Applications In Computer Science Research Articles

CAS4DL: Christoffel adaptive sampling for function approximation via deep learning

The problem of approximating smooth, multivariate functions from sample points arises in many applications in scientific computing, e.g., in computational Uncertainty Quantification (UQ) for science and engineering. In these applications, the target function may represent a desired quantity of interest of a parameterized Partial Differential Equation (PDE). Due to the large cost of solving such problems, where each sample is computed by solving a PDE, sample efficiency is a key concern in these applications. Recently, there has been increasing focus on the use of Deep Neural Networks (DNN) and Deep Learning (DL) for learning such functions from data. In this work, we propose an adaptive sampling strategy, CAS4DL (Christoffel Adaptive Sampling for Deep Learning) to increase the sample efficiency of DL for multivariate function approximation. Our novel approach is based on interpreting the second to last layer of a DNN as a dictionary of functions defined by the nodes on that layer. With this viewpoint, we then define an adaptive sampling strategy motivated by adaptive sampling schemes recently proposed for linear approximation schemes, wherein samples are drawn randomly with respect to the Christoffel function of the subspace spanned by this dictionary. We present numerical experiments comparing CAS4DL with standard Monte Carlo (MC) sampling. Our results demonstrate that CAS4DL often yields substantial savings in the number of samples required to achieve a given accuracy, particularly in the case of smooth activation functions, and it shows a better stability in comparison to MC. These results therefore are a promising step towards fully adapting DL towards scientific computing applications.

An Overview of the Application of Harmony Search for Chemical Engineering Optimization

Harmony search algorithm and its variants have been used in several applications in medicine, telecommunications, computer science, and engineering. This article reviews the global and multi-objective optimization for chemical engineering using harmony search. The main features of the HS method and several of its popular variants and hybrid versions including their relevant algorithm characteristics are described and discussed. A variety of global and multi-objective optimization problems from chemical engineering and their resolution using HS-based methods are also included. These problems involve thermodynamic calculations (phase stability analysis, phase equilibrium calculations, parameter estimation, and azeotrope calculation), heat exchanger design, distillation simulation, life cycle analysis, and water distribution systems, among others. Remarks on future developments of HS and its related algorithms for global and multi-objective optimization in chemical engineering are also provided in this review. HS is a reliable and promising stochastic optimizer to resolve challenging global and multi-objective optimization problems for process systems engineering.

Certificate complexity of elementary symmetric Boolean functions

Boolean functions have important applications in information technology and computer science. Certificate complexity is an important combinatorial measure of Boolean function complexity. In this paper, we first introduce many concise and efficient notations then we study the elementary symmetric Boolean functions and obtain their certificate complexities when their degrees are odd or powers of 2. We show that both upper and lower bounds of certificate complexities can be attained.

Computational Linguistics Based Emotion Detection and Classification Model on Social Networking Data

Computational linguistics (CL) is the application of computer science for analysing and comprehending written and spoken languages. Recently, emotion classification and sentiment analysis (SA) are the two techniques that are mostly utilized in the Natural Language Processing (NLP) field. Emotion analysis refers to the task of recognizing the attitude against a topic or target. The attitude may be polarity (negative or positive) or an emotional state such as sadness, joy, or anger. Therefore, classifying posts and opinion mining manually is a difficult task. Data subjectivity has made this issue an open problem in the domain. Therefore, this article develops a computational linguistics-based emotion detection and a classification model on social networking data (CLBEDC-SND) technique. The presented CLBEDC-SND technique investigates the recognition and classification of emotions in social networking data. To attain this, the presented CLBEDC-SND model performs different stages of data pre-processing to make it compatible for further processing. In addition, the CLBEDC-SND model undergoes vectorization and sentiment scoring process using fuzzy approach. For emotion classification, the presented CLBEDC-SND model employs extreme learning machine (ELM). Finally, the parameters of the ELM model are optimally modified by the use of the shuffled frog leaping optimization (SFLO) algorithm. The performance validation of the CLBEDC-SND model is tested using benchmark datasets. The experimental results demonstrate the better performance of the CLBEDC-SND model over other models.

Application of DFT Calculations in Designing Polymer-Based Drug Delivery Systems: An Overview.

