Reviewerly/exHarmony huggingface dataset:
Solving the sparse QUBO on multiple GPUs for Simulating a Quantum Annealer

Quadratic Unconstraint Binary Optimization (QUBO) is a combinatorial optimization problem such that an <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$n\times n$</tex> upper triangle matrix <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$W$</tex> is given and the objective is to find an n-bit vector <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$X$</tex> that minimizes the energy value <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$E(X)=X^{T}WX$</tex> . A QUBO instance <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$W$</tex> is sparse if instance <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$W$</tex> has few non-zero elements. The D-Wave 2000 <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$Q$</tex> is a quantum annealer that can solve 2048-bit sparse QUBO instances represented as a Chimera graph topology. We present a sparse QUBO solver running on GPUs for 2048-bit sparse QUBO with a Chimera graph topology. We have evaluated the performance of our sparse QUBO solver and the D-Wave 2000Q for solving 2048-bit QUBO instances with various resolutions. The experimental results show that our sparse QUBO solver running on a GPU cloud server with 8 NVIDIA A100 GPUs can find optimal solutions in less than 3ms for all instances while the D-Wave 2000 <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$Q$</tex> cannot find them in 996.7ms. Hence, our QUBO solver can find better solutions than the D-Wave 2000Q in less than 1/300 running time. We can think that our QUBO solver is a quantum annealer simulator with better performance in terms of the accuracy of solutions and the running time. Our result implies that quantum annealer D-Wave 2000 <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$Q$</tex> does not achieve quantum supremacy yet.
Movie Big Data Intelligent Recommendation System Based on Knowledge Graph

The user-oriented recommendation system enables users to obtain their favorite movie programs from a large number of movies. Knowledge graph is a kind of heterogeneous network information, which provides rich structural knowledge for movie recommendation system. It helps to solve the problem of data sparseness and cold start. Based on this, we designed a movie recommendation model based on the knowledge graph. First, we embed the knowledge graph into the recommendation model, then use the interactive attention network represented by the user/item to update the vector representation, obtain rich neighborhood knowledge through multi-layer interactive information dissemination, and finally make predictions. We test on public data sets, and experiments show that our method is better than existing baseline methods.
“In-Network Ensemble”: Deep Ensemble Learning with Diversified Knowledge Distillation

Ensemble learning is a widely used technique to train deep convolutional neural networks (CNNs) for improved robustness and accuracy. While existing algorithms usually first train multiple diversified networks and then assemble these networks as an aggregated classifier, we propose a novel learning paradigm, namely, “In-Network Ensemble” ( INE ) that incorporates the diversity of multiple models through training a SINGLE deep neural network. Specifically, INE segments the outputs of the CNN into multiple independent classifiers, where each classifier is further fine-tuned with better accuracy through a so-called diversified knowledge distillation process . We then aggregate the fine-tuned independent classifiers using an Averaging-and-Softmax operator to obtain the final ensemble classifier. Note that, in the supervised learning settings, INE starts the CNN training from random, while, under the transfer learning settings, it also could start with a pre-trained model to incorporate the knowledge learned from additional datasets. Extensive experiments have been done using eight large-scale real-world datasets, including CIFAR, ImageNet, and Stanford Cars, among others, as well as common deep network architectures such as VGG, ResNet, and Wide ResNet. We have evaluated the method under two tasks: supervised learning and transfer learning. The results show that INE outperforms the state-of-the-art algorithms for deep ensemble learning with improved accuracy.
Decentralized Multi-robot Collision Avoidance Algorithm Based on RSSI

Optimal path planning has always been a key issue in multi-robot research. One of the most important issues is how to use appropriate methods to resolve path collision between multiple robots, which is usually converted into an optimization problem of time and space. This paper solves the problem of path collision at intersections between robots based on the change of RSSI value. First, a fixed priority collision avoidance algorithm based on the robot number is proposed. In order to improve this algorithm, a binary sequence priority collision avoidance algorithm is proposed. In Webots Robot Simulator, self-build robots are used to implement the proposed algorithm.
Hypercane: Intelligent Sampling for Web Archive Collections

Humans can choose individual documents from a web archive collection, but doing so is difficult if they are unfamiliar with the collection. The issue is scale. Most web archive collections consist of thousands of documents. Hypercane is a tool that automates the selection of documents from a web archive collection for summarization or collection exploration.
Ratcliff/Obershelp Algorithm as An Automatic Assessment on E-Learning

