Reviewerly/exHarmony huggingface dataset:
Instant Difficulty Adjustment: Predicting Success Rate of VR Kendama when Changing the Difficulty Level

This paper presents a task difficulty adjustment method that allows the user to reach desired success rate instantly using VR technology. We propose a methodology based on a Gaussian process dynamical model (GPDM) to model the user’s skill from a small number of past performance observations, and predict future performance at a targeted difficulty level under consideration of model uncertainty. As a task to be performed within a VR environment, we focus on Kendama (a cup-and-ball sports game), in which the cup size is changeable to adjust the difficulty level. In the experiment, we evaluated the personalized skill model with participants who performed the VR Kendama. Our results indicate that the GPDM-based approach accurately reflects the users’ skills, and the predicted success rate when changing the difficulty level is close to the actual success rate even with a small number of trials. This instant difficulty adjustment can therefore help users to receive a pleasant user experience.
ShadowClones: an Interface to Maintain a Multiple Sense of Body-space Coordination in Multiple Visual Perspectives

In this paper, we propose ShadowClones, an interface that supports interactions in which a single user can interact with multiple bodies in multiple spaces. Recent teleoperation technologies have allowed a user controlling multiple objects simultaneously, but at the same time, it also exhibited a significant challenge, which can be attributed to the high cognitive load caused by switching and recogning various spaces/perspectives repeatedly and instantly. To tackle this challenge, by taking advantage of pre-attentive visual cues for users’ simultaneous information processing, we designed and evaluated a new user interface, called Shadow Clones, that projects self-body information in unattended areas for increasing the awareness of body-space relationships and allowing users to seamlessly switch across different visual perspectives from avatars or remote robots. We then explored the proposed approach through a simple visual reaching task with a performance evaluation in terms of task completion time and success rate. The results showed superior performance when compared with a condition that presents no projections of users’ body movements in unattended areas. We conclude by discussing possible mechanisms of this enhancement as well as two potential scenarios using the shadow clones approach, including new entertainment content for virtual reality e-sports and multiple robot teleoperation such as in a construction site or a disaster site, without compromising operational performance.
Transcending the ultimate duality

In many philosophical traditions, it is held that reality is non-dual. Of course, to be non-dual, as opposed to dual, is itself to partake of a certain duality. If reality really is non-dual, it must transcend this duality too. But what could this mean? Can one make coherent sense of it? To keep the discussion focussed, I will locate it in one specific tradition: the Mahāyāna Buddhist tradition. The idea that ultimate reality is non-dual goes back to the earliest Mahāyāna sūtras at the turn of the Common Era. Thereafter, the question of what it means to transcend duality plays a central role in Buddhist philosophy. The point that reality must transcend even the duality between duality and non-duality plays a significant role in the Chinese Sanlun philosopher Jizang ( , 549–623). His discussion points the way to an answer to our problem which may be articulated with the techniques of contemporary paraconsistent logic, as we will see.
Faster SCDNet: Real-Time Semantic Segmentation Network with Split Connection and Flexible Dilated Convolution

Recently, semantic segmentation has been widely applied in various realistic scenarios. Many semantic segmentation backbone networks use various forms of dense connection to improve the efficiency of gradient propagation in the network. They achieve excellent segmentation accuracy but lack inference speed. Therefore, we propose a backbone network SCDNet with a dual path structure and higher speed and accuracy. Firstly, we propose a split connection structure, which is a streamlined lightweight backbone with a parallel structure to increase inference speed. Secondly, we introduce a flexible dilated convolution using different dilation rates so that the network can have richer receptive fields to perceive objects. Then, we propose a three-level hierarchical module to effectively balance the feature maps with multiple resolutions. Finally, a refined flexible and lightweight decoder is utilized. Our work achieves a trade-off of accuracy and speed on the Cityscapes and Camvid datasets. Specifically, we obtain a 36% improvement in FPS and a 0.7% improvement in mIoU on the Cityscapes test set.
Research on Holographic Visualization Verification Platform for Construction Machinery Based on Mixed Reality Technology

