Reviewerly/exHarmony huggingface dataset:
Variational Bayesian Based Localization for Intelligent Vehicle Using Lidar and GPS Data Fusion: Algorithm and Experiments

Accurate localization is crucial for safe operation of intelligent vehicle (IV). In practice, global positioning system (GPS) signals sometimes may be contaminated or lost, resulting in inaccurate positions of IV. In this article, the distance of IV’s position between previous frame and current frame is derived from Lidar point cloud registration. A novel slide window variational Bayesian (VB) based localization method is proposed for IV with multiple dynamics by fusing GPS and Lidar. In the proposed method, the state of IV, the motion model identity of IV, the measurement loss identity, and the loss probability of measurement are jointly estimated by the VB technique. The effectiveness of the proposed localization method is validated by simulations and field experiments.
Modeling Global Solar Radiation Using Machine Learning with Model Selection Approach: A Case Study in Tanzania

The measurement of solar radiation is essential information for different applications. Thus, predicting future values of this data is also important, from load control in electrical networks to forecasts in the agricultural area. However, obtaining the necessary data for such predictions requires stations with specific equipment, which is generally expensive and scarce. This conflict between the need for data and the difficulty in obtaining it produces a scenario where new and cheaper technologies must be used to mitigate existing problems. In this context, machine learning methods have been satisfactorily used to predict solar radiation in different locations. This work presents a study of machine learning models in predicting solar radiation values in the Dar es Salaam region, Tanzania. This is the first machine learning for solar radiation prediction in Tanzania to the best of the authors’ knowledge. Here is presented a framework where three different machine learning techniques are used and compared to predict these data: support vector regression, extreme gradient boosting, and ridge regression. The results show that the three models can predict solar radiation with a good error rate and that the use of all available variables reduces the error considerably.
Toward Applying Agglomerative Hierarchical Clustering in Improving the Software Development Effort Estimation

Background: There are many studies on the effect of data clustering on the effort estimation process. Most of them are on partitioning and density-based clustering, and some use hierarchical clustering but fewer details on the linkage methods. Aim: we concentrate on the aspect of the agglomerative hierarchical clustering algorithm’s effectiveness on the accuracy of the effort estimation. Method: We used the agglomerative hierarchical clustering algorithm to group the data into clusters then performed the IFPUG FPA method for effort estimation. The ISBSG dataset was used in this study. The number of clusters is determined using the dendrogram’s cut points. Different cut points and linkage methods were employed to cluster the dataset for the comparison. The estimated results of these clusters were compared with the result from the whole dataset without clustering. Result: with the selected number of clusters, results are consistently better than without clustering with all selected evaluation criteria. Conclusion: the accuracy of the effort estimation can be significantly improved when using agglomerative hierarchical clustering.
SLFNet: A Stereo and LiDAR Fusion Network for Depth Completion

Acquiring dense and precise depth information in real time is highly demanded for robotic perception and automatic driving. Motivated by the complementary nature of stereo images and LiDAR point clouds, we propose an efficient stereo-LiDAR fusion network (SLFNet) to predict a dense depth map of a scene. Specifically, the LiDAR point cloud is first projected onto each image plane of the stereo images to generate sparse RGB-D maps. Then, multi-modal feature fusion is performed between RGB image and sparse RGB-D map of the same viewpoint, and the resultant features are utilized to generate a coarse disparity map for stereo fusion. Next, complementary geometric information in stereo images and sparse RGB-D maps are incorporated to perform occlusion-aware refinement. Finally, an edge-aware refinement module is conducted to encourage the depth discontinuities to be consistent with edges in the image. Experimental results demonstrate that our network can effectively fuse the stereo images and point clouds to produce accurate depth estimations at 6 FPS, which is <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$8\times$</tex-math></inline-formula> faster than existing methods. Comparative results show that our network achieves the state-of-the-art performance on the KITTI and Virtual KITTI2 datasets.
Utilization of Free Platforms in Online Learning

The use of free platforms in online learning raises various problems, especially with regard to the assessment process. This study is intended to describe the teacher's response to the use of free platforms in online learning assessments. Participants in this study consisted of 23 elementary school teachers in Kebumen Regency. This research uses quantitative research which is supported by survey data collection. The data collected was obtained from the results of the questionnaire and was measured using a Likert scale. This study obtained the results that the teacher's response to the use of free platforms in the assessment of online learning in elementary schools in terms of aspects, namely the preparation aspect reached 75.4% (enough), the implementation aspect reached 80.1% (high), the processing aspect reached 75.4% (enough), and the utilization aspect reached 79% (enough). Research findings can be an initial picture for future researchers to analyze or test the effect of using free platforms on online learning assessments.
Myths and mythconceptions: what does it mean to be a programming language, anyhow?

