Reviewerly/exHarmony huggingface dataset:
A Note on Disjunction and Existence Properties in Predicate Extensions of Intuitionistic Logic—An Application of Jankov Formulas to Predicate Logics

Predicate extensions of intuitionistic logic (PEI’s) are intermediate predicate logics having the same propositional part as intuitionistic logic. Intuitively, PEI’s must resemble intuitionistic logic. We discuss PEI’s from the viewpoint of disjunction property (DP) and existence property (EP). Note that DP and EP are regarded as “hallmarks” of constructivity of intuitionistic logic. There are, however, uncountably many PEI’s having both of DP and EP. Moreover, there are two continua of PEI’s: (1) each of which lacks both of DP and EP, and (2) each of which has EP but lacks DP. Now, a natural question arises: Do there exist uncountably many PEI’s each of which has DP and lacks EP? We answer this question affirmatively. Specifically, we construct uncountably many such PEI’s by making use of modified Jankov formulas. This result suggests that although PEI’s are living near to intuitionistic logic, the diversity of their nature seems rich. In other words, logics among PEI’s are fascinating from the logical point of view and yet to be explored.
Blockchain and People’s Livelihood

Livelihood issues and people’s lives are closely related, which have always been an important focus in the process of social construction. In this paper, the first part describes the value of blockchain applications in people’s livelihood, especially analyze the significance of providing reliable and credible business collaboration and value transmission platforms. The second part discusses the typical application scenarios and practices, including Healthcare data sharing, Traceability of drugs, Donation management, Crowd-sourcing compensation, Targeted poverty alleviation and Land requisition and demolishing management.
Reconstructing Parameterized Strings from Parameterized Suffix and LCP Arrays

Reconstructing input from a data structure entails determining whether an instance of the data structure is in fact valid or not, and if valid, discovering the underlying data that it represents. In this paper we consider the parameterized suffix array (pSA) along with its corresponding parameterized longest-common-prefix (pLCP) array and solve the following problem. Given two arrays of numbers as input, A and P, does there exist a parameterized string S such that A is its pSA and P is its pLCP array? If the answer is positive, our algorithm produces a string S whose pSA is A and whose pLCP array is P. Although the naive approach would have to consider an exponential number of possibilities for such a string S, our algorithm’s time complexity is only $$O(n^2)$$ for input arrays of size n.
Glints Detection in Noisy Images Using Graph Matching

Glints detection is important in gaze estimation with virtual reality equipment. In some complex scenarios, the glints detection may fail due to multiple reasons, such as the ambiguous correspondence between the multiple light sources or their reflected glints in the captured image, as well as the distinction between the good glints and the noisy glints caused by the lights reflect on the glasses that the users may wear. In this article, the glints detection is formulated as a graph matching problem between the two graphs constructed from the candidate glints and the rough predicted ones from the information given by the last frame. In addition, the optimal parameters for the matching are estimated using an Expectation Maximum algorithm-like algorithm. Finally, with the optimal parameters, the Kuhn–Munkras algorithm is used to get the optimal matching, and the successfully matched true glints are considered the correctly detected glints. Experiments are performed to validate the effectiveness of the proposed method.
A Fuzzy Logic Risk Assessment System for Type 2 Diabetes

This chapter details the design and evolution of a Mamdani fuzzy logic system to allow users to assess their own risk of developing type 2 diabetes. From the research behind the system to its design, conception and testing to refine the system’s accuracy. The system allows users to enter details about their lifestyle and their vital statistics such as height and weight to determine an approximate risk level of developing type 2 diabetes so they can make healthier and more mindful lifestyle choices. Choosing to make better choices will improve their quality of life and save healthcare providers or themselves a vast amount of money in the long run, depending on whether it is a national healthcare service or private provider. This system is not medically tested, and usage of this system without rigorous medical testing and alterations based on that testing is not advised.
Design of Single-Electron Reservoir Computing Circuit and Evaluation of Its Learning Function

We propose a single-electron (SE) reservoir computing (RC) circuit. The circuit consists mainly of SE oscillators (SEOs) whose behavior resembles that of a neuron. In our RC circuit, the reservoir layer has arrayed SEOs. We let the circuit learn a sawtooth wave and a sine wave and confirmed an effect of learning of the circuit by computer simulation.
Font Generation with Missing Impression Labels

