Reviewerly/exHarmony huggingface dataset:
A Hybrid Approach of Distributional Semantics and Event Semantics for Telicity

The aspectual class of verb phrases plays a key role for tense and aspect semantics like those long studied in formal semantics, determining whether an event is completed and whether its consequences hold at the time. Recently, logic-based inference systems have enabled us to computationally realize the compositional semantics studied in formal semantics. However, since the aspectual class of verb phrases is determined not only by the lexical properties of verbs but also by the context, how to computationally determine the aspectual class is a non-trivial issue. In this paper, we introduce a semantic parsing system to map the aspectual class of English verbs to semantic representations and an inference system that effectively performs inference for aspectual entailment in an effective way. We use Combinatory Categorial Grammar (CCG) as syntax and Neo-Davidsonian event semantics as semantics. We provide an aspectual analyzer to determine the aspectual class of verbs in input sentences and discuss how to handle the ambiguity of telicity.
Flipping the Switch on Local Exploration: Genetic Algorithms with Reversals

One important feature of complex systems is problem domains that have many local minima and substructure. Biological systems manage these local minima by switching between different subsystems depending on their environmental or developmental context. Genetic algorithms (GA) can mimic this switching property as well as provide a means to overcome problem domain complexity. However, standard GA requires additional operators that will allow for large-scale exploration in a stochastic manner. Gradient-free heuristic search techniques are suitable for providing an optimal solution in the discrete domain to such single-objective optimization tasks, particularly compared to gradient-based methods which are noticeably slower. To do this, the authors turn to an optimization problem from the flight scheduling domain. The authors compare the performance of such common gradient-free heuristic search algorithms and propose variants of GAs. The Iterated Chaining (IC) method is also introduced, building upon traditional chaining techniques by triggering multiple local searches instead of the singular action of a mutation operator. The authors will show that the use of multiple local searches can improve performance on local stochastic searches, providing ample opportunity for application to a host of other problem domains. It is observed that the proposed GA variants have the least average cost across all benchmarks including the problem proposed and IC algorithm performs better than its constituents.
The Pneumatic Twin

We explain the behavior of gases and look at a safety valve for a biogas tank and a ventilation system for a building. Safety valves are used to prevent a gas-filled tank from bursting when the pressure in a gas becomes too high. When designing and constructing ventilation systems for buildings, there is often the problem of routing air through branched networks of pipes. These must be planned and professionally designed before a building is constructed. The length and diameter of the pipes play a major role in the speed and pressure drop.
Multilingual Text Generation for Abstract Wikipedia in Grammatical Framework: Prospects and Challenges

Abstract Wikipedia is an initiative to produce Wikipedia articles from abstract knowledge representations with multilingual natural language generation (NLG) algorithms. Its goal is to make encyclopaedic content available with equal coverage in the languages of the world. This paper discusses the issues related to the project in terms of an experimental implementation in Grammatical Framework (GF). It shows how multilingual NLG can be organized into different abstraction levels that enable the sharing of code across languages and the division of labour between programmers and authors with different skill requirements. The plan is to start with a simple but functional multilingual NLG system and to proceed towards more and more sophisticated language and wider coverage of topics, also allowing a human in the loop to create content via a Controlled Natural Language (CNL).
Integrating Deep Neural Networks with Dependent Type Semantics

Recent studies in computational semantics have explored how the advantages of both type-logical semantics and deep neural networks can be combined to develop a theory or system of natural language understanding that can perform complex inferences while being learnable. In this study, we propose a theory of language understanding that fuses neural language models and dependent type semantics (DTS), a proof-theoretic semantics of natural language based on dependent type theory, by replacing names and predicates in DTS with tensor-based distributional representations and neural classifiers over them. Under this unified view, interesting correspondences are found between proof-theoretic and machine learning notions. For instance, the value of the loss function provides proof for an atomic predicate, and the neural parameters are regarded as proof-theoretic assumptions about the real world.
White Roses, Red Backgrounds: Bringing Structured Representations to Search

