Reviewerly/exHarmony huggingface dataset:
FEU-Net: Glomeruli region segmentation network based on pseudo-labelling and channel attention mechanisms

The classification and detection of glomeruli of kidney tissue is a key process for correct diagnosis of diseases in renal pathology. However, it is still a big problem to perform comprehensive and accurate glomerular ultramicroscopic pathological diagnosis based on high-resolution whole-field digital slices (whole Slide Image). The reason lies in the grayscale image texture corresponding to the glomerular ultrastructure. Complicated, there are many types of related lesions, and it is difficult to identify and judge subtle pathological changes. The traditional semantic segmentation model cannot achieve the ideal segmentation effect. Based on this, this paper proposes a semantic segmentation method FEU-Net (FCN-Efficient U-Net) for pathological slices, which improves the accuracy of glomerular region segmentation and realizes end-to-end segmentation. FEU-Net uses EfficientNet, which has undergone transfer learning, as the encoder part to enhance image feature extraction capabilities. The decoder uses U-Net to promote the fusion of deep and shallow features while reducing the amount of network parameters, and redesign the convolution module to improve the gradient transfer capability. Compared with other classic methods in the SEED data set, it verifies that some classic models are difficult to fit in this segmentation task. At the same time, experiments show that modifying the feature extraction method can greatly improve the results. Among them, the modified method in this article is in the accuracy of segmentation. This is an increase of 18.968.96% over the original U-Net. At the same time, this article conducted ablation experiments on the ZENODO data set and the mendeley data set, verifying that each module in the improved algorithm helps to improve the segmentation effect of pathological slices. In the SEED data set, the FEU-Net method in this paper improves the Dice coefficient, accuracy rate and recall rate by 5.175.17%, 2.72.7%, 3.693.69%, 4.084.08% respectively compared with the benchmark model; in the BOT data set, The three indicators of the method in this paper have increased by 0.47%, 0.060.06%, 4.304.30%, and 6.086.08% respectively. The FEU-Net proposed in this paper improves the accuracy of segmentation of the pathological slices of gastric cancer and has good generalization performance.
A Diffusion Approximation Theory of Momentum Stochastic Gradient Descent in Nonconvex Optimization

Momentum stochastic gradient descent (MSGD) algorithm has been widely applied to many nonconvex optimization problems in machine learning (e.g., training deep neural networks, variational Bayesian inference, etc.). Despite its empirical success, there is still a lack of theoretical understanding of convergence properties of MSGD. To fill this gap, we propose to analyze the algorithmic behavior of MSGD by diffusion approximations for nonconvex optimization problems with strict saddle points and isolated local optima. Our study shows that the momentum helps escape from saddle points but hurts the convergence within the neighborhood of optima (if without the step size annealing or momentum annealing). Our theoretical discovery partially corroborates the empirical success of MSGD in training deep neural networks.
Credit Card Fraud Detection with Resampling Techniques

With the advancement of technology and consumer strength, the use of credit cards has rapidly increased. Credit Cards have shortened a transaction process making it simpler but has made the system prone to frauds. The information regarding one's credit card can easily be stolen and misused. Due to a gazillion data points of transactions available with the banks, Machine learning and Deep Learning Algorithms may prove very helpful in finding patterns and preventing fraudulent transactions. This paper is focused on delivering a comparative study on various supervised machine learning algorithm's performance for identifying fraudulent transactions by handling a highly imbalanced data-set by applying sampling techniques and work with a new approach in solving the problem for better results.
Towards a Blockchain Voting Roadmap

Recent concerns about election integrity provide motivation for more secure voting. Electronic voting has been projected for some time but still not widely adopted. Blockchain features like transaction authentication, block integrity, consensus mechanism has some potential to form part of a solution. Voting systems have disparate requirements, especially in terms of off-chain processes (e.g., voter authentication and non-coercion processes). Contributions of this article include classification of voting applications by governance and decisional context, 4W's framework for analysis of normal operations, technology roadmap considerations for blockchain voting, and identification of areas for further study.
Aeroengine Fault Correlation Analysis Based on Extension FP-Growth

