International Institute of
A Characters Prefix Based Methodology for Enhancing the Execution Performance of Any String Sorting Algorithm
Thabit, Khalid (Saudi Arabia)
ABSTRACT:
In any computing framework that involves searching, uses SQL, or requires faster recovery after a system crash, sorting plays a vital role. Furthermore, since the majority of data is retained as strings, faster string sorting is a boon. Strings are accessed by reference, which means that any string access requires two memory requests, slowing down every string sorting algorithm.
Characters, on the other hand, are accessed by value, and each character access requires a single memory search, implying that character sorting should be faster than string sorting for the same number of elements and algorithm. Based on this hypothesis, a methodology was introduced that can be applied to any sorting algorithm that can sort either string or record of character arrays to speed up string sorting.
A Comparison of Runtime of Simplex versus Karmarkar, Affine Scaling and Ruiz-Paredes Inner Point Methods
Morales-Marquez, Luis-Enrique; Carrasco-Limon, Odón-David; Bermudez-Juarez, Blanca; Alanis-Urquieta, José-David (Mexico)
ABSTRACT:
In this paper three inner point methods: Karmarkar, Affine Scaling and Ruiz-Paredes, are taken to explain their performance versus the Simplex method. To this end, two specific types of problems with 3 different dimension sizes were used to show which of them gives better results in terms of time.
The results illustrate that these inner point methods are severely limited and unstable when using projection matrixes, with Roos’ problems with dimension 10 or less. Simplex is faster and more exactly, but in dimension 15 or more, simplex begins to have distorted results, where Affine Scaling achieves a better approximation with a remarkably similar time, but with Klee- Minty problems, simplex achieved accurate results in the shortest time. According to the results of the work, there are different classes of problems for different methods that have a better performance, the reasons of this fact could be analyzed in future works.
A Meta-Analysis of Evolution of Deep Learning Research in Medical Image Analysis
Zeng, David; Noteboom, Cherie; Sutrave, Kruttika; Godasu, Rajesh (United States)
ABSTRACT:
With a text mining and bibliometrics approach, we review the literature on the evolution of deep learning in medical image literature from 2012 – 2020 in order to understand the current state of the research and to identify the major research themes in image analysis to answer our research questions: RQ1: What are the learning modes that are evident in the literature? RQ2: What are the emerging learning modes in the literature? RQ3: What are the major themes in medical imaging literature? The analysis of 8704 resulting from a data collection process from peer-reviewed databases, our analysis discovered the six major themes of image segmentation studies, studies with image classification, evaluation procedures such as sensitivity and specificity, optical coherence tomography studies, MRI imaging studies, and Chest imaging studies. Additionally, we assessed the number of articles published each year, the frequent keywords, the author networks, the trending topics, and connections to other topics. We discovered that segmenting and classifying the images are the most common tasks. Transfer learning is the most researched area and cancer is the highly targeted disease and Covid-19 is the most recent research trend.
A Novel IDS Model Oriented to Drone Nodes Networks Threated for DoS Attacks
Pola, Eddy A.; López, Máximo; González, J. Gabriel; González, Nimrod; Mújica, Dante; Santamaría, Guillermo (Mexico)
ABSTRACT:
A very important branch of the IoT is mobile ad-hoc networks, where sensor networks move in a certain space and have been created to operate without a specialized infrastructure. However, there is a specialization of this technology that involves unmanned vehicles and can be divided into two sub-branches: Vehicle and Flying ad-hoc networks. There, end nodes security becomes something of the utmost importance. This article aims to propose detection time as a metric to measure the impact that a denial of service attack could have even with an intrusion detection system. Additionally, the importance of developing an intrusion detection system that revolves around false positives and how this could affect the entire network system is emphasized. Also, a model is proposed to detect denial of service attacks from the security approach of the end node where, instead of starting to track the attack, the node that is allegedly being attacked is secured, safeguarding it without interrupting its operations and subsequently confirming the attack to be identified. In the future, we pretend to explain the correlation between time detection and security.
