Reviewerly/exHarmony huggingface dataset:
Formal Representation of Aspect

In this chapter, I will give formal representations for different ontological situations and the corresponding linguistic event types discussed in Chap. 3 . The aspectual markers and negators discussed in Chap. 4 will also be revisited in the perspective of formal representation. The purpose for doing this is first to describe the situations more accurately, based on which we can study their semantic entailments. Secondly, with the formal representation, we can understand how situation aspect and viewpoint aspect cooperate with each other to form linguistic events.
Community Building in Times of Pandemic: University Camilo José Cela, Spain

Abstract University Camilo José Cela (UCJC) is a private university located in Madrid (Spain) that belongs to the SEK Education Group, an institution with 125 years of tradition and a strong innovation identity. This case study presents the response that UCJC has given to facilitate the adaptation of the educational community (students, families, and teachers) to the situation arising from the pandemic caused by COVID-19. It will explain the coordination actions between students from the School of Education at UCJC and the impact derived from their interventions. Specifically, it will detail students’ participation as teacher assistants in online teaching within the IB pedagogical model to respond to the demands of primary and secondary teachers. This collaboration is the most outstanding due to the number of students and schools involved and the efficacy and efficiency of its implementation. On the other hand, there were other interventions of a smaller scale but a high social impact committed to disadvantaged sectors of the population. For example, our students’ support gave refugee students from Syria reinforcing their training or the psycho-emotional, educational, and legal assistance that volunteers from the bachelor’s degree of law provided to children and families in social exclusion. It is also significant to highlight the UCJC international actions: the teacher training program, EachTeach, provided educational methodologies, resources, and media to refugee teachers at the Kakuma refugee camp (Kenya), helping them to raise awareness about COVID-19, and the Cambodian program dedicated to training volunteers on how to combat the pandemic on these vulnerable contexts, where children live on the streets. Finally, to define broader collaborations and scale these initiatives in the future, this case study will reflect on the reasons for the success achieved, especially in training and pedagogical innovation and in the use of educational technology. The UCJC and SEK Schools collaboration allowed the use of a common technological language, sharing values. The development of training, support, and advice, between the university community (professors and faculty students) and the schools’ community (teachers, students, and families), enabled a wide range of relevant issues to be addressed in dealing with COVID-19 by schools and the broader education community.
Analysis of the Beaufort Cipher Expansion Technique and Its Usage in Providing Data Security in Cloud

Cloud computing usually refers to the usage of computational resources that is delivered as a service over the internet. Virtualization can be considered as the main technology behind cloud computing. There is a need for providing the security of the data in the cloud. Several encryption techniques have been used for providing the data security in the cloud. In this paper, analysis of the Beaufort expansion technique is carried out, and it is used for providing the security for the cloud data. Beaufort expansion technique provides better security against crypt analysis and pattern prediction compared to the original Beaufort cipher.
Security Mechanism for Digital Car Key

The digital car key enhances the convenience of the users. However, the security of wireless communication is at risk. This paper designs and builds a digital car key system, including the vehicle, the server, and the mobile device. Then an attack-defense tree model is used to analyze the security risk and countermeasures. The designed security mechanism includes the RSA algorithm, AES algorithm, freshness check, and key distribution and transmission. It also considers the real-time requirements of the automotive embedded system and uses the hardware-based security module. The system can resist tampering attacks, replay attacks, man-in-the-middle (MITM) attacks, etc. Finally, a MITM attack is implemented to test the security mechanism.
Assesment Model for Domain Specific Programming Language Design

In this paper, we present the results of research in constructing criteria for qualitative assessment of domain specific languages. Proposed compound metric consists of three parts: classic grammar size metrics, for evaluation programming language as a software and for evaluating conciseness. Each part of compound metric evaluates different characteristics needed for assessment of modern domain specific languages. To process evaluation automated software was developed. Research also contains explanation of marks for each component and their influence on the quality of accessing programming language. The analysis of assessment results provided required information for further improvements which will be presented as a part of system for computer-aided design of domain specific languages. Achieved compound metric could be used in assessment of domain specific programming languages of any types and can eliminate human provided mistakes in some areas of design.
Learning to Rank for Text Summarization: Revisiting the Features and Methods for Turkish Bank Documents

This study revisits features and learning to rank algorithms for domain specific Turkish text summarization. In this study, we elaborate on the effect of sentence-level and word-level features for text summarization using three learning to rank algorithms that belong to different categories. To this aim, we annotated a corpus of Turkish bank transcripts, and designed three experimental settings. In the first setting, we investigate the performance of sentence-level features, in the second word-level features, and in the last the union of both features in Turkish text summarization. The experimental results show that word-level features perform poorer compared to the sentence-level features.
Machine Learning-Based Profiling Attack Method in RSA Prime Multiplication

