Reviewerly/exHarmony huggingface dataset:
Anchor-Free Small Object Detection Algorithm Based on Multi-scale Feature Fusion

In order to overcome the problems of the small proportion of the object, the variable shooting angle, and height in the UAV aerial image, an anchor-free small object detection algorithm based on the YOLOX network is proposed in this paper. Firstly, aiming at the problem of small object size, a Tri-Head module is proposed to change the size of the detection head to obtain more original information on small objects. Secondly, in order to accurately detect dense continuous small objects in complex scenes, a CBAM-CSP module incorporating the CBAM attention mechanism is proposed to increase the weights for detecting dense continuous regions of interest in images. Then, to alleviate the problems caused by background clutter and pixel blur, BiNet, a jumping multi-scale feature enhancement module, is proposed. This module fully integrates shallow feature information and deep feature information so that detection heads of different scales can obtain enough semantic image information and spatial information. Finally, the experimental results on the VisDrone dataset show that, comparing with the YOLOX network, the mAP is increased by 7.56%±0.06% when the model parameters are reduced by about 5MB, which proves that the proposed algorithm can effectively improve the detection accuracy of small targets.
Single image depth estimation based on sculpture strategy

Supervised deep learning approaches have achieved state-of-the-art performance in single image depth estimation. However, the procedure to train the network is not intervened, due to the black box character of deep learning. To enhance the generalization ability of trained models, we employ a learned prior that is inspired by the process of sculpturing, to guide the model’s learning. From the view of sculpturing, the depth estimation can be divided into two consecutive steps: the initial contour estimation and the detail refinement. The contour of scene can be introduced as the intermediate supervision which enables the network to learn true knowledge and accelerate training. In addition, the edges of the scene contain much semantic information. To better use this structured knowledge, we use an attention mechanism to integrate the edges of the scene to guide training. We also create a more precise ground truth by using self-adaptive Gaussian interpolation. Compared to other datasets, our dataset is denser, and the details of small objects and the ordinal relation in the depths of different objects is preserved. Additionally, the distribution of depth values does not change. The experimental results on the KITTI, Make3D and NYU Depth V2 datasets show that the method achieved better performance with respect to other state-of-the-art depth estimation approaches. Code is available at https://github.com/XTU-PR-LAB/HG-Net.
Collaborative Edge Caching and Transcoding for 360° Video Streaming Based on Deep Reinforcement Learning

360° video streaming provides an immersive viewing experience, but demands high bitrate transmission with low latency. To accommodate the fluctuating network condition and different user requirements, 360° videos are streamed with different quality versions, which makes the bandwidth more demanding. Edge servers with limited resources can be utilized to cache and transcode video contents near users to support 360° video streaming. The edge caching strategy and the transcoding strategy are correlated, as the cached contents limit the transcoding operations, while transcoding extends the use of cached contents at the cost of delay and provides new contents for possible caching. In this article, we consider the problem of collaborative edge transcoding and caching in an edge cluster for tile-based 360° video streaming, which aims to jointly allocate storage and computing resources to reduce quality mismatch level, delay, and transmission cost. The formulated problem is complicated considering dynamic viewers’ Field of View (FoV), video popularity, different quality versions, future user requests, and possible FoV prediction errors. To solve this problem, the process of collaborative transcoding and caching is modeled as a Markov decision process (MDP). Then, a model-free deep reinforcement learning approach, the deep deterministic policy gradient (DDPG), is used to obtain the caching replacement and computing power allocation strategy. Since the action space for caching replacement and power allocation is large, an FoV-guided scheme has been designed to speed the training of the DDPG agent. The simulation results with real viewing traces show that the proposed scheme can increase the cache hit ratio, reduce transmission cost, and improve users’ viewing experience effectively.
GenoREC: A Recommendation System for Interactive Genomics Data Visualization

