Reviewerly/exHarmony huggingface dataset:
Robot Motion Planning as Video Prediction: A Spatio-Temporal Neural Network-based Motion Planner

Neural network (NN)-based methods have emerged as an attractive approach for robot motion planning due to strong learning capabilities of NN models and their inherently high parallelism. Despite the current development in this direction, the efficient capture and processing of important sequential and spatial information, in a direct and simultaneous way, is still relatively under-explored. To overcome the challenge and unlock the potentials of neural networks for motion planning tasks, in this paper, we propose STP-Net, an end-to-end learning framework that can fully extract and leverage important spatio-temporal information to form an efficient neural motion planner. By interpreting the movement of the robot as a video clip, robot motion planning is transformed to a video prediction task that can be performed by STP-Net in both spatially and temporally efficient ways. Empirical evaluations across different seen and unseen environments show that, with nearly 100% accuracy (aka, success rate), STP-Net demonstrates very promising performance with respect to both planning speed and path cost. Compared with existing NN-based motion planners, STP-Net achieves at least 5×, 2.6× and 1.8× faster speed with lower path cost on 2D Random Forest, 2D Maze and 3D Random Forest environments, respectively. Furthermore, STP-Net can quickly and simultaneously compute multiple near-optimal paths in multi-robot motion planning tasks.
Knowledge and skills of the digital transformation of the Greek public school in the post covid era

The present research focuses on the digital transformation the Greek Public School underwent, within a few months, due to the Covid - 19 crisis. The need for a digitally transformed education system that meets the requirements of the 21 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">st</sup> century and is characterized using educational and computing technologies [1], was highlighted by the Covid - 19 coronavirus pandemic. Furthermore, the transformation of educational units into learning organizations [2] which are digitally update and, hence, can effectively cope with the new circumstances, was also introduced. The aim of this research is to examine the perceptions the principals of the Primary Schools in the Region of Western Greece have, regarding the need for the Greek Primary School to follow the trends of the new digital era and become sufficient in their use. It, also, examines the principals’ level of ICT knowledge and skills as well as their willingness to respond to the challenges that call for an education system compatible with the new digital era and the necessity of school units to function as learning organizations in the era of the 4th Industrial Revolution. The research was conducted with the use of a questionnaire with close-ended questions. According to the results, the principals were found to have positive attitudes and views towards the available digital tools and platforms used in education in the last two years even though, most of them have low level ICT knowledge and skills. Moreover, they believe that the digital transformation of education is a slow-moving process due to certain obstacles which, also, prevent the transformation of educational units into learning organizations.
Improved Protocols for Distributed Secret Sharing

In Distributed Secret Sharing schemes, secrets are encoded with shares distributed over multiple nodes of a network. Each involved party has access to a subset of the nodes and thus to a subset of the shares and is able to reconstruct a specific secret. Usually, these schemes are evaluated by measuring the required storage overhead, as well as the encoding and decoding complexities. In this paper, we provide new Distributed (multi) Secret Sharing Protocols for <inline-formula><tex-math notation="LaTeX">$(k,n)$</tex-math></inline-formula> -threshold access structures that improve on previous results, characterized by nearly-optimal storage overhead, achieving both storage optimality and a better encoding/decoding complexity. The protocols are also simpler than previous ones and allow for easier encoding.
A Quantum Software Modeling Language

This chapter discusses a series of fundamental principles for quantum software modeling languages. These principles are then exemplified in the development of “Q-UML”—a quantum software modeling language based on the popular unified modeling language (UML).
Mixed Data Imputation Using Generative Adversarial Networks

Missing values are common in real-world datasets and pose a significant challenge to the performance of statistical and machine learning models.Generally, missing values are imputed using statistical methods, such as the mean, median, mode, or machine learning approaches.These approaches are limited to either numerical or categorical data.Imputation in mixed datasets that contain both numerical and categorical attributes is challenging and has received little attention.Machine learning-based imputation algorithms usually require a large amount of training data.However, obtaining such data is difficult.Furthermore, no considerate work has been conducted in the literature that focuses on the effects of the training and testing size with an increasing amount of missing data.To address this gap, we propose that increasing the amount of training data will improve the imputation performance.We first used generative adversarial network (GAN) methods to increase the amount of training data.We considered two state-of-theart GANs (tabular and conditional tabular) to add synthetic samples using observed data with different synthetic sample ratios.We then used three state-of-the-art imputation models that can handle mixed data: MissForest, multivariate imputation by chained equations, and denoising auto encoder (DAE).We propose a robust experimental setup on four publicly available datasets with different training-testing data divisions that have increasing missingness ratios.Extensive experimental results show that incorporating synthetic samples with training data achieves better performance compared to the baseline methods for mixed data imputation in both categorical and numerical variables, especially for large missingness ratios.
Optimal Correction Cost for Object Detection Evaluation

