Reviewerly/exHarmony huggingface dataset:
A First-Order Logic Clause Set Preprocessing Method Based on Clause Deduction Distance

Currently, many preprocessing methods in first-order automated theorem provers use symbol-based method, which is used to measure the relation of symbols in goal clauses (negated conjectures) and symbols in other clauses, and simplify clause set by setting threshold of the correlation metric. From the perspective of deduction between literals to measure the relation of different clauses, a novel method defining the relation between goal clauses with other clauses in clause set, and its application for clause set preprocessing by setting threshold are proposed in this paper, including literal deduction distance, clause deduction distance and their relationship. In the algorithm implementation, literals in the clause set are classified with a tree construction by the literal deduction distance, which intuitively describe the deduction relationship of literals in clause set. Importantly, the time complexity of the algorithm is polynomial. Apply the proposed method as a preprocessing method to the top theorem prover-Vampire 4.1, and take the CASC-26 (in 2017) competition theorems (FOF division) as the test object, experimental results show that the proposed preprocessing method has a good capability to simplify clause set and indeed enhances the capability of plain Vampire 4.1 and Vampire 4.1 with unrestricted threshold to a certain extent.
Exploring Citation Networks with Hybrid Tree Pattern Queries

Scientific impact of publications is often measured using citation networks. However, traditional measures typically rely on direct citations only. To fully leverage citation networks for assessing scientific impact, it is necessary to investigate also indirect scientific influence, which is captured by citation paths. Further, the analysis and exploration of citation networks requires the ability to efficiently evaluate expressive queries on them. In this paper, we propose to use hybrid query patterns to query citation networks. These allow for both edge-to-edge and edge-to-path mappings between the query pattern and the graph, thus being able to extract both direct and indirect relationships. To efficiently evaluate hybrid pattern queries on citation graphs, we employ a pattern matching algorithm which exploits graph simulation to prune nodes that do not appear in the final answer. Our experimental results on citation networks show that our method not only allows for more expressive queries but is also efficient and scalable.
A Twist of the Coronavirus Privacy Crisis: New Lows in Privacy

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Classification of Relationship in Argumentation Using Graph Convolutional Network

The Argument Relationship Prediction is one of the tasks of Argumentation Mining that aim to find connections between arguments (or parts thereof). This task is considered as one of the most complex stages of argumentation. Concomitant to that, the Graph Convolutional Network (GCN) has been successfully applied to graph-based applications. In this study, we join the relationship prediction challenge with the ability of GCN to classification. We propose ArgGCN, a framework based in GCN method applied to the classification of relationships between arguments. The arguments are considered as short texts, and we abstracted the recognition of unitary elements from them (such as claims and evidence). In this study, we achieved promising results on the UKP Aspect, AFS, and Microtext corpus.
Automated Classifier Development Process for Recognizing Book Pages from Video Frames

One of the latest developments made by publishing companies is introducing mixed and augmented reality to their printed media (e.g. to produce augmented books). An important computer vision problem that they are facing is classification of book pages from video frames. The problem is non-trivial, especially considering that typical training data is limited to only one digital original per book page, while the trained classifier should be suitable for real-time utilization on mobile devices, where camera can be exposed to highly diverse conditions and computing resources are limited. In this paper we address this problem by proposing an automated classifier development process that allows training classification models that run real-time, with high usability, on low-end mobile devices and achieve average accuracy of 88.95% on our in-house developed test set consisting of over 20 000 frames from real videos of 5 books for children. At the same time, deployment tests reveal that the classifier development process time is reduced approximately 16-fold.
Link Prediction in Bibliographic Networks

Analysing bibliographic networks is important for understanding the process of scientific publications. A bibliographic network can be studied using the framework of Heterogeneous Information Networks (HINs). In this paper, we comparatively evaluate two different algorithms for link prediction in HINs on an instance of a bibliographic network. These two algorithms represent two distinct categories: algorithms that use path-related features of the graph and algorithms that use node embeddings. The results suggest that the path-based algorithms achieve significantly better performance on bibliographic networks.
Optimized Text Classification Using Deep Learning

As there is tremendous hike in the amount of data created in the world, the need for text classification is on rise. Data from all the online sources: e-mails, web pages, social media, chats, and more results in a huge amount of unstructured text. To extract the information from the text that is unstructured in nature is very cumbersome and time-taking. Therefore, text classification becomes a pre-requisite for the businesses to improve the decision-making process. Different deep learning-based models for text classification with respect to different activation functions are analyzed in the paper.
Research on Key Technologies of Knowledge Graph Construction Based on Natural Language Processing

