Reviewerly/exHarmony huggingface dataset:
A machine learning ensemble approach for 5- and 10-year breast cancer invasive disease event classification

Designing targeted treatments for breast cancer patients after primary tumor removal is necessary to prevent the occurrence of invasive disease events (IDEs), such as recurrence, metastasis, contralateral and second tumors, over time. However, due to the molecular heterogeneity of this disease, predicting the outcome and efficacy of the adjuvant therapy is challenging. A novel ensemble machine learning classification approach was developed to address the task of producing prognostic predictions of the occurrence of breast cancer IDEs at both 5- and 10-years. The method is based on the concept of voting among multiple models to give a final prediction for each individual patient. Promising results were achieved on a cohort of 529 patients, whose data, related to primary breast cancer, were provided by Istituto Tumori "Giovanni Paolo II" in Bari, Italy. Our proposal greatly improves the performances returned by the baseline original model, i.e., without voting, finally reaching a median AUC value of 77.1% and 76.3% for the IDE prediction at 5-and 10-years, respectively. Finally, the proposed approach allows to promote more intelligible decisions and then a greater acceptability in clinical practice since it returns an explanation of the IDE prediction for each individual patient through the voting procedure.
Data Anomaly Detection Based on Isolation Forest Algorithm

Compared with other anomaly detection methods, the traditional isolation forest algorithm improves the execution efficiency, but it still is time-consuming. Thus, an improved isolation forest algorithm was proposed to solve the problem. Specifically, we analyzed the user access data stream of the website platform through data preprocessing. Then PCA was used to reduce the feature dimension, and a parallel processing approach based on an isolation forest algorithm was used to detect the data. The detection efficiency of the proposed method is verified through the standard simulation data set.
The Hybrid Stylus: A Multi-Surface Active Stylus for Interacting with and Handwriting on Paper, Tabletop Display or Both

The distinct properties and affordances of paper provide benefits that enabled paper to maintain an important role in the digital age. This is so much so, that some pen-paper interaction has been imitated in the digital world with touchscreens and stylus pens. Because digital medium also provides several advantages not available to physical paper, there is a clear benefit to merge the two mediums. Despite the plethora of concepts, prototypes and systems to digitise handwritten information on paper, these systems require specially prepared paper, complex setups and software, which can be used solely in combination with paper, and, most importantly, do not support the concurrent precise interaction with both mediums (paper and touchscreen) using one pen only. In this paper, we present the design, fabrication and evaluation of the Hybrid Stylus. The Hybrid Stylus is assembled with the infinity pencil tip (nib) made of graphite and a specially designed shielded tip holder that is attached to an active stylus. The stylus can be used for writing on a physical paper, while it still maintains all the features needed for tablet interaction. Moreover, the stylus also allows simultaneous digitisation of handwritten information on the paper when the paper is placed on the tablet screen. In order to evaluate the concept, we also add a user-friendly manual alignment of paper position on the underlying tablet computer The evaluation demonstrates that the system achieves almost perfect digitisation of strokes (98.6% of strokes were correctly registered with only 1.2% of ghost strokes) whilst maintaining excellent user experience of writing with a pencil on the paper.
Dynamic resource provisioning and secured file sharing using virtualization in cloud azure

Abstract Virtual machines (VMs) are preferred by the majority of organizations due to their high performance. VMs allow for reduced overhead with multiple systems running from the same console at the same time. A physical server is a bare-metal system whose hardware is controlled by the host operating system. A physical server runs on a single instance of OS and application. A virtual server or virtual machine encapsulates the underlying hardware and networking resources. With the existing physical server, it is difficult to migrate the tasks from one platform to another platform or to a datacentre. Centralized security is difficult to setup. But with Hypervisor the virtual machine can be deployed, for instance, with automation. Virtualization cost increases as well as a decrease in hardware and infrastructure space costs. We propose an efficient Azure cloud framework for the utilization of physical server resources at remote VM servers. The proposed framework is implemented in two phases first by integrating physical servers into virtual ones by creating virtual machines, and then by integrating virtual servers into cloud service providers in a cost-effective manner. We create a virtual network in the Azure datacenter using the local host physical server to set up the various virtual machines. Two virtual machine instances, VM1 and VM2, are created using Microsoft Hyper-V with the server Windows 2016 R. The desktop application is deployed and VM performance is monitored using the PowerShell script. Tableau is used to evaluate the physical server functionality of the worksheet for the deployed application. The proposed Physical to Virtual to Cloud model (P2V2C) model is being tested, and the performance result shows that P2V2C migration is more successful in dynamic provisioning than direct migration to cloud platform infrastructure. The research work was carried out in a secure way through the migration process from P2V2C.
Load balancing using dynamic algorithms for cloud environment: A survey

