Subfield Of Computer Science Research Articles

Effectiveness and Efficiency of Artificial Intelligence in Boosting Customs Performance: A Case Study of RECTS at Uganda Customs Administration

The artificial intelligence (AI) revolution has great economic potential for customs operations. Indeed, some have already hailed AI ‘the new oil’ since it has become a resource for businesses to streamline processes. This may be an imperfect analogy, but it does capture the excitement and high expectations surrounding the AI-driven economy. AI is a sub-field of computer science that enables intelligence exhibited by machines. For example, in the East African community, the Regional Electronic Cargo Tracking System (RECTS) is used to monitor cargo in transit from Mombasa in Kenya through Uganda to Rwanda and now in the Democratic Republic of Congo (DRC). This has transformed transit cargo management through deterring of dumping and diversion of transit cargo in real time. Previously, cargo would sometimes, if not most of the time, go unaccounted for while in transit, which caused government revenue leakage and business losses. This paper examines the global standards and actions in the uptake of AI for trade facilitation in smoothing customs operations. This study looks at how the uptake of AI has affected customs administration in terms of transit management and security of cargo as well as trade facilitation, which greatly reduces the cost of doing business by the private sector. Based on the study findings, the paper offers several recommendations for policy makers, including undertaking Private Public Partnerships (PPP), integration of RECTS with other Customs systems, well-planned change management and developing a pool of AI experts in Customs.

A survey of accepted authors in computer systems conferences

Computer Science researchers rely on peer-reviewed conferences to publish their work and to receive feedback. The impact of these peer-reviewed papers on researchers’ careers can hardly be overstated. Yet conference organizers can make inconsistent choices for their review process, even in the same subfield. These choices are rarely reviewed critically, and when they are, the emphasis centers on the effects on the technical program, not the authors. In particular, the effects of conference policies on author experience and diversity are still not well understood. To help address this knowledge gap, this paper presents a cross-sectional study of 56 conferences from one large subfield of computer science, namely computer systems. We introduce a large author survey (n = 918), representing 809 unique papers. The goal of this paper is to expose this data and present an initial analysis of its findings. We primarily focus on quantitative comparisons between different survey questions and comparisons to external information we collected on author demographics, conference policies, and paper statistics. Another focal point of this study is author diversity. We found poor balance in the gender and geographical distributions of authors, but a more balanced spread across sector, experience, and English proficiency. For the most part, women and nonnative English speakers exhibit no differences in their experience of the peer-review process, suggesting no specific evidence of bias against these accepted authors. We also found strong support for author rebuttal to reviewers’ comments, especially among students and less experienced researchers.

Fast and effective distribution-key recommendation for amazon redshift

How should we split data among the nodes of a distributed data warehouse in order to boost performance for a forecasted workload? In this paper, we study the effect of different data partitioning schemes on the overall network cost of pairwise joins. We describe a generally-applicable data distribution framework initially designed for Amazon Redshift, a fully-managed petabyte-scale data warehouse in the cloud. To formalize the problem, we first introduce the Join Multi-Graph , a concise graph-theoretic representation of the workload history of a cluster. We then formulate the "Distribution-Key Recommendation" problem - a novel combinatorial problem on the Join Multi-Graph - and relate it to problems studied in other subfields of computer science. Our theoretical analysis proves that "Distribution-Key Recommendation" is NP-complete and is hard to approximate efficiently. Thus, we propose BaW, a hybrid approach that combines heuristic and exact algorithms to find a good data distribution scheme. Our extensive experimental evaluation on real and synthetic data showcases the efficacy of our method into recommending optimal (or close to optimal) distribution keys, which improve the cluster performance by reducing network cost up to 32x in some real workloads.

자동창작시대의 예술 작품 -인공지능은 예술 창작의 주체가 될 수 있는가?