Drug delivery systems transfer medications to target locations throughout the body. These systems are often made up of biodegradable and bioabsorbable polymers acting as delivery components. The introduction of density functional theory (DFT) has tremendously aided the application of computational material science in the design and development of drug delivery materials. The use of DFT and other computational approaches avoids time-consuming empirical processes. Therefore, this review explored how the DFT computation may be utilized to explain some of the features of polymer-based drug delivery systems. First, we went through the key aspects of DFT and provided some context. Then we looked at the essential characteristics of a polymer-based drug delivery system that DFT simulations could predict. We observed that the Gaussian software had been extensively employed by researchers, particularly with the B3LYP functional and 6-31G(d, p) basic sets for polymer-based drug delivery systems. However, to give researchers a choice of basis set for modelling complicated organic systems, such as polymer–drug complexes, we then offered possible resources and presented the future trend.

Digital educational history as a component of the digital student's profile in the context of education transformation

Introduction. The recent active development of digital educational infrastructure allows educational institutions to collect and store large amounts of data related to the learning process. In this regard, the task of describing, structuring and combining various data about the student into a single digital profile, seems relevant as it will make possible applying an integrated approach to solving the problems of data-driven management of the educational process. Materials and methods. A comparative analysis of the literature to analyze the existing terminology in the field of digital transformation of education and methods for modeling objects and processes in education, as well as to identify the components of the digital student's profile has been carried out. To assess the intensity of activity performed by students in the digital learning environment (DLE) of the university, we have conducted the survey of the 172 students of Siberian Federal University majoring in several areas of training: Computer Science and Computer Engineering, Applied Computer Science, Information Security, and Metallurgy. Results. We proposed the so-called student digitalization pyramid, which includes the following hierarchy levels: digital footprint, digital profile, digital model, digital twin. We described the structure of the digital profile of a student which includes two components – a digital personality portrait and a digital educational history and mentioned possible links between them. We introduced a classification of its components and identified the sources of obtaining these components from the university EIEE. We introduced and described in detail the concept of digital educational history which is a multidimensional structured dynamically changing data on the educational activities of the student and his current learning outcomes. We indicated the problem of completeness of digital educational history data, the presence of which is confirmed by the results of the survey. Three most frequently used sources of educational materials in addition to the university e-learning courses were identified as video hosting (used by 85.5% of respondents), educational interaction in small groups (62.2%) and learning forums (45.3%). At the same time, obtaining educational history data from these sources is a serious problem which is difficult to overcome. Conclusions. In the future, digital educational history data will become the basis for modeling a student (their learning process) and developing their digital twin. Nevertheless, there are a number of problems which concern the collection and verification of the related data, the ethics of their use and applicability for solving learning management problems. We propose possible solutions to these problems

Ideal technologies, ideal women: AI and gender imaginaries in Redditors’ discussions on the Replika bot girlfriend

There is extensive literature on how expectations and imaginaries about artificial intelligence (AI) guide media and policy discussions. However, it has not been considered how such imaginaries are activated when users interact with AI technologies. We present findings of a study on how users on a subreddit discussed ‘training’ their Replika bot girlfriend. The discussions featured two discursive themes that focused on the AI imaginary of ideal technology and the gendered imaginary of the ideal bot girlfriend. Users expected their AI Replikas to both be customizable to serve their needs and to have a human-like or sassy mind of their own and not spit out machine-like answers. Users thus projected dominant notions of male control over technology and women, mixed with AI and postfeminist fantasies of ostensible independence onto the interactional agents and activated similar scripts embedded in the devices. The vicious feedback loop consolidated dominant scripts on gender and technology whilst appearing novel and created by users. While most research on the use of AI is conducted in applied computer science to improve user experience, this article outlines a media and cultural studies lens for a critical understanding of these emerging technologies as they become embedded in communication and meaning-making.