Aeroengine configuration is complex, and there are many failure reasons and failure mechanisms of components, which bring difficulty to aeroengine fault diagnosis. Using machine learning method to analyze historical operation and maintenance data and mining fault association rules can provide effective support for aeroengine fault diagnosis. In this paper, an aeroengine fault correlation analysis method based on extension FP-Growth algorithm is proposed by combining extension superiority evaluation method with FP-Growth algorithm. Aiming at the problems of low value and weak relevance of operation and maintenance data, this paper uses FP-Growth to mine frequent items and search for feature items closely related to engine failure; aiming at the problems of slow search and low efficiency of transaction set in traditional FP-Growth, this paper removes the redundant items of transaction set, Improve the process of FP-Growth algorithm; for the problems of single evaluation index and over reliance on confidence in association rule mining, this paper introduces extension superiority evaluation method to comprehensively analyze the frequency characteristics, security, economy and maintenance, so as to improve the actual utility of association rules; and through data analysis and example verification, it proves the effectiveness and effectiveness of the algorithm proposed in this paper Advantages.
An Improved Deep-Layer Architecture for Real-Time End-to-End Person Recognition System

• Finding matches for a person in a real-time surveillance video is challenging. • Challenges include changes in background, illumination, pose, occlusion, views etc. • Assigning unique identifier to same person is known as person re-identification. • Deep learning with customized layers captures robust features of person's image. • Similarity score is computed using the spatial differences across summary of patches. • Threshold value is fixed to remove irrelevant matches. Surveillance of human activities in real time has drawn tremendous attention in the field of research. As manual monitoring of surveillance videos is expensive and prone to error, automation of surveillance is preferred. Person recognition is one of the fundamental problems related to automation of surveillance. It is defined as the system that generates correspondence between two images captured by different cameras at different times. Matching of probe image with the people in the surveillance video is really challenging due to variations in background, costume of people, pose, camera views, lighting, etc. A deep-learning-based end–end person recognition system is proposed to suit the real-world environment. This paper discusses the architecture of proposed system with the issues encountered during the implementation. Experiments were conducted based on different situations to illustrate the results of the proposed system with suitable evaluation metric. CUHK03 dataset was used for experiment. Real-time data were collected and tested to prove the robustness of the proposed system.
Classical and Quantum based Differential Cryptanalysis Methods

The security of a significant proportion of cryptography in use today depends directly or indirectly on the presumed difficulty of either factoring or extracting discrete logarithms in polynomial time on quantum computers. This paper discusses a quantum version of differential cryptanalysis which propounds quadratic speedup over the existing classical one. Linear cryptanalysis and differential cryptanalysis are general form cryptanalysis which is primarily applicable to block cipher, but also to stream ciphers and cryptographic hash functions. Linear cryptanalysis is a known-plaintext attack, in which attacker studies probabilistic linear relations known as linear approximations between some bits of plaintext, some bits of cipher text and some bits of cipher key. And in differential cryptanalysis the role of attacker is to analyze how differences in input information can affect the resulting difference at the output. Differential Cryptanalysis is a chosen-plaintext attack. However, differential cryptanalysis is more compelling so in this paper we are proposing a quantum version of the same. The computational complexity of classical differential cryptanalysis reaches O(NK). But by applying variations of Grover's search that is either full or partial Quantum Search the number of queries required to find the cipher key will be reduced.
Establishment of Business English Corpus Based on Foreign Economic Demand Dependent on Automatic Generation Algorithm of Conceptual Semantic Network

The establishment of a business English corpus is an essential means for integrating and extracting social language resources in the language discipline. In this paper, the requirements of tea trade for language and tea culture are explored through steps such as concept correlation computation, threshold selection, optimization, and adjustment for tea trade English relying on the automatic generation algorithm of the conceptual semantic network, and establishes a business English corpus to enhance the influence of business English culture and improve its economical application value. The practice suggests that the proposed algorithm can support the corpus establishment effectively, which has laid a solid foundation for the systematic establishment of the economic structure of tea trade.
The Primacy of Data in Deep Learning NLP for Conversational AI

Computational Linguistics and Natural Language Processing have changed considerably in the past few decades. Early research focused on representing and using linguistic knowledge in computational processes such as parsers, while these days the field focuses on practically-useful tasks such as information retrieval and chatbots. Currently our Deep Learning models have little to do with linguistic theory
Federated mobile sensing for activity recognition

Despite advances in hardware and software enabling faster on-device inference, training Deep Neural Networks (DNN) models has largely been a long-running task over TBs of collected user data in centralised repositories. Federated Learning has emerged as an alternative, privacy-preserving paradigm to train models without accessing directly on-device data, by leveraging device resources to create per client updates and aggregate centrally. This has been applied to various tasks, ranging from next-word prediction to automatic speech recognition (ASR). In this tutorial, we recognise on-device sensing as a privacy-sensitive task and build a federated learning system from scratch to showcase how to train a model for accelerometer-based activity recognition in a federated manner. In addition, we present the current landscape and challenges in the realm of federated learning and mobile sensing and provide guidelines on how to build such systems in a privacy-preserving and scalable manner.
Micro Activities Recognition in Uncontrolled Environments