Computational creativity is emerging as a sub-field of computer science; it attempts to make machines the subjects of artistic creations. Therefore, we examine whether machines can be considered the subjects of artistic creations through experiments on audiences’ appreciation of works by artificial intelligence (AI) and humans. According to our qualitative and quantitative findings, audiences reported no significant differences in appreciating works by AI and humans. Thus, it seems inappropriate to declare that “AI cannot be the subject of an artistic creation.” Prior to asking, “Can machines create art?” it is necessary to ask “Isn’t a human a kind of machine?” It is contradictory if we deny that humans are machines, because they are also formed by evolution, a mechanical algorithm. If humans are a type of machine, and if they also make art, eventually, this will come to mean that machines create art. In conclusion, it is not natural to deny the hypothesis, “Machines can be the subjects of artistic creations.”

Clinical Text Engineering Using Natural Language Processing Tools in Healthcare Domain: A Systematic Review

Everyday large amount of data is produced in healthcare industry which contain valuable information about the patient. The healthcare domain area is complex and can be difficult to apprehend. There are plenty of specialized disciplines in medical. Applying text mining and natural language processing to health records needs special care and understanding of the domain. There is a large demand for tools that can make healthcare better and more efficient .Biomedical informatics and computational biology provide excellent opportunities for presenting applications of NLP in the biomedical domain. Natural language processing (NLP) is a subfield of computer science concerned with the interactions between computers and human (natural) languages. A large amount of the information in an electronic patient record system is unstructured in the form of free text. In this paper we provide entail of NLP and its various methods used to identify and extract clinical information from unstructured clinical data .Thus natural language processing tools are used to convert data into a format that computers can understand; that is structured data. In this study it is found from literature that the areas of fundamental BioNLP is innovative and challenging from an NLP perspective.

NLP ‘RECIPES’ FOR TEXT CORPORA: APPROACHES TO COMPUTING THE PROBABILITY OF A SEQUENCE OF TOKENS

Investigation in the hybrid architectures for Natural Language Processing (NLP) requires overcoming complexity in various intellectual traditions pertaining to computer science, formal linguistics, logic, digital humanities, ethical issues and so on. NLP as a subfield of computer science and artificial intelligence is concerned with interactions between computers and human (natural) languages. It is used to apply machine learning algorithms to text (and speech) in order to create systems, such as: machine translation (converting from text in a source language to text in a target language), document summarization (converting from long texts into short texts), named entity recognition, predictive typing, et cetera. Undoubtedly, NLP phenomena have been implanted in our daily lives, for instance automatic Machine Translation (MT) is omnipresent in social media (or on the world wide web), virtual assistants (Siri, Cortana, Alexa, and so on) can recognize a natural voice or e-mail services use detection systems to filter out some spam messages. The purpose of this paper, however, is to outline the linguistic and NLP methods to textual processing. Therefore, the bag-of-n-grams concept will be discussed here as an approach to extract more details about the textual data in a string of a grouped words. The n-gram language model presented in this paper (that assigns probabilities to sequences of words in text corpora) is based on findings compiled in Sketch Engine, as well as samples of language data processed by means of NLTK library for Python. Why would one want to compute the probability of a word sequence? The answer is quite obvious – in various systems for performing tasks, the goal is to generate texts that are more fluent. Therefore, a particular component is required, which computes the probability of the output text. The idea is to collect information how frequently the n-grams occur in a large text corpus and use it to predict the next word. Counting the number of occurrences can also envisage certain drawbacks, for instance there are sometimes problems with sparsity or storage. Nonetheless, the language models and specific computing ‘recipes’ described in this paper can be used in many applications, such as machine translation, summarization, even dialogue systems, etc. Lastly, it has to be pointed out that this piece of writing is a part of an ongoing work tentatively termed as LADDER (Linguistic Analysis of Data in the Digital Era of Research) that touches upon the process of datacization[1] that might help to create an intelligent system of interdisciplinary information.

Artificial intelligence and machine learning: The new paradigm in orthodontic practice

Artificial intelligence (AI) and machine learning (ML) are powerful tools that can be utilized to overcome some of the clinical problems that orthodontists face daily. With the availability of more data, better AI and ML systems should be expected to be developed that will help orthodontists to practise more efficiently and improve the quality of care. AI is a subfield of computer science concerned with developing computers and programs that have the ability to perceive information and reason, and ultimately, convert that information into intelligent actions. The future may be purely digitized, at the comforts of our home, with orthodontists developing neural programs with orthodontic decision markers to aid in developing AI for patients to take less visits, make more use of their time using orthodontic appliances, and enhance the quality of work. This article will briefly discuss the contributions AI and ML in orthodontics, its history and various uses in orthodontics in specific, and the possibility of development.