Interpretation of genomics data is critically reliant on the application of a wide range of visualization tools. A large number of visualization techniques for genomics data and different analysis tasks pose a significant challenge for analysts: which visualization technique is most likely to help them generate insights into their data? Since genomics analysts typically have limited training in data visualization, their choices are often based on trial and error or guided by technical details, such as data formats that a specific tool can load. This approach prevents them from making effective visualization choices for the many combinations of data types and analysis questions they encounter in their work. Visualization recommendation systems assist non-experts in creating data visualization by recommending appropriate visualizations based on the data and task characteristics. However, existing visualization recommendation systems are not designed to handle domain-specific problems. To address these challenges, we designed GenoREC, a novel visualization recommendation system for genomics. GenoREC enables genomics analysts to select effective visualizations based on a description of their data and analysis tasks. Here, we present the recommendation model that uses a knowledge-based method for choosing appropriate visualizations and a web application that enables analysts to input their requirements, explore recommended visualizations, and export them for their usage. Furthermore, we present the results of two user studies demonstrating that GenoREC recommends visualizations that are both accepted by domain experts and suited to address the given genomics analysis problem. All supplemental materials are available at https://osf.io/y73pt/.
Assessing the stability of noisy quantum computation

Quantum computation has made considerable progress in the last decade with multiple emerging technologies providing proof-of-principle experimental demonstrations of such calculations. However, these experimental demonstrations of quantum computation face technical challenges due to the noise and errors that arise from imperfect implementation of the technology. Here, we frame the concepts of computational accuracy, result reproducibility, device reliability and program stability in the context of quantum computation. We provide intuitive definitions for these concepts in the context of quantum computation that lead to operationally meaningful bounds on program output. Our assessment highlights the continuing need for statistical analyses of quantum computing program to increase our confidence in the burgeoning field of quantum information science.
“I Have Only Checked after the Event”: Consumer Approaches to Safe Online Shopping

Online shopping has now become very common, with consumers increasingly opting to purchase products on the World Wide Web instead of visiting traditional "bricks and mortar" stores, particularly during the COVID-19 pandemic.This has, however, also provided significant opportunities for offenders to abuse the inherent trustbased nature of online shopping, whereby consumers typically do not see the products physically prior to purchasing them.As such, this article sets out to examine the actions and behaviors that individuals take to prevent online shopping fraud and what, if any, discrepancy exists between prevention messaging and consumer behavior.To accomplish this, the study utilizes secondary survey data (n = 3478 respondents) obtained from a private-sector initiative called ScamAdviser.The results find that many respondents do not use appropriate behaviors to reduce their risk when shopping online and that furthermore there is reason to believe that consumers are not served well by the online safety advice that they are given.The paper argues that there is scope to increase guardianship through better prevention advice being communicated to online shoppers.
A Survey on Privacy Preserving Voting Scheme Based on Blockchain Technology

The blockchain methodology utilizes cryptographic hashes to establish end-to-end demonstration to facilitate security for votes. The aim of the project is to establish a successful voting methodology with the help of blockchain mechanism. With this mechanism, every vote is reserved as a new block and gets updated in the database. The system assures that voting system maintains the one person, one-vote (democracy) principle. This is accomplished by matching the voter's unique face biometric at the start of each voting attempt to avoid double voting. In this, we have created an online voting system through blockchain methodology which is used to solve the issues that are faced by the pre-developed methodology. For each unique vote, different enactment is done. Miners will reject a vote if it is suspected as being malicious. Blockchain mechanism used here makes the vote trustworthy and reliable, and it will also aid to raise the number of voters besides it enhances trust toward the government. In this project, we apply the hash function using SHA—256 algorithms for secure password hashing. It is necessary to have unique hash for every vote that is being recorded by cryptographic hash by which each vote can be verified distinctly. This characteristic facilitates the general voting process’s verifiability. In addition to this, the vote that is being counted is more secure and not an individual including the operator knows about the details of the vote that is being recorded.
Formation of a teacher's digital culture by means of mass open online courses

Introduction. Education is a strategic resource for sustainable development, as it forms a new culture of society, sets new guidelines. The low qualification of teachers in the field of digital culture is a hindering process in the digital economy. It is the university that should comprehensively solve the problems of training a future teacher, taking into account the methodology and implementation of practical activities in the direction of the formation of the digital culture of the teacher. The purpose of our research is to generalize the use of the opportunities of mass open online courses in the continuous development of a teacher's digital culture, starting from the first year of study at a pedagogical university. To achieve this goal, we have chosen a methodology for calculating online course assessment indicators proposed by Kirkpatrick and Jack Phillips. Materials and Methods. The analysis of psychological and pedagogical literature on the problem of the formation of the digital culture of the future teacher made it possible to determine its relevance for the modern development of digital society. One of their tools for its formation, the authors propose the use of open online courses within the framework of the organization of mixed education at the university. Results. In the course of the study, we identified the initial level of digital culture of students of 3-4 courses of the faculties of mathematics, physics, computer science and primary school teacher training South Ural State Humanitarian Pedagogical University. The topics of mass open online courses were determined on the basis of the identified level of digital culture and personal needs of students in such courses, which made it possible to form the content of an individual educational trajectory. The survey of students who took part in our study revealed the willingness of students to use the opportunities of mass open online courses in their academic and further professional activities for the development of digital culture. Discussion and Conclusion. In the course of this study, indicators for evaluating online courses based on the Kirkpatrick and Philips methodology were identified, recommendations were given to eliminate emerging risks when taking mass open online courses. In conclusion, it is concluded that mass open online courses can be used as a means of developing a teacher's digital culture.
Deep learning‐based smishing message identification using regular expression feature generation

