Reviewerly/exHarmony huggingface dataset:
On Understanding the Influence of Controllable Factors with a Feature Attribution Algorithm: a Medical Case Study

Feature attribution XAI algorithms enable their users to gain insight into the underlying patterns of large datasets through their feature importance calculation. Existing feature attribution algorithms treat all features in a dataset homogeneously, which may lead to misinterpretation of consequences of changing feature values. In this work, we consider partitioning features into controllable and uncontrollable parts and propose the Controllable fActor Feature Attribution (CAFA) approach to compute the relative importance of controllable features. We carried out experiments applying CAFA to two existing datasets and our own COVID-19 non-pharmaceutical control measures dataset. Experimental results show that with CAFA, we are able to exclude influences from uncontrollable features in our explanation while keeping the full dataset for prediction.
Relation between quantum coherence and quantum entanglement in quantum measurements

Quantum measurement is a class of quantum channels that sends quantum states to classical states. We set up resource theories of quantum coherence and quantum entanglement for quantum measurements and find relations between them. For this, we conceive a relative entropy type quantity to account for the quantum resources of quantum measurements. The quantum coherence of a quantum measurement can be converted into the entanglement in a bipartite quantum measurement through coherence-non generating transformations. Conversely, a quantum entanglement monotone of quantum measurements induces a quantum coherence monotone of quantum measurement. Our results confirm that the understanding on the link between quantum coherence and quantum entanglement is valid even for quantum measurements which do not generate any quantum resource.
S5CL: Unifying Fully-Supervised, Self-supervised, and Semi-supervised Learning Through Hierarchical Contrastive Learning

In computational pathology, we often face a scarcity of annotations and a large amount of unlabeled data. One method for dealing with this is semi-supervised learning which is commonly split into a self-supervised pretext task and a subsequent model fine-tuning. Here, we compress this two-stage training into one by introducing S5CL, a unified framework for fully-supervised, self-supervised, and semi-supervised learning. With three contrastive losses defined for labeled, unlabeled, and pseudo-labeled images, S5CL can learn feature representations that reflect the hierarchy of distance relationships: similar images and augmentations are embedded the closest, followed by different looking images of the same class, while images from separate classes have the largest distance. Moreover, S5CL allows us to flexibly combine these losses to adapt to different scenarios. Evaluations of our framework on two public histopathological datasets show strong improvements in the case of sparse labels: for a H &E-stained colorectal cancer dataset, the accuracy increases by up to $$9\%$$ compared to supervised cross-entropy loss; for a highly imbalanced dataset of single white blood cells from leukemia patient blood smears, the F1-score increases by up to $$6\%$$ (Code: https://github.com/manuel-tran/s5cl ).
The Royalflush System of Speech Recognition for M2met Challenge

This paper describes our RoyalFlush system for the track of multi-speaker automatic speech recognition (ASR) in the M2MeT challenge. We adopted the serialized output training (SOT) based multi-speakers ASR system with large-scale simulation data. Firstly, we investigated a set of front-end methods, including multi-channel weighted predicted error (WPE), beamforming, speech separation, speech enhancement, etc., to process training, evaluation, and test sets. However, according to their experimental results, we only selected the WPE and beamforming approach as our front-end methods. Secondly, we made great efforts in the data augmentation for multi-speaker ASR, including adding noise and reverberation, over-lapped speech simulation, multi-channel speech simulation, speed perturbation, front-end processing, etc., which brought us a significant performance improvement. Finally, to make full use of the performance complementary of different model architecture, we trained the standard conformer based joint CTC/Attention (Conformer) and U2++ ASR model with a bidirectional attention decoder, a modification of Conformer, to fuse their results. Compared with the official baseline system, our system got a 12.22% absolute Character Error Rate (CER) reduction on the evaluation set and 12.11% on the test set.
Unified Multimodal Punctuation Restoration Framework for Mixed-Modality Corpus

The punctuation restoration task aims to correctly punctuate the output transcriptions of automatic speech recognition systems. Previous punctuation models, either using text only or demanding the corresponding audio, tend to be constrained by real scenes, where unpunctuated sentences are a mixture of those with and without audio. This paper proposes a unified multimodal punctuation restoration framework, named UniPunc, to punctuate the mixed sentences with a single model. UniPunc jointly represents audio and non-audio samples in a shared latent space, based on which the model learns a hybrid representation and punctuates both kinds of samples. We validate the effectiveness of the UniPunc on real-world datasets, which outperforms various strong baselines (e.g. BERT, MuSe) by at least 0.8 overall F1 scores, making a new state-of-the-art. Extensive experiments show that UniPunc’s design is a pervasive solution: by grafting onto previous models, UniPunc enables them to punctuate on the mixed corpus. Our code is available at github.com/Yaoming95/UniPunc
Network Slicing for Industrial IoT and Industrial Wireless Sensor Network: Deep Federated Learning Approach and Its Implementation Challenges