Adopting Agile Practices: Lessons Learned Transforming Organizations That Do Not Develop Software
Cherinka, R.; Prezzama, J. (United States)
ABSTRACT:
Is Agile viable for everyone? This question forms the basis of ongoing evaluation of Agile adoption across various public sector organizations. Previous work examined a case study of an operationally focused organization as part of Development and Operations (DevOps), highlighting challenges and lessons learned with respect to Day 2+ operations. In this paper, an additional case study is used to examine the progress of a similar operational organization seeking to implement Agile practices to meet their dynamically changing mission needs. Based on experience and lessons learned, this paper focuses on researching and determining the viability of applying Agile practices in real world scenarios where software development is not the main objective of the organization. Lessons related to challenges dealing with scaling complexity, tooling, methods, organizational transformation and more are discussed. Additionally, potential recommendations are provided to aid in the success of Agile adoption where appropriate.
An Optimal Path Planning Approach for a Two Wheeled Mobile Robot Using Free Segment and Turning Point Algorithm
Siddiqui, Rida; Moin, Lubna (Pakistan)
ABSTRACT:
Path planning is a key subject in the research of mobile robotic systems where, enormous applications require mobility in known and indoor environments. Therefore, safe navigation along with robust control is essentially required for any such application. In this work we present Free segment and Turning point algorithm that caters the navigation problem in presence of hurdles in a static known environment. Our approach primarily deals with finding a safe and short path for the robot so that it travels safely throughout the defined trajectory.
Analysis of Risks to Data Privacy Throughout European Countries
Patterson, Wayne (United States)
ABSTRACT:
Fears of having personal information discovered by malicious or even benign actors on the Internet has raised substantial concerns about the sensitivity of personal information. Perhaps the first and very surprising research on this was published a number of years ago by LaTanya Sweeney. She was able to show that approximately 87% of persons in the United States could be uniquely identified through the discovery of a triad of data points T = (PC, G, B) where PC = the United States’ five digit postal code; G = gender; and B = birth date (including year). Her results, first published in 2000, have been stable since that time. Sweeney only considered the data applicable to the United States. In this work, we have used an approach similar to Sweeney’s, but we are able to estimate the challenge to data privacy for all 51 European countries, including both European Community and non-European Community countries, as well as several counties only partially in Europe.
Context-Free Grammars from the Computing Theory Perspective
Radev, Ivan (United States)
ABSTRACT:
This article introduces the context-free languages and the related context-free grammars and pushdown automata. We explore the majority of the issues related with the context-free languages. The context-free grammars are used to generate context-free languages. The context-free grammars have very critical applications in implementing programming languages, natural languages processing, the Web search engines based on pattern-matching, recognizing many forms of computations and the nature of computation, as well as in the theory of computing. We also discuss different approaches for modeling complex structures beyond context-free grammars.
Cyber Security in the Age of Covid 19 Pandemic: An Empirical Model to Manage the Risk in Banks
Iacoviello, Giuseppina; Bruno, Elena; Cavallini, Iacopo (Italy)
ABSTRACT:
This paper examines cyber security issue in banks during Covid 19 pandemic. The objective of the work is to propose a model for the management and control of cyberisk, based on the identification of the most complete methods of evaluation, in qualitative and quantitative terms, and thus able to better describe the possible impacts of its manifestation on the bank's business.
The document provides some answers to the issues of covid-19 impact on bank's cyber security and the possibility to define a cyberisk governance structure able to support the challenges and the new policies to include in the Risk appetite framework, for more incisive mitigation of cyberisk during the pandemic. This study applies a case study approach. It is based on quantitative data and qualitative information obtained through semi-structured interviews and questionnaires addressed to the chief risk officer and its officers for operational and IT risk. The research contributes to the literature by proposing a framework for the management and control of cyber risk based on a mapping of IT assets and operational risk and their relationships.