Finitely distinguishable erasing pattern languages

Pattern languages have been an object of study in various subfields of computer science for decades. This paper introduces and studies a decision problem on patterns called the finite distinguishability problem: given a pattern π, are there finite sets T+ and T− of strings such that the only pattern language containing all strings in T+ and none of the strings in T− is the language generated by π? This problem is related to the complexity of teacher-directed learning, as studied in computational learning theory, as well as to the long-standing open question whether the equivalence of two patterns is decidable. We show that finite distinguishability is decidable if the underlying alphabet is of size other than 2 or 3, and provide a number of related results, such as (i) partial solutions for alphabet sizes 2 and 3, and (ii) decidability proofs for variants of the problem for special subclasses of patterns, namely, regular, 1-variable, and non-cross patterns. For the same subclasses, we further determine the values of two complexity parameters in teacher-directed learning, namely the teaching dimension and the recursive teaching dimension.

Computational Complexity Theory and the Philosophy of Mathematics†

AbstractComputational complexity theory is a subfield of computer science originating in computability theory and the study of algorithms for solving practical mathematical problems. Amongst its aims is classifying problems by their degree of difficulty — i.e., how hard they are to solve computationally. This paper highlights the significance of complexity theory relative to questions traditionally asked by philosophers of mathematics while also attempting to isolate some new ones — e.g., about the notion of feasibility in mathematics, the $\mathbf{P} \neq \mathbf{NP}$ problem and why it has proven hard to resolve, and the role of non-classical modes of computation and proof.

User Preferences in Intelligent Environments

ABSTRACTIntelligent Environments and other Computer Science sub-fields based on the concepts of context and context-awareness are created with the explicit or implicit intention of providing services which are satisfying to the intended users of those environments. This article discusses the pragmatic importance of Preferences within the process of developing Intelligent Environments as a conceptual tool to achieve that system-user alignment and we also look at the practical challenges of implementing different aspects of the concept of Preferences. This study is not aimed at providing a definitive solution, rather to assess the advantages and disadvantages of different available options with the view to inform the next wave of developments in the area.

Three Level Based Security for Improvised User Privacy in Human Computer Interaction

Privacy can be a key aspect of the user experience with computers, online systems, and new technologies. Knowing what to consider about users and their views of computer systems can only improve privacy mechanism. Human Computer Interaction (HCI) is the sub field of Computer Science that studies how people interact with and through computational technologies. In this paper, a three-level model-based approach is provided for the end user privacy in the human computer interaction as the end user’s privacy in the area of HCI is emerging as critical design element for interacting systems in areas as diverse as e-commerce, health care, office work, personal communications etc. The data privacy of end user as well as the resource access privacy is needed to be considered. As of now, no proper solution exists for all types of interface users which is a major privacy issue. Hence, to provide the end users, their personal data protection as well as the resource access security, this paper focuses on analyzing and developing the theory of technological acceptance related to user privacy.

APPLICATIONS OF ARTIFICIAL INTELLIGENCE IN HUMAN LIFE

Artificial Intelligence (AI) has revolutionized in information technology.AI is a subfield of computer science that includes the creation of intelligent machines and software that work and react like human beings. AI and its Applications gets used in various fields of life of humans as expert system solve the complex problems in various areas as science, engineering, business, medicine, video games and Advertising. But “Do any traffic lights use Artificial Intelligence??”, I thought a lot of this when waiting in a red light.
 This paper gives an overview of Artificial Intelligence and its applications used in human life. This will explore the current use of Artificial Intelligence technologies in Network Intrusion for protecting computer and communication networks from intruders, in the medical area-medicine, to improve hospital inpatient care, for medical image classification, in the accounting databases to mitigate the problems of it, in the computer games, and in Advertising. Also, it will show artificial intelligence principle and how they were applying in traffic signal control, how they solve some traffic problem in actual. This paper gives an introduction to a self-learning system based on RBF neural network and how the system can simulate the traffic police’s experience. This paper is focusing on how to evaluate the effect of the control with the changing of the traffic and adjust the signal with the different techniques of Artificial Intelligence.