Numerous researchers have worked on different load balancing algorithms in cloud Environment. But, no efficient algorithm for load balancing in cloud environment is developed yet. For optimizing the solution and fully utilizing the resources authors moved to the nature inspired algorithms. This paper presents a survey of different dynamic and nature inspired algorithms with their advantages and disadvantages. The objective of this study is to bring out the conclusion that disadvantages of various nature inspired algorithms can be overcome if different algorithms can be used as a hybrid to suit the usage of application and new optimization technique can be formulated by the combination of two or more algorithm. Researchers are also proposing a new hybrid technic which is more effective and robust and will try to balance the load without being complex. Proposed algorithm will use Ant Colony and Bird swarm Optimization techniques through which the degree of imbalance will be reduced. By using the proper fitness function makespan can be minimized and resource utilization can be maximized. Good part of the Ant colony optimization and Bird Swarm optimization is that by using these algorithms best optimal solution can be find out and both can be suited for static and dynamic environment and adaptive as well.
CodeCity: A comparison of on-screen and virtual reality

Over the past decades, researchers proposed numerous approaches to visualize source code. A popular one is CodeCity, an interactive 3D software visualization representing software system as cities: buildings represent classes (or files) and districts represent packages (or folders). Building dimensions represent values of software metrics, such as number of methods or lines of code. There are many implementations of CodeCity, the vast majority of them running on-screen. Recently, some implementations using virtual reality (VR) have appeared, but the usefulness of CodeCity in VR is still to be proven. Our comparative study aims to answer the question “Is VR well suited for CodeCity, compared to the traditional on-screen implementation?” We performed two experiments with our web-based implementation of CodeCity, which can be used on-screen or in immersive VR. First, we conducted a controlled experiment involving 24 participants from academia and industry. Taking advantage of the obtained feedback, we improved our approach and conducted a second controlled experiment with 26 new participants. Our results show that people using the VR version performed the assigned tasks in much less time, while maintaining a comparable level of correctness. VR is at least equally well-suited as on-screen for visualizing CodeCity, and likely better.
Multiagent Path Finding Using Deep Reinforcement Learning Coupled With Hot Supervision Contrastive Loss

Multiagent path finding (MAPF) is employed to find collision-free paths to guide agents traveling from an initial to a target position. The advanced decentralized approach utilizes communication between agents to improve their performance in environments with high-density obstacles. However, it dramatically reduces the robustness of multiagent systems. To overcome this difficulty, we propose a novel method for solving MAPF problems. In this method, expert data are transformed into supervised signals by proposing a hot supervised contrastive loss, which is combined with reinforcement learning to teach fully-decentralized policies. Agents reactively plan paths online in a partially observable world while exhibiting implicit coordination without communication with others. We introduce the self-attention mechanism in the policy network, which improves the ability of the policy network to extract collaborative information between agents from the observation data. By designing simulation experiments, we demonstrate that the learned policy achieved good performance without communication between agents. Furthermore, real-world application experiments demonstrate the effectiveness of our method in practical applications.
Sparsing and Smoothing for the seq2seq Models

Current neural language models are trained to minimize cross-entropy and use softmax to compute the locally normalized probabilities over the target. While this setup provides solid results in several natural language processing (NLP) tasks, one unsatisfying aspect is its dense output. This density is wasteful, making models hard to interpret and assigning probability mass to many implausible outputs. To overcome this problem, we propose T-softmax, a simple but effective method to draw considerably sparse probability out of neural language models than softmax. Our method avoids dense output by truncating the unreliable tail of the probability distribution to improve the model's performance. In addition, we generalize logits with temperature, a critical regularization technique, from the softmax to T-softmax. To show our approach as a drop-in replacement for softmax, we evaluate them on three NLP tasks: summary generation, question answer, and math word problem. Experimental results show that our proposed model significantly improves performance without sacrificing speed; notably, in all experiments, our method outperforms the softmax.
Cost and Performance Analysis on Decentralized File Systems for Blockchain-Based Applications: State-of-the-Art Report

Blockchain technology is an immutable append-only decentralized ledger that theoretically makes it an ideal data storage system. The nature of blockchain design ensures stored data is reliable, trustworthy, and transparent. However, the blockchain's full-replication feature makes it ineffective to store a large volume of data directly on-chain. This paper provides a systematic review and analysis of existing decentralized file systems. Our focus was on systems that can support large, high-frequency data writing while still providing swift and easy data retrieval for blockchain-based applications. The challenge, in our view, is to find ways to achieve those efficiency outcomes while still retaining the key decentralized features of blockchain design. With that in mind, we assessed the costs involved in using nine state-of-the-art decentralized file systems and we also considered their latency performance.
Blockchain-Enabled Open Quality System for Smart Manufacturing: Applications and Challenges

