Machine Learning And Personality Traits Assessment In Computer Science Thesis PdfBy Nicolette D. In and pdf 10.05.2021 at 13:33 9 min read
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- Machine Learning Methods for Classification of Unstructured Data
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Once production of your article has started, you can track the status of your article via Track Your Accepted Article. Help expand a public dataset of research that support the SDGs. The spread of the use of artificial intelligence techniques is now pervasive and unstoppable. However, it brings with it opportunities but also risks and problems that must be addressed in order not to compromise an effective evolution.
Personality traits are generally referred to as relatively stable patterns of thoughts, feelings, and behaviours that have been associated with a wide range of important life outcomes and choices. Specifically, personality traits have repeatedly been related to the individual e. Hence, in the recent years, there has been a massive increase in the interest to develop models which use online data on human behaviour and preferences i.
The integration of several disciplines and their integration and deployment into the living environment are the ingredients for the design and development of future generation solutions.
In this special issue, we intend to strengthen the link between the sentiment analysis field and the mental health research area. Within the Digital Health domain, several works demonstrated how the real-time monitoring of mood conditions led to an improvement of the overall patients and citizens quality of life.
As an example, we want to mention the impact that emotion monitoring has on the improvement of daily healthy behavior e. The education sector is relying more and more on online learning. Educational and training institutions are being motivated to endorse online learning strategies thanks to its technical, economic, and operational feasibility. Personal computers, laptops and personal smart devices have had a steady increase in storage and computational capacity capabilities over the years, where it has become common with terabytes in storage space.
Recent years have witnessed a dramatic increase of graph applications due to advancements in information and communication technologies. In a variety of applications, such as social networks, communication networks, internet of things IOTs , and human disease networks, graph data contains rich information and exhibits diverse characteristics.
The term Fog Computing has been introduced to identify a paradigm for designing applications able to exploit both the virtually infinite resources on the cloud and the limited edge computation power by operating also on the devices living in between these two sides.
In the modern computing era, characterized by saturated performance and high production costs, Approximate Computing has been representing the most attractive breakthrough for efficient system design. We invite submission of high-quality manuscripts reporting relevant research in the area of generation of knowledge graphs by using deep learning techniques. In the vision of Industry 4. In the last decades, the complexity of scientific and engineering problems has largely increased.
In many areas of engineering and science, High-Performance Computing HPC and Simulations have become determinants of industrial competitiveness and advanced research. Advances in HPC architectures, storages, networking, and software capabilities are leading to a new era of HPC and simulations, with new challenges both in computing and system modeling.
Today, this is especially critical and key considering that HPC systems continue to scale up in terms of nodes and cores, in the path toward exascale. Science gateways serve as connection points, assembling the various components of advanced cyberinfrastructure - data collections, instruments, supercomputers, clouds, and analytical tools - behind streamlined, user-friendly interfaces.
They are typically a community-developed web portal or a suite of desktop applications. Gateways can provide scalable access to many things: a highly-tuned parallel application running on a supercomputer; a remote instrument like a telescope or electron microscope; a curated data collection; tools to create workflows and visualizations linking these different resources; and collaborative venues to discuss results, share curricula and presentations and more.
Gateways enable not only researchers with a common scientific goal but also students and members of the broader community by providing access to top-tier resources.
Gateways provide both a user-centric and a community-centric view with social networking of the cyberinfrastructure. There is much that is common in gateway development regardless of the domain area. For this special issue related to the workshop we encourage research papers that address one or more of these networking needs; and developments that are essential in the information systems infrastructure for the scientific discovery process.
Participants to the workshop are invited to submit extended version of their work. Other submissions are also welcome. Cognition is emerging as a new and promising methodology with the development of cognitive-inspired computing, cognitive-inspired interaction and systems, which has the potential to enable a large class of applications and has emerged with a great potential to change our life.
However, recent advances on artificial intelligence AI , fog computing, big data, and cognitive computational theory show that multidisciplinary cognitive-inspired computing still struggle with fundamental, long-standing problems, such as computational models and decision-making mechanisms based on the neurobiological processes of the brain, cognitive sciences, and psychology.
How to enhance human cognitive performance with machine learning, common sense, natural language processing etc. The objective of this special issue is to bring together state-of-the-art research contributions that address these key aspects of cognitive-inspired computing and applications. In the last few years we have observed an increasing presence of intelligent applications in our daily lives: from accurate product recommendations, cyber-threat detection, to sophisticated software assistants.
Many of these applications have certainly had an impact in our lifestyle, but they have mostly remained in the realm of the digital world.
Despite the fact that we are increasingly digital citizens, cities and urban areas will always be our main ecosystem and, therefore, the main aspect affecting our daily lives.