5G networks are envisioned to support heterogeneous Industrial IoT (IIoT) and Industrial Wireless Sensor Network (IWSN) applications with a multitude Quality of Service (QoS) requirements. Network slicing is being recognized as a beacon technology that enables multi-service IIoT networks. Motivated by the growing computational capacity of the IIoT and the challenges of meeting QoS, federated reinforcement learning (RL) has become a propitious technique that gives out data collection and computation tasks to distributed network agents. This chapter discuss the new federated learning paradigm and then proposes a Deep Federated RL (DFRL) scheme to provide a federated network resource management for future IIoT networks. Toward this goal, the DFRL learns from Multi-Agent local models and provides them the ability to find optimal action decisions on LoRa parameters that satisfy QoS to IIoT virtual slice. Simulation results prove the effectiveness of the proposed framework compared to the early tools.
Value-creation in the health data domain: a typology of what health data help us do

Abstract It has become a trope to speak of the increasing value of health data in our societies. Such rhetoric is highly performative: it creates expectations, channels and justifies investments in data technologies and infrastructures, and portrays deliberations on political and legal issues as obstacles to the flow of data. Yet, important epistemic and political questions remain unexamined, such as how the value of data is created, what data journeys are envisioned by policies and regulation, and for whom data types are (intended to be) valuable. Drawing on two empirical cases, (a) interviews with physicians on the topic of digital selfcare, and (b) expectations of stakeholders on the use of Real-World Data in clinical trials, as well as existing literature, we propose a typology of what health data help us to do. This typology is intended to foster reflection about the different roles and values that data use unfolds. We conclude by discussing how regulation can better accommodate practices of valuation in the health data domain, with a particular focus on identifying regulatory challenges and opportunities for EU-level policy makers, and how Covid-19 has shed light on new aspects of each case.
How Will Video Conference Fatigue Affect Participants of MICE in the With-COVID-19 Era? Focusing on Video Conference Quality, Social Presence Theory, and Flow

Is our mental health at risk due to spending a significant amount of time online due to the COVID-19 pandemic? In the new era that we are living in, where we live a life that coexists with the virus, we are participating in video conferences held online rather than on-site in order to slow the spread of the virus. Video conferencing has become our necessity since March 2020, and is becoming a new standard, especially in the MICE industry. Recently, however, people who have excessively used video conference platforms are complaining of video conference fatigue, which is a new negative emotion such as stress, anxiety, and worry as well as general work fatigue. Therefore, this study focused on the mechanism of video conferencing in MICE, which is rapidly digitally converted by the virus, and the digital psychological factors of the participants. This study derived the quality attributes of video conferencing in MICE and empirically analyzed the relationship with digital psychological factors of the video conference participants, such as video conference fatigue, social presence, and flow. One hundred and thirty-eight valid questionnaires collected from participants of several international academic conferences held in EXCO, Daegu, Korea, from 23 to 28 May 2021, were analyzed. The main results are as follows. First, unlike general video conference fatigue, MICE video conference fatigue was not found to be related to the preceding and following variables. This is due to the characteristics of the MICE video conference and the expertise of the participants. Second, social presence was identified as an important variable in MICE video conferencing. Although media-mediated, the feeling of being present with the presenter and participants was found to affect the participants' flow in the video conference. Third, in this study, the fun factor was identified as the most important video conference quality that can enhance the social presence of the video conference participants of MICEs.
Reasoning with Multi-Structure Commonsense Knowledge in Visual Dialog

Visual Dialog requires an agent to engage in a conversation with humans grounded in an image. Many studies on Visual Dialog focus on the understanding of the dialog history or the content of an image, while a considerable amount of commonsense-required questions are ignored. Handling these scenarios depends on logical reasoning that requires commonsense priors. How to capture relevant commonsense knowledge complementary to the history and the image remains a key challenge. In this paper, we propose a novel model by Reasoning with Multi-structure Commonsense Knowledge (RMK). In our model, the external knowledge is represented with sentence-level facts and graph-level facts, to properly suit the scenario of the composite of dialog history and image. On top of these multi-structure representations, our model can capture relevant knowledge and incorporate them into the vision and semantic features, via graph-based interaction and transformer-based fusion. Experimental results and analysis on VisDial v1.0 and VisDialCK datasets show that our proposed model effectively outperforms comparative methods.
Analysis of Teaching Practices During the COVID-19 Pandemic: Teachers’ Goals and Activities in Virtual Classrooms