Detecting Mobile Pandemic Misinformation by Using Mobile Text/Data Mining
Bansal, Benu; Hu, Wen-Chen; Kaabouch, Naima (United States)
ABSTRACT:
More than two million people died of the COVID-19 by 2021. The heavy casualties have put people on great and urgent alert and people try to find all kinds of information to keep them from being inflected by the coronavirus or cure the disease if they have already been inflected. However, the information sent to them intentionally or unintentionally may not be accurate. Some of the wrong information may even cause fatal results. This research tries to find out whether the mobile pandemic information sent to people’s devices is correct as smartphones becoming the major information source for people. The proposed method uses various mobile text/data mining technologies including lexical analysis, stopword elimination, stemming, and decision trees to classify the mobile pandemic information to one of the following classes: (i) true, (ii) neutral, (iii) misinformative, (iv) disinformative, and (v) fake. Preliminary experiment results show the accuracy of the proposed method is above the threshold value 50%, but is not optimal. It is because the problem, misinformation detection, is intrinsically difficult. Further refinements are needed before it is put to work.
Encryption Challenges: What Are We Facing?
Blair, Risa (United States)
ABSTRACT:
Computer encryption is vital for protecting users, data, and infrastructure in the digital age. Using traditional computing, even common desktop encryption could take decades for specialized ‘crackers’ to break and government and infrastructure-grade encryption would take billions of times longer. In light of these facts, it may seem that today’s computer cryptography is a rock-solid way to safeguard everything from online passwords to the backbone of the entire internet. Unfortunately, many current cryptographic methods will soon be obsolete. In 2016, the National Institute of Standards and Technology (NIST) predicted that quantum computers will soon be able to break the most popular forms of public key cryptography. The encryption technologies we rely on every day—HTTPS, TLS, Wi-Fi protection, VPNs, cryptocurrencies, PKI, digital certificates, smartcards, and most two-factor authentication—will be virtually useless unless you prepare. Cryptography and encryption are by no means static technology and knowledge. Both are forever evolving based on the current state of the art technology, programming, algorithms, and research. It rather takes a village and a constant effort to maintain our digital environment security. In order to manage with known threats, engineers, hardware and software producers, mathematicians, and especially network and system engineers must work collaborative to evaluate new knowledge and design methodologies. Then, after adequate testing, they need to tweak and implement the next level of encryption. The virtual encrypted village may not be maintained in a silo. It takes a dedicated technology village to work together – designers, engineers, mathematicians, network and systems engineers to maintain digital security. It is necessary to invest in ongoing skills and capabilities to ensure our digital environment is safe. Investing in encryption and the tools and systems that rely on it are a necessity. Furthermore, there are some countries that choose to go above and beyond. Those countries, including Australia, are carefully crafting and putting laws and policies in place focused specifically on undermining encryption. Cryptography is not stagnant. It does not age so well. There needs to be constant attention devoted to maintaining systems. It is challenging enough to try to design systems that are secure and that will truly stand the test of time, without the consideration of encryption! Businesses are faced with the challenges of promoting innovations and rapidly deploying products, systems, and services. When businesses compromise on cryptographic decisions, the end results can be catastrophic. Sensitive data may be exposed or there may be inappropriate access to systems or services permitted. However, being overly cautious can unnecessarily delay bringing a product or service to market. Therein lies the challenge. From the government standpoint, there were some key areas of concern reported back in 2016 that bring the issue of cyber-attacks and encryption right up to the minute, today in 2021. There is a greater likelihood that companies and government are serving as the driving forces for improving computer security and encryption, as well as holding each other accountable. How could effective encryption save the day? Would effective encryption have saved the 80 million Anthem customers from having their PII hacked by the Chinese? Indeed, with state-of-the-art end-to-end and data at rest encryption implementation, there would have been a far greater likelihood that the customers’ PII would not have been taken or, if taken, impossible to interpret. To that end, there are new surveillance techniques currently being investigated. With end users accessing more and more devices and the challenge of IoT, the issue is not put to rest. However, along with effective encryption, perhaps multimodal biometric authentication provides new opportunities for security enhancement. It becomes a matter of the encryption experts staying ahead of the hackers.
