Reviewerly/exHarmony huggingface dataset:
Will Online Learning Become a New Norm in Higher Education?

The Covid-19 pandemic has been seen by some as the event that will finally make online delivery become a dominant mode in universities. This study reports the preferences of students for online teaching, collaboration, and assessment using survey data gathered after a lengthy period of full online delivery and at a time when students were free to choose conventional, online and hybrid approaches to learning, collaboration and assessment. Most previous research has explored student preferences with respect to specific courses or at the institutional level, but the present study examines students’ preferences with respect to their discipline: arts, science and technology, business, hospitality and mathematics. Analyses revealed that students prefer conventional lectures providing that recordings are made available online, but that students prefer small group teaching, collaborative work, assessment, support and other activities to be delivered conventionally. These preferences were broadly similar across students drawn from the different discipline areas, but there was also some variation between the disciplines. Overall the findings suggest that students prefer conventional on-campus teaching, learning and assessment to full online delivery.
Encryption scheme based on small Ree groups

research-article Encryption scheme based on small Ree groups Share on Authors: Gennady Khalimov Kharkiv National University of Radio electronics, Ukraine Kharkiv National University of Radio electronics, UkraineView Profile , Yevgen Kotukh Sumy State University, Ukraine Sumy State University, UkraineView Profile , Ibraim Didmanidze Batumi Shota Rustaveli State University, Georgia Batumi Shota Rustaveli State University, GeorgiaView Profile , Svitlana Khalimova Kharkiv National University of Radio electronics, Ukraine Kharkiv National University of Radio electronics, UkraineView Profile Authors Info & Claims ICCTA 2021: 2021 7th International Conference on Computer Technology ApplicationsJuly 2021 Pages 33–37https://doi.org/10.1145/3477911.3477917Online:15 October 2021Publication History 0citation16DownloadsMetricsTotal Citations0Total Downloads16Last 12 Months16Last 6 weeks3 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Research on Building Energy Consumption Optimization Based on Improved Particle Swarm Optimization Algorithm

Aiming at the poor efficiency of traditional PSO algorithm in energy consumption optimization of office buildings in cold areas, a building energy consumption optimization method based on improved PSO was proposed. Firstly, the basic principle of PSO algorithm is analyzed, and then the PSO algorithm is improved by GE or AG operators. On the basis of the improvement, different test functions were used to optimize the algorithm, and then the maximum PSO parameters were verified. Finally, the above improvements are applied to a building example. The results show that the improved PSO energy consumption optimization method can not only improve the fast travel performance of the algorithm, but also reduce the overall search time.
An Approach for Composing Facial Expressions by Collecting Face Images on a Video

Recently, many video conference systems and video on demand systems have been used to support audio-visual telecommunication. However, many of these systems do not consider the immersiveness and presence states. So, by these systems can’t be seen the state of other users as in an office and a classroom. The tele-immersion system enables telecommunication with a high presence of users and a high immersiveness into a VR (Virtual Reality) space. However, by using a VR headset, the face is hidden by the VR headset when the user is displayed on the tele-immersion system. In this paper, we consider an approach for composing facial expressions collected by face images on a video, which is required for a tele-immersion system using the VR headset. We prepare the face images from a video using a commercial software and compose some face images using our developed software.
Dark Reciprocal-Rank: Teacher-to-student Knowledge Transfer from Self-localization Model to Graph-convolutional Neural Network

In visual robot self-localization, graph-based scene representation and matching have recently attracted research interest as robust and discriminative methods for self-localization. Although effective, their computational and storage costs do not scale well to large-size environments. To alleviate this problem, we formulate self-localization as a graph classification problem and attempt to use the graph convolutional neural network (GCN) as a graph classification engine. A straightforward approach is to use visual feature descriptors that are employed by state-of-the-art self-localization systems, directly as graph node features. However, their superior performance in the original self-localization system may not necessarily be replicated in GCN-based self-localization. To address this issue, we introduce a novel teacher-to-student knowledge-transfer scheme based on rank matching, in which the reciprocal-rank vector output by an off-the-shelf state-of-the-art teacher self-localization model is used as the dark knowledge to transfer. Experiments indicate that the proposed graph-convolutional self-localization network (GCLN) can significantly outperform state-of-the-art self-localization systems, as well as the teacher classifier. The code and dataset are available at https://github.com/KojiTakeda00/Reciprocal_rank_KT_GCN.
Deep Interactive Video Inpainting: An Invisibility Cloak for Harry Potter