Modern software does not stand alone; it is embedded in complex physical and sociotechnical systems. It relies on computational support from interdependent subsystems as well as non-code resources such as data, communications, sensors, and interactions with humans. Both general-purpose programming languages and mainstream programming language research focus on symbolic notations with well-defined abstractions that are intended for use by professionals to write programs that solve precisely specified problems. There is a strong emphasis on correctness of the resulting programs, preferably by formal reasoning. However, these languages, despite their careful design and formal foundations, address only a modest portion of modern software and only a minority of software developers. Several persistent myths reinforce this focus. These myths express an idealized model of software and software development. They provide a lens for examining modern software and software development practice: highly trained professionals are outnumbered by vernacular developers. Writing new code is dominated by composition of ill-specified software and non-software components. General-purpose languages may be less appropriate for a task than domain-specific languages, and functional correctness is often a less appropriate goal than overall fitness for task. Support for programming to meet a specification is of little help to people who are programming in order to understand their problems. Reasoning about software is challenged by uncertainty and nondeterminism in the execution environment and by the increasingly dominant role of data, especially with the advent of systems that rely on machine learning. The lens of our persistent myths illuminates the dissonance between our idealized view of software development and common practice, which enables us to identify emerging opportunities and challenges for programming language research.
Directed Acyclic Graph-based Neural Networks for Tunable Low-Power Computer Vision

Processing visual data on mobile devices has many applications, e.g., emergency response and tracking. State-of-the-art computer vision techniques rely on large Deep Neural Networks (DNNs) that are usually too power-hungry to be deployed on resource-constrained edge devices. Many techniques improve DNN efficiency of DNNs by compromising accuracy. However, the accuracy and efficiency of these techniques cannot be adapted for diverse edge applications with different hardware constraints and accuracy requirements. This paper demonstrates that a recent, efficient tree-based DNN architecture, called the hierarchical DNN, can be converted into a Directed Acyclic Graph-based (DAG) architecture to provide tunable accuracy-efficiency tradeoff options. We propose a systematic method that identifies the connections that must be added to convert the tree to a DAG to improve accuracy. We conduct experiments on popular edge devices and show that increasing the connectivity of the DAG improves the accuracy to within 1% of the existing high accuracy techniques. Our approach requires 93% less memory, 43% less energy, and 49% fewer operations than the high accuracy techniques, thus providing more accuracy-efficiency configurations.
Design and Logic Synthesis of a Scalable, Efficient Quantum Number Theoretic Transform

The advent of quantum computing has engendered a widespread proliferation of efforts utilizing qubits for optimizing classical computational algorithms. Number Theoretic Transform (NTT) is one such popular algorithm that accelerates polynomial multiplication significantly and is consequently, the core arithmetic operation in most homomorphic encryption algorithms. Hence, fast and efficient execution of NTT is highly imperative for practical implementation of homomorphic encryption schemes in different computing paradigms. In this paper, we, for the first time, propose an efficient and scalable Quantum Number Theoretic Transform (QNTT) circuit using quantum gates. We introduce a novel exponential unit for modular exponential operation, which furnishes an algorithmic complexity of O(n). Our proposed methodology performs further optimization and logic synthesis of QNTT, that is significantly fast and facilitates efficient implementations on IBM’s quantum computers. The optimized QNTT achieves a gate-level complexity reduction from power of two to one with respect to bit length. Our methodology utilizes 44.2% fewer gates, thereby minimizing the circuit depth and a corresponding reduction in overhead and error probability, for a 4-point QNTT compared to its unoptimized counterpart.
Examining the Maturity of Bitcoin Price through a Catastrophic Event: The Case of Structural Break Analysis During the COVID-19 Pandemic

In this research, we examine the maturity of Bitcoin by using structural break analysis to examine Bitcoin price. Our results reveal that the number of structural breaks of Bitcoin has remained the same at five. The Solidification phase started 8 months prior to the onset of the pandemic, signifying the small effect of the pandemic on Bitcoin price. During the pandemic, Bitcoin was more susceptible to underlying economic factors than to either COVID-related variables alone or to a combination of both factors. We conclude that Bitcoin has reached a certain level of maturity as an investment vehicle.
Missing standard features compared with similar apps? A feature recommendation method based on the knowledge from user interface