As “Industry 4.0” progresses, construction machinery is evolving toward large-scale, automation, and integration, resulting in the equipment becoming increasingly sophisticated, and the designs more difficult. Labor costs, transportation, and time will be huge challenges for construction machinery, and mixed reality technology is one of several possible ways to solve this challenge. The research presented in this paper develops a holographic visual verification platform for a digital prototype of construction machinery based on virtual terminal equipment, through investigating the synchronous remote collaboration of multiple terminal devices in a mixed reality scenario. These included semi-physical virtual-real fusion assembly, multi-person real-time voice communication, dynamic loading of MR model based on a cloud server, virtual imitation control, interface design, and human-computer interaction. The effectiveness of this paper’s method is demonstrated through remote collaborative design cases. These included a double drum roller, loader, and milling planer welding production line, as well as tractor modeling review and virtual simulation manipulation of an aerial work platform. The experimental results show that this visual verification platform is a feasible, low-cost and scalable solution, which brings a qualitative breakthrough to the design, research and development, production and other stages in the field of construction machinery.
FASTER: A Dynamic Fairness-assurance Strategy for Session-based Recommender Systems

When only users’ preferences and interests are considered by a recommendation algorithm, it will lead to the severe long-tail problem over items. Therefore, the unfair exposure phenomenon of recommended items caused by this problem has attracted widespread attention in recent years. For the first time, we reveal the fact that there is a more serious unfair exposure problem in session-based recommender systems (SRSs), which learn the short-term and dynamic preferences of users from anonymous sessions. Considering the fact that in SRSs, recommendations are provided multiple times and item exposures are accumulated over interactions in a session, we define new metrics both for the fairness of item exposure and recommendation quality among sessions. Moreover, we design a dynamic F airness- A ssurance ST rategy for s E ssion-based R ecommender systems ( FASTER ). FASTER is a post-processing strategy that tries to keep a balance between item exposure fairness and recommendation quality. It can also maintain the fairness of recommendation quality among sessions. The effectiveness of FASTER is verified on three real-world datasets and five original algorithms. The experiment results show that FASTER can generally reduce the unfair exposure of different session-based recommendation algorithms while still ensuring a high level of recommendation quality.
Comparison Between CNN, ViT and CCT for Channel Frequency Response Interpretation and Application to G.Fast

Convolutional Neural Networks (CNN) and more recently Visual Transformers (ViT) have been heavily used in specific areas like computer vision. Through this work, we explore and compare the CNNs and ViT models applied to a telecommunication signal, more specifically to interpret a G.fast channel frequency response. As both CNNs and ViT bring advantages, we have deepened the research by using a combination of both convolutions and transformers using Compact Convolutional Transformers (CCT) models. This study demonstrates that using transformer based models on a 1-D signal processing use case, we have significantly gained in accuracy compared to traditional convolution based models.
A Cloud-Assisted Data Processing Scheme for Smart Grid with Flexible Access Control

Compared with traditional power grid, smart grid provides more efficient power transmission and real-time data acquisition. However, data processing in smart grid involves heavy overheads in storage and computation, and user privacy needs to be considered. In order to address these issues, many privacy preserving data aggregation schemes have been proposed for smart grid, and it has been suggested to outsource the computation and storage to the cloud platform for performance improvement. However, most of the existing solutions still suffer some limitations. First, a single cloud server is normally used to process the data, and sensitive information might be leaked if this server is malicious. Second, proper access control is crucial for data protection, but many schemes have not considered this feature. Therefore, the existing solutions still cannot meet the requirements in practical applications. In this paper, we propose a cloud-assisted secure data processing scheme for smart grid with flexible access control. Smart meters use encryption to protect the data, while the decryption is performed in a distributed fashion. Moreover, attribute based encryption (ABE) is applied to realise flexible access control for the power consumption data. Therefore, our scheme achieves both privacy protection and access control. Since most of the expensive operations can be outsourced to the cloud platform, our scheme is very efficient for practical use.
Dynamic Multi-skill Knowledge Tracing for Intelligent Educational System

Knowledge tracing (KT) is the task of tracing students' evolving knowledge proficiency in learning interactions. In KT research, the modeling of exercise-student relations always plays a key role. How to construct the exercise and student representation is still a pending problem. To solve this problem, we propose a novel Dynamic Multi-skill Knowledge Tracing (DMKT) method in this paper. First, the Res-embedding layer is exploited to make the exercise representation more complete. Then, a new approach is proposed for simulating students' learning process. Furthermore, a Learning Absorption Indicator (LAI) is designed to effectively model the student's knowledge mastery. To verify the performance of our method, we implement DMKT with several baselines on three real-world datasets. Experimental results demonstrate the superiority and effectiveness of the proposed method.
A Novel deep ensemble classifier for recommending fashion products

