Reviewerly/exHarmony huggingface dataset:
Adaptation of a robotic dialog system for medication reminder in elderly care

Social robots can assist older adults in their daily life. Verbal conversation is a natural and convenient way for older adults to interact with social robots. However, most of the existing conversation-based robot services, such as medication reminders, are rule-based systems. These systems require many hand-crafted rules and a significant amount of expert knowledge, therefore they cannot adapt to older adults’ characteristics and dialog history. There are many reinforcement learning (RL) based methods for task-oriented dialogues, but they mainly focus on completing the tasks through text-based conversations. Those methods cannot be directly used for elderly care applications involving human–robot interactions (HRI). Considering the above shortcomings, we proposed a dialog system adaptation method (DSAM) for social robots. The DSAM is based on reinforcement learning which considers the characteristics of older adults, the dialog history and user preference to adapt the dialog policy and improve the dialog module. We implemented DSAM in our custom-made ASCCBot social robot. To evaluate DSAM, we firstly tested the dialog agent which was trained by a user simulator with different settings. The results show that the obtained agent achieves a good result with the desired dialog flow compared to the baseline agent. Based on the obtained dialog policy, the adaptation process is evaluated. The results show that with a good success rate, the number of dialog turns is decreased and the NLU module performance is improved by the adaptation process, which proves the effectiveness of DSAM. We also tested DSAM with human subjects. The results show that the average adaptation success rate is 94.7% and the preference distance reaches 0 after 6 rounds of adaptation while creating reminders successfully with a limited amount of user feedback.
Are FinTech, Robotics, and Blockchain index funds providing diversification opportunities with emerging markets?Lessons from pre and postoutbreak of COVID-19

The study has been inspired by the emergence of technology-based assets, namely, FinTech, Robotics, and Blockchain in the 4th Industrial Revolution. We are examining diversification opportunities with nonconventional technology funds based on FinTech, Robotics, and Blockchain while investing in MSCI Emerging Markets Index, and finally gauging the most resilient fund during the pre-and post-outbreak of COVID-19. The five technology-driven funds considered are ARK FinTech Innovation Exchange Traded Funds (ARKF), Global X FinTech Exchange Traded Funds (FINX), First Trust NASDAQ Artificial Intelligence and Robotics Exchange Traded Funds (ROBT), Global X Robotics and Artificial Intelligence (BOTZ), and Ishares Robotics and Artificial Intelligence (IRBO) to investigate diversification opportunities with MSCI Emerging Markets Index. The time-varying dynamic spillover using the Vector Auto Regression Model for average, low, and high volatility quantiles and the network of volatility connectedness based on quantile VAR have been applied to capture diversification and identifying the most resilient fund. The study found that ARKF and FINX provide diversification opportunities. In each quantile, these two funds are evidence of diversification, and BOTZ, also shows diversification evidence. Moreover, FINX is the throughout resilient fund, and ARKF is the most resilient in extreme quantiles. Throughout the quantiles, it is perceived a significant impact of COVID-19 on the total connectedness of Funds with the emerging market index.
Research progress in digital pathology: A bibliometric and visual analysis based on Web of Science

The development of whole slide image and deep neural network technologies has contributed to the paradigm shift in diagnostic pathology and has received much attention from researchers, with related publications increasing yearly and "exploding" in recent years. However, few studies have systematically reviewed "digital pathology" using bibliometric tools. In this study, we will use multiple approaches to visualize and analyze "digital pathology" to provide a comprehensive and objective picture of the field's historical evolution and future development.We use VOSviewer, CiteSpace, Gephi, and R to analyze the authors, institutional and national collaboration networks, keyword co-occurrence, and co-citation analysis to visualize the current status of global digital pathology research.Digital pathology-related research is mainly active in "molecular, biological, and immunology" journal groups, "pharmaceutical, medical, and clinical" journal groups, and "psychology, education, and health" journal groups; in addition to "digital pathology," "diagnosis," "deep learning," "histopathology," and "surgical pathology" are also active research topics; the U.S. has significant research results in digital pathology, with the top 10 publishing institutions all coming from the U.S. In the past two decades, global digital pathology-related research can be divided into two major research areas. One is about system verification and optimization of WSI, and the other is about the application and development of artificial intelligence technology in digital pathology. Among them, based on the development of computer technology and the update of the machine learning concept, the research results for deep neural network technologies have been more concentrated in recent years. The robust performance of deep neural networks in feature extraction and image analysis provides a new research direction for improving digital pathology-aided diagnosis systems, which is where the research hotspots have been in recent years.The bibliometric analysis may help better understand the current status of research within the field of digital pathology and provide references and lessons for future related research.
Real-time post-processing for physical-layer secure key distribution in fiber networks