In this paper, we propose a machine learning-based profiling attack on the prime multiplication operation of RSA's key generation algorithm. The proposed attack takes advantage of the fact that a prime word value, which is the data storage unit, is loaded in the process of the multiplication operation for generating a modulus. We selected a commonly used product-scanning method as a multiplication algorithm. Then we collected the power consumption traces and constructed a profile of the secret prime value based on machine learning. In addition, the success rate of the attack was measured within a single trace to perform a realistic attack during the key generation operation. The secret prime values were derived with a maximum success rate of 99.8% in a single trace. Based on this, this paper suggests that if the secret value is an operand of the multiplication operation, there may be vulnerability against side-channel attacks because of the characteristics of the multiplication algorithm.1
Analysis of the Productivity of Pair Programming Versus Solo Programming in a University Classroom

The present study explains the development of an experimental process about the comparison of programming styles, which was objective to measure productivity in software creation applying two different styles: Pair Programming and Solo Programming. There was a search for information by different relevant online databases about experimental studies that cover the comparison in the use of known agile methodologies. For each study, it was verified that the address experiments about the implementation of programming styles for software development. The results show that experimentation in this area, in most studies, generates significant results in software development by comparing of the productivity of both programming styles, to determinate the feasibility that programming in pairs can have on individual programming and validate that it is applicable in companies dedicated to software development. In addition, this study provides suggestions for futures experimentation where more detailed parameters and longer execution times are established to obtain different results on the implementation of programming styles belonging to the extreme programming.
Photonic VCSEL-neuron for spike-rate representation of digital image data

With the increasing importance and capabilities of artificial intelligence (AI) approaches across all the domains of our society, the focus is now also moving towards the hardware used for running these algorithms. Neuromorphic computers, inspired by the computational capabilities of the brain, offer a viable, high efficiency alternative to usual von Neumann-based electronic processors. Besides the conventional CMOS-based electronics, novel technologies are also being investigated for neuromorphic computing [1] . Optics and photonics are one these promising alternative technologies for such systems, offering desirable properties including high bandwidth and low power operation. Among the photonic technologies for neuromorphic hardware, vertical cavity surface emitting lasers (VCSELs) provide mature fabrication technology, suitability for integration and high-speed operation.
How to Realize the Integration of Database System and Web in Information System

This paper briefly introduces how to combine database system with Web/Intranet technology, publish dynamic database information on Web, and construct information system of Web/Intranet mode.
Financial table extraction in image documents

Table extraction has long been a pervasive problem in financial services. This is more challenging in the image domain, where content is locked behind cumbersome pixel format. Luckily, advances in deep learning for image segmentation, OCR, and sequence modeling provides the necessary heavy lifting to achieve impressive results. This paper presents an end-to-end pipeline for identifying, extracting and transcribing tabular content in image documents, while retaining the original spatial relations with high fidelity.
The elliptic net algorithm revisited

Efficient implementation of pairings is a fundamental ingredient in pairing-based cryptographic protocols. The Elliptic Net algorithm is an alternative method to Miller’s algorithm for computing the (Optimal) Ate pairing in polynomial time. In this paper, we utilize several tricks to speed up the Elliptic Net algorithm. Firstly, we eliminate the inversion in the improved Elliptic Net algorithm, which allows for further improvements under certain circumstances. Second, we apply lazy reduction to the Elliptic Net algorithm for a better performance. Finally, we propose a new derivation of the formulas for computing the (Optimal) Ate pairing on the twisted curves. In addition, we provide implementations of all versions of the Elliptic Net algorithm on personal computers based on RELIC toolkit. Our implementations indicate that on this research line the Elliptic Net algorithm is about $$80\%$$ faster than the previous fastest ones on the twisted 381-bit BLS12 curve and $$71.5\%$$ faster on the twisted 676-bit KSS18 curve on 64-bit platforms, respectively.
Navigational Behavior of Humans and Deep Reinforcement Learning Agents