There have been many major changes in the education sector due to the closure of educational institutions to suppress the spread of the Covid 19 pandemic, one of which is teaching methods in Indonesia. Problems arise when schools have to use e-learning for students and online assessments have to be done. It is known that difficulties are experienced when taking semester exams or when teachers give assignments online. This study applies text mining techniques using the Ratcliff/ Obershelp algorithm to determine the similarity value between two strings, namely between student answers and the teacher's answer key. A collection of answer data obtained from one of the 6th grade teachers at elementary school and applied to the Examz web application. This application is built to find out the status of answers based on the error tolerance that has been predetermined by the teacher. The results of this study indicate that the Ratcliff/ Obershelp algorithm has achieved a correction accuracy rate of 90% in Natural Science subjects, 93% in social science, 83% in Civil Education, 91% in SBK, and 97 in Religion. The final result for the average accuracy of the application is 91.00% in determining the status of student answers.
Evaluation of Measurement Converted KF, EKF, UKF, CKF, and PF in GMTI Filtering

The ground moving target indicator (GMTI) radar plays an important role in surveillance, precision tracking, and classification of ground moving targets. The measurements of a GMTI radar are range, azimuth, and radial velocity, which are nonlinear functions of the Cartesian target and sensor states. In our 2003 SPIE paper we analyzed the performance of the converted measurement based Kalman filter (CMKF), extended Kalman filter (EKF), unscented Kalman filter (UKF), and transition density based particle filter (TD-PF). In this paper, we extend our previous work by considering the cubature Kalman filter (CKF). We also compute the posterior Cramér-Rao lower bound (PCRLB) to judge the accuracy of the filters relative to the best achievable accuracy. The results of this analysis are useful in an operational system where a filter has to chosen based on state estimation accuracy, scalability, and computational complexity. We also analyze the accuracy of the TD-PF by varying the number of particles. We perform Monte Carlo simulations to analyze the accuracy and computational complexity of various filters. Based on the root time-averaged mean square (RTAMS) error of position and velocity, we find that the CMKF, EKF, UKF, and CKF have nearly the same accuracy in cases analyzed. To achieve the same level of accuracy, the TD-PF requires more than 40,000 particles with about 508 times more computational cost relative to the EKF. Contrary to conventional thinking, our results show that for cases analyzed the EKF produces the best accuracy with stable performance and lowest computational cost.
Anticipating Connectivity in (UX) Design Practices: Reframing Challenges by Introducing Theory Cards

This paper presents a design anthropological study with User Experience design departments from five large companies in Denmark, ranging from manufacturers of medical equipment through toys to control systems for industrial infrastructures We explore the challenges they face as products, services, and user research are increasingly connected. Our research shows that current methods, development processes, and organizational structures do not sufficiently support User Experience design teams in dealing with emerging design and organizational challenges that follow from increased digitalization. As a result, UX designers are struggling to anticipate the future of product interaction, user data, and their organizational role. In this paper, we explore how playing with theoretical concepts and introducing a new vocabulary may facilitate fundamental shifts in perspective necessary to instigate change. We deploy ‘theory cards’ in an experiment with one of the companies to see if theories might serve as instruments for seeing field material and design problems in ways more supportive for future design endeavours.
An improved Schnorr-based multi-signature scheme with application to blockchain

A multi-signature scheme allows a group of signers to jointly generate a signature on a common message, which can be verified by anyone, making the signature more compact. Multi-signature has the advantage of fast verification, and it helps reduce bandwidth and the cost of validation and storage. Recently, the multi-signature has drawn much attention because its advantages are desirable for blockchain and cryptocurrency. However, the traditional Schnorr-based multi-signature scheme suffers from an excessive number of communications. This paper proposes an improved Schnorr-based multi-signature scheme ASMS, which reduces one round of communication in a traditional scheme, supporting public key aggregation. Thus, it is suitable for e-business and e-government affairs scenarios. As an application, we implemented our solution in Fabric, an enterprise blockchain platform, using chaincode technology.
Short Term Cloud Motion Forecast based on Boid's Algorithm for use in PV Output Prediction

Forecasting cloud motion and dynamics is crucial for many areas of study. Solar energy production depends on the cloud coverage over the area which impacts PV output, by clouds limiting the incoming solar irradiance. In this paper we propose an adaptation of a nature-inspired technique called the Flocking Behavior Algorithm (or Boids Algorithm) for the problem of cloud motion forecasting, for nowcasting and intra-day prediction windows. Without limiting the application of the algorithm we validate it on a sequence of satellite images and show its efficiency for short term forecasting.
Towards an Empirical Perspective on Multi-Level Modeling and a Comparison with Conventional Meta Modeling

