Reviewerly/exHarmony huggingface dataset:
A joint training network for learning more distinguishable relation features in relation classification

Relation classification is an important task in natural language processing, which aims to predict the semantic relation between a given entity pair in a sentence. There are datasets, like TACRED, that contain a large number of “no_relation” type samples. Most existing methods treat “no_relation” and normal relation types equally, and directly apply the softmax function over all relation types. In this paper, we propose a novel joint training network to learn more distinguishable relation features for relation classification. Specially, we convert the original multi-class classification problem into two joint optimized modules, binary classification of whether a relation is “no_relation” and multi-class classification of normal relation types. To further differentiate between similar normal relation types, we introduce a self-supervised contrastive learning method to learn more distinguishable features for them. We jointly optimize the above modules. Experimental results agree well with our design intention and demonstrate that our joint training network not only achieves superior performance against existing competitive models, but also is robust to “no_relation” problem.
A data sharing method for remote medical system based on federated distillation learning and consortium blockchain

With the development of Medical Internet of Things (MIoT) technology and the global COVID-19 pandemic, hospitals gain access to patients’ health data from remote wearable medical equipment. Federated learning (FL) addresses the difficulty of sharing data in remote medical systems. However, some key issues and challenges persist, such as heterogeneous health data stored in hospitals, which leads to high communication cost and low model accuracy. There are many approaches of federated distillation (FD) methods used to solve these problems, but FD is very vulnerable to poisoning attacks and requires a centralised server for aggregation, which is prone to single-node failure. To tackle this issue, we combine FD and blockchain to solve data sharing in remote medical system called FedRMD. FedRMD use reputation incentive to defend against poisoning attacks and store reputation values and soft labels of FD in Hyperledger Fabric. Experimenting on COVID-19 radiography and COVID-Chestxray datasets shows our method can reduce communication cost, and the performance is higher than FedAvg, FedDF, and FedGen. In addition, the reputation incentive can reduce the impact of poisoning attacks.
Application of Deep Learning in First-Break Picking of Shallow OBN Data

Abstract Summary As well known, the modeling of the near-surface from first-break plays a significant role on the sub-surface imaging, reservoir characterization, and monitoring. Small errors in first-break picking can greatly impact the seismic velocity model building, so it is necessary to pick high-quality travel times. Geoscientists from around the world continues trying their best to address the near-surface challenges. Due to the rapid development of high-efficiency acquisition technique, such as WBH (wide-azimuth, broadband and high-density) acquisition technique and blended source acquisition technique, the quantity of seismic data, especially 3D seismic exploration, has leapt from GB to TB(some to PB), which sets a big challenge for first-break picking. Traditional first-break picking methods can't meet the production. In recent years, with the development of computer capacity and algorithm, artificial intelligence has changed our lives in many ways. In seismic exploration, artificial intelligence, like deep learning, has played a more and more important role now, from fault prediction, attribute identification to velocity and first break picking. Generally, deep learning is a new neural network which has multiple hidden layers, mostly over 3 layers, compared with traditional neural network. Deep learning includes Deep Belief Network (DBN), Convolutional Neural Network (CNN), Recurrent Neural Network (RNN) and so on. In this paper, Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) have been combined for first-break picking in a large 3D OBN project of Caspian Sea. A high precision near seabed velocity model is built based on the auto-picked first break with tomography inversion, which provides a good solution for static problem of the survey.
The practice of using the ergonomics of the environment in the educational process of the university in the implementation of the discipline "Elective courses (modules) in physical culture and sports"

The subject of the study is the process of physical education at the university. The object of the study is the educational and training work on physical culture and sports at the university.The purpose of the study is to study the ergonomics of the environment and its impact on the biomechanics of motor actions implemented in the educational process of university students in the discipline "Elective courses (modules) in physical culture and sports". In this work, the authors presented in the form of a set of research methods, which included such as theoretical (system analysis, generalization), empirical methods (observation, included observation, pedagogical experiment) and statistical methods. The obtained data recorded during the study, after analysis and statistical processing, allowed us to state the importance of taking into account the influence of the ergonomics of the environment and formulate a conclusion about the need for its use, recognizing the effectiveness of its application in the practice of the educational process in the implementation of the program material of the discipline "Elective courses (modules) in physical culture and sports" in the walls of higher school. The scope of the results. At the training sessions on the discipline "Elective courses (modules) in physical culture and sports" in high school.The main conclusions of the study are the use of regional features of the ergonomics of the environment in the educational process. Such a format of research contributes to the development of the relationship between the theory and practice of physical culture and sports in the organization of the educational process of students at the university, giving it not only a practice-oriented character, but also a potentially developing science in competitive market circumstances. The novelty of this study lies in the use of various sports complexes, recreational walking and training grounds above different sea levels, it is possible to achieve an increase in the level of acclimatization changes in the body of students, from which it can be concluded that the ergonomics of the environment is used in the educational process of the university.
Location-based Sentiment Analysis of 2019 Nigeria Presidential Election using a Voting Ensemble Approach