Abstract The increase in the number of undesired SMS termed smishing message and the data imbalance problem has generated a great demand for the development of more reliable anti‐spam filters. State of the art machine learning approaches are being employed to recognize and separate spam messages. Most recent studies target message classification by using numerous properties and features of the words but fail to consider the circumstantial features like long‐range dependencies between the words that are extremely important in identifying smishing messages. The idea is to develop an intelligent model that will distinguish between smishing messages and ham messages, by adopting a combined approach of regular expression (Regex), machine learning (ML) and deep learning (DL) models. Regex rules are generated using the dataset's spam messages for the purpose of refining the dataset. Support vector machine (SVM), Multinomial Naive Bayes and Random Forest are included under machine learning models and long short‐term memory (LSTM), bidirectional long short‐term memory (Bi‐LSTM), stacked LSTM and stacked Bi‐LSTM are included under deep learning models. The comparison between machine learning models and deep learning models is also carried out based on the performance evaluation parameters namely accuracy, precision, recall and F 1 score of the models. It is observed that deep learning models perform better than machine learning models and the introduction of a regular expression to the dataset increases the efficiency of both the deep learning models and machine learning models.
Soft Computing for Intelligent Edge Computing

Recently, reinforcement learning (RL) has been applied to task-oriented dialogue systems by using latent actions to solve shortcomings of supervised learning (SL). In this paper, we propose a multi-domain task-oriented dialogue system, called Dialogue System with Optimizing a Recurrent Action Policy using Efficient Context (DORA), that uses SL, with subsequently applied RL to optimize dialogue systems using a recurrent dialogue policy. This dialogue policy recurrently generates explicit system actions as a both word-level and high-level policy. As a result, DORA is clearly optimized during both SL and RL steps by using an explicit system action policy that considers an efficient context instead of the entire dialogue history. The system actions are both interpretable and controllable, whereas the latent actions are not. DORA improved the success rate by 6.6 points on MultiWOZ 2.0 and by 10.9 points on MultiWOZ 2.1.
Blockchain-based donations traceability framework

The rapid growth of modern technologies has encouraged non-profit organizations (NPOs) to harness such technologies to better serve the charity sector, especially in relation to charity donation processes. Non-profit organizations primarily rely on fundraising that may involve opaque operations, which leads to the exacerbation of fears that donations may be used for illegal purposes or not reach deserving people. The necessity of charity donations traceability system is inevitable to overcome such concerns, which have an adverse impact on doners’ trust in the donation process. In this study, we propose a blockchain-based donation traceability framework intended to enable all involved parties to trace the progress of charity donations from the moment they are given by donors to the moment they reach the intended recipients. The system is built on a public-permissioned blockchain on the Ethereum platform, with every transaction being recorded as a block in the chain. These blocks of information are immutable and visible to all parties, and they also enable timely and traceable transactions. The proposed framework’s effectiveness is evaluated using a hybrid qualitative approach and proves to improve the traceability of charity donations overcoming the uncertainty associated with current systems.
A Quantitative Analysis of Offensive Cyber Operation (OCO) Automation Tools

The ecosystem for automated offensive security tools has grown in recent years. As more tools automate offensive security techniques via Artificial Intelligence (AI) and Machine Learning (ML), it may result in vulnerabilities due to adversarial attacks. Therefore, it is imperative that research is conducted to help understand the techniques used by these security tools. Our work explores the current state of the art in offensive security tools. First, we employ an abstract model that can be used to understand what phases of an Offensive Cyber Operation (OCO) can be automated. We then adopt a generalizable taxonomy, and apply it to automation tools (such as normal automation and the use of artificial intelligence in automation). We then curated a dataset of tools and research papers and quantitatively analyzed it. Our work resulted in a public dataset that includes analysis of (n=57) papers and OCO tools that are mapped to the the MITRE ATT&CK Framework enterprise techniques, applicable phases of our OCO model, and the details of the automation technique. The results show a need for a granular expansion on the ATT&CK Exploit Public-Facing application technique. A critical finding is that most OCO tools employed Simple Rule Based automation, hinting at a lucrative research opportunity for the use of Artificial Intelligence (AI) and Machine Learning (ML) in future OCO tooling.
CosmoScout VR: A Modular 3D Solar System Based on SPICE