The application of computer science in fractional distillation of petroleum

The purpose of the study is to assess the application of Computer science in the fractional distillation of petroleum. The study sought to: Determine the merit of using Computer science in fractional distillation of petroleum. Evaluate the cost effectiveness to the application of Computer science in fractional distillation of petroleum to the welfare of citizens. Determine if Computer science can help in controlling the natural effects of fractional distillation of petroleum on the environment. Computer science has gained wider application in so many areas of human endeavor ranging from the homes to more scientific and industrial applications, hence its application of Computer science in fractional distillation of petroleum cannot be overemphasized. Irrespective of the benefits of the application of Computer science in fractional distillation of petroleum as a whole, it has become pertinent to ask how effective and efficient its application of Computer science in fractional distillation of petroleum has been. This research work tries to investigate how effective the application of Computer science in fractional distillation of petroleum. The project exertion covered statement of the problem, purpose of the study, research questions/hypotheses were formulated to enable the researcher find out facts about the study, highlighted in division one. Subdivision two was based on the literature review of this study. The study covers the research methodology which guided the study, the next stage was based on the presentation of data and its’ analysis as well as the discussion of findings. The final stage of the research was concerned with the conclusion as well as recommendations and suggestions for further studies

Sistem Pendukung Keputusan Dalam Pemilihan Peserta Beasiswa Magister Menggunakan Metode SAW

A scholarship is a gift given by the government or a university to help reduce the cost of education where the higher the level of education taken, the more money that must be provided to fulfill, the opportunity to be able to continue master's education is the dream of many young scholars. Every opportunity that is open to get a master's scholarship makes many prospective scholarship recipients compete to register themselves as participants, but not all participants who apply to receive scholarships get the same opportunity because the quota is limited. It is necessary to make a selection in determining the award of scholarships to the right people and avoid fraud in the selection process, this can be solved by using the application of computer science, namely a decision support system assisted by applying the SAW method in obtaining more systematic results based on previous research. the results obtained in this study

Some Methods of Improving the Communicative and Speech Competence of Students Studying Applied Computer Science in a Non-linguistic University

The article discusses the methods of intensification of monologue and dialogic speech of students. Emphasis is placed on some progressive ways of organizing classroom work in English classes to improve the communicative and speech competence of students. The author gives example of the introduction of modern digital educational technologies into the process of teaching a foreign language in order to develop student’s speaking skills, increase motivation for learning a foreign language and the quality of education.

Detecting Affine Equivalence Of Boolean Functions And Circuit Transformation

Abstract Affine equivalence of Boolean functions has various applications in computer science and modern cryptography, such as circuit design and S-boxes. Existing methods for detecting affine equivalence of Boolean functions work in some cases but not when the truth table of a Boolean function is sparse. To improve previous methods and overcome this limitation, we propose a method by transforming the Boolean function to a function with the property that its function values at the orthonormal basis are all equal to 1 or 0, which narrows down the search space of affine transformations. Our first algorithm has the advantage of getting a smaller search space than previous methods and is especially useful for sparse functions. Specifically, when the Boolean functions are sparse, the search space can be reduced exponentially in average and experiments show the efficiency of our first algorithm. We then present another algorithm to transform one circuit into its equivalent affine circuit by synthesizing a reversible circuit and inserting it in front of the original circuit. To our knowledge, this is the first work to automatically synthesize an affine equivalent circuit for any given circuit and the first to do this by combining reversible circuit and non-reversible circuit.

Identifying Arrhythmias Based on ECG Classification Using Enhanced-PCA and Enhanced-SVM Methods