Rapid advances in the field of Deep Reinforcement Learning (DRL) over the past several years have led to artificial agents (AAs) capable of producing behavior that meets or exceeds human-level performance in a wide variety of tasks. However, research on DRL frequently lacks adequate discussion of the low-level dynamics of the behavior itself and instead focuses on meta-level or global-level performance metrics. In doing so, the current literature lacks perspective on the qualitative nature of AA behavior, leaving questions regarding the spatiotemporal patterning of their behavior largely unanswered. The current study explored the degree to which the navigation and route selection trajectories of DRL agents (i.e., AAs trained using DRL) through simple obstacle ridden virtual environments were equivalent (and/or different) from those produced by human agents. The second and related aim was to determine whether a task-dynamical model of human route navigation could not only be used to capture both human and DRL navigational behavior, but also to help identify whether any observed differences in the navigational trajectories of humans and DRL agents were a function of differences in the dynamical environmental couplings.
S.Uvarov’s Imperial Ideology as a Principle of Modernization of the Educational Environment

of Manuscript of the Vernadsky National Library of Ukraine.Archival selections contain numerous ministerial circulars and orders to the trustees of the Kyiv educational district 1 .
Research on Safety Classification for Vehicle Anti-collision Data by Improved Interval Fuzzy Reasoning

The automobile anti-collision safety system can reduce the traffic accident rate and maintain traffic safety. Research of it is very practical and important. This paper introduces a new safety data classification method. A new weighted interval value matching classification method was proposed based on interval-valued fuzzy set theory. The weights of each feature are determined by the relationship between classes and inside classes. The matching function is achieved by common interval intersection operations. At last, simulation is given to illustrate the effectiveness. The new fuzzy classifier can be applied to safety data classification for vehicle crash avoidance.
Revisit the Scalability of Deep Auto-Regressive Models for Graph Generation

As a new promising approach to graph generations, deep auto-regressive graph generation has drawn increasing attention. It however has been commonly deemed as hard to scale up to work with large graphs. In existing studies, it is perceived that the consideration of the full non-local graph dependences is indispensable for this approach to work, which entails the needs for keeping the entire graph's info in memory and hence the perceived “inherent” scalability limitation of the approach. This paper revisits the common perception. It proposes three ways to relax the dependences and conducts a series of empirical measurements. It concludes that the perceived “inherent” scalability limitation is a misperception; with the right design and implementation, deep auto-regressive graph generation can be applied to graphs much larger than the device memory. The rectified perception removes a fundamental barrier for this approach to meet practical needs.
The Research Importance and Possible Problem Domains for NoSQL Databases in Big Data Analysis

The days are gone where we depend on traditional database techniques to solve the contemporary database problems. With the evolution of unstructured and semi-structured data forms and due to the frequent gathering of voluminous data from time to time, new techniques to store, process and manage data are becoming most crucial nowadays. NoSQL forms are emerging as the basic source for researchers to invent new database techniques, which can be applied in addition to the conventional techniques in order to solve the existing real-time database problems today. Occasionally, NoSQL can also be termed as Not Only SQL. It is a modern approach to handle data without organizing the source data in the form of tables and sidesteps the use of SQL. In this article, the advantages and disadvantages of NoSQL forms are discussed. The possible research directions in NoSQL domain are outlined to facilitate NoSQL research from the big data perspective.
Mood and force in defeasible arguments

Argumentation schemes bring artificial intelligence into day to day conversation. Interpreting the force of an utterance, be it an assertion, command, or question, remains a task for achieving this goal. But it is not an easy task. An interpretation of force depends on a speaker’s use of words for a hearer at the moment of utterance. Ascribing force relies on grammatical mood, though not in a straightforward or regular way. We face a dilemma: on one hand, deciding force requires an understanding of the speaker’s words; on the other hand, word meaning may shift given the force in which the words are spoken. A precise theory of how mood and force relate helps us handle this dilemma, which, if met, expands the use of argumentation schemes in language processing. Yet, as our analysis shows, force is an inconstant variable, one that contributes to a scheme’s defeasibility. We propose using critical questions to help us decide the force of utterances.
Domain Specific Program Synthesis

Program Synthesis refers to the task of constructing a program in a specific programming language, given its intent in a particular format. This emerging field can be applied in diverse domains and is currently being investigated with different techniques. A program synthesizer would simplify the efforts of programmers and help them focus on the program's core logic, without worrying about language syntax and other domain specifics. We applied the concepts of program synthesis in the context of solving a propositional logic word problem. We have developed a tool that is capable of understanding, parsing and evaluating a propositional logic word problem. With the user's natural language input, this tool processes the query and evaluates truth values of the question expressions. The working of the tool can be explained in three major phases: natural language processing, machine learning to obtain postfix notations of the Boolean expressions involved, and further evaluation of the postfix notations to determine the answers. Our goal was to explore the domain agnostic capabilities of our program-synthesis-based techniques of learning used in the implementation of this tool.
Parameter prediction of power grid branch based on cross neural network