Nigeria president Buhari defeated his closest rival Atiku Abubakar by over 3 million votes. He was issued a Certificate of Return and was sworn in on 29 May 2019. However, there were claims of widespread hoax by the opposition. The sentiment analysis captures the opinions of the masses over social media for global events. In this paper, we use 2019 Nigeria presidential election tweets to perform sentiment analysis through the application of a voting ensemble approach (VEA) in which the predictions from multiple techniques are combined to find the best polarity of a tweet (sentence). This is to determine public views on the 2019 Nigeria Presidential elections and compare them with actual election results. Our sentiment analysis experiment is focused on location-based viewpoints where we used Twitter location data. For this experiment, we live-streamed Nigeria 2019 election tweets via Twitter API to create tweets dataset of 583816 size, pre-processed the data, and applied VEA by utilizing three different Sentiment Classifiers to obtain the choicest polarity of a given tweet. Furthermore, we segmented our tweets dataset into Nigerian states and geopolitical zones, then plotted state-wise and geopolitical-wise user sentiments towards Buhari and Atiku and their political parties. The overall objective of the use of states/geopolitical zones is to evaluate the similarity between the sentiment of location-based tweets compared to actual election results. The results reveal that whereas there are election outcomes that coincide with the sentiment expressed on Twitter social media in most cases as shown by the polarity scores of different locations, there are also some election results where our location analysis similarity test failed.
Review of Probability Elicitation and Examination of Approaches for Large Bayesian Networks

Probability elicitation is the process of formulating a person's knowledge and beliefs about one or more uncertain quantities into a joint probability distribution for those quantities. The important point is that the goal of elicitation is to capture a person's knowledge and beliefs based upon their current state of information. Consequently, the results of elicitation need be only good enough to make reasoned decisions or reasonable inferences. This chapter identifies how to elicit probabilities for large conditional probability tables in Bayesian networks. This chapter looks at Bayesian networks which are statistical models to describe and visualize in a compact graphical form the probabilistic relationships between variables of interest; the nodes of a graphical structure correspond to the variables, while directed edges between the nodes encode conditional independence relationships between them.
Attack Adaptation Patterns for Defensive Operations

The mission of cyber defenders is as monumental as it is unprecedented: protect information systems from ongoing cyber-attacks while simultaneously fortifying those systems against tomorrow's unrealized threats. The pressing imperative to react quickly to these constant attacks makes it difficult to find the time and resources needed to build capabilities focused on anticipating tomorrow's threats before they result in costly systems damage or information leakage. This chapter focuses on the role defensive measures play in spurring cyber-attack adaptation, especially with respect to malware. While the need to react to today's attacks will likely persist indefinitely, this chapter aims to begin helping defenders and researchers channel its wealth of tacit knowledge, deep expertise, and sophisticated capabilities into establishing more anticipative defensive postures.
Impact of Function Variability in Value-Focused Models

Value focused models (VFMs) are often used to compare different alternatives in terms of their overall value for achieving a top-level objective. Since VFMs are developed by subject matter experts (SMEs), they are inherently subjective. The impact of this subjectivity on the decision process is typically evaluated by performing a sensitivity analysis on the VFM weights generated by the SMEs. This chapter discusses two additional sources of variability that can also significantly impact the final decision accuracy pertaining to building a software-based system. A methodology is also provided for estimating and reducing the magnitude of these effects on the final decision for building the system.
Testing a Planck scale mechanism by applying to matter a law for light: A new gravity theory that closely mimics standard theory

