Areas Of Computer Science Research Articles

Ludic-pedagogical strategies for the strengthening of mathematical logical reasoning

Currently, the branches of mathematics are in the worst performance and more in the logical reasoning part, being a fundamental part of computational development. This work focuses on intertwined activities that are part of a deep and detailed process that aims to strengthen logical and mathematical reasoning through the development of computational thinking among primary school teachers. The methodology used has an ethnographic design with aqualitative approach, which is based on a diagnosis through a survey and an interview that help plan training workshops in basic computational thinking skills through digital platforms. The main results show that it is necessary to create transversality in the area of mathematics and computer science, to create the need to strengthen mathematical logical reasoning in the different grades of primary school, and to improve the teaching processes in terms of technological tools within the classroom.
 Keywords: computational thinking, mathematical logical reasoning, problem-solving

Finding Relevant Documents in a Search Engine Using N-Grams Model and Reinforcement Learning

The field of information retrieval (IR) is an important area in computer science, this domain helps us to find information that we are interested in from an important volume of information. A search engine is the best example of the application of information retrieval to get the most relevant results. In this paper, we propose a new recommendation approach for recommending relevant documents to a search engine’s users. In this work, we proposed a new approach for calculating the similarity between a user query and a list of documents in a search engine. The proposed method uses a new reinforcement learning algorithm based on n-grams model (i.e., a sub-sequence of n constructed elements from a given sequence) and a similarity measure. Results show that our method outperforms some methods from the literature with a high value of accuracy.

Covid19/IT the digital side of Covid19: A picture from Italy with clustering and taxonomy.

The Covid19 pandemic has significantly impacted on our lives, triggering a strong reaction resulting in vaccines, more effective diagnoses and therapies, policies to contain the pandemic outbreak, to name but a few. A significant contribution to their success comes from the computer science and information technology communities, both in support to other disciplines and as the primary driver of solutions for, e.g., diagnostics, social distancing, and contact tracing. In this work, we surveyed the Italian computer science and engineering community initiatives against the Covid19 pandemic. The 128 responses thus collected document the response of such a community during the first pandemic wave in Italy (February-May 2020), through several initiatives carried out by both single researchers and research groups able to promptly react to Covid19, even remotely. The data obtained by the survey are here reported, discussed and further investigated by Natural Language Processing techniques, to generate semantic clusters based on embedding representations of the surveyed activity descriptions. The resulting clusters have been then used to extend an existing Covid19 taxonomy with the classification of related research activities in computer science and information technology areas, summarizing this work contribution through a reproducible survey-to-taxonomy methodology.

Normal Forms of Conditional Belief Bases Respecting Inductive Inference

Normal forms of syntactic entities play an important role in many different areas in computer science. In this paper, weaddress the question of how to obtain normal forms and minimal normal forms of conditional belief bases in order to,e.g., ease reasoning with them or to simplify their comparison. We introduce notions of equivalence of belief basestaking nonmonotonic inductive inference operators into account. Furthermore, we also consider renamings of beliefbases induced by renamings of the underlying signatures. We show how renamings constitute another dimension of normalforms. Based on these different dimensions, we introduce and illustrate various useful normal forms and show their properties, advantages, and interrelationships.

Artificial intelligence in glaucoma

Background:Artificial Intelligence (AI) is an area of computer science that encompasses the creation of intelligent machines that work and react like humans. It deals with the development algorithms that seek to simulate human brain and also mimic cognitive functions typically associated with the human mind such as learning and problem solving.Purpose:Do we need artificial intelligence in Glaucoma? Glaucoma is the second most common cause of blindness in the world. Its prevalence was over 60 million in 2010 and over 80 million by 2020. It is so common, yet so easily overlooked. More importantly, about 50% of patients in developed countries and 90% in developing countries are unaware of having glaucoma. Early detection can delay the progression of glaucoma. Hence the time is ripe to advovate glaucoma screening.Synopsis:The application of AI in ophthalmology mainly concentrates on the diseases with a high incidence, such as diabetic retinopathy, age-related macular degeneration, glaucoma, retinopathy of prematurity, age-related or congenital cataract etc AI involves mainly 1. machine learning that are algorithms with the ability to learn without being explicitly programmed and 2. deep learning in which artificial neural networks adapt and learn from vast amounts of data. But there are limitations to screening - such as disparity between ophthalmologist:patient ratio and also the availability of the specialty services. The large amount of data acquired from patients makes it nearly impossible for ophthalmologists to screen them with equal efficacy and consistency.Highlights:AI in glaucoma aims at including factors such as clinical data, genomic data, life style behaviors, risk factors, and medical history to predict the risk of developing glaucoma, help customise the most appropriate management protocol for a given patient, and estimate prognosis and surgical success.Video Link: https://youtu.be/IwYS7wDMhkY