Exploring Critical Success Factors of Reciprocal Synergy in Strategic Alliances: The Renault-Nissan-Mitsubishi Strategic Alliance
Čirjevskis, Andrejs *; Fialeix, Julien ** (* Latvia, ** Spain)
ABSTRACT:
This paper aims to unbundle the antecedents of competence-based synergy in the strategic alliance formation process by employing the ARCTIC framework. The current research provides a new empirical application of the ARCTIC framework to reveal the success factors of reciprocal synergies of the Renault-Nissan-Mitsubishi strategic alliance in the automotive industry. By taking a resource-based view on the sources of competitive advantage, the current paper contributes to theoretical and practical issues of global strategic alliances as part of the existing literature of strategic management and international business. Promising areas of future research are as follows. What would be the consequences for Renault and Nissan of a possible separation? What is the impact of the French and Japanese governments on the alliance performance and how do they continue influencing the alliance’s synergies?
Heating Asymmetry in Magnetoresistive Random Access Memories
Hadámek, Tomáš *; Selberherr, Siegfried *; Goes, Wolfgang **; Sverdlov, Viktor * (* Austria, ** United Kingdom)
ABSTRACT:
Time-dependent current induced heating in a magnetic tunnel junction-based memory cell is investigated by numerically solving the heat transport equation with finite element methods. The Joule heat sources and the sources due to electron tunneling are considered. It is shown that a CoFeB|MgO|CoFeB cell connected to 20 nm long metal electrodes reaches a stationary temperature in 100 ps after a constant current pulse is applied. A similar time is required to cool the cell to the ambient temperature after the current is turned off. The saturation temperature increases with the current pulse power. Due to an asymmetry of the heat generated by tunneling electrons, the temperature profile is not symmetric. The asymmetry of heat generation increases linearly with the voltage up to 1 V and slowly starts to saturate at higher voltages. Because of the increasing asymmetry, the maximum saturation temperature rises faster and is not linear with respect to the pulse power.
Innovative Behaviour of Latvian Companies During COVID-19 Pandemic
Lace, Natalja; Pokromovica, Iveta (Latvia)
ABSTRACT:
Demand for innovative technologies and digital transformation increased during the COVID-19 pandemic. This paper analyzes the findings of a research project conducted by researchers from Riga Technical University within the National research program “Towards the Post-pandemic Recovery: Economic, Political and Legal Framework for Preservation of Latvia’s Growth Potential and Increasing Competitiveness (reCOVery-LV)”. The analysis of the data of the first survey revealed the innovative solutions adopted to overcome the crisis caused by the COVID-19 pandemic in companies. The second survey showed that 2/3 of the surveyed companies in the reporting period (2019-2020) have implemented at least one product or business process innovation, or performed an innovation that is still ongoing. The analysis of inventions, trademarks, and design applications (2016-2020) indicates that the creation of intellectual products will continue in the crisis. Based on the analysis of expert interviews, sets of factors hindering and promoting the development of innovation were created.
Intelligent Extended XGBoost Algorithm for Psychiatric Diagnosis
Azar, Ghassan; El-Bathy, Naser; Chen, Mengyi; Runwal, Sonali Dilip; Azar, Nawal (United States)
ABSTRACT:
The current most accepted method of diagnosing mental health disorders is interviewing patients with an undetermined mental health issue and associating symptoms described in the Diagnostic and Statistical Manual of Mental Disorders (DSM). We propose an Intelligent Extended XGBoost Algorithm (IEXGBA) using Architected Rapid Application Development (ARAD) model to improve the effectiveness and implementation of real-life diagnoses. It creates a better tool for diagnosing and potentially growing the knowledge base of disorders. The goal of the proposed IEXGBA is to generalize patterns in training data so that psychiatrists/psychologists/therapists can correctly predict new data that never been presented to the existing algorithm. Overfitting occurs when the algorithm adjusts excessively to the training data, seeing patterns that do not exist. Our training dataset has the unbalanced target binary variable, which can undermine some models' predictability. We will perform an oversampling, which consists of new samples to increase 0 minority class. For this, we will use the Synthetic Minority Oversampling Technique (SMOTE). SMOTE consists of synthesizing elements for the minority class, based on those that already exist. It works randomly picking a point from the minority class and computing the k-nearest neighbors for this point. The synthetic points are added between the chosen point and its neighbors. Development of a prototype examined and validated the concepts of this research study. The proposed concepts improve the efficiency and quality of mental disease diagnoses. The results obtained from the IEXGBA are optimal.