In this technological development era, new trends and approaches related to quality management are emerging. Although many studies have focused on quality management in Industry 4.0, new technologies face difficulties in resolving problems arising from lack of trust, transparency, integrity, track-and-traceability, connectivity, and responsibility in evaluating quality. In this article, we discuss the potential of blockchain to deliver business value with transparent quality by introducing a new quality concept called “open quality”. A systematic literature review presents the increased visibility of open quality across the manufacturing system, based on which a “blockchain-enabled open quality” (OQB) framework in the fourth industrial revolution is proposed. The research contributes by providing a platform to share manufacturing information regarding quality within the entire system, thus ensuring that all members and processes of the system obtain verified information to enhance trust, transparency, and track-and-traceability. By this, manufacturers are able to meet the expectations of the customer and stakeholder with an improved product and system quality. In addition, the potential threats associated with OQB system implementation and their possible solutions are also discussed. Moreover, research gaps that can be explored in future study and the opportunities for new concepts of quality in Industry 4.0 are presented.
Analysis of the Public Opinion Evolution on the Normative Policies for the Live Streaming E-Commerce Industry Based on Online Comment Mining under COVID-19 Epidemic in China

Recent years have witnessed the intensive development of live streaming E-commerce, an emerging business mode. Although it contributes to economic growth, various forms of chaos show up and disturbs the market order. Therefore, from 1 July 2020, the official release of the first domestic document on live streaming E-commerce, the Code of Conduct for Online Live Streaming Marketing, to the end of the first half of 2021, China has witnessed so intensive release of relevant policies that are rare over the past years. Introducing these policies will inevitably attract the general public’s attention and discussions. Based on online comments, this paper uses the LDA models to extract topics from online comments related to live streaming E-commerce and identifies sentiment polarity and sentiment intensity by the analysis models of different emotion dictionaries to study policy implementation effects and the main topics of concern before and after the policy implementation. The analysis results show that people between the age of 20 and 40 attach more importance to the implementation of the normative policy for live streaming E-commerce. Women, the main force of live streaming users, are less enthusiastic about the policy implementation than men. Moreover, the analysis results of the LDA models and online HDP (online hierarchical Dirichlet process) models demonstrate that the most discussed topics are the contribution of live streaming E-commerce to traditional economic transformation, public welfare activities, resumption of work and production, and poverty alleviation, as well as fraud, counterfeit goods, supervision, rights protection and other incidents in this industry. Overall, the majority of the public holds a positive attitude towards the policy implementation. After further analysis of comments under the relevant topics, it is found that compared with the first two policies released on 1 July and 5 November in 2020, although the proportion of netizens with positive emotions during the implementation of the follow-up policy has increased, the increment is not significant, indicating that the implementation of the new normative policy in a short term will hardly curb the occurrence of industry chaos. In turn, the governments should transfer their attention to actual regulatory problems, and intensify efforts to implement normative policies.
Successive Binary Partition K-means Method for Clustering with Less Cluster Size Bias

We propose a clustering algorithm with less cluster size bias. Conventional clustering algorithms, such as the K-means method, aim to minimize the mean square error between the centroid of the cluster and the samples in the cluster, which may lead to a biased number of samples in the resulting clusters. In response to this, several algorithms have been proposed for optimization, such as the balanced k-means method, which gives equal-sized clusters as a result. However, it has a drawback of slow speed. This paper proposes a relatively fast heuristic algorithm with less bias in the number of samples in the clusters. This method successively divides clusters with large cluster sizes into two parts, and when the desired number of clusters is reached, the centroid is optimized again. The last optimization allows us to adjust the bias and error of the clusters. Two experiments were conducted for evaluation. In the first experiment, we applied the proposed method and the k-means, k-means++, and balanced k-means methods to the generated data and measured the computation time. The results showed that the proposed method was slower than the k-means method but faster than the other methods. In the second experiment, the image data from CIFAR-100 were clustered and compared with k-means. We measured the error and cluster size bias, and the results showed that we could control the trade-off between error and cluster size bias.
Graph prototypical contrastive learning