Computer Science Papers in Web of Science: A Bibliometric Analysis

In this article we present a bibliometric study of 1.9 million computer science papers published from 1945 to 2014 and indexed in Web of Science. We analyze both the quantity and the impact of these publications according to document types, languages, disciplines, countries, institutions, and publication sources. The most frequent author keywords, cited references, and cited papers as well as the distribution of the number of references and citations per paper and of the age of cited references are also explored. Since conference proceedings play a tremendous role in this scientific field, we investigate the time and place of computer science conferences in terms of the most prolific months and locations. And, last but not least, the production of journal articles and conference papers over the whole time period and the level of collaboration in different computer science disciplines are inspected. One of the main results is the finding that “Artificial Intelligence” is the most productive subfield of computer science, but “Interdisciplinary Applications” has the highest relative impact.

A Research on data mining using machine learning

Data mining is mathematical techniques of reading patterns in large sets of data involving methods belonging to various fields like artificial intelligence, machine learning, and statistics. It is a subfield of computer science. This field is in high demand in 21st century because it is implementable in all fields of science, social media and business. We are using concepts of machine learning to train our system against a set of English phrases and words which may have weight .This weight is how we decide whether phrases have a positive, negative or neutral net sentiment ratio. We then read data from sources and perform statistical analysis on this data to take some useful decisions. These choices can display to be of vital importance in numerous fields from industrial employer, Medical, Engineering and Media. Data mining is exploration and evaluation of massive statistics gadgets, so that it will discover significant sample and policies. The basic concept is to catch out powerful way to mix PC’s (personal computer) capacity to technique information along human eyes potential to encounter styles. The layout and art work effectively with big data unit that is purpose of record data mining. Information discovery from database is wider process of data mining. Data Mining is approach of reading facts from specific perspectives and encapsulates results as helpful facts. It has been defined as the nontrivial technique of figuring out legitimate, novel, probably beneficial, and ultimately comprehensible patterns in document.

Machine Listening

Beyond the sub-field of computer science concerned with the extraction of meaningful information from audio data, that term "machine listening" invokes the knotty questions of what it is to listen, what (if anything) separates listening by machines and by humans, and how listening is entangled with the materiality of the voice. The timely emergence of WaveNet is provocative regarding each of these—it is, perhaps more than anything else, a listening machine. Furthermore, it reveals the limits of a media materialist approach to sonicity, as exemplified by Wolfgang Ernst, when it comes to media that are artificially intelligent. As a corrective, I propose Henri Lefebvre’s “rhythmanalysis,” a theory of the everyday which helps to take into account the ambiguities of WaveNet.

Multi‐scale navigation of large trace data: A survey

SummaryDynamic analysis through execution traces is frequently used to analyze the runtime behavior of software systems. However, tracing long running executions generates voluminous data, which are complicated to analyze and manage. Extracting interesting performance or correctness characteristics out of large traces of data from several processes and threads is a challenging task. Trace abstraction and visualization are potential solutions to alleviate this challenge. Several efforts have been made over the years in many subfields of computer science for trace data collection, maintenance, analysis, and visualization. Many analyses start with an inspection of an overview of the trace, before digging deeper and studying more focused and detailed data. These techniques are common and well supported in geographical information systems, automatically adjusting the level of details depending on the scale. However, most trace visualization tools operate at a single level of representation, which are not adequate to support multilevel analysis. Sophisticated techniques and heuristics are needed to address this problem. Multi‐scale (multilevel) visualization with support for zoom and focus operations is an effective way to enable this kind of analysis. Considerable research and several surveys are proposed in the literature in the field of trace visualization. However, multi‐scale visualization has yet received little attention. In this paper, we provide a survey and methodological structure for categorizing tools and techniques aiming at multi‐scale abstraction and visualization of execution trace data and discuss the requirements and challenges faced to be able to meet evolving user demands.