Male vs. Female Perception of Problems Highlighted for Solving and Innovating
Oganisjana, Karine; Kozlovskis, Konstantins (Latvia)
ABSTRACT:
Problem driven approach with customer engagement is argued to be one of the principal strategies of innovation opportunity identification. This paper analyzes the findings of a two-year-long research project conducted in Riga Technical University with the data collection from different countries of Europe, Asia and America (n=1050) to explore whether there are principal differences in the perception of innovation opportunities by males and females. The qualitative content analysis of the respondents’ texts revealed three groups of problems shared by males and females: 1) universal problems which are perceived similarly by both genders, 2) problems, which are perceived more by males, and 3) problems, which are perceived more by females. So, innovation opportunity identification is gender related.
Multidisciplinary Threat Recognition in Homeland Protection Systems
La Manna, Mario (Italy)
ABSTRACT:
The use of a multisensor system, composed of a set of heterogeneous sensors and other devices has already been demonstrated to improve sensibly the recognition capability, through the exploitation of its spatial/capability diversity, given by the presence of multiple devices and coordinated processes which perform threat detection/recognition. In this paper, we evaluate the performance of a multidisciplinary system, which uses a combination of a multisensory classification algorithm and a multidisciplinary fusion rule. This fusion rule combines the decisions coming from different channels with the reasoning process of a machine learning/human in the loop agent. The multidisciplinary fusion rule takes into account the different channel decisions, taken by different sensors and/or devices, and the intelligence provided by the machine learning/ human in the loop channel. The purpose of this channel is to highlight the channels which, inside the machine learning process and through the interaction with the human in the loop agent, show better performance in terms of recognition capabilities in the specific scenario. The performance evaluation of the multidisciplinary threat recognition system is carried out by considering different case studies. The evaluation demonstrates that a multidisciplinary system can classify different threats, by using a set of methods and algorithms, with a high probability of correct classification, when compared to a completely automated recognition criterium.
Numerical Solution of the Partial Differential Equation Bilaplacian Type by the Finite Element Method for the Simulation of Accelerometer-Type MEMS
Alanís Urquieta, José David; Bermúdez Juárez, Blanca; Vazquez Mora, Paulo Daniel; Hernández Flores, Armando (Mexico)
ABSTRACT:
In this paper, the numerical solution of the Partial Differential Equation Bilaplacian type by the finite element method is presented in order to simulate the accelerometer-type MEMS behavior. The above-mentioned solution is used for emulates the behavior of the deformation of an Accelerometer-type MEMS. The technique is the physically-based modelling as a methodology of simulation with visualization that was used for solve the current problem. The first step is to solve the partial differential equation, which represents the structure, by the finite element method. This numerical method was instrumented in Octave, taking into account the primitive functions that it contains, and taking in advantage the powerful language and that is free software resource. For this problem, the software built, it has results suitable for these types of problems and has well rates of error. Once these types of results have been obtained, the next step will be the rendering and interpretation of the results in graphical way. In spite of the complexity and size in memory used by the numerical method, this procedure it results be a good alternative for this case and maybe in other similar cases. In future works it is looking for parallelize some numerical methods.
On the Pitfalls of Videoconferences for Challenge-Based Face Liveness Detection
Carta, Kévin; Barral, Claude; El Mrabet, Nadia; Mouille, Stéfane (France)
ABSTRACT:
Since the global COVID 19 pandemic, videoconference has become a daily routine for a large part of the world’s population, whether for work or personal life. However, despite its many advantages, videoconference offers a significant biometric source to attackers. Indeed, we will see in this article that recording the face of a person during a videoconference can make it possible to carry out high quality attacks, in particular deepfakes and morphing, in order to attack remote facial recognition systems, secured by a challenge-based liveness detection module.