Abstract As we all know, building a domain knowledge graph from a large amount of text requires a very large amount of work, including entity recognition, entity disambiguation, relationship extraction, and event extraction, etc. It is difficult to build a very comprehensive domain knowledge graph from scratch. Fortunately, with the rapid progress of natural language processing technology, we can use a large number of natural language processing tools to help us build a domain knowledge graph. This article mainly studies the extraction of domain terms in the process of constructing the knowledge graph. The natural language processing techniques used are mainly new word discovery, word segmentation, and keyword extraction. This paper improves the existing imperfect natural language processing technologies and applies them to the process of constructing the domain knowledge graph in order to construct the domain knowledge graph accurately and efficiently.
Health Access Broker: Secure, Patient-Controlled Management of Personal Health Records in the Cloud

Secure and privacy-preserving management of Personal Health Records (PHRs) has proved to be a major challenge in modern healthcare. Current solutions generally do not offer patients a choice in where the data is actually stored, and also rely on at least one fully trusted element that patients must also trust with their data. In this work, we present the Health Access Broker (HAB), a patient-controlled service for secure PHR sharing that (a) does not impose a specific storage location (uniquely for a PHR system), and (b) does not assume any of its components to be fully secure against adversarial threats. Instead, HAB introduces a novel auditing and intrusion-detection mechanism where its workflow is securely logged and continuously inspected to provide auditability of data access and quickly detect any intrusions .
Commutative Encryption Method Based on Hidden Logarithm Problem

A candidate for post-quantum commutative encryption algorithm is proposed, which is based on the hidden discrete logarithm problem defined in a new 6-dimensional finite noncommutative associative algebra.The properties of the algebra are investigated in detail and used in the design of the proposed commutative cipher.The formulas describing the set of p 2 different global right-sided units contained in the algebra and local left-sided units are derived.Homomorphisms of two different types are considered and used in the commutative cipher.The encrypted message is represented in the form of a locally invertible element T of the algebra and encryption procedure includes performing the exponentiation operation and homomorphism map followed by the left-sided multiplication by a randomly selected local right-sided unit.The introduced commutative cipher is secure to the knownplaintext attacks and has been used to develop the post-quantum no-key encryption protocol providing possibility to send securely a secret message via a public channel without using any pre-agreed key.The proposed commutative encryption algorithm is characterized in using the single-use keys that are selected at random directly during the encryption process.
MKM: Multiple Kernel Memory for Protecting Page Table Switching Mechanism Against Memory Corruption

Countermeasures against kernel vulnerability attacks on an operating system (OS) are highly important kernel features. Some kernels adopt several kernel protection methods such as mandatory access control, kernel address space layout randomization, control flow integrity, and kernel page table isolation; however, kernel vulnerabilities can still be exploited to execute attack codes and corrupt kernel memory. To accomplish this, adversaries subvert kernel protection methods and invoke these kernel codes to avoid administrator privileges restrictions and gain complete control of the target host. To prevent such subversion, we present Multiple Kernel Memory (MKM), which offers a novel security mechanism using an alternative design for kernel memory separation that was developed to reduce the kernel attack surface and mitigate the effects of illegal data manipulation in the kernel memory. The proposed MKM is capable of isolating kernel memory and dedicates the trampoline page table for a gateway of page table switching and the security page table for kernel protection methods. The MKM encloses the vulnerable kernel code in the kernel page table. The MKM mechanism achieves complete separation of the kernel code execution range of the virtual address space on each page table. It ensures that vulnerable kernel code does not interact with different page tables. Thus, the page table switching of the trampoline and the kernel protection methods of the security page tables are protected from vulnerable kernel code in other page tables. An evaluation of MKM indicates that it protects the kernel code and data on the trampoline and security page tables from an actual kernel vulnerabilities that lead to kernel memory corruption. In addition, the performance results show that the overhead is 0.020 $$\mu $$ s to 0.5445 $$\mu $$ s, in terms of the system call latency and the application overhead average is 196.27 $$\mu $$ s to 6,685.73 $$\mu $$ s , for each download access of 100,000 Hypertext Transfer Protocol sessions.
Lifelog Moment Retrieval With Interactive Watershed-Based Clustering and Hierarchical Similarity Search