The "Cardio Vascular Diseases (CVDs)" had already attained worrisome proportions in both advanced and emerging nations in recent times. Physically inactive behaviors, altered eating, and occupational routines, and reduced daily fitness were all recognized as crucial contextual elements, in addition to genetics. Considering CVDs have such a significant morbidity and mortality, accurate and early diagnosis of cardiac disease by "ElectroCardioGram (ECG)" allows clinicians to decide suitable therapy for a multitude of cardiovascular disorders. The interpretation of ECG signal is an important bio-signal processing area that involves the application of computer science and engineering to detect and visualize the functional status of the heart. Therefore, in the present work, a detailed study on ECG signals denoising and abnormalities detection using different techniques were performed. Annoying distortions and noisy particles are common in ECG signals. The "Biased Finite Impulse Response (BFIR)" preprocessing filtering is employed in this research to eliminate the noises in the raw ECG signals. The "Nonlinear-Hamilton" segmentation method is employed to segment the 'R' peak signals. To decrease the extraneous features included in the segmented ECG data, the innovative "Enhanced Principal Component Analysis (EPCA)" was applied for feature extraction. A unique "Enhanced version of the Support Vector Machine (ESVM)" framework with a "Weighting Kernel" based technique is proposed for classifying the ECG data. The 'Q', 'R', and 'S' waves in the given ECG data will be identified by this framework, allowing it to characterize the cardiac rhythm. The evaluation metrics of the EPCA-ESVM proposed method is comparatively analyzed with our previous approach EPSO. To estimate the results for the dataset from MIT-BIH it was experimented with by the EPSO and the EPCA-ESVM methods focused upon different parameters such as Accuracy, F1-score, etc. The final findings of the EPCA-ESVM method were good than the EPSO method in which the accuracy is higher even though unbalanced data were present.

Using ACL2 To Teach Students About Software Testing

We report on our experience using ACL2 in the classroom to teach students about software testing. The course COSC2300 at the University of Wyoming is a mostly traditional Discrete Mathematics course, but with a clear focus on computer science applications. For instance, the section on logic and proofs is motivated by the desire to write proofs about computer software. We emphasize that the importance of software correctness falls along a spectrum with casual programs on one end and mission-critical ones on the other. Corresponding to this spectrum is a variety of tools, ranging from unit tests, randomized testing of properties, and even formal proofs. In this paper, we describe one of the major activities, in which students use the ACL2 Sedan's counter-example generation facility to investigate properties of various existing checksum algorithms used in error detection. Students are challenged to state the relevant properties correctly, so that the counter-example generation tool is used effectively in all cases, and ACL2 can find formal proofs automatically in some of those.

Using digital forensics in higher education to detect academic misconduct

Academic misconduct in all its various forms is a challenge for degree-granting institutions. Whilst text-based plagiarism can be detected using tools such as Turnitin™, Plagscan™ and Urkund™ (amongst others), contract cheating and collusion can be more difficult to detect, and even harder to prove, often falling to no more than a ‘balance of probabilities’ rather than fact. To further complicate the matter, some students will make deliberate attempts to obfuscate cheating behaviours by submitting work in Portable Document Format, in image form, or by inserting hidden glyphs or using alternative character sets which text matching software does not always accurately detect (Rogerson, Int J Educ Integr 13, 2017; Heather, Assess Eval High Educ 35:647-660, 2010).Educators do not tend to think of academic misconduct in terms of criminality per se, but the tools and techniques used by digital forensics experts in law enforcement can teach us much about how to investigate allegations of academic misconduct. The National Institute of Standards and Technology’s Glossary describes digital forensics as ‘the application of computer science and investigative procedures involving the examination of digital evidence - following proper search authority, chain of custody, validation with mathematics, use of validated tools, repeatability, reporting, and possibly expert testimony.’ (NIST, Digital Forensics, 2021). These techniques are used in criminal investigations as a means to identify the perpetrator of, or accomplices to, a crime and their associated actions. They are sometimes used in cases relating to intellectual property to establish the legitimate ownership of a variety of objects, both written and graphical, as well as in fraud and forgery (Jeong and Lee, Digit Investig 23:3-10, 2017; Fu et. al, Digit Investig 8:44–55, 2011 ). Whilst there have been some research articles and case studies that demonstrate the use of digital forensics techniques to detect academic misconduct as proof of concept, there is no evidence of their actual deployment in an academic setting.This paper will examine some of the tools and techniques that are used in law enforcement and the digital forensics field with a view to determining whether they could be repurposed for use in an academic setting. These include methods widely used to determine if a file has been tampered with that could be repurposed to identify if an image is plagiarised; file extraction techniques for examining meta data, used in criminal cases to determine authorship of documents, and tools such as FTK™ and Autopsy™ which are used to forensically examine single files as well as entire hard drives. The paper will also present a prototype of a bespoke software tool that attempts to repurpose some of these techniques into an automated process for detecting plagiarism and / or contract cheating in Word documents.Finally, this article will discuss whether these tools have a place in an academic setting and whether their use in determining if a student’s work is truly their own is ethical.