Deep learning has proven to be very useful for the image understanding in efficient manners. Assembly of complex machines is very common in industries. The assembly of automated teller machines (ATM) is one of the examples. There exist deep learning models which monitor and control the assembly process. To the best of our knowledge, there exists no deep learning models for real environments where we have no control over the working style of workers and the sequence of assembly process. In this paper, we presented a modified deep learning model to control the assembly process in a real-world environment. For this study, we have a dataset which was generated in a real-world uncontrolled environment. During the dataset generation, we did not have any control over the sequence of assembly steps. We applied four different states of the art deep learning models to control the assembly of ATM. Due to the nature of uncontrolled environment dataset, we modified the deep learning models to fit for the task. We not only control the sequence, our proposed model will give feedback in case of any missing step in the required workflow. The contributions of this research are accurate anomaly detection in the assembly process in a real environment, modifications in existing deep learning models according to the nature of the data and normalization of the uncontrolled data for the training of deep learning model. The results show that we can generalize and control the sequence of assembly steps, because even in an uncontrolled environment, there are some specific activities, which are repeated over time. If we can recognize and map the micro activities to macro activities, then we can successfully monitor and optimize the assembly process.
Patenting Inventions Generated by Artificial Intelligence: The Way Forward

In the last two decades, we have witnessed significant advancement in Artificial Intelligence (AI), which has opened up unprecedented frontiers, accelerated disruptive innovations, solved complex problems, and reduced human efforts, in almost every industry. While the impact of AI on science and technology is still unfolding, there are currently cases where AI systems have independently generated inventions with the aid of techniques such as machine learning, deep learning, and neural networks. Ordinarily, these inventions should qualify for patent protection. However, the current patent laws world over only contemplate natural persons as inventors. This article will examine whether AI-generated innovations will satisfy the fundamental patent requirements of ownership, non-obviousness, patentable subject matter, and disclosure, and the difficulties patent offices will and are facing with the advent of AI-generated inventions. Most importantly, this article will suggest how inventions generated by AI systems should be protected.
Modeling Inter-Claim Interactions for Verifying Multiple Claims

To inhibit the spread of rumorous information, fact checking aims at retrieving evidence to verify the truthfulness of a given statement. Fact checking methods typically use knowledge graphs (KGs) as external repositories and develop reasoning methods to retrieve evidence from KGs. As real-world statement is often complex and contains multiple claims, multi-claim fact verification is not only necessary but more important for practical applications. However, existing methods only focus on verifying a single claim (i.e. a single-claim statement). Multiple claims imply rich context information and modeling the interrelations between claims can facilitate better verification of a multi-claim statement as a whole. In this paper, we propose a computational method to model inter-claim interactions for multi-claim fact checking. To focus on relevant claims within a statement, our method first extracts topics from the statement and connects the triple claims in the statement to form a claim graph. It then learns a policy-based agent to sequentially select topic-related triples from the claim graph. To fully exploit information from the statement, our method further employs multiple agents and develops a hierarchical attention mechanism to verify multiple claims as a whole. Experimental results on two real-world datasets show the effectiveness of our method for multi-claim fact verification.
Analysis of In-Place Randomized Bit-Flipping Decoders for the Design of LDPC and MDPC Code-Based Cryptosystems

We present a variant of the classic in-place bit-flipping decoder, frequently used with Low- and Moderate-Density Parity Check (LDPC/MDPC) codes, which allows a statistical analysis of the achievable decoding failure rate (DFR) in worst-case conditions. Such evaluation is of paramount importance in code-based post-quantum cryptography (PQC) where the ability to achieve indistinguishability under adaptive chosen ciphertext attacks strictly depends on being able to ensure very low DFR values (e.g., in the order of \(2^{-128}\) or lower) that, as such, are practically impossible to validate via numerical simulation. We provide theoretical evidence of the proposed approach and demonstrate its correctness through numerical examples. Moreover, we investigate the effect of changing the bit flipping decision threshold on the provided worst case analysis. Finally, we give design parameters for code-based cryptosystems employing Quasi-Cyclic LDPC/MDPC codes, able to achieve the security levels required in the NIST PQC standardization initiative which is currently in progress.
Deep Semantic Fusion Representation Based on Special Mechanism of Information Transmission for Joint Entity-Relation Extraction