Search has become a key component of many on-line user experiences. Search queries are usually textual and hence should benefit from improvements in natural language processing. However, many of the NLP algorithms used in production systems fail for queries that require structured understanding of the query and document or that require reasoning. These issues arise because of the way information is stored in the search index and the need to return results quickly. The issues are exacerbated when searching over non-textual documents, including images and structured data. The use of embedding-based techniques has helped with some types of searches, especially when the query vocabulary does not match that of the documents and when searching over images. However, these techniques still fail for many searches, especially ones requiring reasoning. Simply combining classic word-level search and embedding-based search does not solve these issues. Instead, in this position paper, I argue that we need to create hybrid systems from traditional search techniques, embedding-based search, and the addition of structured data and reasoning. Enabling such hybrid systems will require a deep understanding of linguistic representations of meaning, of information retrieval optimization, and of the types of information encoded in the queries and documents. It is my hope that this paper inspires further collaboration across disciplines to improve these complex search problems.
Decomposing Events into GOLOG

Linguists working in a variety of semantic theories have converged on a non-atomic view of predicates. According to these theories, the object denoted by a verb can be decomposed into some complex expression which might or might not include one or more primitive predicates expressing acts or results of acts. I develop an account of event-semantic decomposition into GOLOG programs, a variety of the Situation Calculus. The result is an implemented theory of decomposition which can compute well-known inferences concerning telicity.
A Unified Cluster of Valence Resources

A ‘Valence Catalogue’ is a design for succinctly representing information about the valence frames of a language. It can be coordinated with in-depth annotated corpora and computational parsers. Such a cluster is realized by the verb Valence Catalogue NorVal of Norwegian Hellan (Natural Language Processing in Artificial Intelligence—NLPinAI 2021, Springer, 2022) together with a computational parser for Norwegian, NorSource, and the multi-lingual grammar system TypeGram. Another such cluster is realized by a verb Valence Catalogue of the West African language Ga (GaVal) together with the language specific parser GaGram and with coverage also by TypeGram. The resources are all based on the general formalism of Typed Feature Structures, whereby grammars and valence catalogues are formally tied together, and specifications in the resources are succinctly comparable also cross-linguistically. In describing the multilingual aspects of these architectures, we thereby demonstrate that systems of this formal nature are fully attainable no matter where on the ladder of ‘high-low-resourced’ a language finds itself. In a comparative perspective, we also demonstrate discrepancies between the valence systems of the two languages, made visible through the succinctness and coverage of Valence Catalogues, and perspectives they open for typological research and for cross-linguistically informed resource generation.
Secure Image Classification Using Deep Learning

Machine learning and security are the buzzwords these days. Just like other fields, privacy concern is a major issue in machine learning systems as well. Current privacy techniques focus on allowing multiple input parties to collaboratively train machine learning models without releasing their private data in its original form. One of the most sensitive data in this regard is medical images. Usage of such data for collectively training models might be against the policies of hospitals, which assure patients that their information would be kept confidential. In such a scenario, privacy preserving machine learning poses several advantages over the conventional methods. In this paper, we have implemented a secure machine learning model based on the multi-party protocol described in SecureML (Mohassel and Zhang in 2017 IEEE symposium on security and privacy. IEEE, pp 19–38, 2017, [1]), on the medical dataset of X-ray images for pneumonia. The performance of these privacy preserving techniques against conventional machine learning algorithms is evaluated.
The Use of LBP Features in Transform Domain for Object Recognition

Local binary pattern (LBP) is very useful in various applications of machine vision problems. It is an effective multi-resolution texture descriptor. Various types of local binary patterns have been proposed till now. Even though deep learning framework has gained more attention in generic object recognition nowadays, in this article, we studied the effectiveness of LBP features in the transform domain for the task of object recognition. The comparison was done based on the performance measure such as recall, precision, equal error rate and F-measure. Images have been pre-processed using Log Gabor filter, and then, different types of LBPs are applied on the transformed images, and the statistical features are extracted and classified with support vector machine (SVM). The evaluation is done on the Graz databases. It is observed that rotation invariant LBP and direction coded LBP perform better with lesser error rate and higher recall, precision and F-measure.
A Hybrid Classification Approach for Fasteners Based on Transfer Learning with Fine-Tuning and Deep Features