Mean Average Precision (mAP) is the primary evaluation measure for object detection. Although object detection has a broad range of applications, mAP evaluates detectors in terms of the performance of ranked instance retrieval. Such the assumption for the evaluation task does not suit some downstream tasks. To alleviate the gap between downstream tasks and the evaluation scenario, we propose Optimal Correction Cost (OC-cost), which assesses detection accuracy at image level. OC-cost computes the cost of correcting detections to ground truths as a measure of accuracy. The cost is obtained by solving an optimal transportation problem between the detections and the ground truths. Unlike mAp, OC-cost is designed to penalize false positive and false negative detections properly, and every image in a dataset is treated equally. Our experimental result validates that OCscost has better agreement with human preference than a ranking-based measure, i.e., mAP for a single image. We also show that detectors' rankings by OC-cost are more consistent on different data splits than mAP. Our goal is not to replace mAP with OC-cost but provide an additional tool to evaluate detectors from another aspect. To help future researchers and developers choose a target measure, we provide a series of experiments to clarify how mAP and OC-cost differ.
Casdoc

Code examples are of great value to programmers trying to learn an unfamiliar API. Effective code examples are often surrounded with plain text explanations of the relevant concepts, techniques, and API elements involved in the example. However, authoring concise yet complete explanations is a challenging balancing act. To address this challenge, we propose Casdoc, a novel authoring technique and presentation format for annotated code examples. Casdoc-formatted code examples are HTML documents designed to embed unobtrusive explanations into the code. They thus contain more explanations to address the varying needs of a larger audience, without disrupting individual readers with information they already know. Explanations are split into short annotations and organized into an intuitive tree-like structure, thus supporting a streamlined authoring process. We used Casdoc to produce 105 Java code examples as part of the course material for an undergraduate computer science course. Students preferred the new format over traditional code examples. Their interaction with code examples suggests that the intuitive structure of Casdoc annotations reduces the need for navigation aids such as search fields.
CS-KG: A Large-Scale Knowledge Graph of Research Entities and Claims in Computer Science

In recent years, we saw the emergence of several approaches for producing machine-readable, semantically rich, interlinked description of the content of research publications, typically encoded as knowledge graphs. A common limitation of these solutions is that they address a low number of articles, either because they rely on human experts to summarize information from the literature or because they focus on specific research areas. In this paper, we introduce the Computer Science Knowledge Graph (CS-KG), a large-scale knowledge graph composed by over 350M RDF triples describing 41M statements from 6.7M articles about 10M entities linked by 179 semantic relations. It was automatically generated and will be periodically updated by applying an information extraction pipeline on a large repository of research papers. CS-KG is much larger than all comparable solutions and offers a very comprehensive representation of tasks, methods, materials, and metrics in Computer Science. It can support a variety of intelligent services, such as advanced literature search, document classification, article recommendation, trend forecasting, hypothesis generation, and many others. CS-KG was evaluated against a benchmark of manually annotated statements, yielding excellent results.
Virtual reality enhances active student learning

This paper describes the advantages of using virtual reality (VR) environments like Second Life (SL) and VR headsets to motivate and engage students into active learning. Two advantages of virtual reality are that it allows users to see and interact in a three-dimensional world. Research results for the use of VR are also presented and compared to those of the traditional textbook teaching method. In addition, the authors share their successful problem-based learning (PBL) activities carried out in SL and their creative lesson ideas for using VR headsets with students.
Drone Brush: Mixed Reality Drone Path Planning

In this paper we present Drone Brush, a prototype mixed reality interface for immersive planning of drone paths for tasks such as collaborative photogrammetry and inspection. This interface employs Microsoft's HoloLens 2 to allow users to draw paths for drone navigation in 3D using hand gestures. Users can place waypoints with a simple pinch gesture, and similarly, delete and move existing waypoints. To validate paths, we leverage the HoloLens spatial map to check for potential collisions ahead of time, greatly reducing the likelihood of a collision during drone navigation. Paths are simplified and cleaned up using density-based clustering to prevent complex or redundant drone movement. In this Late-Breaking Report, we present the design and implementation of our system that integrates mixed reality, natural hand gestures, and drone path planning, which we plan to evaluate in a user study in the near future.
Learning from Multiple Annotator Noisy Labels via Sample-Wise Label Fusion