The next barrier for intelligent applications is pervading urban areas to optimize resources, foster sustainable practices, fighting inequalities, creating new opportunities, and, generally, improving the welfare of their inhabitants. Advances in the capability of data computing and processing have ignited an explosion in paradigm, driving Financial Technology FinTech forward at an ever-accelerating rate with unprecedented new financial services. The result is that FinTech is now widely perceived as the next phase in the evolution of financial services, in which financial affairs and technology are seamlessly integrated.
It has become clear that established financial institutions will need to continue driving innovation and meeting consumer needs while simultaneously satisfying new regulatory requirements. In light of the rise in interest around FinTech, both the research community and industry must intensify the attention given to overcoming the trust, security and privacy challenges germane to FinTech to unleash its full potential.
There is currently no consensus on best practices regarding how FinTech can be applied with robust security and privacy preservation. The cognitive computing is computational technology that provides an artificial physical response, permitting a subject to test events and various activities comparable to those that can be established in reality.
By employing sensors and intelligent algorithms, the machines or computers can sense similar to human behaviour seeing, hearing and even feeling. In recent years, the Internet of Medical Things IoMT support the out-of-hospital concept that modify and provide higher care standards. This is executed with individual data-driven treatment schemes and high performance optimised devices customised to act as individual requirements. The IoMT are designed mainly to sense the individual health status data where it can be sent to the clinical for interpretation issue.
With the aid of cognitive algorithms, a pre-learned intelligent system can be developed for improving the diagnosis process and automate it. Moreover, the valuable information from the clinical database is used for individual health prevention and protection through emergency situations.
Internet of Vehicles IoVs is expected to analyze and utilize the various information, especially multimedia inside and outside vehicles itself through wireless communication techniques. Specifically, through the vehicle-to-vehicle V2V , infrastructure-to-vehicle I2V and vehicular-to-infrastructure V2I communications, which are the foundation and key support technologies determining the overall performance of vehicular networks, road safety and traffic efficiency are significantly improved assisted advanced artificial intelligence AI.
Business Process Management BPM has been referred to as a "holistic management" approach to aligning an organization's business processes with the needs of users. It promotes business effectiveness and efficiency while striving for innovation, flexibility, and integration with technology. However, the challenge for large-scale business process management is the complexity in addressing both the dynamic execution environment and the elastic requirement of users. Over the last decade, many new computing paradigms including cloud computing, mobile computing, mobile-cloud computing, and recent edge and fog computing have significantly impacted the IT industry, especially how organizations are running their business applications.
These computing paradigms bring both challenges and opportunities for business process management. Advances in distributed systems technology have allowed for the provisioning of IT services on an unprecedented scale and with increasing flexibility.
As the global market for infrastructure, platforms and software services is continuously evolving supporting new application domains, the need to understand and deal with the new implications and multitude of new challenges is quickly growing.
This Special Issue aims to solicit contributions that are interdisciplinary, combining business and economic aspects with engineering and computer-science related themes. Contributions to this Special Issue can include extensions to existing technologies, successful deployment of technologies, economic analyses, analyses of technology adoptions, and theoretical models. We welcome papers that combine micro- and macro-economic principles with resource management strategies in computer science and engineering.
Case studies, which demonstrate practical use of economic strategies, benefits and limitations, are particularly encouraged.
The purpose of this Issue is to gather original work and build a strong multidisciplinary community in this increasingly important area of a future information and knowledge economy. Cyber Physical Systems CPS refer to the seamless integration of computation with physical processes, possibly with humans in the loop. In these systems, embedded computers and networks monitor through sensors and control through actuators the physical processes, usually with feedback loops where physical processes and computations affect each other.
A key point in these systems is the control of physical processes from the monitoring of variables and the use of computational intelligence to obtain a deep knowledge of the monitored environment, thus providing timely and more accurate decisions and actions. The growing interconnection of physical and virtual worlds, and the development of increasingly sophisticated intelligence techniques, has opened the door to the next generation of CPS, that is referred to as smart cyber-physical systems sCPS.
The Internet of Things IoT is a term that has been introduced in recent years to define objects that are able to connect and transfer data via the Internet. In the IoT, cloud computing environment has made the task of handling the large volume of data generated by connecting devices easy and provides the IoT devices with resources on-demand.
Many fields of science have been experiencing and continue to experience a large influx of data. Managing, transporting, and architecting systems, as well as building tools to deal with the delivery of these data has become increasingly important. Additionally, the ecosystem of information and communications systems is becoming more complex. Wide area networks are now an integral and essential part of this data-driven supercomputing ecosystem connecting information sources, data stores, processing, simulation, visualization and user communities together.