Deep learning, which has seen frequent use in recent studies, has helped solve the problem of classifying objects of many different types and properties. Most studies both create and train a convolutional neural network (CNN) from scratch. The time spent training the network is thus wasted. Transfer learning (TL) is used both to prevent the loss of time due to training the dataset and to more effectively classify small datasets. This study performs classification using a dataset containing eighteen types of fastener. Our study contains three different TL scenarios. Two of them use TL with fine-tuning (FT), while the third does so with feature extraction (FE). The study compares the classification performance of eighteen different pre-trained network models (i.e., one or more versions of EfficientNet, DenseNet, InceptionResNetV2, InceptionV3, MobileNet, ResNet50, Xception, and VGGNet) in detail. When compared to other research in the literature, our first and second scenarios provide excellent implementations of TL-FT, while our third scenario, TL-FE, is hybrid and produces better results than the other two. Furthermore, our findings are superior to those of most previous studies.
Effect of Coupling Discretization on Coherent-Ising-Machine-Implemented Hopfield Model

The coherent Ising machine (CIM) is being developed for solving combinatorial optimization problems scalably at high speed. The actual equipment of the measurement-feedback type of CIM (MFB-CIM) uses a field-programmable gate array (FPGA) to calculate the local field. Because the product of the coupling strength matrix and the measured amplitude vector of the optical parametric oscillator pulses must be calculated in real-time to achieve feedback, the coupling strength must have a low-bit discrete representation in the FPGA. By reducing the number of bits of coupling strength, the amount of memory needed to store the coupling matrix can be reduced and the calculation speed increased so that the MFB-CIM can implement more spins. Therefore, it is necessary to reduce the number of bits of coupling strength as much as possible while maintaining the performance in solving combinatorial optimization problems. In this paper, to evaluate the effect of discretization of the coupling strength in the MFB-CIM, we quantitatively evaluate the performance of the Hopfield model with discrete coupling when it is implemented in the CIM. The Hopfield model, which is a canonical model of Ising computation in information statistical mechanics, is defined in a coupling manner called the correlation-type interaction, in common with other models such as Sourlas code, a code division multiple access multiuser detector, and L0 regularization-based compressed sensing. We derived the macroscopic equations (MEs) of the Hopfield model with discrete coupling when it is implemented in the CIM by using the non-equilibrium statistical mechanics method we previously proposed. Moreover, we clarified the relationship between the number of bits of the coupling strength and the critical memory capacity for various pump rates.
An approach to building a model of communication flows in solving tasks in project management

This article discusses an approach to building a model of communication flows when solving project management tasks in the context of the need for subsequent regulation in order to increase the efficiency of project implementation in general. Definitions are given that distinguish such concepts as a communication flow and a single communication link with subsequent formalization in a formulaic form. Examples of forms and methods of communication between project participants are given, followed by the example of a hierarchical approach to the formation of a model of communication flows. The article also discusses the basis for building a model, including the organizational structure of the project, taking into account resources and the process of setting tasks, and in part of the latter, an algorithm of actions is presented, distributed over key stages, including visualization of the process diagram. Examples of control actions are given in order to demonstrate the possible regulation of the proposed model.
Automatic reading task prediction on tablet (PC) under the influence of noise and illumination via optimized LSTM

Everyone has long recognized smart phones and other mobile technology's capability to enhance cognitive abilities. The majority of studies on smart phones and tablets while driving have focused on distraction, reaction times, and overall safety. In this study, the effects of light and noise on the tablet reading tasks completed by males and females (18 to 45 years old) are investigated. Initially, the collected data are pre-processed via data cleaning process. Subsequently, the most relevant features like improved correlation, improved entropy and statistical features are retrieved from the pre-processed information. The final prediction with respect to the automatic identification of variations in pupil diameter of the left eye, right eye, and reading ability (count of characters read/second) is made using three mathematical models: optimized Long Short-Term Memory1 (LSTM1), optimized LSTM2 and optimized LSTM3 respectively. The optimized LSTM1, optimized LSTM2 and optimized LSTM3 are trained with the fused features. Further, to improve the prediction efficiency of the three mathematical models, weight of them is fine-tuned employing aunique approach known as Grey Wolf Optimization Appended Cuckoo (GWAC). At last, a comparative assessment is undergone to verify the efficiency of the projected model in terms of various measures.
Design and Development of Internet Voting System

The explosion in the use of information technology and the widespread use of the Internet makes information and communication technologies to get their inevitable benefits. Some of these benefits are accuracy, speed, cost saving, etc. Election and voting are one of the cases which have recently tended to be performed electronically. Web services, due to their PROS, may have a key role in usage and deployment of e-voting systems. The voting system is the backbone of every democracy and organization. The voting system has experienced many efficient changes in the past few decades. There are various voting techniques used such as Paper Ballot Voting System, E-Voting System also known as Electronic Voting System, Internet Voting System. In this paper, we have put some trends of various voting systems and their scopes. The primary goal of this research is to make the voting system multipurpose and make it work multiplatform on any system.
Cyber Security