Our goal is to generate fonts with specific impressions, by training a generative adversarial network with a font dataset with impression labels. The main difficulty is that font impression is ambiguous and the absence of an impression label does not always mean that the font does not have the impression. This paper proposes a font generation model that is robust against missing impression labels. The key ideas of the proposed method are (1) a co-occurrence-based missing label estimator and (2) an impression label space compressor. The first is to interpolate missing impression labels based on the co-occurrence of labels in the dataset and use them for training the model as completed label conditions. The second is an encoder-decoder module to compress the high-dimensional impression space into low-dimensional. We proved that the proposed model generates high-quality font images using multi-label data with missing labels through qualitative and quantitative evaluations. Our code is available at https://github.com/SeiyaMatsuda/Font-Generation-with-Missing-Impression-Labels.
A Transformer-based Approach for Identifying Target-oriented Opinions from Travel Reviews

Performing target-oriented opinion word extraction (TOWE) from online travel reviews is a valuable reference for both tourists and attraction administration department. This paper formulates a novel research topic of identifying target-opinion pair from Chinese travel review corpus. Learning target-oriented representation accurately, locating the opinion word and extracting the complete opinion are three major challenges. Hence, we leverage aspect-based query, pos-tag and relative position and devise appropriate structure to fuse them in an encoder-decoder framework. Specifically, in the encoder, the target-fused (aspect, review) pair and the pos-tag label are encoded by transformers to model the global dependency, in the decoder, a BiLSTM is adopted to enhance contextual representation by incorporating relative position information. A real-world Chinese travel dataset for TOWE task is constructed, and the experimental results demonstrate the efficacy of the proposed model. Extensive ablation experiments are also conducted to study the effect of different components of the model.
Improving Code Clone Detection Accuracy and Efficiency based on Code Complexity Analysis

Code cloning is a common activity in software development, which refers to the copying and reusing of code fragments with appropriate modifications. In the era of big code, some code clone detection techniques are no longer suitable for large-scale software or repositories. Code clone filtering can not only significantly improve the efficiency of code clone detection but also effectively improve the accuracy of code clone detection. In this paper, we propose CCFilter which is a code clone filtering tool based on code complexity analysis. In CCFilter, the code complexity of all functions is analyzed, and the functions to be checked are filtered according to the threshold of complexity before detecting code clones. In order to evaluate CCFilter's performance, a set of experiments is conducted. CCFilter is more accurate and efficient than a filtering strategy based on code size. The experimental results also show that filtering improves the scalability and efficiency of code clone detection.
Routing and Scheduling of Key Assignment in Optical Networks secured by Quantum Key Distribution

Quantum key distribution (QKD) has potential of providing long-term security as it is based on the fundamental principles of quantum mechanics. Security of information transmitted through optical networks has attracted attention recently due to its vulnerability to attacks post quantum computers. Hence, the integration of QKD into optical networks can significantly improve the security of existing and future optical networks. In QKD-secured optical networks, the quantum channel and the classical channel are integrated into a single optical fiber to reduce the deployment cost of the network infrastructure. However, due to limited network resources in a single optical fiber, the efficient provisioning of network resources (wavelengths and time slots) and blocking are two major issues in such networks. In this work, a resource assignment strategy with scheduling, namely, resource assignment with scheduling (RA-WS) has been proposed for utilization of network resources efficiently. In the RA-WS strategy, the QKD lightpath requests (QLPRs) have been served according to the proposed scheduling criterion. Moreover, the proposed scheduling criterion for routing, wavelength and time-slot assignment (RWTA) can reduce the effect of QLPRs blocking because of the limited network resources. The performance of the proposed RA-WS strategy has been analyzed in terms of blocking probability (BP), success probability of update secret key ( <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$SP_{USK}$</tex> ), and time-slot utilization ratio (TSUR) with different reserved wavelengths for quantum signal channel (QSCh), i.e., <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$W_{Q}$</tex> and different time slots ( <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$n_{tr}$</tex> ). Simulations performed on NSFNET network topology indicate that the RA-WS strategy performs better than the baseline strategy, i.e., resource assignment without scheduling (RA-WOS) in terms of all the considered metrics.
Sound-Guided Semantic Video Generation

The recent success in StyleGAN demonstrates that pre-trained StyleGAN latent space is useful for realistic video generation. However, the generated motion in the video is usually not semantically meaningful due to the difficulty of determining the direction and magnitude in the StyleGAN latent space. In this paper, we propose a framework to generate realistic videos by leveraging multimodal (sound-image-text) embedding space. As sound provides the temporal contexts of the scene, our framework learns to generate a video that is semantically consistent with sound. First, our sound inversion module maps the audio directly into the StyleGAN latent space. We then incorporate the CLIP-based multimodal embedding space to further provide the audio-visual relationships. Finally, the proposed frame generator learns to find the trajectory in the latent space which is coherent with the corresponding sound and generates a video in a hierarchical manner. We provide the new high-resolution landscape video dataset (audio-visual pair) for the sound-guided video generation task. The experiments show that our model outperforms the state-of-the-art methods in terms of video quality. We further show several applications including image and video editing to verify the effectiveness of our method.
Regarding the digitalization of the cognitive gaming space of educational institutions