Furthermore, networks are required to connect research instruments such as photon sources, and large visualization displays. Cybersecurity is an essential requirement when living in a digital world. Should one user trust a service on internet? How well secured are my personal data in the digital world?
All these questions request new technical and methodological solutions involving many aspects among cryptography, information theory, protocols… In order to establish a secure link between users and cyber services, biometrics becomes a key technology. However, it has also many drawbacks such as possible false rejection of legitimate users and false acceptance of impostors, privacy concerns and possible attacks spoofing, replay.
Cognitive Internet of Things CIoT is the use of cognitive computing technologies, which is derived from cognitive science and artificial intelligence, in combination with data generated by connected devices and the actions those devices can perform.
CIoT is viewed as the current IoT integrated with cognitive and cooperative mechanisms to promote performance and achieve intelligence. Furthermore, assisted by cloud computing and big data, CIoT is expected to provide deeper insights and high-level intelligence from the vast amount of data being created by IoT to create value for people, cities, and industry.
Therefore, CIoT is considered to drive the next generation of data analytics and technical capabilities, and infuse intelligence and decision making into the physical world to continually transform businesses and enhance the human experience in real-time.
The Internet technologies combining distributed computing settings such as cloud computing have further increased the performance of the system. The benefits of using data-driven applications have a strong impact on various industries, including the financial industry, manufacturing, consulting agency, and healthcare. One of the vital issues in data-driven applications is to find out efficient methods of optimizations in both executions and outputs sides.
The challenges are varied, which could include but are limited to speeding up the data mining efficiency in big data, secure data transmissions among multiple stakeholders, adoptable network designs for multi-channel communications, etc. Using data wisely is considered one of the potential solutions to the potential risks or restrictions in the field. The edge server layer is expected to be a medium that intelligently optimize the use of the computing resources.
This technology innovation has begun to drive new levels of performance and productivity in multiple domains. Meanwhile, cloud computing also is becoming a major enabler of various industrial innovations. The threats exist at different layers due to more parties are involved in the service processes.
Both detecting threats and finding out solutions are significant. Autonomous Cloud is an exciting area of development and research that utilizes artificial intelligence, machine learning and data analytics to aid in intelligent cloud management and decision making.
Such techniques can support automation of operations such as services mapping, scaling, network design, data organization and security management.
Call for Papers
Once production of your article has started, you can track the status of your article via Track Your Accepted Article. Help expand a public dataset of research that support the SDGs. The spread of the use of artificial intelligence techniques is now pervasive and unstoppable. However, it brings with it opportunities but also risks and problems that must be addressed in order not to compromise an effective evolution. Personality traits are generally referred to as relatively stable patterns of thoughts, feelings, and behaviours that have been associated with a wide range of important life outcomes and choices. Specifically, personality traits have repeatedly been related to the individual e.
MS Thesis What makes our university stand out is a clear vision to offer a good value education. Home MS Thesis. Internationalization vs. Its Competitors. Numerical study of MHD Nanofluid flow due to stretching permeable surface under the effect of viscous dissipation, joule heating and thermal radiations.
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I am a senior research scientist in Machine Learning and Computer Vision. Email: csegalin netflix. During my PhD I investigated the interplay between aesthetic preferences and individual differences.
Artificial general intelligence AGI is the hypothetical  ability of an intelligent agent to understand or learn any intellectual task that a human being can. It is a primary goal of some artificial intelligence research and a common topic in science fiction and futures studies. In contrast to strong AI, weak AI  also called narrow AI  is not intended to perform human-like cognitive abilities and personality , rather, weak AI is limited to the use of software to study or accomplish specific pre-learned problem solving or reasoning tasks expert systems.
Recently, the automatic prediction of personality traits has received a lot of attention. Specifically, personality trait prediction from multimodal data has emerged as a hot topic within the field of affective computing. In this paper, we review significant machine learning models which have been employed for personality detection, with an emphasis on deep learning-based methods. This review paper provides an overview of the most popular approaches to automated personality detection, various computational datasets, its industrial applications, and state-of-the-art machine learning models for personality detection with specific focus on multimodal approaches. Personality detection is a very broad and diverse topic: this survey only focuses on computational approaches and leaves out psychological studies on personality detection.
I am hiring software engineers and applied scientists in San Francisco.
Machine Learning Methods for Classification of Unstructured Data
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review recent applications of machine learning to personality assessment, place machine learning Early research, conducted primarily by scholars in the field of computer science, introduced MLPA Our thesis is that refining MLPA via the.
Сьюзан, я хочу кое о чем тебя спросить. - Звук его голоса гулко раздался в комнате оперативного управления, и все тут же замерли, повернувшись к экрану. - Сьюзан Флетчер, выйдете за меня замуж. В комнате зашушукались.
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