Physical-layer secure key distribution (PLSKD) in fiber networks provides robust security and full compatibility with current fiber infrastructure. The post-processing is generally required to improve the key consistency, security, and randomness in PLSKD. However, offline digital signal processing was generally adopted in the previous PLSKD schemes, while the real-time high-speed post-processing is necessary towards the practical PLSKD applications. In this paper, a real-time pipeline post-processing is designed and demonstrated using field-programmable gate array (FPGA) boards. A complete set of post-processing is applied, including double-threshold quantization, information reconciliation (IR) with Bose–Chaudhuri–Hocquenghem code, and privacy amplification (PA) with SM3 hash algorithm. The system architecture is detailed by combining all these processing steps, where the trade-offs for the post-processing scheme are analyzed. We also assess the performance of post-processing implementation on FPGA in terms of throughput and hardware resource utilization. The throughput of post-processing is optimized by pipeline design approach with the parallel processing capability of FPGA, where the maximum data throughputs are 50, 57.1, and 332.5 Mb/s for the quantization, IR, and PA, respectively. On the FPGA platform, the final key rate is up to 12.2 Mb/s. The proposed real-time post-processing FPGA prototype is a good candidate for the practical application of PLSKD in fiber networks.
An exploration-based SVVR approach to promote students’ chemistry learning effectiveness

In science education, chemistry is a discipline that involves macroscopic matter and microscopic particles. It is difficult for the traditional teaching approach to help learners comprehend abstract chemical knowledge. Researchers have attempted to use Virtual Reality (VR) to help learners realize meaningful knowledge via visualizing the learning content. However, merely engaging students in experiencing the scientific contexts is not enough. To fill this gap, the present study adopted Spherical Video-based Virtual Reality (SVVR) to create authentic learning contexts for learners, and proposed an exploration-based SVVR approach which aimed to promote students’ understanding of abstract concepts and improve their competence of completing chemistry experiments. A quasi-experimental design was employed to evaluate the effectiveness of the proposed approach. A total of 136 junior high school students from three classes were recruited for the experiment. The experimental group adopted the exploration-based SVVR approach, control group I adopted the conventional SVVR approach, and control group II adopted traditional instruction. The results showed that the exploration-based SVVR approach not only improved students’ learning achievement, but also increased their learning motivation, learning attitude, and creative thinking tendency. Besides, students adopting this approach had more positive behaviors, including finding problems, making assumptions, investigating and verifying, and discussion.
A Virtual Reality Educational Game for the Ethics of Cultural Heritage Repatriation

The technology of virtual reality and the gamification of education has had proven educational benefits and has the ability to immerse students in a participatory learning experience. To capitalize on the strengths of the new digital medium, including immersion, engagement, and presence, a new educational game aims to teach the ethics of cultural heritage repatriation through the lens of art history. The use of games to address current issues and conceptualize a framework for understanding the complexities of geopolitics is not new but aligning these considerations with the pressing need to protect cultural heritage as seen in modern-day Ukraine is. This study investigates the process of game design and development from preproduction to postproduction. The final version of The Museum of the Lost provides a model for other institutions with game design and art history departments to collaborate and create educational experiences that optimize the user experience and learning outcomes.
A systematic comparison of the accuracy of monocular RGB tracking and LiDAR for neuronavigation

With the advent of augmented reality (AR), the use of AR-guided systems in the field of medicine has gained traction. However, the wide-scale adaptation of these systems requires highly accurate and reliable tracking. In this work, the tracking accuracy of two technology platforms, LiDAR and Vuforia, are developed and rigorously tested for a catheter placement neurological procedure. Several experiments (900) are performed for each technology across various combinations of catheter lengths and insertion trajectories. This analysis shows that the LiDAR platform outperformed Vuforia; which is the state-of-the-art in monocular RGB tracking solutions. LiDAR had 75% less radial distance error and 26% less angle deviation error. Results provide key insights into the value and utility of LiDAR-based tracking in AR guidance systems.
Intent-Driven Secure System Design: Methodology and Implementation