Recently, the “lifelogging” and “lifelog” terminologies are frequently used to represent the activity of continuously recording people's everyday experiences, and the dataset contained these recorded experiences, respectively. Hence, providing an excellent tool to retrieve life moments from lifelogs to fast and accurately bring a memory back to a human when required, has become a challenging but exciting task for researchers. In this paper, a new method to meet this challenge by utilizing the hypothesis that “a sequence of images taken during a specific period can share the same context and content” is introduced. This manuscript per the authors introduces a new system to overcome the drawbacks. The experimental results confirm the high productivity of the proposed method in both stable and accuracy aspects as well as the advantage of having an interactive schema to push the accuracy when there is a conflict between a query and how to interpret such a query. Especially, this system requires only a few interactive steps to retrieve relevant images with high accuracy completely.
Research on the comprehensive evaluation method of college students’ achievements based on multi-view RSR

Abstract In view of the problems and shortcomings of the current college students’ achievements evaluation methods, a multi-view rank sum ratio(RSR) comprehensive evaluation method of students’ achievements was proposed. The method integrated the two factors of curriculum importance and students’ learning ability when determining the weight. Then combined with the RSR ranking method, the comprehensive evaluation ranking of students’ achievements was carried out. This method overcomes the subjectivity of weight setting and maximally reflects the integrity of information. Finally, through the empirical analysis, the method was compared with the average score method and intellectual education evaluation method, and the effectiveness of the method was verified through the ranking results comparison.
Digitalization and the Anthropodicy Problem in Andrei Platonov’s Works

The article identifies the main features of the Russian writer Andrei Platonov’s (1899-1951) comprehension of the anthropological consequences of the radical social transformation during the years of the “Great Turn,” or “Great Break” (i.e., the years of Stalin’s reforms that started in 1929). Platonov’s evaluation is unique in its scale and depth. He was among the first authors to draw attention to the typological commonness of Soviet and German totalitarianisms. Their similarities are not only rooted in the design of the respective regimes. Vice versa, the design itself is generated by the possibilities of inhuman rationalistic activism in mass society. Platonov’s texts written in 1929-1934 were devoted, rather than to mass collectivism or political and socio-cultural reorganization, to anthropology and the possibility of reorganizing man, together with his cosmos. The main idea of these literary works is search for a universal way of human existence in general, including the living and the dead. In these texts, Platonov deeply conceived and felt the complete emptiness and inhumanity of doctrinaire rationalistic activism, when it is accepted as a practical maxim for the universal human will. This body of texts does not represent a dystopian view of a possible future, yet it relates the shock of an encounter with an unexpectedly ambiguous future and the author’s longing and suffering in his attempts to understand it. Such attempts lead to the need for a new anthropodicy as a justification for a human existence, notwithstanding man’s limitations and finiteness. In this respect, the results of Platonov’s reflections are extremely relevant in relation to the analysis of humanitarian factors and the consequences of currently ongoing digitalization of practically all spheres of life, as well as in terms of searching for new foundations of human life under these conditions. Platonov’s works turn out to be more relevant than the alarmism of the philosophers of the Frankfurt School of Critical Theory and than the contemporary demonization by the conceptions of digital posthumanity and transhumanism. Platonov’s relevance is due to the depth of the topics and problems he raised, and their meaning is just beginning to be revealed today.
Adaptive image denoising using a deep neural network with a noise correction map

Smartphone camera is becoming the primary choice for photography among general users due to its convenience and rapidly improving image quality. However, it is more prone to noise compared to a professional DSLR camera due to a smaller sensor. Image noise, especially in low-light situations, is a critical problem that must be addressed to obtain high quality photos. Image denoising has thus remained an important low level vision topic over years with both traditional and learning based techniques used for mitigating this problem. We propose an adaptive Deep Neural Network based Noise Reduction (DNN-NR) algorithm to address the denoising problem in smartphone images. Image noise was modeled from photos captured under different light settings using a Poisson-Gaussian noise model which better approximates the signaldependence (photon sensing) and stationary disturbances in the sensor data. Using this noise model, synthetic noisy datasets were prepared to mimic photos captured under varying light conditions and train the network. A noise correction map based on camera and image information like ISO, vignetting map and image gray level was provided as an input to the network. This correction map provides an indication of the local noise level to help the network adaptively denoise photos. Experimental results show that our adaptive neural network based denoising approach produced a significantly better denoised image with higher PSNR and MOS quality scores in comparison to a standard denoising method like CBM3D across varying light conditions. In addition, using a locally varying noise map helped in preserving more detail in denoised images.
Fuzzy ARTMAP Network And Clustering For Streaming Classification Under Emerging New Classes