Although prior work exist focusing on how, when, and why multi-level modeling (MLM) could be applied, presently, surprisingly little is known about how learning processes of MLM proceed, what modeling difficulties learners experience, or what the comprehensibility of multi-level models, compared to their conventional counterparts, is. As a response, we take an empirical perspective on MLM. We are planning for a study collecting data on subjects’ modeling processes in order to (1) identify, by using the concept of cognitive breakdowns, modeling difficulties these subjects face while using MLM, and (2) by combining various theories from cognitive linguistics, e.g., to analyze how participants decide upon selected abstractions. In this position paper, we present the design of the planned study as well as theoretical lenses we will follow. We also share first experiences from a conducted pilot study, in terms of its focus and set-up.
Personalized Learning Based on Learning Analytics and Chatbot

Personalized learning requires technology in order to feed information about the learners’ preferences, achievements and needs. However, many available works have focused either on fully automated processes such as prediction or recommendation of learning plan. In fact, integration of human into the machine learning loop would improve the performance of the assistive technology compared to separated models. In this paper we propose an approach for personalized learning that integrates human and machine learning, and utilize learning analytics, chatbot and recommendation system. We present our proposed idea and work in progress.
A Meta-Learning Architecture based on Linked Data

In Machine Learning (ML), there is a lot of research that seek to automate specific processes carried out by data scientists in the generation of knowledge models (predictive, classification, clustering, etc.); however, an open problem is to find mechanisms that allow conferring the ability of self-learning. Thus, a meta-learning mechanism is required to allow ML techniques to self-adapt in order to improve their performance in problem solving, and even in some cases, to induce the learning algorithm itself. In this context, our research defines a meta-learning architecture using Linked Data (LD) for the automatic generation of knowledge models. Specifically, this intelligent architecture is formed by the layers of Knowledge Sources, Meta-Knowledge and Knowledge Modelling, to unify all processes to guarantee a Meta-Learning process. The Knowledge Sources layer is responsible for providing semantic knowledge about the processes of generation of knowledge models; the Meta-Knowledge layer is responsible for controlling the different processes and strategies for the automatic generation of knowledge models; and finally, the Knowledge Modelling layer is responsible for executing ML tasks defined by the Meta-Knowledge layer, among which are the tasks of feature engineering, ML algorithm configuration, model building, among others. Additionally, this article presents a case study to analyze the behavior of the different layers of the architecture, to generate knowledge models. Thus, the main contribution of this research is the definition of a Meta-Learning architecture for ML techniques, which takes advantage of the semantic information described as LD when generating the knowledge models. The preliminary results are very encouraging.
Pyramid Information Distillation Attention Network for Super-Resolution Reconstruction of Remote Sensing Images

Image super-resolution (SR) technology aims to recover high-resolution images from low-resolution originals, and it is of great significance for the high-quality interpretation of remote sensing images. However, most present SR-reconstruction approaches suffer from network training difficulties and the challenge of increasing computational complexity with increasing numbers of network layers. This indicates that these approaches are not suitable for application scenarios with limited computing resources. Furthermore, the complex spatial distributions and rich details of remote sensing images increase the difficulty of their reconstruction. In this paper, we propose the pyramid information distillation attention network (PIDAN) to solve these issues. Specifically, we propose the pyramid information distillation attention block (PIDAB), which has been developed as a building block in the PIDAN. The key components of the PIDAB are the pyramid information distillation (PID) module and the hybrid attention mechanism (HAM) module. Firstly, the PID module uses feature distillation with parallel multi-receptive field convolutions to extract short- and long-path feature information, which allows the network to obtain more non-redundant image features. Then, the HAM module enhances the sensitivity of the network to high-frequency image information. Extensive validation experiments show that when compared with other advanced CNN-based approaches, the PIDAN achieves a better balance between image SR performance and model size.
Moment-based analysis of Bayesian network properties

We use algebraic reasoning to translate Bayesian network (BN) properties into linear recurrence equations over statistical moments of BN variables. We show that this translation can always be done for various BNs, such as discrete, Gaussian, conditional linear Gaussian, and dynamic BNs. An important part of our work comes with representing BNs as while loops in probabilistic programs with polynomial assignments over random variables and parametrised distributions. We prove that closed-form summaries of probabilistic loops precisely characterize higher-order moments of BN variables. As such, we automatically solve several BN-related problems, including exact inference, sensitivity analysis, filtering, and computing the expected number of rejecting samples in sampling-based procedures. We evaluate our work on a number of BN benchmarks, using automated invariant generation within Prob-solvable loop analysis. This paper is an extended version of the "Analysis of Bayesian Networks via Prob-Solvable Loops" manuscript published at ICTAC 2020 [1].
BPTE: Bitcoin Price Prediction and Trend Examination using Twitter Sentiment Analysis