Given the typical complexity of networked systems in terms of number of components and their interconnections, manually designing their architecture is inherently difficult, and the design process requires expert knowledge and skills. If we also consider the security requirements that networked systems must meet, the task becomes even more demanding, since the manual audit and security mitigation of the architecture are time and labor intensive. This led to research on automated system design, including ways to cover the related security aspects. In this paper we present a methodology for secure system design that uses an intent-based representation of the network service requirements as input, which is annotated with security requirements, and applies the Design Space Exploration (DSE) approach to generate the system design. Security is handled via a MITRE ATT&CK-based security knowledge base, and a set of security check functions, so that the resulting system design meets not only the functional and quantitative requirements, but also the specified security requirements. We implemented this methodology as the secure system designer SecureWeaver by extending the functionality of an existing intent-based system designer that targeted IT/NW services, named Weaver. A case study of a typical corporate network scenario is used to illustrate the feasibility of the methodology in producing a system design that mitigates the associated security threats. The performance evaluation we conducted for this scenario demonstrates that the added security check overhead does not have a significant impact on the overall performance characteristics of the framework.
Deformable Pyramid R-CNN for 3D object detection (ChinaMM2022)

3D object detector performance has gradually improved, but it still faces huge challenges for accurate detection from point clouds due to incomplete shapes and sparse point clouds. To solve this problem, recent two-stage approaches usually divide the region of interest (RoI) into regular grids and aggregate the internal features of the RoI within a certain radius for refinement proposals. However, for objects with sparse points, only the features inside the RoI cannot satisfy the need for accurate detection. In view of this objective fact, we present a new two-stage 3D object detection framework from point clouds to 3D voxels inside and outside RoI, named Deformable Pyramid R-CNN. Specially, we firstly propose the Voxel Feature Pyramid, which dynamically selects multi-layers of 3D features based on the sparsity of non-empty voxels within the RoI for proposal refinement. Secondly, a new Deformable Voxel RoI Pooling is introduced to abstract rich context information from the voxels of interest outside RoI, to better align semantic information by deformation grid points set with multiple receptive fields for accurate detection. Our method achieves progressive performance in both KITTI Dataset and Waymo Open Dataset. Especially on KITTI Dataset, our method outperforms PV-RCNN by 0.47%, 1.63%, 1.34% on val set easy, moderate and hard levels for car detection, respectively. Code will be available at https://github.com/june2678/DP-RCNN.
Knowledge graph completion using topological correlation and multi-perspective independence

Knowledge graphs (KGs) are large-scale semantic networks designed to describe real-world facts. Existing KGs contain only a fraction of what is really happening in the real life. Knowledge graph completion (KGC) has attracted attention because it can automatically infer and predict missing links in the KGs. The classical geometric, tensor decomposition and convolutional neural network (CNN) based models generate expressive feature embeddings, but these models treat triples independently and thus fail to cover the hidden information that is inherently implicit in the local neighborhood surrounding a triple. Recent works have introduced Graph Convolutional Network (GCN) into KGC task for leveraging the rich structural information in complex graphs. However, existing GCN-based models have limitations in relational topology distinction and multi-perspective feature aggregation. As multi-relational graphs, KGs display intrinsic heterogeneous structures and rich entity types. Therefore, ignoring the rich structural information and perspective features will greatly limit the expressive power of these models. In this paper, we propose multi-relational GCNs for modeling topological correlation and multi-perspective independence (CorIn). Specifically, we propose a multi-integration ring relational topology to fine-grained select neighbors according to relational correlation patterns, and capture the independence of multi-perspective feature groups by mutual information minimization. Our model adaptively utilizes an embedding learning design that can leverage a variety of entity-relation composition operations from classical KGC models. We evaluate our model on the public benchmark datasets and achieve marked performance gains in comparison to state-of-the-art methods.
Improving transferability of adversarial examples with powerful affine-shear transformation attack

Image classification models based on deep neural networks have made great improvements on various tasks, but they are still vulnerable to adversarial examples that could increase the possibility of misclassification. Various methods are proposed to generate adversarial examples under white-box attack circumstances that have achieved a high success rate. However, most existing adversarial attacks only achieve poor transferability when attacking other unknown models with the black-box scenario settings. In this paper, we propose a new method that generates adversarial examples based on affine-shear transformation from the perspective of deep model input layers and maximizes the loss function during each iteration. This method could improve the transferability and the input diversity of adversarial examples, and we also optimize the above adversarial examples generation process with Nesterov accelerated gradient. Extensive experiments on ImageNet Dataset indicate that our proposed method could exhibit higher transferability and achieve higher attack success rates on both single model settings and ensemble-model settings. It can also combine with other gradient-based methods and image transformation-based methods to further build more powerful attacks.
Optimizing the Online Learners’ Verbal Intention Classification Efficiency Based on the Multi-Head Attention Mechanism Algorithm