Streaming classification with emerging new classes is a challenging problem receiving extensive attention. To date, most approaches simply detect novel classes without further recognizing them, and ignore conceptual drift and conceptual evolution problem. Therefore, in this paper, we propose an effective semi-supervised framework that combines ARTMAP neural network and clustering method to detect and identify simultaneously multiple known and unknown classes in data streams and update the network online. Experiments have been conducted on four benchmark datasets with different data forms including MNIST, CIFAR10, network attack analysis, and geospatial information of forests. The empirical evaluation shows effectiveness of the proposed approach, whose results are much better than many previous studies.
Maximum Entropy Regularization and Chinese Text Recognition

Chinese text recognition is more challenging than Latin text due to the large amount of fine-grained Chinese characters and the great imbalance over classes, which causes a serious overfitting problem. We propose to apply Maximum Entropy Regularization to regularize the training process, which is to simply add a negative entropy term to the canonical cross-entropy loss without any additional parameters and modification of a model. We theoretically give the convergence probability distribution and analyze how the regularization influence the learning process. Experiments on Chinese character recognition, Chinese text line recognition and fine-grained image classification achieve consistent improvement, proving that the regularization is beneficial to generalization and robustness of a recognition model.
SOME DIFFICULTIES FACED BY THE STUDENTS IN LEARNING PRESENT PERFECT TENSE

The research purpose is to know the difficulties faced by the students in learning Present Perfect Tense and the cause of the problems. The design of the research is descriptive research. The number of the population was thirty-six students. The researchers used tests, questionnaires, and interviews to collect the data. After analyzing the data, it found that the average score of test results was 44.67, which was classified as a less classification. Then the ability in using To Be (Has/Have) of present perfect tense was placed in a good rating by the mean score 68.29. Moreover, the students' ability in understanding regular verbs was in a less classification by the mean score 34.03. It also happens for the knowledge in understanding irregular verbs which were classified as less classification by the mean score 39.72. Based on the data obtained from the questionnaires, it shows that most of the students felt that Present Perfect Tense is a problematic material of English. It was proven by seeing that more than 50% of the students said that. It caused by some factors; (1) English materials are less favoured by the students, (2) they are lack of English vocabulary mastery, (3) they have difficulties in recognizing to be of Present Perfect Tense, and (4) they could control the form of verbs changes.
Gaussian Process Multiclass Classification : Evaluation of Binarization Techniques and Likelihood Functions

In binary Gaussian process classification the prior class membership probabilities are obtained by transforming a Gaussian process to the unit interval, typically either with the logistic likelihoo ...
The NodeHopper: Enabling Low Latency Ranking with Constraints via a Fast Dual Solver

Modern recommender systems need to deal with multiple objectives like balancing user engagement with recommending diverse and fresh content. An appealing way to optimally trade these off is by imposing constraints on the ranking according to which items are presented to a user. This results in a constrained ranking optimization problem that can be solved as a linear program (LP). However, off-the-shelf LP solvers are unable to meet the severe latency constraints in systems that serve live traffic. To address this challenge, we exploit the structure of the dual optimization problem to develop a fast solver. We analyze theoretical properties of our solver and show experimentally that it is able to solve constrained ranking problems on synthetic and real-world recommendation datasets an order of magnitude faster than off-the-shelf solvers, thereby enabling their deployment under severe latency constraints.
Evaluation of the Tiller Switch Layout of a Tractor Using Eye-Fixation Related Potentials

The human-machine interface (HMI) of a tractor cockpit includes not only the steering wheel and pedals, but also many levers and switches. According to a report from the National Agriculture and Food Research Organization of Japan, the HMI layout often differs from the operator's expectation. This gap between expectations and reality has led to operator errors and market complaints. However, few reports have ergonomically evaluated these arrangements. Our previous work showed that this gap can be evaluated by the amplitude of the P300 component of the event-related potentials (ERPs) for discrimination tasks by using a picture of the vehicle cockpit on the display. However, in real-world driving and operation situations, eye movements occur. In such cases, the eye-fixation related potential (EFRP) may be used. The EFRP appears at the end of a saccade, which is a rapid movement of the eyes between fixation points. Its positive component, called the lambda response, is reported to reflect the human attention level to visual targets. We hypothesize that if the HMI layout differs from the user's expectation, operation becomes difficult and the user's attention to the target during operation will be reduced. In order to confirm this hypothesis, an experiment was conducted during the actual operation of a tractor while changing the tiller switch position. The participants were 13 adult males with experience in tractor operations. The amplitude of the lambda wave was reduced when the switch position was different from the user's expectations, reflecting the decrease in attention caused by the increased difficulty of operating the tiller. We suggest that the amplitude of the lambda response can be used as the evaluation index for optimizing the switch position.