The Planck scale is thought to be where space, time and matter as we know them begin, and seems to be vital to physics. Our ideas about that scale are unclear. String theory is dependent on supersymmetry, which unexpectedly has not been found—this leaves no reliable picture. In some views the world is chaotic there, making it hard to explain the order at larger scales. Direct experiment cannot reach that scale, but indirect experiment and mathematics can. It would be wrong to assume, because of string theory, that only complex mathematics is relevant. This paper is the second of three from a conceptual basis with a small group of simple but lateral assumptions about the structure of space, and the nature of light and matter. It leads to interpretations, in some areas to rederivations, in others to new mathematics that closely mimics existing physics. Two spinoffs from the main theory are dimensional quantum mechanics [J. M. Kerr, Phys. Essays 33 , 1 2019)], and here Planck scale gravity (PSG), in which a gravity mechanism reproduces Newtonian theory and parts of general relativity (GR). In a simple approach, the result is essentially to test the idea that matter at a small scale is similar to light, as both are waves in the fabric of the dimensions, traveling in different directions through the structure of space, which takes the form of parallel cylinders. If one assumes matter travels at c around the circumference of the cylinders, making loops at the Planck scale not unlike the closed string, one can apply to matter a law normally applied to light. What comes out is the mathematics of gravity, with what might be seen as “smoking gun evidence” (p. 11), showing that every point on any trajectory through a gravitational field is connected to every other point on it. PSG is mathematically different from GR, but it mimics it across a range of physics, in most cases to eight decimal places. It is conceptually equivalent to GR in many areas including gravitational waves, and diverges in a few places, leading to testable predictions.
Struggles, Potential, and Research Angles in the Amalgamation of Blockchain Technology With 6G Networks

The world is experiencing a dramatic transformation as a result of the emergence of intellectual information technology among the main industries where high-end user engagement is anticipated to boost service qualities in all fields. Blockchain is one of the most brilliant technology enablers to overcome most of the current limitations and provide the functional needs of 6G. This chapter examines the importance of blockchain in tackling pressing 6G concerns as well as potential future applications and new research fields like artificial intelligence, data storage and analytics, and internet of everything. In addition to offering a solitary answer for application security and privacy, blockchain also has its own unique set of security and privacy flaws. This chapter examines the advantages, drawbacks, and possible routes for getting through the obstacles associated with using blockchain in 6G.
IoT Applications With Cryptography and Blockchain Technology in Healthcare Digital Twin Design

In recent years, big data, artificial intelligence techniques, the Internet of Things (IoT), and other evolving digital technologies have been essential in regular healthcare services. The sensitive characteristic of healthcare data presents many crucial issues, such as security, privacy, interoperability, and reliability, that need consideration in healthcare data management systems. With the rapid development of digital techniques, integration methods, and data acquisition mechanisms, digital twin (DT) technology presents a new way to modernize healthcare services and information systems. One of the critical digital technologies, IoT-based information systems, is vulnerable to privacy and security-related issues. This chapter highlights the importance of an information system framework that consists of IoT with blockchain to tackle vulnerability issues using lightweight cryptography techniques.
Role of Blockchain Technology in 6G Network Management

Blockchain technology is a revolutionary technology for the sixth-generation (6G) network. Blockchain offers significant advantages of transparency, immutability, flexibility, traceability, and security to the 6G network management. This chapter focuses on the survey of an emerging field of blockchain technology in 6G-based application and network management. Blockchain could enhance resource management and spectrum management more broadly. Blockchain could record the idle spectrum usage between primary users and secondary users. In addition, the network management chapter briefs about the security concern of the 6G network. Further, the impact of emerging technologies on the 6G network are also introduced.
Blockchain-Based E-Healthcare Monitoring System Using Internet of Healthcare Things (IoHT)

In order to incorporate patient data, the internet of healthcare things (IoHT) enables services using wearable healthcare devices. Blockchain 1.0 is based on bitcoin whereas the primary concept has relied on the cryptocurrency applications. Blockchain 2.0, which introduced the idea of the smart contract, was based on the defined single piece of code that is executed and saved in a decentralized ledger. Blockchain 3.0 significantly deals with the non-financial applications such as government, energy, health, etc. This generation of blockchain technology is spreading in the healthcare sector.
Analysis of B2B Blockchain Apps Using Hyperledgers and Their Implications in This Digital Era

In this digital era, at most 80-90% of business transactions are digitized using different mechanisms. In the case of businesses and financial institutions, such digitization poses both merits as well as challenges. Nowadays, the IT industries are focusing on providing a trustworthy solution, namely blockchain-based applications, to provide secured online transactions. This blockchain technology provides a comprehensive solution to B2B applications by providing support to process information across organizations in a highly secured manner. In blockchain, these kinds of secured online transactions are achieved using hyperledgers. In particular, the hyperledger is decentralized as it is replicated across the entire network for all the participants to collaborate in a secured manner. In this chapter, an analysis of the application of hyperledgers in B2B blockchain apps to and their implications in this digital era is provided.
Structure-preserving texture smoothing with scale-aware intensity aggregation structure measurement