Natural Language Processing (NLP) is a challenging and evolving field with the potential of mushroom growth. This technology is expected to assume a pivotal role in bridging the gap between human communication and digital data. In line with that, NLP-driven sentiment analysis has become an attractive research area. On the other hand, bitcoin, arguably the most valuable cryptocurrency, has gained popularity as a major source of investment. In this paper, we propose a framework to perform sentiment analysis on Twitter data. We outline the method and results of predicting bitcoin price for a few days in the future. The framework is expected to be helpful in making informed decisions about our investments and policy based on anticipated future trends.
3D Print-Scan Resilient Localized Mesh Watermarking

Existing 3D print-scan watermarking schemes usually have some limitations, such as the use of auxiliary materials and expensive high-resolution devices, and low visual quality of watermarked models. Considering these limitations, we propose a novel localized mesh watermarking method, which is resilient to 3D print-scan process and suitable for ordinary consumer-level 3D printing and scanning devices. In our scheme, we use the geodesic distances of the model's surface to determine the location and scope of the localized embedded watermark and construct a special tracking signal for the synchronization of the watermark. When detecting the watermark, we amplify the watermark signal through the residual mesh and achieve blind watermark detection. By evaluating various 3D mesh models, we demonstrate that the proposed localized watermarking method can ensure a high watermark extraction accuracy after the 3D print-scan process while maintaining high visual quality.
Socially Engineering a Polarizing Discourse on Facebook through Malware-Induced Misperception

This paper reports the findings of a study testing a novel social engineering attack called Malware-induced Misperception (MIM). The goal of the MIM attack is to induce misperception by using a man-in-the-middle malware that covertly rearranges the linguistic content of an authentic social media post, web page, or an e-mail. The MIM attack was tested in controlled settings (N=311) where the malware covertly manipulated the linguistic content of a Facebook discourse to induce misperception about the climate of opinion on a polarizing issue (freedom of speech on college campuses) by making conservative-leaning comments appear as liberal-leaning. The induced misperception was assessed in the context of the spiral-of-silence theory. The theory predicts that polarizing issues discourage commenting on social media if an individual's opinion diverges from the perceived climate of opinion on that issue. Consistent with the theory, the results suggest that the MIM attack can socially engineer the spiral-of-silence effect by manipulating the comments in a Facebook discourse to appeal to the individual's political ideology and gender identity.Additional Key Words and Phrases: Malware-Induced Misperception (MIM); spiral-of-silence; social engineering; Facebook, web security
The Application of Autoencoders for Hyperspectral Data Compression

In this paper, a neural networks based algorithm for hyperspectral data compression is proposed. Three different types of autoencoders along with principal component method were implemented and compared. Kennedy Space Center dataset is used for evaluation of data compression algorithms and similarity metrics between source and reconstructed signals. For low value of the compression ratio, the training error of neural networks tends to oscillate and the use of the principal component method shows better metric score. For high value of the compression ratio, the multilayer and convolutional autoencoders demonstrate a significant advantage of compression efficiency over the principal component method.
Quantifying and detecting HPC resource wastage in cloud environments

Among many details that users need to consider when using cloud computing, the care not to waste resources requires more attention by administrators and new users. When the application does not fully utilize the provisioned resource, the end-of-the-month bill is unnecessarily increased. Several studies have developed solutions to avoid wastage using predictive techniques. Nonetheless, these approaches require applications’ to have predictive behavior and depend on pre-executions or history data. To circumvent these limitations, we explore how a reactive solution can be used to detect and contain wastage. More specifically, we discuss several important issues that arise when quantifying resource wastage caused by HPC resource wastage on the cloud and propose a reactive strategy to quantify, detect, and contain resource wastage in this context. This solution is designed so that it can be applied in environments with expert and non-expert users with no prior knowledge about the applications.
Research on Blockchain Storage Extension Based on DHT

With the rapid growth of blockchain data, the storage cost of maintaining the full node of a blockchain is getting higher and higher. This has led to the gradual weakening of the decentralization of the blockchain, increasing the risk of 51% attacks on the blockchain system. Based on DHT, we propose a blockchain storage expansion mechanism, using the Kademlia protocol to assign unique ID for every node in the blockchain network, and divide the entire node into several node clusters according to certain rules, each node cluster stores a complete blockchain data, and the nodes in the cluster only need to store a small part of the blockchain data. At the same time, in order to reduce the storage pressure of the nodes in the cluster, a dynamic reorganization mechanism is proposed, and the cluster scale will be dynamically adjusted according to the storage capacity of the nodes in the cluster. Finally, we established an experimental simulation model based on Colored Petri Nets, and verified the superiority of our proposed scheme by comparing it with the other two storage schemes.