With the rapid development of UHV AC/DC hybrid power grid, the safe and stable operation of interconnected large power grid is facing great pressure, which puts forward higher requirements for real-time analysis and dynamic early warning application of power grid control system. At present, the branch parameter identification of power grid regulation and control system is mainly carried out by the least square method, but this method has some shortcomings, such as easy over-fitting. One cross neural network model (CRSNet) is proposed in this paper, which adjusts the branch parameters to different scales in the calculation process and then updates the parameters through cross perception, so that the neural network can consider the characteristic information of other nodes in the power grid and it can fit accurately. Compared with the least square method commonly used in practice and other machine learning algorithms, the experiment shows that the proposed model has greatly improved the fitting accuracy and enhanced robustness, which has a broader application prospect.
The Application of Big Data in Bias Analysis on Mixed Teaching Mode

Bias refers to a factor or factors that inherent within a test thus prevents the accession to the testing validity. Since college English adopted mixed teaching and testing, the shifting between online/offline mode demands systematical analysis, as a newly-added affecting factor for test bias and testing validity. To begin with a brief introduction about test bias and mixed teaching/testing mode in college English course, the article introduces the technical methods and online applications adopted in mixed teaching and testing and shows the comparison between online/offline testing scores and ranks. Two technique and designing- based hypothesis presented show that the technical development presents a possibility in test bias[1]. In the main part, proving process is explicitly presented, with clear steps followed and particular technical method used in testing practice, whether test validity and reliability defects or not will be clearly concluded. The diversity of samples and two hypothesis on bias attributes the research in this article a comprehensive and innovative one.
Adversarial Activity Detection Using Keystroke Acoustics

Using keystroke acoustics to predict typed text has significant advantages, such as being recorded covertly from a distance and requiring no physical access to the computer system. Recently, some studies have been done on keystroke acoustics, however, to the best of our knowledge none have used them to predict adversarial activities, such as password dictionary attacks, data exfiltration, etc. We show that keystrokes in an adversarial environment have unique characteristics that distinguish it from benign environments and these differences can be used to predict adversarial activities and threat levels against a computer system. On a dataset of two million keystrokes consisting of seven adversarial and one benign activity, we use a signal processing approach to extract keystrokes from the audio and a clustering method to recover the typed letters followed by a text recovery module to regenerate the typed words. Furthermore, we use a neural network model to classify the benign and adversarial activities and achieve significant results: (1) we extract individual keystroke sounds from the raw audio with 91% accuracy and recover words from audio recordings in a noisy environment with 71% average top-10 accuracy. (2) We classify adversarial activities with 93.11% to 98.07% average accuracy under different operating scenarios.
Clinical Study on Fast Rehabilitation Program of Integrated Traditional Chinese and Western Medicine after Laparoscopic Hysterectomy based on Data Mining

Uterine fibroids are the most common benign tumors in gynecology, with high incidence rate and showing an increasing trend. Some uterine fibroids can lead to patients with prolonged menstrual cycle, increased menstrual volume, more severe cases will appear hemorrhagic anemia. Larger uterine fibroids will oppress the patient's pelvic cavity, so that patients have frequent urination, fecal discomfort, etc. This disease seriously affects women's life and health. This paper completes the requirement analysis and overall design of the disease data mining system. After that, the system is divided into data processing subsystem, algorithm calling subsystem, knowledge display subsystem, user management subsystem, as well as the realization technology, function modules and main process of the main functions of the system.
Coresets for the Average Case Error for Finite Query Sets

Coreset is usually a small weighted subset of an input set of items, that provably approximates their loss function for a given set of queries (models, classifiers, hypothesis). That is, the maximum (worst-case) error over all queries is bounded. To obtain smaller coresets, we suggest a natural relaxation: coresets whose average error over the given set of queries is bounded. We provide both deterministic and randomized (generic) algorithms for computing such a coreset for any finite set of queries. Unlike most corresponding coresets for the worst-case error, the size of the coreset in this work is independent of both the input size and its Vapnik-Chervonenkis (VC) dimension. The main technique is to reduce the average-case coreset into the vector summarization problem, where the goal is to compute a weighted subset of the n input vectors which approximates their sum. We then suggest the first algorithm for computing this weighted subset in time that is linear in the input size, for n≫1/ε, where ε is the approximation error, improving, e.g., both [ICML'17] and applications for principal component analysis (PCA) [NIPS'16]. Experimental results show significant and consistent improvement also in practice. Open source code is provided.