Texture smoothing aims to smooth out textures in images while preserving prominent structure. However, facing complex images with multi-scale coexistence of texture and structure, the existing methods fail to distinguish small-scale structure from large-scale texture, which leads to undesired texture filtering. To this end, this paper proposes a novel scale-aware method. First, according to the texture and structure characteristics of one-dimensional signals, we propose a new texture metric, called intensity aggregation structure measurement (IASM), which has good performance in recognizing texture and structure. Second, we propose a structure-first-aware relative total variation, which can recognize important structural features with different sizes and shapes more finely, thereby estimating the calculation window of the new texture metric IASM for each pixel. Finally, the IASM with adaptive window cooperates with guided filtering to achieve smooth texture while preserving structure. The experimental results show that our method can protect high-quality structural features that are considered important visually, and at the same time, filter out large-scale textures well, which is better than existing state-of-the-art methods. Besides, our method is straightforward to implement and can be computationally efficient.
Self-Supervised Facial Motion Representation Learning via Contrastive Subclips

Facial motion representation learning has become an exciting research topic, since biometric technologies are becoming more common in our daily lives. One of its applications is identity verification. After recording a dynamic facial motion video for enrollment, the user needs to show a matched facial appearance and make a facial motion the same as the enrollment for authentication. Some recent research papers have discussed the benefits of this new biometric technology and reported promising results for both static and dynamic facial motion verification tasks. Our work extends the existing approaches and introduces compound facial actions, which contain more than one dominant facial action in one utterance. We propose a new self-supervised pretraining method called contrastive subclips that improves the model performance with these more complex and secure facial motions. The experimental results show that the contrastive subclips method improves upon the baseline approaches, and the model performance for test data can reach 89.7% average precision.
The Potential of Adding Mammography to Handheld Ultrasound or Automated Breast Ultrasound to Reduce Unnecessary Biopsies in BI-RADS Ultrasound Category 4a: A Multicenter Hospital-Based Study in China

The appropriate management strategies for BI-RADS category 4a lesions among handheld ultrasound (HHUS) remain a matter of debate. We aimed to explore the role of automated breast ultrasound (ABUS) or the second-look mammography (MAM) adjunct to ultrasound (US) of 4a masses to reduce unnecessary biopsies. Women aged 30 to 69 underwent HHUS and ABUS from 2016 to 2017 at five high-level hospitals in China, with those aged 40 or older also accepting MAM. Logistic regression analysis assessed image variables correlated with false-positive lesions in US category 4a. Unnecessary biopsies, invasive cancer (IC) yields, and diagnostic performance among different biopsy thresholds were compared. A total of 1946 women (44.9 ± 9.8 years) were eligible for analysis. The false-positive rate of category 4a in ABUS was almost 65.81% (77/117), which was similar to HHUS (67.55%; 127/188). Orientation, architectural distortion, and duct change were independent factors associated with the false-positive lesions in 4a of HHUS, whereas postmenopausal, calcification, and architectural distortion were significant features of ABUS (all p < 0.05). For HHUS, both unnecessary biopsy rate and IC yields were significantly reduced when changing biopsy thresholds by adding MAM for US 4a in the total population (scenario #1:BI-RADS 3, 4, and 5; scenario #2: BI-RADS 4 and 5) compared with the current scenario (all p < 0.05). Notably, scenario #1 reduced false-positive biopsies without affecting IC yields when compared to the current scenario for ABUS (p < 0.001; p = 0.125). The higher unnecessary biopsy rate of category 4a by ABUS was similar to HHUS. However, the second-look MAM adjunct to ABUS has the potential to safely reduce false-positive biopsies compared with HHUS.
Adapting Off-the-Shelf Speech Recognition Systems for Novel Words

Current speech recognition systems with fixed vocabularies have difficulties recognizing Out-of-Vocabulary words (OOVs) such as proper nouns and new words. This leads to misunderstandings or even failures in dialog systems. Ensuring effective speech recognition is crucial for the proper functioning of robot assistants. Non-native accents, new vocabulary, and aging voices can cause malfunctions in a speech recognition system. If this task is not executed correctly, the assistant robot will inevitably produce false or random responses. In this paper, we used a statistical approach based on distance algorithms to improve OOV correction. We developed a post-processing algorithm to be combined with a speech recognition model. In this sense, we compared two distance algorithms: Damerau–Levenshtein and Levenshtein distance. We validated the performance of the two distance algorithms in conjunction with five off-the-shelf speech recognition models. Damerau–Levenshtein, as compared to the Levenshtein distance algorithm, succeeded in minimizing the Word Error Rate (WER) when using the MoroccanFrench test set with five speech recognition systems, namely VOSK API, Google API, Wav2vec2.0, SpeechBrain, and Quartznet pre-trained models. Our post-processing method works regardless of the architecture of the speech recognizer, and its results on our MoroccanFrench test set outperformed the five chosen off-the-shelf speech recognizer systems.