In this paper, we propose a new task of deep interactive video inpainting and an application for users to interact with machines. To our best knowledge, this is the first deep learning-based interactive video inpainting framework that only uses a free form of user input as guidance (i.e. scribbles) instead of mask annotations, which has academic, entertainment, and commercial value.
Obstacle Avoidance Methods Based on Geometric Information Under the DMPs Framework

Dynamical Movement Primitives (DMPs) can equip the robot with humanoid characteristics and make it efficiently complete the task. However, research on obstacle avoidance strategies combined with DMPs is still being explored nowadays. In this paper, we proposed new obstacle avoidance methods based on geometric information under the DMPs framework, in order that the robot can still complete the task in an unstructured environment. We first generalized new trajectories by DMPs after demonstration. Then according to the interference area of a static or a moving obstacle after extracting the geometric information of the obstacle, we quantitatively adjusted the generalized trajectories by adding proper offset at a certain direction. Finally we used proportional-derivative (PD) control to ensure that the modified trajectories converge to the original one and the goal. The methods make the robot successfully avoid the interference of a static or a moving obstacle, and besides, they maintain all the advantages of the DMPs framework, such as fast goal convergence, simple mathematical principles and high imitation similarity. We verified the effectiveness of the methods in two dimensional and three dimensional simulation, and set up the real experiments using a static or a moving obstacle respectively, and finally proved the feasibility and validity of the avoidance methods.
Local users, global takeaways

Seeking to understand user populations is a key aim of communication design research and practice. User-centered technical communicators seek to amplify the experiences of technology users to transform their experiences into practical knowledge for engineers, designers, and strategists. How do we grant primacy to the user's experience in this type of work? How do we act upon our accountability to users, through selecting and architecting methods that foreground their narratives of navigating varied technologies, journeys, and expertise? This experience report highlights the methodological practices used by three communication design scholars in their doctoral dissertations to advocate for users’ local as well as the global users’ needs in differing social, cultural, and technological contexts. The experiential insights from these projects can inform user research practice by grounding inquiry in socially just relations with participants.
Deep Augmented Performers

The fusion of music-information processing and human physical functions will enable new musical experiences to be created. We developed an interactive music system called “Deep Augmented Performers,” which provides users with the experience of conducting a musical performance. This system converts music arranged using melody morphing into electrical muscle stimulation (EMS) to control the body movements of multiple performers. The melodies used in the system are divided into segments, and each segment has multiple variations of melodies. The user can interactively control the performers, thus the actual performance.
Entailments with Sentential Predicates

A simple model accounting for entailments between sentential predicates with propositional attitude verbs is proposed. In this model no reference to propositions is made. Sentential predicates denote specific sets of sentences (of a given natural language). This approach avoids the problem of intensionality of propositional attitude operators and permits to use many tools from the generalised quantifier theory.
Implementing path-dependent GADT reasoning for Scala 3

Generalized Algebraic Data Types (GADT) are a popular programming language feature allowing advanced type-level properties to be encoded in the data types of a program. While Scala does not have direct support for them, GADT definitions can be encoded through Scala class hierarchies. Moreover, the Scala 3 compiler recently augmented its pattern matching capabilities to reason about such class hierarchies, making GADT-based programming practical in Scala. However, the current implementation can only reason about type parameters, but Scala’s type system also features singleton types and abstract type members (collectively known as path-dependent types), about which GADT-style reasoning is also useful and important. In this paper, we show how we extended the existing constraint-based GADT reasoning of the Scala 3 compiler to also consider path-dependent types, making Scala’s support for GADT programming more complete and bringing Scala closer to its formal foundations.
A Machine Learning-Based Routing Technique for Off-chain Transactions in Payment Channel Networks