To attract and retain users, deciding what features should be added in the next release of apps becomes very crucial. Different from traditional software, there are rich data resources in app markets to perform market-wide analysis. Considering that capturing key features that apps lack compared with its similar products and making up for them can be conducive to enhance the competitiveness, we propose a method to establish the feature relationships from the level of UI pages and recommend missing key features for the pages of apps based on these relationships. Firstly, we utilize the UI testing tool to collect UI pages for apps in the repository, and give the method to gain the feature information in them. Then, we identify the products similar to the analyzed app based on topic modeling technique. Finally, we establish the relationships between features by analyzing UI pages gained for the analyzed app as well as its similar products, and identify suitable features recommended to UI pages of the analyzed app based on these relationships. The experiment based on Google Play shows that our method can recommend features for apps from the level of UI pages effectively. • Give the method to comprehensively mine the features in UI pages. • Provide the method to identify similar products for apps. • Propose a novel method to capture key features missed by apps.
A novel stereo image self-inpainting network for autonomous robots

Recently, vision-based methods have exhibited promising prospects in assigning autonomous robots more capabilities for better environment perception. Since visual sensors are easily affected under extreme conditions, current image inpainting methods based on CNNs jointly with generative adversarial networks (GANs) usually generate patches quite different from the ground truth (GT), which is harmful to autonomous robots. In this paper, we propose a novel multiscale feature alignment module with an early fusion strategy to align the left and right feature maps to better capture the motion cues between them. Then, the aligned features are fused to fill holes in the left image. To aggregate the multiscale feature maps dynamically, we propose a multiscale feature aggregation module based on an attention mechanism, of which the fusion module is designed as a symmetrical architecture to adaptively incorporate the complementary contextual correlations from different feature branches. In addition, a spatial attention module able to capture the correlations among pixels is introduced into our network to enhance the inpainting capacity and generate more refined details. To evaluate the effectiveness of our proposed method, many experiments are conducted on a stereo image dataset. The quantitative and qualitative results show that our method significantly outperforms the recent state-of-the-art image inpainting methods while running over 22 fps on a single NVIDIA RTX2080Ti GPU.
Learning to branch with Tree-aware Branching Transformers

Machine learning techniques have attracted increasing attention in learning Branch-and-Bound (B&B) variable selection policies, but most of the existing methods lack extensions to heterogeneous problems. Though parameterizing search trees has recently shown a promising alternative for heterogeneous scenarios, it remains challenging to maintain good performance when generalizing to instances comparatively harder to solve than those seen during training. To fill this gap, we propose a tree-aware transformer-based branching framework for branching efficiently and effectively. Specifically, the transformer-based branching is conducted, in which the mutual connections between candidate variables can be evaluated by the self-attention mechanism. Then, we novelly encode the empirical data in the search tree, i.e., branching history, with a binary tree representation. In this way, we can fully utilize features exploited from the parameterized B&B search trees and stronger branching policies can be attained thereby. The proposed models are evaluated on multiple benchmark instances and achieve a significant boost on performance, in terms of smaller B&B search trees and lower primal–dual integrals and gaps for harder problems within a given time limit. Ablation studies further validate the effectiveness of our method.
Classification Based Neural Network Modelling with Continuous and Sequential data

Data mining is a potential process that involves finding useful data from an existing database. The main core work of the paper is classification with Neural Networks and Pattern mining. The study is involved in figuring out the efficient and accurate algorithm from the existing mining algorithms. The sequential mining algorithms are incorporated in areas where the data are in the form of transactions. However, in this paper, the closed and frequent sequential mining algorithms are looked at deeper with the neural network Based Multi-Layer Perceptron to pick out a productive algorithm. There are a lot of potential sequential pattern mining algorithms that can be used on different types of data. And some are good with results when they are worked with certain datasets. The frequent sequential algorithms produce the data based on the ‘minsup’ value provided. Unlike the other mining algorithms, the closed pattern mining algorithms are compact and lossless when producing results. In the frequent sequential pattern mining category, the lattice-based CM-SPADE algorithm stands out with high accuracy in terms of memory management in megabytes and execution time in milliseconds. The CM-ClaSP algorithm performs better than the ClaSP, CloFAST and BIDE+ algorithms in the closed sequential pattern mining algorithm. Now the performance of the CM-SPADE has been added with the classification based format Multi-Layer Perceptron to boost up the Perceptron Networks Classification Style with an outcome of 93% of accuracy.
Inventorizing, situating, transforming : Social semiotics and data visualization