To research teachers' priorities on what was to be taught and learned during the COVID-19 lockdown, we asked Spanish Primary and Secondary teachers to choose and describe the activity they preferred among those carried out with their students during the pandemic. Our interest was to investigate what really happened in the classrooms, the type of learning favored by the practices (reproductive vs. constructive), and the agreement between the teacher's goals and their teaching We obtained 272 activities that we analyzed according to the proposed goals, the types of learning worked (verbal, procedural, and attitudinal), and the kind of teaching promoted (content or student-centered). Results showed that most teachers proposed content-centered activities, oriented above all to verbal learning. There were clear differences between the proposed goals, partly student-centered, and what was really taught, essentially content-centered. We obtained two teaching profiles, one reproductive and the other constructive.
Improvement for tasks allocation system in VM for cloud datacenter using modified bat algorithm

Since its inception, cloud computing has greatly transformed our lives by connecting the entire world through shared computational resources over the internet. The COVID-19 pandemic has also disrupted the traditional learning and businesses and led us towards an era of cloud-based activities. Virtual machine is one of the main elements of virtualization in cloud computing that represents physical server into the virtual machine. The utilizations of these VM's are important to achieved effective task scheduling mechanism in cloud environment. This paper focuses on improvment of the task distribution system in VM for cloud computing using load balancing technique. For that reason modification took place at Bat algorithm fitness function value this section used in load balancer section. When algorithm iteration are complete then time to distribute the task among different VM therefore in this section of algorithm was modified. The second modification took place at the search process of Bat at dimension section. The proposed algorithm is known as modified Bat algorithm. Four parameter are used to check the performance of the system which are throughput, makespan, degree of imbalance and processing time. The proposed algorithm provides efficient result as compaire to other standard technique. Hence the proposed algorithm improved cloud data center accuracy and efficiency.
APPLYING THE LOCAL MARTINGALE THEORY OF BUBBLES TO CRYPTOCURRENCIES

Cryptocurrencies provide a natural setting to test for the existence of price bubbles using the local martingale theory of bubbles because cryptocurrencies have no cash flows. Using a robust statistical algorithm, we test for price bubbles in eight cryptocurrencies, Bitcoin (BTC), Litecoin (LTC), Ethereum (ETH), Ripple (XRP), Bitcoin Cash (BCH), EOS (EOS), Monero (XMR), and Zcash (ZEC), from 1 January 2019 to 17 July 2019. The statistical test first estimates the cryptocurrencies’ volatilities as a function of the price level. Then, these estimates are extrapolated over the positive real line using power functions. Finally, these power functions underly a sequence of hypothesis tests for price bubbles that control for both Type I and Type II errors. Five of the eight currencies (BTC, BCH, EOS, XMR, ZEC) exhibit price bubbles, LTC does not, and the evidence for ETH and XRP is inconclusive. The paper provides strong evidence for the prevalence of bubbles in cryptocurrencies.
Digitally-disadvantaged languages

Digitally-disadvantaged languages face multiple inequities in the digital sphere including gaps in digital support that obstruct access for speakers, poorly-designed digital tools that negatively affect the integrity of languages and writing systems, and unique vulnerabilities to surveillance harms for speaker communities.This term captures the acutely uneven digital playing field for speakers of the world's 7000+ languages.
Ownership, liability, patentability, and creativity issues in artificial intelligence

While Artificial Intelligence technologies find increasing use in different industries such as transportation, healthcare and other services, it gives rise to legal complexities in respect of ownership and liability of AI, patentability of AI inventions, and creativity & ownership of AI-generated works attributable to various components of AI. The autonomous decision-making ability of AI challenges the existing IP framework. Since AI machines can “think” and “act” without any human effort, if any damage or harm occurs to the properties, does the current model of tort liability (product liability, negligence, strict liability) adequately address the legal concerns? Robust regulatory bodies and institutional mechanisms are required to develop rigorous safety standards and establish safety certification processes for AI. Even though AI inventions can be patented, many jurisdictions recognize only a “human” as an inventor and not the AI. With the increasing capability of AI to generate works without human intervention, there seems to be a strong case for granting copyright protection to AI-generated works. Exploring a separate legal framework for AI to reduce ambiguity and increase accountability would be in order.
End user engagement in refugee shelter design: Contextualising participatory process