We present CosmoScout VR -a modular 3D Solar System for interactive exploration and presentation of large space mission datasets.This paper describes the overall architecture as well as several core components of the framework.To foster the application in various scientific domains, CosmoScout VR employs a plugin-based architecture.This not only reduces development times but also allows scientists to create their own data visualization plugins without having to modify the core source code of CosmoScout VR.One of the most important plugins -level-of-detail terrain rendering -is described in greater detail in this paper.Another key feature of CosmoScout VR is the scene graph which is tightly coupled with NASA's SPICE library to allow for high-precision positioning of celestial objects, such as planets, moons, and spacecrafts.SPICE is also used for the seamless navigation throughout the Solar System in which the user automatically follows the closest body.During navigation, the virtual scene is scaled in such a way, that the closest celestial body is always within arm's reach.This allows for simultaneous exploration of multiple datasets in their spatial context at diverse scales.However, the navigation uses all six degrees of freedom which can induce motion sickness.In this paper, we present some counter measures as well as evaluate their effectiveness in a user study.CosmoScout VR is open source, cross-platform, and while it can run on conventional desktop PCs, it also supports stereoscopic multi-screen systems, such as display walls, DOMEs or CAVEs.
English language teachers’ engagement in and preference for experiential learning for professional development

Experiential learning is an assertion in the professional development of teachers. However, there is a scarcity of studies exploring experiential learning from the integrative perspectives in the English as a Foreign Language (EFL) context and at the level of a primary school. This study, therefore, aimed to assess primary school EFL teachers' engagement in experiential learning from these perspectives. A concurrent mixed research design was adopted to explore the cases. 186 teachers were selected for the questionnaire while eight teachers were selected for interviews. Questionnaires, document analysis, and interviews were used to collect data. The data were analyzed both quantitatively and qualitatively. The results indicated that most primary school English language teachers were mostly engaged in professional learning and development activities. Besides, they applied cognitive experiential learning strategies and preferred diverger experiential learning styles dominantly. These findings indicate a deviation from the scope, focus, and major aims of experiential learning and constructivist premises. As a result, the study suggested that the teacher development programs for primary school teachers in the EFL context should incorporate activities that lead to the self-directed and autonomous learning of teachers.
Implications of the Token Economy: A Taxonomy and Research Agenda

In the wake of blockchain technology, token offerings emerged as a new way of corporate finance, whereby companies sell digital tokens representing some kind of value to the public, typically in exchange for cryptocurrency or fiat money. Within a short couple of years, various types of tokens were developed that serve different purposes, and numerous potentials and shortcomings of the so-called token economy have surfaced. Its potential impact reaches far beyond the financial sector and entails profound implications on businesses and the economy as a whole. In this paper, a mixed-methods approach is used, which consists of a quantitative analysis of the properties of 254 tokens in combination with 15 qualitative expert interviews to present a thorough investigation of the constituents and consequences of the token economy. Furthermore, a definition of token economy is proposed; the business, economic, and legal implications are investigated; and the roles of the stakeholders in a token ecosystem are highlighted. Finally, a structured agenda for future research is presented.
The Application of Immersive Interactive Technology in Animation Teaching

In order to improve the interactive and immersive effect of animation teaching, this paper applies immersive interactive technology to animation teaching. In the light processing of animation teaching, this paper uses four-step phase shift and PCA algorithm to process the interference fringes with vibration effects collected by the CCD camera. The influence of white light background noise in the environment on the laser speckle measurement can be effectively filtered by adding a filter device to the imaging optical path. In order to explore the antivibration ability of different phase extraction algorithms of laser speckle interferometry in the presence of vibration in the measurement environment, this paper constructs an application model of immersive interactive technology in animation teaching. From the test results, it can be seen that the application effect of the immersive interactive technology proposed in this paper in animation teaching is relatively obvious.