Algorithmic Study of Online Multi-Facility Location Problems.

Facility location (FL) is a well-known optimization problem that asks to optimally place facilities so as to serve clients at various locations, requesting a facility service, with minimum possible costs. Many variants of FL have been known, appearing as sub-problems in many applications in computer science, management science, and operations research. Most FL models studied thus far assume that clients need to be served by connecting each to one facility. To overcome facility failures and provide a robust solution, we investigate in this paper FL problems that require each client to be connected to multiple facilities, represented by an additional input parameter. The aim of the algorithm is then to provide a robust service to all clients while minimizing the total connecting and facility purchasing costs. This is known as the Multi-Facility Location problem (MFL) and has been studied in the offline setting, in which the entire input sequence is given to the algorithm at once. In this paper, we study MFL in the online setting, in which client requests are not known in advance but are revealed to the algorithm over time. We refer to it as the online multi-facility location problem (OMFL) and study its metric and non-metric variants. We propose the first online algorithms for these variants and measure their performance using the standard notion of competitive analysis. The latter is a worst-case analysis that compares the cost of the online algorithm to that of the optimal offline algorithm that is assumed to know all demands in advance. We further study OMFL in the leasing setting, in which facilities are leased, rather than bought, for different durations and prices, and each arriving client needs to be connected to multiple facilities leased at the time of its arrival. The aim is to minimize the total connecting and facility leasing costs.

Sparsity-Specific Code Optimization using Expression Trees

We introduce a code generator that converts unoptimized C++ code operating on sparse data into vectorized and parallel CPU or GPU kernels. Our approach unrolls the computation into a massive expression graph, performs redundant expression elimination, grouping, and then generates an architecture-specific kernel to solve the same problem, assuming that the sparsity pattern is fixed, which is a common scenario in many applications in computer graphics and scientific computing. We show that our approach scales to large problems and can achieve speedups of two orders of magnitude on CPUs and three orders of magnitude on GPUs, compared to a set of manually optimized CPU baselines. To demonstrate the practical applicability of our approach, we employ it to optimize popular algorithms with applications to physical simulation and interactive mesh deformation.

Project-Based Learning for Graduate Students in Digital Humanities

This essay reports on a five-year summer internship sponsored by the Graduate School of Arts and Sciences at New York University that sought to apply computer science pedagogy in project-based learning [PBL] to digital humanities training of graduate students from diverse humanities disciplines and programs. While much student training in the field tends to occur in academic courses or workshops devoted to particular tools and methods, this program used the iterative process of project development to design an inclusive, efficient context for graduate students with limited experience with technology to learn digital humanities skills appropriate to their professional and scholarly objectives. Describing the framework of PBL computer science pedagogy, the essay considers the technology learning objectives of a broad variety of projects undertaken by 50 MA and PhD students from disciplines ranging from English and History to Fine Arts and Linguistics. Emphasizing the role of peer-learning and cultural differences between STEM and humanities learning contexts, the essay draws on the program coordinators' teaching experience and student commentary to assess learning outcomes of a PBL approach for the professional and scholarly development of humanities graduate students.

Analysis of Computer Science and Technology Modernization Application

Analysis of Computer Science and Technology Modernization Application

Discussion on the Application of Computer Science and Technology in Data Management

Discussion on the Application of Computer Science and Technology in Data Management

Automatic Differentiation for Riemannian Optimization on Low-Rank Matrix and Tensor-Train Manifolds

In scientific computing and machine learning applications, matrices and more general multidimensional arrays (tensors) can often be approximated with the help of low-rank decompositions. Since matrices and tensors of fixed rank form smooth Riemannian manifolds, one of the popular tools for finding the low-rank approximations is to use the Riemannian optimization. Nevertheless, efficient implementation of Riemannian gradients and Hessians, required in Riemannian optimization algorithms, can be a nontrivial task in practice. Moreover, in some cases, analytic formulas are not even available. In this paper, we build upon automatic differentiation and propose a method that, given an implementation of the function to be minimized, efficiently computes Riemannian gradients and matrix-by-vector products between approximate Riemannian Hessian and a given vector.