The main aim of the recommender system is to recommend items that are similar to the given query image. Extracting similar items from a large collection of data is a challenging task. Systems for online shopping are exploring ways to recommend products according to the user's interests. Different statistical methods and similarity metrics were used in earlier days to get similar items, which is leading the recommendation of the products with less accuracy and precision. A Novel Deep ensemble classifier model is proposed for classifying the given product. The model is tested on Fashion product data set and the results are attractive. With this, it is possible to recommend the items with 88.32% accuracy. The suggested strategy outperforms current approaches in the form of accuracy, precision, recall, F1-score, and kappa statistics.
Keyword Extraction for Fine-Grained IoT Device Identification

Internet of Things (IoT) devices are becoming more widespread in networks and are shown to have security considerations as an afterthought. Identifying IoT devices can help users locate security vulnerabilities in their networks. Previous studies have used machine learning and rule-based methods to try and identify unknown devices from passive network traffic. The first issue with these approaches however is that the device must have been seen on a training dataset beforehand; otherwise it cannot be identified. The second issue is that trying to achieve granularity on device identification down to firmware level from passive network traffic has not been researched before, and is a key factor in identifying vulnerable devices. This paper contains a novel technique to solve those two problems. The technique automatically identifies unknown devices from passive network traffic without using a machine learning approach that finds and weights keywords found in each packet per device. These keywords then allow device identification down to a specific firmware version. The approach in this paper achieved 71% accuracy for identifying firmware versions and 74% and 78% for models and makes respectively, across a test dataset of 44 devices.
Smart Data Placement for Big Data Pipelines: An Approach based on the Storage-as-a-Service Model

The development of big data pipelines is a challenging task, especially when data storage is considered as part of the data pipelines. Local storage is expensive, hard to maintain, comes with several challenges (e.g., data availability, data security, and backup). The use of cloud storage, i.e., Storageas-a-Service (StaaS), instead of local storage has the potential of providing more flexibility in terms of such as scalability, fault tolerance, and availability. In this paper, we propose a generic approach to integrate StaaS with data pipelines, i.e., computation on an on-premise server or on a specific cloud, but integration with StaaS, and develop a ranking method for available storage options based on five key parameters: cost, proximity, network performance, the impact of server-side encryption, and user weights. The evaluation carried out demonstrates the effectiveness of the proposed approach in terms of data transfer performance and the feasibility of dynamic selection of a storage option based on four primary user scenarios.
Developing a Secure Architecture for Internet of Medical Things Using Attribute-Based Encryption

The Internet of Medical Things (IoMT) is a new paradigm in the IoT-based healthcare and medical applications which has brought many advantages to this. Increasing in the number of smart medical devices has led to security challenges within IoMT environments. Cryptography and other cuttingedge approaches have been widely used to mitigate the challenges and prevent sensitive data leakage. One of such approaches is the attribute-based encryption (ABE), which enables a fine-grained and flexible access control. However, the ABE does not provide a dynamic encryption mechanism when patients’ conditions change over the period of remote monitoring. To address this, we present a novel security architecture for IoMT that relies on ciphertext policy attributebased encryption (CP-ABE). The architecture applies a dynamic access structure for the encryption in accordance with the status of the medical data collected by IoMT devices. We define some new components mainly hosted on the IoMT gateways in order to encrypt and decrypt the medical data in the architecture. An IoMT use case is presented to show how our proposed architecture supports dynamic encryption in ehealthcare systems.
Educational Data in the Cloud Legal Implications and Technical Recommendations

Moving operations to the cloud has become a way of life for educational institutions. Much of the information these institutions store in the cloud is protected by the Family Educational Rights and Privacy Act (FERPA), which was last amended in 2002, well before cloud computing became ubiquitous. The application of a 1974 law to 21st-century technology presents a plethora of legal and technical questions. This work presents an interdisciplinary analysis of existing statutes (i.e., FERPA) and case law. We find that FERPA excludes information that students and faculty often believe is protected and that lower-court decisions have created further ambiguity. Given current technology, the statute is no longer sufficient to protect student data, and we offer recommendations based on the National Institute of Standards and Technology (NIST) Cybersecurity Framework to improve educational institutions’ management of protected data.
VECFlex: Reconfigurability and Scalability for Trustworthy Volunteer Edge-Cloud supporting Data-intensive Scientific Computing