Data lies at the core of modern deep learning. The impressive performance of supervised learning is built upon a base of massive accurately labeled data. However, in some real-world applications, accurate labeling might not be viable; instead, multiple noisy labels (instead of one accurate label) are provided by several annotators for each data sample. Learning a classifier on such a noisy training dataset is a challenging task. Previous approaches usually assume that all data samples share the same set of parameters related to annotator errors, while we demonstrate that label error learning should be both annotator and data sample dependent. Motivated by this observation, we propose a novel learning algorithm. The proposed method displays superiority compared with several state-of-the-art baseline methods on MNIST, CIFAR-100, and ImageNet-100. Our code is available at: https://github.com/zhengqigao/Learning-from-Multiple-Annotator-Noisy-Labels .
Blockchain-Based Software Effort Estimation: An Empirical Study

Context: The success or failure of any software development project significantly depends on the accuracy of its effort estimates. Software development effort estimation is the foundation for project bidding, budgeting, planning, and cost control. Problem: The literature shows that a lot of work has been done on software effort estimation. But still, there is a need for improvement in effort estimation by introducing new methodologies. The structured group-based and analogy-based effort estimations are the widely used estimation methods. Nevertheless, there are several shortcomings of using these methods such as lack of experts, lack of historical data, and biasness in expert opinion, which negatively affect the estimation results. Motivation: With the advancement of technologies, such limitations could be overcome. Such as leveraging the applicability of blockchain in several domains such as improvement in software development process and network security. Method: In this article, we have proposed a Blockchain-Based Software Effort Estimation (BBSEE) methodology to improve the software effort estimation. We employ the proposed method using Web and blockchain technologies. Moreover, we also proposed an evaluation criteria to assess the efficacy of the proposed method in terms of Mean Magnitude of Relative Error (MMRE), Mean Absolute Error (MAE), and percentage of successful predictions falling (PRED (25)). Result: We performed several case studies and analyses expert opinion of 52 organizations to present the efficacy of the proposed method. Conclusion: We observe that BBSEE method outperforms than expert judgment and analogy-based effort estimation methodologies in terms of software effort estimation.
Research on Integrated Management System of Physical Education Course Information based on Spring MVC Framework

Spring MVC framework is based on the integration of Spring framework and MVC design pattern. It integrates and simplifies the traditional SSM framework and builds a lightweight Web development framework characterized by “request-response” mode, which has been widely used in the construction of Web systems. The Spring MVC framework truly implements the idea of “high cohesion, low coupling” in software engineering. It is suitable for team collaborative development according to the software life cycle process, improves the coupling between modules, and reduces development and maintenance costs. This paper constructs the request processing flow based on the Spring MVC framework, designs the functions of the integrated management system of physical education course information, proposes the physical education professional curriculum system, and completes all the basic work of software development.
Diverse Human Motion Prediction Guided by Multi-level Spatial-Temporal Anchors

Predicting diverse human motions given a sequence of historical poses has received increasing attention. Despite rapid progress, existing work captures the multi-modal nature of human motions primarily through likelihood-based sampling, where the mode collapse has been widely observed. In this paper, we propose a simple yet effective approach that disentangles randomly sampled codes with a deterministic learnable component named anchors to promote sample precision and diversity. Anchors are further factorized into spatial anchors and temporal anchors, which provide attractively interpretable control over spatial-temporal disparity. In principle, our spatial-temporal anchor-based sampling (STARS) can be applied to different motion predictors. Here we propose an interaction-enhanced spatial-temporal graph convolutional network (IE-STGCN) that encodes prior knowledge of human motions (e.g., spatial locality), and incorporate the anchors into it. Extensive experiments demonstrate that our approach outperforms state of the art in both stochastic and deterministic prediction, suggesting it as a unified framework for modeling human motions. Our code and pretrained models are available at https://github.com/Sirui-Xu/STARS.
Alzheimer's Dementia Detection through Spontaneous Dialogue with Proactive Robotic Listeners