The modern development of digital technologies also demonstrates the relevance of its use in the educational process of children. The purpose of the article is to analyze the digitalization of the cognitive and gaming space of educational institutions, the formation of a future perspective model of their development and use. The article uses pedagogical (theoretical) and general scientific research methods: analysis, synthesis, induction, deduction, concretization, abstraction, forecasting. In the results, it is proposed to form a certain conditional model of the cognitive-game space, which will include the establishment of the necessary prerequisites, a comprehensive analysis of the spatial environment, the development and implementation of the necessary material and technical conditions, the processing of results and the elimination of shortcomings. Also, the process of understanding will take place according to the model: observation of children, survey, and determination of educational level work with documents and materials to improve and optimize the educational process. The conclusions particularly emphasize the need to develop digital competencies among children, as working with modern digital technologies will require special skills, given the fact that digital technologies are constantly improving.
Analysis of automated detection methods for hepatocellular carcinoma

In human body , liver is the primary organs which performs numerous function in the body such as filtering toxins in blood, neutralize toxins, Store and release needed glucose, regulate blood clotting and so on .The liver may get affected by Hepatitis and chronic alcohol. It leads liver diseases like liver cancer and cirrhosis. There are lot of medicines and treatment available in the market. Correct diagnosis of liver disease help the physicians to save many people lives. So we focused on studying various analytical methods for identifying liver diseases. However, we started to analysis the automated detection system used for the analysis of biomedical images. We have taken many research articles and found Pre-processing, segmentation, feature extraction and classifier are the various procedures used for liver image analysis. Different algorithm used in each steps has been well studied that helpful for the development of Computer Aided Diagnosis (CAD) system helps for analysis and detection of Hepatocellular carcinoma. The CAD system provides an accurate diagnosis of liver images extracting the texture features of it. In this paper, different available automated methods are taken for analyzing liver images.
First Time-Users of the Mobile Internet in the Global South

First-time users of the mobile Internet have not received much attention in studies of the adoption and diffusion of the technology in the Global South. This would not much matter if such users comprised only a small fraction of the total number or if they behaved in a similar way to other users in the region. The evidence provided, however, suggests that neither of these alternatives carry much weight. For, not only do very large numbers of persons use the mobile Internet for the first time in any given year, but they also undergo a very different learning process from those who have used the technology for a longer period of time. In short, those in the former category tend to suffer from a relative paucity of digital skills, that results in a narrow choice of Internet uses, based heavily on entertainment rather than development. In the concluding policy section, I emphasize that an important tool for imparting skills to first-time users involves one-on-one-contact with mobile agents or street vendors. Two cases are cited in which this form of contact appears to have a marked, positive impact.
Commander-Soldiers Reinforcement Learning for Cooperative Multi-Agent Systems

In ball sports, such as basketball, the coach can guide players to better offend and defend from a holistic perspective to win the game. Inspired by such scenarios, we introduce a coach-like concept into the decision-making process of cooperative multi-agent systems. We propose a new framework Commander-Soldiers Reinforcement Learning (CSRL), for Multi-Agent systems. Specifically, we introduce a virtual role, Commander, which can obtain and encode global information every T steps and send the encoded global guidance to Soldiers (real agents). Furthermore, we propose Policy Guidance Network (PGN), which can customize the encoded global guidance from Commander based on observations for each Soldier, providing each Soldier with specified guidance to the decision-making process. The Soldier takes into account not only the local action-observation histories but also the specified guidance from PGN when making decisions. We validate CSRL on the challenging StarCraft II micromanagement benchmark, proving that our approach can take advantage of intermittent global information to improve collaborative performance.
Trusted Computing Management Model for Power Data Based on Consortium Blockchain

With the advent of the digital economy era, the large data industry market has great potential for development. To promote the continuous innovation of power Internet business, it is necessary to build trustworthy management and control of large energy data to drive the development of new business. The need for effective utilization of data value urgently requires internal and external interaction and sharing of power grid data, in which the trustworthy calculation of data becomes a focus issue. This paper presents a trusted computing management model for power data, which uses Consortium Blockchain to achieve trusted computing of data, and manages the computing in the trusted computing process jointly with wireless communication resources to optimize the overall utility of the system under limited resources. We carry out an experimental simulation and compare the model with the traditional trusted computing model to verify that the model improves the system performance and resource utilization efficiency.
A Robust Growing Memory Network for Lifelong Learning of Intelligent Agents