Future of Computer Science Profession

As society becomes more reliant on computers and the Internet, A computer science degree is a great way to start a career in IT. You can find many different roles that suit your interests and strengths. The best thing about computer science is that it teaches you to think logically and solve problems.
 Computer hardware and software technology quickly expand in power and complexity as this process unfolds. Because developments happen so quickly, predicting where the area of computer science will go in the next 10 to 20 years can be challenging. The area of computer science is expected to undergo significant changes shortly.

Facial Recognition Using Image Processing

The growing interest in computer vision of the past decade. Fueled by the steady doubling rate of computing power every 13 months, face detection and recognition has transcended from an esoteric to a popular area of research in computer vision and one of the better and successful applications of image analysis and algorithm based understanding. Because of the intrinsic nature of the problem, computer vision is not only a computer science area of research, but also the object of neuro- scientific and psychological studies, mainly because of the general opinion that advances in computer image processing and understanding research will provide insights into how our brain work and vice versa. Because of general curiosity and interest in the matter, the author has proposed to create an application that would allow user access to a particular machine based on an in-depth analysis of a person’s facial features. This application will be developed using Intel’s open source computer vision project, OpenCV and Microsoft’s .NET framework..

A Review Paper on Cloud Computing

On demand or on pay per use of resource such as: network, storage and server these all facilities are provided by cloud computing through internet is called cloud computing. Although, cloud computing is facilitating the Information Technology industry, the research and development in this arena is yet to be satisfactory. We have contribution in this paper is an advanced survey focusing on cloud computing concept and most advanced research issues. This paper provides a better understanding of the cloud computing and identifies important research issues in this burgeoning area of computer science. Section 1 contains the introduction, in the section 2, we provide an overview of cloud computing, section 3 contains the security architecture and section 4 will focus on the research issues and security issue. We conclude the paper on section 5 along with references.

Study of algorithm for coloring in various graph

Graph coloring is an important area of mathematics and computer science. Graph coloring problem is getting more famous to solve the variety of real-world problems like map coloring, timetabling and scheduling. Graph coloring is allied with two types of coloring as vertex and edge coloring. Algorithm is a set of rules that must be followed when solving a particular coloring problem and algorithms plays an important role in graph coloring. The main objective of this paper is to study of various algorithms in graph coloring.

Approximating Minimum Representations of Key Horn Functions

Horn functions form a subclass of Boolean functions and appear in many different areas of computer science and mathematics as a general tool to describe implications and dependencies. Finding minimum sized representations for such functions with respect to most commonly used measures is a computationally hard problem that remains hard even for the important subclass of key Horn functions. In this paper we provide logarithmic factor approximation algorithms for key Horn functions with respect to all measures studied in the literature for which the problem is known to be hard.

How Inclusive are We?

ACM SIGMOD, VLDB and other database organizations have committed to fostering an inclusive and diverse community, as do many other scientific organizations. Recently, different measures have been taken to advance these goals, especially for underrepresented groups. One possible measure is double-blind reviewing, which aims to hide gender, ethnicity, and other properties of the authors. We report the preliminary results of a gender diversity analysis of publications of the database community across several peer-reviewed venues, and also compare women's authorship percentages in both single-blind and double-blind venues along the years. We also obtained a cross comparison of the obtained results in data management with other relevant areas in Computer Science.