As the aging of society continues to accelerate, Alzheimer's Disease (AD) has received more and more attention from not only medical but also other fields, such as computer science, over the past decade. Since speech is considered one of the effective ways to diagnose cognitive decline, AD detection from speech has emerged as a hot topic. Nevertheless, such approaches fail to tackle several key issues: 1) AD is a complex neurocognitive disorder which means it is inappropriate to conduct AD detection using utterance information alone while ignoring dialogue infor-mation; 2) Utterances of AD patients contain many disfluencies that affect speech recognition yet are helpful to diagnosis; 3) AD patients tend to speak less, causing dialogue breakdown as the disease progresses. This fact leads to a small number of utterances, which may cause detection bias. Therefore, in this paper, we propose a novel AD detection architecture consisting of two major modules: an ensemble AD detector and a proactive listener. This architecture can be embedded in the dialogue system of conversational robots for healthcare.
DU-QS22: A Dataset for Analyzing QC-MDPC-Based Quantum-Safe Cryptosystems

Cryptographically Relevant Quantum Computers (CRQC) will likely compromise the security of current Public-Key Encryption (PKE) mechanisms and make them unusable in the near future. In view of this, the National Institute of Standards and Technology (NIST) is currently undertaking the standardization of post-quantum Key Encapsulation Mechanisms (KEM) such that they can withstand quantum-capable attackers. One potential standardization candidate (i.e., BIKE) is based upon Quasi-Cyclic Moderate Density (QC-MDPC) codes and offers benefits in terms of security and key-size compared with other candidates. Since this candidate is highly dependent upon the performance of the decoder employed in the decapsulation subroutine, we in this paper, present a dataset for benchmarking the performance of various instantiations of decoders that may be proposed by the wider research community in future. To the best of our knowledge, no other dataset exists for researchers to benchmark their decoders for QC-MDPC-based cryptosystems.
From social networks to knowledge graphs: A plea for interdisciplinary approaches

Contextual information is a crucial factor in interdisciplinary research on social networks. At the same time, missing linked data and missing methods for large-scale knowledge networks are a serious limitation. Only few researches have addressed the questions between data science, graph theory and social network analysis. This paper seeks to discuss how this gap can be closed. For this, we use an interdisciplinary approach trying to improve the interdisciplinary exchange and method exchange. We investigate how new methods from computer science, in particular knowledge graphs, can be used within the field of SNA. The contributions of this paper are (1) a knowledge graph model representation of a multi-layer social network, (2) the showcase of examples from different domains and (3) a perspective for more interdisciplinary discussion. We present an innovative solution to discuss coming challenges in SNA with methods from the digital humanities, in particular computer science.
A Hand Gesture Recognition Method for Mmwave Radar Based on Angle-Range Joint Temporal Feature

As a sensor, millimeter-wave (mmWave) radar can realize the function of touchless gesture control, and it has become a hot research spot in the field of Human-Computer Interaction (HCI). This paper proposes a robust mmWave gesture recog-nition method, which can recognize gestures end-to-end with high accuracy in a complex environment. It is worth mentioning that the Angle-Range joint temporal (ART) feature is extracted from radar echoes to describe gestures, which is a 3D matrix feature including azimuth, distance and speed in-formation. Then, the CNN-LSTM network is used to real-ize gesture classification. The experimental results show that this method has an accuracy of 98.5% for the recognition of four gesture types. The robust performance of the proposed method is validated by data samples collected in complex en-vironment and random population, and the average recognition accuracy remains above 88.7%.
A High-Performantal and Server-Centric Based Data Center Network

With the increasing demand of data center interconnection in the network cloud era, data center networks (DCNs) play key roles in computing and communication, becoming the center of a great deal of resources and business. Therefore, how to design a DCN with good performance has been a significant issue in networks. The hypercube is a popular interconnection network topology with low diameter, symmetry and recursive structure, and scalability. As a variant of the hypercube, the crossed cube not only retains the excellent properties of the hypercube, but also performs better in terms of Hamiltonian-connectivity, embeddability, diameter, etc. In this paper, we first propose a novel and server-centric DCN, called CSDC, which is based on the crossed cube network. The performance of CSDC can be characterized by the properties of its logical structure, so we then give its logical structure <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$C_{n}$</tex-math></inline-formula> , and determine the connectivity and edge-connectivity of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$C_{n}$</tex-math></inline-formula> . Furthermore, we explore the fault-tolerant paths and disjoint paths between any two distinct nodes in <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$C_{n}$</tex-math></inline-formula> , and obtain the diameter of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$C_{n}$</tex-math></inline-formula> . Moreover, we propose a method to construct two completely independent spanning trees (CISTs) in <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$C_{n}$</tex-math></inline-formula> . Extensive evaluations demonstrate that CSDC is a magnetic DCN for building large-scale data centers.