The general success criterion for an artificial intelligence system is its ability to mimic human brain learning. Throughout a lifetime, the human brain is capable of continual learning. The acquired information is kept, augmented, finetuned, and utilized to complete new tasks in the future. At the moment, machine learning models perform well when given precisely structured, balanced, and homogenized data. However, when several jobs with incremental data are provided, the performance of the majority of these models suffers. Inspired by the Complementary Learning Systems (CLS) theory in neuroscience, episodic-semantic memory-based frameworks have received much attention and research. On the other hand, conventional methods are needed to perform data batch normalization and are sensitive to vigilance hyperparameters across different datasets. This paper proposes a Robust Growing Memory Network (RGMN) that continuously learns incoming data without normalization and is unlikely to be affected by the vigilance hyperparameter. The RGMN is a self-organizing topological network that models human episodic memory, and its network size can grow and shrink in response to data. The long-term memory buffer retains the largest and smallest data values that will use for learning. To evaluate the performance of the proposed method, we conducted comparative experiments on real-world datasets, and results showed that the proposed method outperforms existing memory-based baseline frameworks in terms of accuracy.
Handling Interpretability of Deep Learning Model in real-time application by implementing big data Techniques

The level of interpretability refers to how well a person can consistently predict the outcome of the model. The more interpretable a model of machine learning is, the simpler it is to understand why particular judgments or forecasts have been made. Deep neural networks' (DNNs') interpretability is crucial because it gives users the ability to recognize the models' fundamental advantages and disadvantages, predict their future behavior, and identify and fix any issues. Yet, a DNN's opaque or "black-box" design makes it difficult to understand what it truly performs. To solve this problem, we offer a unique method that makes better use of the rich semantic data present in human descriptions to enhance the comprehensibility of DNNs. By focusing just on object detection problem, we firstly derive from the human explanations a set of semantically significant themes that include a variety of visual ideas, and then we incorporate them into the model with an interpretive loss. Finally, we present a prediction variance maximization technique to understand every neuron's learnt property. Utilizing interpretable data, experimental findings demonstrate its efficacy in object recognition, which is transferable to video action recognition. Users can quickly correct inaccurate predictions using a human-in-the-loop approach by having a comprehensive knowledge of the learnt features.
Combinatorial Coverage for Assured Autonomy

With the advancement of Artificial Intelligence and Ma-chine Learning (AI/ML), we are observing a rapid increase of autonomous systems in safety-critical domains, such as smart medical equipment, self-driving vehicles, and unmanned aircraft. These systems are required to be made ultra reliable using state of the art verification and validation methodologies. Existing verification, validation, and assurance efforts, such as DO-178C guidance for avionics software, depend on structural coverage based testing, such as MC/DC coverage. Such structural coverage criteria require that test cases are chosen to ensure that a specified level of statements, decisions, and paths are systematically exercised. Neural network and other machine learning based systems, however, are not well suited to be tested with such structural coverage dependent criteria [1], [2]. This is because the performance of machine learning functions such as neural networks depends on the data used to train and test the model, rather than in specifically coded behavior. Behaviors of such systems will change depending on inputs used in the training.
Named entity recognition of specific fields integrating word and improved entity key information attention mechanism

Named entity recognition (NER) is one of the basic tasks of knowledge extraction. In view of corpus in some specific fields have sparse semantics and limited text standardization, the existing entity recognition methods have the problems of wrong potential word interference, matching potential words in some specific fields difficultly, and the key information of entities with different lengths in sentences may have interference in the representation of attention mechanism. This paper proposes a NER model integrating word and improved entity key information attention mechanism, which is called binocular attention-based BiLSTM with CNN network (BACBN). Firstly, the character feature embedding is generated through bidirectional encoder representation of transformers (BERT), and a word-level character feature attention mechanism is proposed to highlight the character features constituting words. Secondly, based on the bidirectional long short-term memory network (BiLSTM), an n-gram pooling feature attention mechanism is proposed. The prominent features corresponding to different convolution kernel sizes are obtained through convolution neural network (CNN), and the context features are weighted according to the prominent features, so as to obtain the information that contributes more to entity recognition with different lengths. The experimental results on four specific field Chinese corpus NER datasets show that BACBN model can improve the result of entity recognition.