The internet has revolutionized the way we communicate and conduct business. However, this revolution has come with a cost: the emergence of cyber threats that can compromise our security and privacy. This paper examines the current state of cyber security, discussing the threats posed by malicious actors, the technologies used to protect against them, and best practices for individuals and organizations to ensure their security. Furthermore, this paper explores the need for organizations and governments to work together to increase cyber security in the digital age.
Expense Management System Using OCR

We present an intelligent expense tracker to efficiently manage the monthly expenses. Our system will help everyone who are planning to know their expenses and save from it. The user will be given the facility to set a monthly limit and if the user crosses that limit our app will notify the user about the same. The user can give receipts as an input, using our Androidapp. will sort it into different categories. Here user can also define their own categories like food, clothing, rent and bills and the user can also set limits for a particular category. User will be provided with visual statistics of expenses by transaction date or by category. This project is not indentedfor a particular user or age group but anyone and everyone who wants to track their expense can use this app. So, the general idea of this Project is to help people view and study their overall expenditure pattern by developing a mobile application to analyse all the purchases made by the user by simply scanning the receipts.
ADVANCED PEDAGOGICAL EXPERIENCES IN ORGANIZING AND DEVELOPING THE EDUCATIONAL PROCESS ON THE BASE OF DIGITAL TECHNOLOGIES

This article describes the advanced pedagogical experiences of developed countries in organizing and developing the educational process based on digital technologies. You can also familiarize yourself with the results of research conducted on the organization and development of the educational process based on digital technologies in developed countries such as Germany and Russia. Moreover, by studying the successful experiences of these developed countries, you can get information about the aspects that we can apply to the development of the quality of education in our country based on digital technologies.
A Study on Aspect of Artificial Neural Networks for Machine Learning

Artificial Neural Network (ANN) uses the processing of the brain as a basis to develop algorithms that can be used to model complex patterns and prediction problems. In our brain, there are billions of cells called neurons, which processes information in the form of electric signals. External information/stimuli is received by the dendrites of the neuron, processed in the neuron cell body, converted to an output and passed through the Axon to the next neuron. The next neuron can choose to either accept it or reject it depending on the strength of the signal. Neural network architecture is presented as one approach to the design and implementation of intelligent control systems. Neural networks can be considered as massively parallel distributed processing systems with the potential for ever-improving performance through dynamical learning. The nomenclature and characteristics of neural networks are outlined. Two simple examples are presented to illustrate applications to control systems: one is fault isolation mapping, and the other involves optimization of a Hopfield network that defines a clock less analog-to-digital conversion. Deep learning is one of the types of artificial intelligence and machine learning; basically, deep learning behaves like a human brain, or we can say that it processes the data like the human brain. In deep learning, we collect data from different resources in different complex patterns, such as photos and text, and make predictions as well. As we are also able to automate the different tasks which require the human brain.
TrackSign-labeled web tracking dataset

Recent studies [8] show that more than 95% of the websites available on the Internet contain at least one of the so-called web tracking systems. These systems are specialized in identifying their users by means of a plethora of different methods. Some of them (e.g., cookies) are very well known by most Internet users. However, the percentage of websites including more "obscure" and privacy-threatening systems, such as fingerprinting methods identifying a user's computer, is constantly increasing. Detecting those methods on today's Internet is very difficult, as almost any website modifies its content dynamically and minimizes its code in order to speed up loading times. This minimization and dynamicity render the website code unreadable by humans. Thus, the research community is constantly looking for new ways to discover unknown web tracking systems running under the hood. In this paper, we present a new dataset containing tracking information for more than 76 million URLs and 45 million online resources, extracted from 1.5 million popular websites. The tracking labeling process was done using a state-of-the-art discovery web tracking algorithm called TrackSign [8]. The dataset also contains information about online security and the relation between the domains, the loaded URLs, and the online resource behind each URL. This information can be useful for different kinds of experiments, such as locating privacy-threatening resources, identifying security threats, or determining characteristics of the URL network graph.