Blockchain is a foundational technology that has the potential to create new prospects for our economic and social systems. However, the lack of scalability limits the capability to deliver a target throughput and latency compared to the traditional financial systems. Layer-two is a collective term for solutions designed to enhance scalability by handling transactions off the main chain. For example, bidirectional payment channels allow the execution of fast transactions between two parties, thus forming the so-called payment channel networks (PCNs). Therefore, efficient routing protocols are needed to find a payment path from the sender to the receiver, with the lowest transaction fees. These protocols have to consider, among other factors, the unexpected online/offline behavior of the constituent payment nodes and the payment channel imbalance. This study proposes a novel machine learning-based technique for off-chain transaction routing. It is a fully distributed approach to be used within PCNs. For this purpose, the effect of the offline nodes and channel imbalance on the payment channels network are modeled. The simulation results demonstrate that the proposed technique strikes a good balance when considering the success ratio, transaction fees, routing efficiency, transaction overhead, and transaction maintenance overhead compared to other techniques that have previously been proposed for the same purpose.
Unsupervised High-Fidelity Facial Texture Generation and Reconstruction

Many methods have been proposed over the years to tackle the task of facial 3D geometry and texture recovery from a single image. Such methods often fail to provide high-fidelity texture without relying on 3D facial scans during training. In contrast, the complementary task of 3D facial generation has not received as much attention. As opposed to the 2D texture domain, where GANs have proven to produce highly realistic facial images, the more challenging 3D domain has not yet caught up to the same levels of realism and diversity. In this paper, we propose a novel unified pipeline for both tasks, generation of texture with coupled geometry, and reconstruction of high-fidelity texture. Our texture model is learned, in an unsupervised fashion, from natural images as opposed to scanned textures. To our knowledge, this is the first such unified framework independent of scanned textures. Our novel training pipeline incorporates a pre-trained 2D facial generator coupled with a deep feature manipulation methodology. By applying our two-step geometry fitting process, we seamlessly integrate our modeled textures into synthetically generated background images forming a realistic composition of our textured model with background, hair, teeth, and body. This enables us to apply transfer learning from the 2D image domain, thus leveraging the high-quality results obtained in this domain. We provide a comprehensive study on several recent methods comparing our model in generation and reconstruction tasks. As the extensive qualitative, as well as quantitative analysis, demonstrate, we achieve state-of-the-art results for both tasks.
Using Augmented Reality in an Inquiry-Based Physics Laboratory Course

The use of Augmented Reality (AR) in inquiry-based learning scenarios is increasingly gaining interest in research with recent studies highlighting advantages in various learning scenarios. In particular the effects of AR on the learning gain and the cognitive load regarding electrical circuits in physics laboratory courses have recently been investigated. However, this research focused on more clinical studies and therefore might be limited in its ecological validity with regards to practical use. These studies also showed contrasting results with one study reporting a higher knowledge acquisition in a tablet-based AR setting while another study reported a higher knowledge acquisition and a reduction in extraneous cognitive load in a two-dimensional non-AR setting compared to a smartglasses-based AR environment. Consequently, the importance of context specific aspects must be considered more deeply. We present a randomized controlled trial in a graded physics laboratory course evaluating the effects of a smartglasses-based AR environment on cognitive load and conceptual knowledge acquisition compared to a two-dimensional non-AR setting while also exploring affective variables. The sample consists of a total of \(N=56\) students in two groups performing a set of eight traditional inquiry-based experiments exploring the relationships in basic circuit theory. Both groups showed no differences in cognitive load or knowledge acquisition and no differences regarding their affective state before or during the experiment. However, both groups achieved significant learning gains which is not guaranteed. While these results contrast previous research showing benefits of AR, they do not rule out AR being beneficial in other cases.
“Dawn of South Lake”——Design and Implementation of Immersive Interactive System Based on Virtual Reality Technology

With the development and application of VR technology, the impact of the combination of various fields has become more and more obvious. This virtual three-dimensional space effect produced by computer technology is not limited by time and space. Therefore, under the development trend of the "Internet +" era, the combination of VR technology and various fields allows it to be flexibly applied to all places where people need it. By giving full play to its advantages and making use of its strengths, it is possible to break the routine and integrate the traditional model The way of thinking and development direction lead to a higher and farther place.
Item-Based Collaborative Filtering Blockchain for Secure Movie Recommendation System

