. On the one hand, the introduction of particle energy increases the chance of updating the particle&#x2019;s position, which has not gained a satisfying solution, accelerating the convergence rate. On the other hand, particle diffusion prevents particles trapping in local minima. To verify the efficiency of the proposed algorithm, Improved Arithmetic Optimization Algorithm (IAOA) is compared with nine state-of-the-art algorithms on 23 benchmark functions. Numerical results and Wilcoxon&#x2019;s rank-sum tests show that IAOA performs significantly better than the original AOA and other comparative algorithms in convergence rate and solution accuracy.
Emotion Analysis based on Incremental Online Learning in Social Networks

Social network messages contain reliable information about the emotions that authors have experienced and expressed; based on them, we can conclude about some psycho-emotional characteristics of their authors. In this work, we use the model of emotions proposed by P. Ekman. We present a new iterative learning model that classifies messages from social networks into six basic emotions: joy, sadness, fear, anger, disgust, and surprise. The model considers not only “plain text” as input, but also various emotional indicators: emoticons and emoji. As a result of incremental learning, the averaged performance of the model increased by 8.9% in ROC AUC and by 7.3% in F1-score. The novelty of the approach lies in the implementation of incremental learning on the raw data permanently accepted from the social networks. It helps to maintain the model updated and improve its characteristics.
A Fine-Grained Horizontal Scaling Method for Container-Based Cloud

The container scaling mechanism, or elastic scaling, means the cluster can be dynamically adjusted based on the workload. As a typical container orchestration tool in cloud computing, Horizontal Pod Autoscaler (HPA) automatically adjusts the number of pods in a replication controller, deployment, replication set, or stateful set based on observed CPU utilization. There are several concerns with the current HPA technology. The first concern is that it can easily lead to untimely scaling and insufficient scaling for burst traffic. The second is that the antijitter mechanism of HPA may cause an inadequate number of onetime scale-outs and, thus, the inability to satisfy subsequent service requests. The third concern is that the fixed data sampling time means that the time interval for data reporting is the same for average and high loads, leading to untimely and insufficient scaling at high load times. In this study, we propose a Double Threshold Horizontal Pod Autoscaler (DHPA) algorithm, which fine-grained divides the scale of events into three categories: scale-out, no scale, and scale-in. And then, on the scaling strength, we also employ two thresholds that are further subdivided into no scaling (antijitter), regular scaling, and fast scaling for each of the three cases. The DHPA algorithm determines the scaling strategy using the average of the growth rates of CPU utilization, and thus, different scheduling policies are adopted. We compare the DHPA with the HPA algorithm under different loads, including low, medium, and high. The experiments show that the DHPA algorithm has better antijitter and antiload characteristics in container increase and reduction while ensuring service and cluster security.
Visualizing Life in the Deep: A Creative Pipeline for Data-Driven Animations to Facilitate Marine Mammal Research, Outreach, and Conservation

In this paper, we introduce a creative pipeline to incorporate physiological and behavioral data from contemporary marine mammal research into data-driven animations, leveraging functionality from industry tools and custom scripts to promote scientific insights, public awareness, and conservation outcomes. Our framework can flexibly transform data describing animals’ orientation, position, heart rate, and swimming stroke rate to control the position, rotation, and behavior of 3D models, to render animations, and to drive data sonification. Additionally, we explore the challenges of unifying disparate datasets gathered by an interdisciplinary team of researchers, and outline our design process for creating meaningful data visualization tools and animations. As part of our pipeline, we clean and process raw acceleration and electrophysiological signals to expedite complex multi-stream data analysis and the identification of critical foraging and escape behaviors. We provide details about four animation projects illustrating marine mammal datasets. These animations, commissioned by scientists to achieve outreach and conservation outcomes, have successfully increased the reach and engagement of the scientific projects they describe. These impactful visualizations help scientists identify behavioral responses to disturbance, increase public awareness of human-caused disturbance, and help build momentum for targeted conservation efforts backed by scientific evidence.
Construction and Practice of Blended Learning Mode of English Writing Course Based on Information Technology

The rapid development of information technology has greatly promoted innovation and reform in education. Under such background, blended learning was born naturally. Based on the new situation of higher education, teaching concept of blended learning and Shannon-weaver's communication theory, taking the school-based English Writing course as an example, this paper constructs the framework of blended learning mode supported by the Chaoxing Fanya Platform and Xuexitong App and explores its effect. The result shows that blended learning mode is welcomed by students and it can mobilize students’ learning and cultivate students autonomous learning ability. Some suggestions, namely perfecting teaching design and creating learning community, making good use of data and teaching pertinently and improving students’ sense of contentment and achievement, are presented to promote the deep integration of information technology and English writing course teaching.
Text/Non-text Scene Image Classification Using Deep Ensemble Network

Ghosh, Mridul Chatterjee, Somnath Mukherjee, Himadri Sen, Shibaprasad Obaidullah, Sk MdIn the interpretation of images, contextual comprehension serves a key task in a natural scene. The text present in scene images often bestows qualitative details which can be analyzed in many aspects of image processing. The text and non-text natural image classification help in pruning out unnecessary processing to get the key textual information. In this work, we designed a convolutional neural network (CNN), named TNTNet, and also a transfer learning-based method was considered for this purpose. We proposed an ensemble network by combining the TNTNet and the pre-trained VGG-19 model. The accuracies were computed for different train-test ratios. The highest accuracy of 99.45% was obtained in our ensemble network.
Regarding the Virtual Reality Environment Design and Evaluation Based on STEAM Learning