Citation regression analysis of computer science publications in different ranking categories and subfields

A number of bibliometric studies point out that the role of conference publications in computer science differs from that in other traditional fields. Thus, it is interesting to identify the relative status of journal and conference publications in different subfields of computer science based on the citation rates categorised by the China Computer Federation (CCF) classifications and venue types. In this research, we construct a dataset containing over 100,000 papers recommended by the CCF catalogue and their citation information. We also investigate some other factors that often influence a paper’s citation rate. An experimental study shows that the relative status of journals and conferences varies greatly in different subfields of computer science, and the impact of different publication levels varies according to the citation rate. We also verify that the classification of a publication, number of authors, maximum h-index of all authors of a paper, and average number of papers published by a publication have different effects on the citation rate, although the citation rate may have a different degree of correlation with these factors.

Is this conference a top-tier? ConfAssist: An assistive conflict resolution framework for conference categorization

Classifying publication venues into top-tier or non top-tier is quite subjective and can be debatable at times. In this paper, we propose ConfAssist, a novel assisting framework for conference categorization that aims to address the limitations in the existing systems and portals for venue classification. We start with the hypothesis that top-tier conferences are much more stable than other conferences and the inherent dynamics of these groups differs to a very large extent. We identify various features related to the stability of conferences that might help us separate a top-tier conference from the rest of the lot. While there are many clear cases where expert agreement can be almost immediately achieved as to whether a conference is a top-tier or not, there are equally many cases that can result in a conflict even among the experts. ConfAssist tries to serve as an aid in such cases by increasing the confidence of the experts in their decision. An analysis of 110 conferences from 22 sub-fields of computer science clearly favors our hypothesis as the top-tier conferences are found to exhibit much less fluctuations in the stability related features than the non top-tier ones. We evaluate our hypothesis using systems based on conference categorization. For the evaluation, we conducted human judgment survey with 28 domain experts. The results are impressive with 85.18% classification accuracy. We also compare the dynamics of the newly started conferences with the older conferences to identify the initial signals of popularity. The system is applicable to any conference with atleast 5 years of publication history.

Clustering technique for interpretation of cloudburst over Uttarakhand

Data Mining has been used extensively in various business and scientific applications for last few years. Data mining has been found to be providing a deep insight into understanding the hidden facts in huge databases. Data mining is an interdisciplinary subfield of computer science that discovers patterns in large data sets by using methods at the intersection of artificial intelligence, machine learning, statistics, and database systems. In this paper, data mining technique for Interpretation of Weather Forecasts for one of the most disastrous weather phenomenon viz. cloudburst has been applied. Every year, cloudburst over hilly areas and coastal regions causes loss of lives and property. The forecasting and warning of these events is very difficult. There is no satisfactory technique for anticipating the occurrence of cloudbursts because of their small scale. A very fine network of radars is required to be able to detect the likelihood of a cloudburst and this would be prohibitively expensive. The warning of cloudburst could only be provided at a small lead time say a few hours in advance based on the interpretation of latest satellite imagery data, powerful radar (Doppler category), if available, or by using Model Output Statistics (MOS) models. Another dimension to forecasting this weather event has been identified by applying clustering technique on primary data forecasted by global and regional models of weather forecasting. A recent case of Cloudburst over Uttarakhand that caused a huge loss has been analyzed using k-means clustering technique of data mining. It has been observed that with the mining of Numerical Weather Prediction model forecast data, the signals of formation of cloudburst can be found3-4 days in advance.

Multiple-Time-Series Clinical Data Processing for Classification: A Review

Data mining is a multidisciplinary subfield of computer science. It is used in various fields such as medical research, financial, telecommunication, scientific application. Classification is a method used in data mining. Data mining includes wide varieties of data such as clinical, scientific, biological, remote sensing etc. Clinical data can be used for clinical data mining. Clinical data mining helps the clinicians for diagnosis, therapy and prognosis of various diseases. Most popular primary liver cancer is hepatocellular carcinoma (HCC). It is the fifth most common tumour in the world. HCC can be treated by using Radiofrequency ablation (RFA). Recurrence prediction of hepatocellular carcinoma (HCC) after RFA treatment is an important task. This problem can be solved by using a classification technique that classifies persons into two groups: 1) HCC recurrence and 2) no evidence of recurrence of HCC. In this paper a review is being carried out in various techniques used in HCC recurrence prediction are discussed.