Performance Comparison of Monolith and Microservices Based Applications
Barczak, Andrzej; Barczak, Michał (Poland)
ABSTRACT:
In the following work analysis of the performance of microservices and monolith web applications are done. The article's primary goal is to analyze the behavior of two similar web applications created in microservices and monolith architecture. In the beginning, both monolith and microservices architectures are described. The following section concerns two web applications that were made to examine their performance. One of them is based on monolith architecture, whereas the second on microservices. Analysis are done using Application Insights Azure Module. To compare the performance of applications different metrics are taken into consideration. Very crucial metric is RAM Memory availability on the server. Another indicator taken into consideration is the percentage of CPU usage. One of the most important performance indicators, from the end-user perspective, is response time. All the tests of applications were done automatically using JMeter tool. Both simple requests and more complicated scenarios were tested.
Predicting the Actual Behavior of Customers to Purchase Through an Online Platform
Liu, Yuan Yuan *; Lace, Natalja ** (* China, ** Latvia)
ABSTRACT:
Nowadays, artificial intelligence is no longer a new concept. With the development of science and technology, the application of artificial intelligence in marketing and other business fields has gradually expanded. The predictive power of artificial intelligence in customer purchase behavior has expanded thousands times. AI technologies, such as face recognition, advertising, and content precise delivery are constantly changing people's lives. For many commercial firms, artificial intelligence technologies are changing the marketing strategies and the rules of business. As new applications of artificial intelligence continue to emerge, there is an increasing interest to explore how predictive it is in customer online purchase behavior. The study aims at presenting the conceptual framework of artificial intelligence, revealing its working mechanism in combining customers and firms, showing the machine learning algorithms predicting customer purchase behavior. The study takes one of the smart tourism destinations and its artificial intelligence technologies’ performance in selling tickets during a 5-day “May Day” holiday in China in 2021, which further explains the intelligent marketing and the future trends of artificial intelligence influencing firms’ and customers’ marketing strategies and daily life.
Social Engineering Attacks: A Systematic Mapping Study
Villacís, Johanna *; Pintag, Marco *; Fernández-Peña, Félix *; Andrade, Roberto *; Sánchez, Manuel **; Fuertes, Walter *; Benavides, Eduardo *; Ron, Mario * (* Ecuador, ** Spain)
ABSTRACT:
Social engineering has become the most prominent attack since human beings are the weakest link in the chain of technology’s security. In this paper, we conduct a systematic mapping study of vulnerabilities and countermeasures with human or software intervention. Our objectives are 1) to explore trends in technology security against social engineering attacks, 2) to categorize existing evidence, and 3) to identify potential directions for future research. In this SMS, we considered 960 studies published between 2016 and 2020. A total of 33 primary studies were finally chosen. We classified these primary sources into three categories. Our survey reveals an imbalance between these categories - most of the existing research focuses on phishing attack detection, with fewer studies targeting human behavior and attack prevention. Our survey also exposed several gaps in existing research and suggested areas for improvement.
Solving Knapsack Problems Using Radius Particle Swarm Optimization Fuse with Simulated Annealing
Munlin, Mudarmeen (Thailand)
ABSTRACT:
We present a novel approach to fuse the Radius Particle Swarm Optimization and Simulated Annealing (RPSO-SA) to solve the Knapsack Problems (KPs). The features RPSO-SA create an innovative approach, which can generate high-quality solutions in shorter times and more stable convergence characteristics. The RPSO takes advantage of group-swarm to keep the balance between the global exploration and the local exploitation. The SA gently improves the candidate solution by searching for optimal solutions within a local neighborhood. The RPSO-SA combines the strong global search ability of RPSO and the strong local search ability of SA to reach faster optimal solution. In addition, there are two ways of accepting a new solution. The method has been tested against the knapsack problems. The results indicate that the combined approach outperforms individual implementations of radius particle swarm optimization and simulated annealing.
Spotlight on Information Security Integration in the German Health Sector
Scholl, Margit (Germany)
ABSTRACT:
Based on extensive research of the literature on the current status of the health sector in Germany, the four spotlight areas of CRITIS, the pandemic situation, pandemic planning, and communication and learning are discussed in more detail in connection with information security. They may be used to create an integrative research map for holistic approaches in future research projects. With this in mind, the aim of this paper is to summarize aspects with a sound basis in the literature. The focus here is on general lessons learned from previous awareness-raising projects in information security.