Diversification across mining pools: optimal mining strategies under PoW

Abstract Mining is a central operation of all proof-of-work (PoW)-based cryptocurrencies. The vast majority of miners today participate in “mining pools” instead of “solo mining” in order to lower risk and achieve a more steady income. However, this rise of participation in mining pools negatively affects the decentralization levels of most cryptocurrencies. In this work, we look into mining pools from the point of view of a miner: We present an analytical model and implement a computational tool that allows miners to optimally distribute their computational power over multiple pools and PoW cryptocurrencies (i.e. build a mining portfolio), taking into account their risk aversion levels. Our tool allows miners to maximize their risk-adjusted earnings by diversifying across multiple mining pools. Our underlying techniques are drawn from both the areas of financial economy and computer science since we use computer science-based approaches (i.e. optimization techniques) to experimentally prove how parties (and in particular miners) interact with cryptocurrencies in a way of increasing their Sharpe ratio. To showcase our model, we run an experiment in Bitcoin historical data and demonstrate that a miner diversifying over multiple pools, as instructed by our model/tool, receives a higher overall Sharpe ratio (i.e. average excess reward over its standard deviation/volatility).

Heuristic algorithms in evolutionary computation and modular organization of biological macromolecules: Applications to in vitro evolution.

Evolutionary computing (EC) is an area of computer sciences and applied mathematics covering heuristic optimization algorithms inspired by evolution in Nature. EC extensively study all the variety of methods which were originally based on the principles of selectionism. As a result, many new algorithms and approaches, significantly more efficient than classical selectionist schemes, were found. This is especially true for some families of special problems. There are strong arguments to believe that EC approaches are quite suitable for modeling and numerical analysis of those methods of synthetic biology and biotechnology that are known as in vitro evolution. Therefore, it is natural to expect that the new algorithms and approaches developed in EC can be effectively applied in experiments on the directed evolution of biological macromolecules. According to the John Holland's Schema theorem, the effective evolutionary search in genetic algorithms (GA) is provided by identifying short schemata of high fitness which in the further search recombine into the larger building blocks (BBs) with higher and higher fitness. The multimodularity of functional biological macromolecules and the preservation of already found modules in the evolutionary search have a clear analogy with the BBs in EC. It seems reasonable to try to transfer and introduce the methods of EC, preserving BBs and essentially accelerating the search, into experiments on in vitro evolution. We extend the key instrument of the Holland's theory, the Royal Roads fitness function, to problems of the in vitro evolution (Biological Royal Staircase, BioRS, functions). The specific version of BioRS developed in this publication arises from the realities of experimental evolutionary search for (DNA-) RNA-devices (aptazymes). Our numerical tests showed that for problems with the BioRS functions, simple heuristic algorithms, which turned out to be very effective for preserving BBs in GA, can be very effective in in vitro evolution approaches. We are convinced that such algorithms can be implemented in modern methods of in vitro evolution to achieve significant savings in time and resources and a significant increase in the efficiency of evolutionary search.

A Bibliometric Approach to Track Research Trends in Computer-Aided Early Detection of Cancer Using Biomedical Imaging Techniques

The paper’s primary goal is to conduct a comprehensive bibliometric analysis of scholarly publications related to computer-aided early detection of cancer using biomedical imaging techniques. Among many diseases, cancer is the second major cause of death in the world. Nevertheless, with advancements in cancer screening, the survival rate for many types of cancer is enhancing. If cancer is noticed in its early stage, it sets out the best chances for healing. Screening involves a physical examination, laboratory tests, imaging tests, and biopsy. According to research, imaging tests are the most accurate cancer diagnosis methods. This work seeks to acquire results from scholarly articles extracted from the Scopus database for the last two decades by analyzing growth in publications and sources, author’s endowment, keyword analysis, article citation frequencies, etc. To exhibit the bibliometric study, open-source tools, namely BiblioShiny, VOSviewer, Word Cloud, are utilized. The analysis indicates, 78.52% of publications are of article and review type. Breast cancer, segmentation, melanoma, and deep learning are often used keywords in scholarly articles. Substantial work has been done in China, followed by Germany and the USA under the Computer Science area; also, it shows the elevation in several publications since 2019. Our study will furnish a broad range of perceptions for upcoming researchers by tracking the research study trends. This bibliometric analysis will be worthwhile for an apprentice to survey ongoing research under cancer detection using biomedical images.