Although ‘‘volunteer edge-cloud’’ (VEC) computing has emerged as a new paradigm for scientific computing in recent times, wider adoption is being hindered due to the abundance of heterogeneous volunteer resources and lack of trust in their ability to satisfy workflows’ performance and security requirements. In this paper, we propose the VECFlex, a flexible resource management framework that can reconFigure resource security policies to meet workflow requirements and efficiently allocate resources from a large pool with disparate configurations. It employs Reinforcement Learning (RL)-driven resource behavioral modeling to rank the trustworthiness of resources in terms of their availability, flexibility of applying security policies, and consistency of task execution times. VECFlex also applies a security-based modified Particle Swarm optimization (PSO) scheduler to allocate optimal resources to workflow tasks from a large pool of disparate resources. We evaluate the performance of VECFlex using an AWS testbed that demonstrates VECFlex’s ability to fully satisfy workflows’ security requirements and $\sim$2x improvement in workflow execution latency.
CrossFit: Fine-grained Benchmarking of Serverless Application Performance across Cloud Providers

Serverless computing emerged as a promising cloud computing paradigm for deploying cloud-native applications but raises new performance challenges. Existing performance evaluation studies focus on micro-benchmarking to measure an individual aspect of serverless functions, such as CPU speed, but lack an in-depth analysis of differences in application performance across cloud providers. This paper presents CrossFit, an approach for detailed and fair cross-provider performance benchmarking of serverless applications based on a providerindependent tracing model. Our case study demonstrates how detailed distributed tracing enables drill-down analysis to explain performance differences between two leading cloud providers, AWS and Azure. The results for an asynchronous application show that trigger time contributes most delay to the end-to-end latency and explains the main performance difference between cloud providers. Our results further reveal how increasing and bursty workloads affect performance stability, median latency, and tail latency.
Arktos: A Hyperscale Cloud Infrastructure for Building Distributed Cloud

Scalability and management cost in cloud computing are few of the top challenges for the cloud providers and large enterprises. In this paper, we present Arktos, a cloud infrastructure platform for managing large-scale compute clusters and running millions of application instances as containers and/or virtual machines (VM). Arktos is envisioned as a stepping-stone from current “ single-region” focused cloud infrastructure towards next generation distributed infrastructure in the public and/or private cloud environments. We present details related to the Arktos system architecture and features, important design decisions, and the results and analysis of the performance benchmark testing. Arktos achieves high scalability by partitioning its architecture into two independent components, the resource partition (RP) and the tenant workload partition (TP), with each component scaling independently. Our performance testing using a benchmark tool demonstrates that Arktos with just two RPs and two TPs system setting can already manage a cluster of 50K compute nodes and is able to run 1.5 million workload containers with 5 times system throughput (QPS) <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> compared with an existing container management system. Three key characteristics differentiate Arktos from other open source cloud platforms such as OpenStack and Kubernetes. Firstly, Arktos architecture is a truly scalable architecture that supports a very large cluster by scaling to more RPs and TPs in the system, Secondly, it unifies the runtime infrastructure to run and manage both VM and container applications natively, therefore eliminating the cost of managing separate technology stacks for VMs and containers. Lastly, Arktos has a unique “ virtual cluster” style multi-tenancy design that provides both strong tenancy isolation, including network isolation and transparent resource view.
Proactive Autonomic Cloud Application Management

Applications running in the Cloud can adapt to the varying demands by autonomic management of their resource configurations. The reconfiguration can be done as a reaction to the changed situation, or proactively to ensure the good performance of the application at some future point. However, it is difficult to predict the future behaviour of the application as it depends both on the changing contexts and the reconfiguration actions. This paper describes the approach for proactive autonomic Cloud application management and introduces a distinction between ‘independent metrics’,’performance metrics’ influenced by the reconfigurations, and ‘performance indicators’ related to the application’s utility and reconfigurations. It is shown how performance metrics and performance indicators can be learned as regression functions, and used in the proactive autonomic Cloud application optimization. Finally, the results of an evaluation by simulation show that the proposed approach is more accurate than reactive application control, and it gives better results in terms of the application’s utility.
Face mask detection using transfer learning and OpenCV in live videos

In 2019, we have seen the biggest epidemic of the century, which claimed many lives worldwide. The epidemic has in fact changed our life in many ways. It changed the way we interact with people. Wearing a mask is now the new normal. Though now the vaccine for the disease is available, still wearing a mask can save us from Covid19, its variants, and other contagious diseases.Especially at places where the large gathering is expected wearing a mask can be made mandatory and our proposed framework can do its monitoring through CCTV cameras.So in this research, we build a deep learning-based framework to detect whether some person is wearing a mask or not through the live video stream. We used a total of three state-of-the-art transfer learning methods to train our system and used OpenCV to detect faces in the live video stream. We found that efficientnetB1 achieved the highest accuracy of 97.75%.