To analyse speech intention based on discussion texts in online collaborative discussions, automatic classification of discussion texts is conducted to assist teachers improve their abilities to diagnose and analyse the discussion process. The current study proposes a deep learning network model that incorporates multi-head attention mechanism with bidirectional long short-term memory (MA-BiLSTM). The proposed algorithm acquires contextual semantic connections from a global perspective and the role of key feature words within sentences from a local perspective to further strengthen the semantic features of the texts. The proposed model was employed to classify 12,000 interactive texts generated during online collaborative discussion activities. Results show that MA-BiLSTM achieved an overall classification accuracy of 83.25%, which is at least 2.83% higher than those of other baseline models. However, the classification of consultative and administrative interactive texts is minimally effective. MA-BiLSTM achieved better than the existing classification methods for interactive text classification.
Detecting functional and security‐related issues in smart contracts: A systematic literature review

Abstract Blockchain is a platform of distributed elaboration, which allows users to provide software for a huge range of next‐generation decentralized applications without involving reliable third parties. Smart contracts (SCs) are an important component in blockchain applications: they are programmatic agreements among two or more parties that cannot be rescinded. Furthermore, SCs have an important characteristic: they allow users to implement reliable transactions without involving third parties. However, the advantages of SCs have a price. Like any program, SCs can contain bugs, some of which may also constitute security threats. Writing correct and secure SCs can be extremely difficult because, once deployed, they cannot be modified. Although SCs have been recently introduced, a large number of approaches have been proposed to find bugs and vulnerabilities in SCs. In this article, we present a systematic literature review on the approaches for the automated detection of bugs and vulnerabilities in SCs. We survey 68 papers published between 2015 and 2020, and we annotate each paper according to our classification framework to provide quantitative results and find possible areas not explored yet. Finally, we identify the open problems in this research field to provide possible directions to future researchers.
Detecting Malicious Use of DoH Tunnels Using Statistical Traffic Analysis

DNS plays a fundamental role in the operation of ubiquitous networks. All devices connected to these networks need DNS to work, both for traditional domain name to IP address translation, and for more advanced services such as resource discovery. At first, the DNS communication protocol presented certain security problems: integrity, authenticity and confidentiality. DNSSEC provides security but still does not guarantee confidentiality. To solve this problem, DNS over TLS (DoT) and DNS over HTTPS (DoH) were defined. In recent years, DNS tunneling, a covert form of encapsulating data transmission, has been used to encapsulate malicious traffic in a DNS connection. DoT and DoH versions complicate the detection of these tunnels because the encrypted data prevents performing an analysis of the content of the DNS traffic. Previous work has used machine learning techniques to identify DoH tunnels, but these have limitations. In this study, we identify the most significant features that singularize malicious traffic from benign traffic by statistical analysis. Based on the selected features, we obtain satisfactory results in the classification between benign and malicious DoH traffic. The study reveals that it is possible to differentiate traffic based on certain statistical parameters.
Experimental Study of Radial Distortion Compensation for Camera Submerged Underwater Using Open SaltWaterDistortion Data Set

This paper describes a new open data set, consisting of images of a chessboard collected underwater with different refractive indices, which allows for investigation of the quality of different radial distortion correction methods. The refractive index is regulated by the degree of salinity of the water. The collected data set consists of 662 images, and the chessboard cell corners are manually marked for each image (for a total of 35,748 nodes). Two different mobile phone cameras were used for the shooting: telephoto and wide-angle. With the help of the collected data set, the practical applicability of the formula for correction of the radial distortion that occurs when the camera is submerged underwater was investigated. Our experiments show that the radial distortion correction formula makes it possible to correct images with high precision, comparable to the precision of classical calibration algorithms. We also show that this correction method is resistant to small inaccuracies in the indication of the refractive index of water. The data set, as well as the accompanying code, are publicly available.
Ontology-Based Personalized Job Recommendation Framework for Migrants and Refugees

Participation in the labor market is seen as the most important factor favoring long-term integration of migrants and refugees into society. This paper describes the job recommendation framework of the Integration of Migrants MatchER SErvice (IMMERSE). The proposed framework acts as a matching tool that enables the contexts of individual migrants and refugees, including their expectations, languages, educational background, previous job experience and skills, to be captured in the ontology and facilitate their matching with the job opportunities available in their host country. Profile information and job listings are processed in real time in the back-end, and matches are revealed in the front-end. Moreover, the matching tool considers the activity of the users on the platform to provide recommendations based on the similarity among existing jobs that they already showed interest in and new jobs posted on the platform. Finally, the framework takes into account the location of the users to rank the results and only shows the most relevant location-based recommendations.