This chapter is an overview of social semiotics as a productive framework for research on data visualization. It provides conceptual instruments that can be used to explore the relationship between the formal properties of data visualization and the meanings and practices that these may promote or hinder among users. In particular, the chapter argues that a social semiotic framework can be used to inventorize, situate, and transform visualization resources. Overall, it links descriptive, interpretive, and critical objectives to generate a framework aimed at understanding how data visualization ‘works’ from a formal standpoint, what meanings are consistently associated with particular semiotic resources, and how both key semiotic ‘rules’ and dominant meanings may be questioned and changed.
Digital vs Print resources at African University Institutions: A Discussion Document

Digital resources and the speed and ease with which researchers anywhere can now access vast amounts of information have transformed the nature of the research process. The advent of electronic resources and digital libraries and repositories has also dramatically changed the function of academic libraries in terms of organization, service delivery, and collection development, with much information and many resources now accessed online. Nowadays, librarians and information professionals are expected to provide services to library users who increasingly demand instant access to information on multiple digital platforms. The attraction of electronic resources and the archiving of these resources, is easy to understand: they are eco-friendly, time and cost saving, offer multi-access, round the clock availability, and provide a facility to access resources without requiring a physical presence in the library.
Demonstration of optimal non-projective measurement of binary coherent states with photon counting

Quantum state discrimination is a central problem in quantum measurement theory, with applications spanning from quantum communication to computation. Typical measurement paradigms for state discrimination involve a minimum probability of error or unambiguous discrimination with a minimum probability of inconclusive results. Alternatively, an optimal inconclusive measurement, a non-projective measurement, achieves minimal error for a given inconclusive probability. This more general measurement encompasses the standard measurement paradigms for state discrimination and provides a much more powerful tool for quantum information and communication. Here, we experimentally demonstrate the optimal inconclusive measurement for the discrimination of binary coherent states using linear optics and single-photon detection. Our demonstration uses coherent displacement operations based on interference, single-photon detection, and fast feedback to prepare the optimal feedback policy for the optimal non-projective quantum measurement with high fidelity. This generalized measurement allows us to transition among standard measurement paradigms in an optimal way from minimum error to unambiguous measurements for binary coherent states. As a particular case, we use this general measurement to implement the optimal minimum error measurement for phase-coherent states, which is the optimal modulation for communications under the average power constraint. Moreover, we propose a hybrid measurement that leverages the binary optimal inconclusive measurement in conjunction with sequential, unambiguous state elimination to realize higher dimensional inconclusive measurements of coherent states.
SBGTool v2.0: An Empirical Study on a Similarity-Based Grouping Tool for Students’ Learning Outcomes

Visual learning analytics (VLA) tools and technologies enable the meaningful exchange of information between educational data and teachers. This allows teachers to create meaningful groups of students based on possible collaboration and productive discussions. VLA tools also allow a better understanding of students’ educational demands. Finding similar samples in huge educational datasets, however, involves the use of effective similarity measures that represent the teacher’s purpose. In this study, we conducted a user study and improved our web-based similarity-based grouping VLA tool, (SBGTool) to help teachers categorize students into groups based on their similar learning outcomes and activities. SBGTool v2.0 differs from SBGTool due to design changes made in response to teacher suggestions, the addition of sorting options to the dashboard table, the addition of a dropdown component to group the students into classrooms, and improvement in some visualizations. To counteract color blindness, we have also considered a number of color palettes. By applying SBGTool v2.0, teachers may compare the outcomes of individual students inside a classroom, determine which subjects are the most and least difficult over the period of a week or an academic year, identify the numbers of correct and incorrect responses for the most difficult and easiest subjects, categorize students into various groups based on their learning outcomes, discover the week with the most interactions for examining students’ engagement, and find the relationship between students’ activity and study success. We used 10,000 random samples from the EdNet dataset, a large-scale hierarchical educational dataset consisting of student–system interactions from multiple platforms at the university level, collected over a two-year period, to illustrate the tool’s efficacy. Finally, we provide the outcomes of the user study that evaluated the tool’s effectiveness. The results revealed that even with limited training, the participants were able to complete the required analysis tasks. Additionally, the participants’ feedback showed that the SBGTool v2.0 gained a good level of support for the given tasks, and it had the potential to assist teachers in enhancing collaborative learning in their classrooms.