Debate about participatory design (PD) has focussed considerably upon process. Factors such as facilitation skills and commitment to a participatory ethos have been highlighted as factors mediating stakeholder engagement. Discussion has been driven overwhelmingly by experience of PD in the global North with citizens who enjoy full rights. This article considers engagement with encamped refugees in the global South. Here context has a significant impact upon engagement. Drawing on a four-year research project concerning shelter design we discuss dimensions of context that bear particularly upon PD efforts. As we illustrate, despite contextual constraint, it is possible to produce valuable knowledge about design together with residents. Moreover, attention to context can help achieve a more nuanced approach to the evaluation of PD initiatives.
A Controllable Secure Blockchain-Based Electronic Healthcare Records Sharing Scheme

The sharing of electronic healthcare records (EHRs) is important to healthcare and medical research. However, institutions are faced with difficulties in privacy protection and efficiently secure data exchange. The main objective of this study is to propose a controllable secure blockchain-based EHRs sharing scheme. For this purpose, blockchain technologies are combined with interplanetary file systems (IPFS) to provide efficient secure EHRs sharing. Firstly, the IPFS-based EHR file system (IEFS) is designed to save and share large-size EHR files among medical institutions. With the high-throughput content-addressed block storage model and appropriate redundant backup of IPFS, IEFS is tamper-resistant and free of a single point of failure. Secondly, the blockchain is used to implement the blockchain-based EHR abstract system (BEAS) to manipulate EHR abstracts access. In BEAS, the EHR file addresses generated by IEFS are encrypted and saved in EHR abstracts for privacy protection. Since EHR abstracts are encrypted by patients' public keys, the sharing of EHR files is under the control of patients. In our experiment, a prototype system is developed to validate the proposed scheme. The experimental results showed that (1) EHRs are securely shared under the control of patients and (2) EHR files are retrieved at an acceptable speed supported by IPFS technology. In this paper, solutions to some important practical issues such as incapacitated patients, encryption key forgetting/missing, and efficient interaction of doctors with EHRs sharing scheme are also seriously discussed.
Robustness evaluation for deep neural networks via mutation decision boundaries analysis

While recent years have witnessed the power of deep neural networks in representation learning, it is well known that their robustness is a congenital defect. Formal verification sheds some light to tackle this issue, which achieves it by a rigorous mathematical reasoning. Nevertheless, such technique still suffers from the efficiency and scalability problems. In light of this, we develop a novel solution to make a pre-analysis before performing verification. Specifically, we argue that the points near the actual decision boundary of the neural network are more likely to not satisfy robustness. As such, we focus on locating unstable points in the input set, instead of point-by-point verification. Borrowing from mutation testing, we adopt the analysis of the mutation decision boundaries to evaluate the local robustness of the inputs. Also, we design a robustness metric to guide the selection of unstable points. Then, the effective adversarial examples can be generated by perturbing these unstable points. We conduct extensive experiments on two neural network verification benchmarks, demonstrating the rationality, effectiveness and efficiency improvement of our solution.
An Extensive Study on Pretrained Models for Natural Language Processing Based on Transformers

In recent years, Pretraining Language Models based on Transformers -Natural Language Processing (PLMT-NLP) have been highly successful in nearly every NLP task. In the beginning, Generative Pre-trained model-based Transformer, BERT- Bidirectional Encoder model Representations using Transformers was used to develop these models. Models constructed on transformers, Self-supervise knowledge acquiring, and transfer learning establish the foundation of these designs. Transformed-based pre-trained models acquire common linguistic illustrations from vast amounts of textual information through self-supervised model and apply this information to downstream tasks. To eliminate the need to retrain downstream models, these models provide a solid foundation of knowledge. In this paper, the enhanced learning on PLMT-NLP has been discussed. Initially, a quick introduction to self-supervised learning is presented, then diverse core concepts used in PLMT-NLP are explained. Furthermore, a list of relevant libraries for working with PLMT-NLP has been provided. Lastly, the paper discusses about the upcoming research directions that will further improve these models. Because of its thoroughness and relevance to current PLMT-NLP developments, this survey study will positively serve as a valuable resource for those seeking to understand both basic ideas and new developments better.
An investigation into Unmanned Aerial System (UAS) forensics: Data extraction &amp; analysis

Recent developments of drone technologies have shown a surge of commercial sales of drone devices, which have found use in many industries. However, the technology has been misused to commit crimes such as drug trafficking, robberies, and terror attacks. The digital forensics industry must match the speed of development with forensic tools and techniques. However, it has been identified that there is a lack of an agreed framework for the extraction and analysis of drone devices and a lack of support in commercial digital forensics tools available. In this research, an investigation into the extraction tools available for drone devices and analysis techniques has been performed to identify best practices for handling drone devices in a forensically sound manner. A new framework to perform a full forensic analysis of small to medium sized commercial drone devices and their controllers has been proposed to give investigators a plan of action to perform forensic analysis on these devices. The proposed framework overcomes some limitations of other drone forensics investigation frameworks presented in the literature.