This study aims to design a STEAM virtual reality (VR) learning environment for engineering students, as well as evaluate the practicability of the learning environment. This study adopted Fuzzy Delphi Method by inviting six experts in the fields of VR application, STEAM education, and engineering education to execute the expert questionnaire survey, in a bid to build up a capability indicator of STEAM education and carry out the STEAM VR environment practicability evaluation serving as a foundation of the further development of curriculum. According to our study findings, the most important capability indicator in STEAM is “Hands-on Skills”, followed by “Problem Solving”, “Daily Life Application”, “Sensory Learning” and “Interdisciplinarity”. In terms of the practicability evaluation of STEAM VR learning environment establishment, the highest scoring element is “Interactivity”, followed by “Engagement”, with “Imagination” being the lowest. This study serves as a reference for future educators and researchers in establishing a STEAM VR learning environment.
SocialChain: Decoupling Social Data and Applications to Return Your Data Ownership

Social data produced from widely emerged social media activities are expected to promote information dissemination and engagement, or even make business intelligence more powerful. However, the recent increase in social media incidents of illegal surveillance and data breaches raises questions about the current data ownership model, in which centralized applications collect and control large amounts of user data. In this paper, we present SocialChain, which is a decentralized social data storage and sharing system based on blockchain that decouples user data and social applications to return data ownership to the user. We adopt Personal Data Store to extend off-chain storage for the social data, set up an identity establishment mechanism that can support WebID-based authentication functions using a unique identity assignment (i.e., WebID) as well as certificateless cryptography, and design a general framework that leverages smart contracts to help securely store and share social data in an automated manner. We develop a software prototype based on Ethereum and conduct case studies to test the effects of the adopted techniques on the performance. Experimental results show that SocialChain can provide easy-to-use interfaces while introducing relatively low latency, cost, and overhead and that it can support real-world social media applications.
Application of Bi-factor MIRT and Higher-order CDM Models to an In-house EFL Listening Test for Diagnostic Purposes

The present study examined the performance of the bi-factor multidimensional item response theory (MIRT) model and higher-order (HO) cognitive diagnostic models (CDM) in providing diagnostic information and general ability estimation simultaneously in a listening test. The data used were 1,611 examinees’ item-level responses to an in-house EFL listening test in China and five content experts’ item-attribute coding results of the test form. The bi-factor MIRT model was compared with five CDMs with and without a higher-order structure in terms of model fit, attribute classification and general ability estimation. The results showed that the bi-factor MIRT model provided the best model-data fit, followed by the HO-G-DINA model, the saturated G-DINA model, and other reduced CDMs. The HO-G-DINA model produced attribute classification results more similar to the G-DINA model, whereas the bi-factor MIRT model offered better results in discriminating examinees’ general listening ability. The findings of this study highlighted the feasibility of using the bi-factor MIRT model as an attractive alternative for diagnostic assessment, especially in language assessment where attributes are assumed to be continuous.
Fine-Tuning the ISO/IEC Standard LightMAC

LightMAC, by Luykx et al., is a block cipher based message authentication code (MAC). The simplicity of design and low overhead allows it to have very compact implementations. As a result, it has been recently chosen as an ISO/IEC standard MAC for lightweight applications. LightMAC has been shown to achieve query-length independent security bound of \( O(q^2/2^n) \) when instantiated with two independently keyed n-bit block ciphers, where q denotes the number of MAC queries and the query-length is upper bounded by \( (n-s)2^{s} \) bits for a fixed counter size s. In this paper, we aim to minimize the number of block cipher keys in LightMAC. First, we show that the original LightMAC instantiated with a single block cipher key, referred as 1k-LightMAC, achieves security bound of \( O(q^2/2^n) \) while the query-length is at least \( (n-s) \) bits and at most \( (n-s)\min \{2^{n/4},2^s\} \) bits. Second, we show that a minor variant of 1k-LightMAC, dubbed as LightMAC-ds, achieves security bound of \( O(q^2/2^n) \) while query-length is upper bounded by \( (n-s)2^{s-1} \) bits. Of independent interest, our security proof of 1k-LightMAC employs a novel sampling approach, called the reset-sampling, as a subroutine within the H-coefficient proof setup.
BIAS Autoscaler

Burstable instances have recently been introduced by cloud providers as a cost-efficient alternative to customers that do not require powerful machines for running their workloads. Unlike conventional instances, the CPU capacity of burstable instances is rate limited, but they can be boosted to their full capacity for small periods when needed. Currently, the majority of cloud providers offer this option as a cheaper solution for their clients. However, little research has been done on the practical usage of these CPU-limited instances. In this paper, we present a novel autoscaling solution that uses burstable instances along with regular instances to handle the queueing arising in traffic and flash crowds. We design BIAS Autoscaler, a state-of-the-art framework that leverages burstable and regular instances for cost-efficient autoscaling and evaluate it on the Google Cloud Platform. We apply our framework to a real-world microservice workload, and conduct extensive experimental evaluations using Google Compute Engines. Experimental results show that BIAS Autoscaler can reduce the overall cost up to 25% and increase resource efficiency by 42% while maintaining the same service quality observed when using conventional instances only.
Towards increasing adoption of online VR in higher education

The COVID-19 outbreak triggered an increased and sudden need for adopting either fully digital or hybrid approaches to teaching. At the same time, the use of ‘traditional’ digital technology for teaching has several barriers and difficulties. Our experience teaching remotely during the pandemic led us to reflect on the combination of digital tools and online VR environments for higher education. In this paper we discuss the use of an online browser-based VR platform, Mozilla Hubs. We reflect on our experience and propose guidelines for educators based on what we learned.