Unsupervised graph representation learning methods based on contrastive learning have drawn increasing attention and achieved promising performance. Most of these methods only model instance-level feature similarity while ignoring the underlying semantic structure of the whole data. In this paper, we propose a Graph Prototypical Contrastive Learning (GPCL) framework for unsupervised graph representation learning. Besides modeling instance-level feature similarity, GPCL explores the underlying semantic structure of the whole data. Specifically, we introduce an instance-prototype contrastive objective to learn representations that are invariant to intra-class variance and discriminative to inter-class variance. Meanwhile, a prototype-prototype contrastive objective is proposed to encourage clustering consistency between instances in the same cluster and their augmentations. To optimize the model, we formulate GPCL as an online Expectation–Maximization framework. We iteratively perform E-step to estimate the posterior probability of prototype assignments through online clustering and M-step to optimize model parameters through graph prototypical contrastive learning. We evaluate GPCL on various graph benchmarks, and the experimental results verify the superiority of our method.
An Efficient Authentication Scheme Using Blockchain as a Certificate Authority for the Internet of Drones

The Internet of Drones (IoD) has recently gained popularity in several military, commercial, and civilian applications due to its unique characteristics, such as high mobility, three-dimensional (3D) movement, and ease of deployment. Drones, on the other hand, communicate over an unencrypted wireless link and have little computational capability in a typical IoD environment, making them exposed to a wide range of cyber-attacks. Security vulnerabilities in IoD systems include man-in-the-middle attacks, impersonation, credential leaking, GPS spoofing, and drone hijacking. To avoid the occurrence of such attacks in IoD networks, we need an extremely powerful security protocol. To address these concerns, we propose a blockchain-based authentication scheme employing Hyperelliptic Curve Cryptography (HECC). The concepts of a blockchain as a Certificate Authority (CA) and a transaction as a certificate discussed in this article are meant to facilitate the use of a blockchain without CAs or a Trusted Third Party (TTP). We offer a security analysis of the proposed scheme, which demonstrates its resistance to known and unknown attacks. The proposed scheme resists replay, man-in-the-middle, device impersonation, malicious device deployment, Denial-of-Service (DoS), and De-synchronization attacks, among others. The security and performance of the proposed scheme are compared to relevant existing schemes, and their performance is shown to be better in terms of security attributes as well as computation and communication costs than existing competitive schemes. The total computation cost of the proposed scheme is 40.479 ms, which is 37.49% and 49.79% of the two comparable schemes. This shows that the proposed scheme is better suited to the IoD environment than existing competitive schemes.
Configurable Readout Error Mitigation in Quantum Workflows

Current quantum computers are still error-prone, with measurement errors being one of the factors limiting the scalability of quantum devices. To reduce their impact, a variety of readout error mitigation methods, mostly relying on classical post-processing, have been developed. However, the application of these methods is complicated by their heterogeneity and a lack of information regarding their functionality, configuration, and integration. To facilitate their use, we provide an overview of existing methods, and evaluate general and method-specific configuration options. Quantum applications comprise many classical pre- and post-processing tasks, including readout error mitigation. Automation can facilitate the execution of these often complex tasks, as their manual execution is time-consuming and error-prone. Workflow technology is a promising candidate for the orchestration of heterogeneous tasks, offering advantages such as reliability, robustness, and monitoring capabilities. In this paper, we present an approach to abstractly model quantum workflows comprising configurable readout error mitigation tasks. Based on the method configuration, these workflows can then be automatically refined into executable workflow models. To validate the feasibility of our approach, we provide a prototypical implementation and demonstrate it in a case study from the quantum humanities domain.
Resource Allocation for Multi-Traffic in Cross-Modal Communications

Cross-modal communications that incorporate audio-visual and tactile signals will bring a more holistic immersive experience to people. However, due to the different transmission requirements of these signals, it is a challenging task to rationalize the allocation of transmission resources. Therefore, this work proposes a joint transmission scheme to deal with the resource allocation problem of diverse signals. Network slicing and puncturing architecture are introduced in the scheme to achieve flexible resource allocation and reduce the wasting of resources. To reduce the negative impact of puncturing transmission on users, we construct the optimization problem related to transmission rate and reliability. This problem can realize the reasonable allocation of radio resources and meet the transmission requirements of the two types of signals. Next, we divide the optimization problem into two parts: video traffic resources allocation and tactile traffic puncturing resources allocation. To solve both of the problems, we leverage the channel matching (CM) algorithm and puncturing resource allocation (PRA) algorithm. In addition, we discuss the advantages and disadvantages of both ways to occupy puncturing resources, namely, occupy resources proportionally (ORP) and occupy resources blocks for transmission (ORB). Finally, the effectiveness of the proposed scheme is verified by comparing the excepted rate and resources loss ratio of system users with different schemes.