Chatbot integration with Google Dialogflow environment for conversational intervention

Chatbots are computer programs that execute protocols for supporting human-machine conversations and perform various functions such as searching the web, ordering food, making appointments, and many more. To facilitate timely responses and actions, and enable interactive human-like conversations, chatbots require Natural Language Processing (NLP) to understand user's messages and respond appropriately. NLP is an area of computer science and artificial intelligence concerned with the interactions between computers and human languages. Google Dialogflow is a natural language understanding platform that makes it easy to design and integrate a conversational user interface into your mobile app, web application, device, bot, interactive voice response system, and so on. Dash Messaging is a smart chatbot platform that enables creation of chatbots based on the provided protocol for long-term conversations. In this paper, we discuss how to integrate Google Dialogflow NLP service to a case study chatbot launched with our Dash Messaging platform.

O-Minimal Invariants for Discrete-Time Dynamical Systems

Termination analysis of linear loops plays a key rôle in several areas of computer science, including program verification and abstract interpretation. Already for the simplest variants of linear loops the question of termination relates to deep open problems in number theory, such as the decidability of the Skolem and Positivity Problems for linear recurrence sequences, or equivalently reachability questions for discrete-time linear dynamical systems. In this article, we introduce the class of o-minimal invariants , which is broader than any previously considered, and study the decidability of the existence and algorithmic synthesis of such invariants as certificates of non-termination for linear loops equipped with a large class of halting conditions. We establish two main decidability results, one of them conditional on Schanuel’s conjecture is transcendental number theory.

A history of solving some famous problems in mathematical analysis

This paper provides a historical view of what functional analysis is, a biography of Per H. Enflo and a short view of four fundamental problems in the geometry of Banach spaces. It gives the story of how Enflo, after several years of work, managed to solve three of these problems. It is a story of trying many ideas, testing them on simpler problems, trying to combine different techniques for handling different aspects of the problems, making many failed attempts and finally succeeding. In 1966 Enflo found a general strategy for solving many problems in infinite dimensions, by combining appropriate finite-dimensional techniques with induction procedures. When applied to the old, fundamental problems in functional analysis, this has led to new concepts, problems, techniques and results in analysis. These developments have been applied also in several other areas of mathematics and computer science.

Experimental evidence on the cognitive effectiveness of diagrams

Diagrams continue to play an important role in different areas of computer science. In this paper, we discuss how the cognitive processing of such diagrams can be conceptually understood. To this end, we refer to the CogniDia framework, a recent framework that describes the consecutive phases of cognitive processing. We discuss experimental results for the four categories of the framework. We conclude with an outlook on future research.

Reversible Computing in Debugging of Erlang Programs

Reversible computation is a computing paradigm where execution can progress backward as well as in the usual, forward direction. It has found applications in many areas of computer science, such as circuit design, programing languages, simulation, modeling of biochemical reactions, debugging, and robotics. In this article, we give an overview of reversible computation focusing on its use in reversible debugging of concurrent programs written in the Erlang programing language.

On Relations for Moments of Generalized Order Statistics for Lindley–Weibull Distribution

Moments of generalized order statistics appear in several areas of science and engineering. These moments are useful in studying properties of the random variables which are arranged in increasing order of importance, for example, time to failure of a computer system. The computation of these moments is sometimes very tedious and hence some algorithms are required. One algorithm is to use a recursive method of computation of these moments and is very useful as it provides the basis to compute higher moments of generalized order statistics from the corresponding lower-order moments. Generalized order statistics provides several models of ordered data as a special case. The moments of generalized order statistics also provide moments of order statistics and record values as a special case. In this research, the recurrence relations for single, product, inverse and ratio moments of generalized order statistics will be obtained for Lindley–Weibull distribution. These relations will be helpful for obtained moments of generalized order statistics from Lindley–Weibull distribution recursively. Special cases of the recurrence relations will also be obtained. Some characterizations of the distribution will also be obtained by using moments of generalized order statistics. These relations for moments and characterizations can be used in different areas of computer sciences where data is arranged in increasing order.