Joint entity and relation extraction is still a challenging problem in natural language processing, which goal is to extract all possible relational triplets from original texts. Nevertheless, previous work rarely considered the integration of relation information to capture fine-grained correlations over token and relation spaces before extracting the entity pair, resulting in the unreasonable matching of entities and relations. In this paper, we propose a deep semantics fusing representation method based on a special mechanism of information transmission for joint entity relation extraction (DSFR). Specially, we called this special information transmission mechanism with a gate structure as UMIT, and then fuse the fine-grained information of tokens and relations by stacking multiple layers of UMIT. Finally, we extract the head and tail entities of a sentence under a certain relation by sequence labeling. Experiments on two publicly available New York Times (NYT) and WebNLG corpus show that our proposed approaches can effectively extract overlapping triplets and achieve better performance.
The design of the cse-ucla evaluation model using topsis and ahp methods for optimizing digital library services in badung regency

Abstract The Regional Archives and Library Office of Badung Regency must optimize the services provided in order to achieve excellent service quality. In its implementation, the office encountered difficulties in prioritizing aspects of services that require optimization. The purpose of this study was to design a Center for the Study of Evaluation-University of California in Los Angeles (CSE-UCLA) evaluation model using Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and Analytical Hierarchy Process (AHP) to provide recommendations on which aspects of services need to be optimized. The research method used stages that included problem identification, problem formulation, literature review, data collection, data analysis, design, and conclusions. The TOPSIS and AHP methods had the ability to rank a variety of aspects in the CSE-UCLA model. Aspect with the lowest ranking was considered as the aspect that is prioritized for optimization. That aspects used five service criteria: tangible, reliability, assurance, responsiveness, and empathy, all of which were classified in the “benefit” category. Each criterion had a value of 0.200, 0.200, 0.200, 0.200, 0.200 for the weight values for system assessment, program planning, program implementation, and program improvement, while for program certification: 0.556, 0.111, 0.111, 0.111, 0.111, respectively.
Smart Scape: Fall Detection Using Sensors with Shoes for Accident Falls

Falls are the foremost cause of injury-related mortality among adults of age 65 and more grown, and the age-related fall death is rising to 64 deaths per 1, 00,000 older adults. Senior citizens who have experienced falls several times feel scared about falling again and lose confidence in self-care. Fall detection and reliable surveillance are necessary to eliminate the effects of falls in the elderly. In this paper, we propose a fall detection system, the SMART SCAPE, which provides an easy access to detect falls and boosts the confidence in elderly and physically disabled individuals. SMART SCAPE design is user-friendly, comfortable, and economically intelligent shoe, when compared with the other fall detection models. A rule based detection algorithm has been used for communicating the data through an Internet of Things (IoT) device to alert the caretaker through various methods based on the situation analysis such as voice announcement, location plotting, and condition monitoring using artificial intelligence photography, auto ambulance, and auto complaint registration when a fall is detected.
Digital Preservation of Experiences in Virtual Worlds: Extending Digital Rights to Include Right to Replay

Experiences within Virtual Worlds spaces are increasingly becoming normalised as the standard form of human interaction, not only for play but also for work and education. The COVID pandemic did not cause this, but because of this existential crisis and Climate change, the development of virtual worlds is speeding up. Their role in society is changing, and they are becoming just as important as real-world spaces. Still, due to their digital nature, they are inherently ephemeral, they only exist for a short time in software and hardware. Then, they are simply lost to the ether. These worlds and the experiences of the users within them needs to be preserved. For digital preservation of these virtual worlds to succeed, a new approach is necessary, based not on preserving the world's data but on the players’ experiences within. A user-centric approach to the digital preservation of our experiences in Virtual Worlds virtual reality experiences is required. This can be achieved by extending our digital rights to include our right to replay. When codifying the right to allow for replaying experiences within virtual worlds, it can be argued that this action is burdening the companies who create those virtual worlds with undue responsibilities. This paper outlines how such a burden is trivial given modern-day game engines. To demonstrate this fact, an open-source library was developed to record generic objects within a Virtual World. This approach differs from others that approach treating the virtual world as a deterministic environment. The system in this paper records an object position and interactions within a world instead of just the raw inputs that most replay systems use. This article aims to discuss how future technology generating these worlds should target replays systems as a digital preservation method. Other more laborious processes will not be achieved even if they are enshrined into law. This approach hopes to offer to balance the burden between user and producer of virtual worlds.