Test-Driven Development Effects on Software Quality and Developer Productivity Analysis
Fiuza, Mariana; Hirama, Kechi (Brazil)
ABSTRACT:
Since its gained popularity as a technique that would contribute to the increase of quality, being internal and external, and developers' productivity, TDD’s effects have been studied so that the alleged benefits mentioned by its enthusiasts would be proven. Moreover, according to the CHAOS report, published by The Standish Group International in 2015, there has not been found an efficient way to develop high quality software. Based on this scenario, the present study conducted a critical analysis to verify the impacts of TDD with regards to quality, both internal and external, as well as to developers’ productivity, using the evidence present in the literature. The analysis was conducted based on the metrics used by the selected studies to attest the internal quality of the software and if they were related to its perceived quality. Furthermore, the aspects surrounding the usage TDD were also analysed to try to identify how and if they contribute to the decrease of defects found in the resulting software as well as the improvement of developer’s productivity. The quality and productivity benefits to be gained from using TDD found by this study are more related to the amount of tests produced, which would lead to faster defect detection and correction, easing the activities related to software maintenance, as well as to the task granularity and the uniformity of the task’s execution time, and not to the usage of TDD alone.
The Impact and Trend of Virtual Currency
Gottimukkala, Raga; Jenq, John (United States)
ABSTRACT:
Cryptocurrency, which falls into the category of virtual currency, has become increasingly popular in recent years. There are zero or very minimal transaction fees and the transactions are anonymous. In this report, we will discuss the impact of adopting the virtual currency in our society. We will investigate virtual currency by analyzing and comparing some top cryptocurrency stocks to see the trend and try to predict their futures. Specifically, Python Jupyter Notebook will be the tool to analyze the data. Data will be preprocessed and explored. Machine learning modules will be used to learn the datasets and the moving trends will be predicted.
The Performance of Digital Campaign for Smart Tourism and on-Line Purchasing – The Case of China
Liu, Yuan Yuan *; Lace, Natalja ** (* China, ** Latvia)
ABSTRACT:
Given the rapid development of digitization, social media has become a key platform for digital campaigns to promote business and products, with an aim of maximizing profits. Yet, empirical evidence on the performance of a digital campaign for smart tourism and on-line purchasing remains under explored. Using an innovative design of an ecosystem for both smart tourism and on-line purchasing promotion becomes an important tool especially during mobility restriction due to COVID-19. The research was carried out studying the construction and operation of an ecosystem for smart tourism and on-line purchasing of one case of China. Key findings identify components of the ecosystem and its working mechanisms promoting smart tourism and on-line purchasing, and reveal the performance of the digital campaign. Although a larger data amount should be grasped by designing a more comprehensive ecosystem, results of the research show the effectiveness of social media as a marketing platform for smart tourism essence as “products”, and how an ecosystem is constructed and applied at a smart tourism destination, together with its logic combining and promoting smart tourism together with on-line purchasing.
Web Based Approach for Discovering and Prevention of Customs Violations by Application of Emotional Model
Tudjarov, Boris; Panov, Vesko (Bulgaria)
ABSTRACT:
The problem of fraud in the application of customs regimes is particularly relevant at the moment, which requires timely measures to protect the financial interests of the countries. An approach for prevention and discovering of the frauds in customs operations, based on emotional model, is proposed and experimental Web application is developed. The emotional model is briefly described/presented. Monitoring on customs operations is based on perceptual information (information collected during the operation), which is captured and stored, in our case as emotions (information with different aspects related to every customs operation). By using the rules of the emotional model information about feelings and mood is calculated. As an addition to the emotional model, a decision helping module is proposed and realized - a filter which generate alert in case of overlapping of value/values of emotions, feelings or mood. On this way the complicated task of monitoring of all customs operations is reduced to monitor some of them, where the doubt level increase and/or there are sure symptoms for to qualify the customs operation as a fraud. Developed Web application is presented, its usefulness is discussed and tasks for the future development are defined.
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