A movie or web show or song recommendation plays a very important role in day to day daily life in giving a common person enhanced entertainment. Such a kind of technology has the ability to recommend a group of snaps to user according their likes and dislikes, or how much a movie or films is popular. But for an efficient, accurate and precise recommender system, large volumes of data along with high quality are required. As a result role of customer's privacy comes into play. Many companies try to accommodate these concerns with various tactics but are unable to provide high decipherment security. So we are proposing a system that tends to recommend films or movies to as a replacement user using the block-chain technology that will reduce misuse of privacy concerns and will in fact entice them to share more data. It finds and search movie databases available already to cluster all the important information about the preferences and movie, such as, popularity and attractiveness, required for recommendation. It generates films flocks not only comfortable for movie makers or producer to plan a replacement movie but also useful for movie recommendations for the user of a platform.
Improving Regression Performance on Monocular 3D Object Detection Using Bin-Mixing and Sparse Voxel Data

Accurate and fast 3D object detection plays a role of paramount importance for safe and capable autonomous machines. LiDAR point cloud based methods have demonstrated impressive results, yet expensive LiDAR sensors make such approaches infeasible for wide-scale adaptation. Camera based methods on the other hand are performing sub-optimally given safety and accuracy requirements. Traditionally, camera based 3D object detection is performed by generating pseudo-LiDAR point clouds from RGB-D data and using point-cloud based models, however, irregular nature of point cloud data representation makes it challenging to exploit spatial local correlations on 3D space and point cloud based models generally suffer from this. To this end, we propose Sparse Voxel based 3D Object Detection, our approach differs from traditional approaches by converting point cloud information to sparse voxel grid and utilizing sub-manifold sparse convolutions to extract information instead of PointNet based models. Furthermore, we propose Bin-Mixing layers. Bin-Mixing replaces the output layer of a neural network and boosts performance by representing the problem of regression in a fashion that is easier for network to learn.
Design and Development of Spatio-Temporal Fusion and Operation Platform for Ancient and Modern Maps

There is a growing demand for maps from government, enterprises and the pubulic. This paper studies the data management methods of ancient and modern maps to solve the problems of scattered storage, low openness, inconvenient query and use, lack of system inheritance and so on. Based on the digital processing and database management system, the map database was established after the ancient map was edited. Based on SOA service architecture and Browser/Server mode, the spatio-temporal fusion and operation platform for ancient and modern maps is established. The functions of map unified management, visual display, query and analysis, input and multi-mode export are achieved. Using Android and IOS technology to build mobile applications, improve map management and sharing application mode.
Adversarial Skill Learning for Robust Manipulation

Deep reinforcement learning has made significant progress in robotic manipulation tasks and it works well in the ideal disturbance-free environment. However, in a real-world environment, both internal and external disturbances are inevitable, thus the performance of the trained policy will dramatically drop. To improve the robustness of the policy, we introduce the adversarial training mechanism to the robotic manipulation tasks in this paper, and an adversarial skill learning algorithm based on soft actor-critic (SAC) is proposed for robust manipulation. Extensive experiments are conducted to demonstrate that the learned policy is robust to internal and external disturbances. Additionally, the proposed algorithm is evaluated in both the simulation environment and on the real robotic platform.
Dynamic Multi-Loss Weighting for Multiple People Tracking in Video Surveillance Systems

Multiple people tracking is a fundamental yet challenging task in the computer vision field, which served as a primary process for high-level tasks such as human behaviors, action recognition, pose estimation. Person tracking is decomposed into detection and re-identification (re-ID) sub-tasks. Conventionally, the detection learns classification and regression objectives simultaneously; and the re-ID sub-task is treated as a classification task. Therefore, person tracking is multiple task learning corresponding to multiple loss functions (multiple objectives) with one bounding box regression and two classifications. The difference between various tasks is as follows: the ranges of each objective are inconsistent, the contribution of each task to the overall gradient is altered, and the learning pace of each task is different (level of difficulty). It leads to an objective imbalance in multi-task learning. Previous methods proposed weighting factors as new hyper-parameters to balance the ranges of each task. The dimension of search space for manually tuning these hyper-parameters is high, which depends on the number of tasks. Accordingly, selecting reasonable weighting factors is difficult and complicated. This paper introduces dynamic multi-loss weighting (DMW) with simple but effective in which the weighting factors are dynamically changed during training without introducing any hyper-parameters. The dynamic weights are optimized to balance regression and classification objectives, which depend on the difficulty level of each task and the correlation between each task. Additionally, the general convolution operations are spatially invariant to some degree, which hinders the network’s performance. Hence, this work employs the position-sensitive operation improving feature extraction. The proposed method is conducted on the MOT17 challenging benchmark, which outperforms the online multiple people trackers without using additional data.