Context-Sensitive Complementary Information Retrieval for Text Stream

The need of information is such magnitude. It would trigger advances in information technology. Many things can be done by using the internet. The main objective that people use the Internet is to find the information. Unfortunately, not all the information available on the internet it is true and good consumed. There are plenty of information on the internet which is a hoax and contains elements that are contrary to morals and ethics such as terrorism, racism, and pornography. Thus, it required a strong faith and knowledge to be able to sort the incoming information. Due there are many children who also use the internet and conditions where the parents cannot supervise the activities of their children continuously, it would require an application that is embedded in the web browser so that bad content can be blocked automatically. This article focuses on the research of handling pornographic content on the text based webpages. It needed a smart application to be able to distinguish text that contains pornography. Thus, we are implementing artificial intelligence into our research by applying Naive Bayes and information retrieval method. As a result, the application is able to block 88.02% of the pornographic content.

The ability of Patient Dependency Systems to provide information for staffing decisions and budgetary development has been demonstrated. In addition, they have become powerful tools in modern hospital management. This growing interest in Patient Dependency Systems has renewed calls for their automation. As advances in Information Technology and in particular Knowledge-Based Engineering reach new heights, hospitals can no longer afford to ignore the potential benefits obtainable from developing and implementing Patient Dependency Knowledge-Based Systems. Experience has shown that the vast majority of decisions and rules used in the Patient Dependency method are too complex to capture in the form of a traditional programming language. Furthermore, the conventional Patient Dependency Information System automates the simple and rigid bookkeeping functions. On the other hand Knowledge-Based Systems automate complex decision making and judgmental processes and therefore are the appropriate technology for automating the Patient Dependency method. In this paper a new technique to automate Patient Dependency Systems using knowledge processing is presented. In this approach all Patient Dependency factors have been translated into a set of Decision Rules suitable for use in a Knowledge-Based System. The system is capable of providing the decision-maker with a number of scenarios and their possible outcomes. This paper also presents the development of Patient Dependency Knowledge-Based Systems, which can be used in allocating and evaluating resources and nursing staff in hospitals on the basis of patients' needs.

Autistic adults reported having special interests in a range of topics, including computers, music, autism, nature and gardening. Special interests were associated with a number of positive outcomes for autistic adults. They were also related to subjective wellbeing and satisfaction across specific life domains including social contact and leisure. Very high intensity of engagement with special interests was related to lower levels of wellbeing. This highlights the important role that special interests play in the lives of autistic adults.

We demonstrate both theoretically and experimentally that the combination of vibrational spectroscopic techniques on samples can be used to deduce more detailed structural information of interfacial proteins and peptides. Such an approach can be used to elucidate structures of proteins or peptides at interfaces, such as at the solid/liquid interface or in cell membranes. We also discuss that the controlled perturbations may provide more measured parameters for structural studies on such proteins and peptides. In this paper, we will demonstrate that optical spectroscopic techniques such as polarized Fourier transform infrared spectroscopy (FTIR), sum frequency generation (SFG) vibrational spectroscopy, and higher order nonlinear vibrational spectroscopies can be used to deduce different and complementary structural information of molecules at interfaces (e.g., orientation information of certain functional groups and secondary structures of interfacial proteins). Also, we believe that controlled perturbations on samples, such as variation of sample temperature, application of electrical fields, and alternation of substrate roughness, can provide more detailed information regarding the interfacial structures of proteins and peptides. The development of nonlinear vibrational spectroscopies, such as SFG and four-wave mixing vibrational spectroscopy, to examine interfacial protein and peptide structures, and introduction of external perturbations on samples should be able to substantially advance our knowledge in understanding structures and thus functions of proteins and peptides at interfaces.

Modern mankind has joined the general historical process called computerization. In contrast to the industrial society, in which the main thing was the production of material goods, in today’s post-industrial society the production of information becomes the main activity (by information we mean knowledge as information about people, objects, facts, events), phenomena and processes, regardless of their form representation that can be recorded on a tangible medium), and this process is called computerization (hence the term - information society). Such a shift in emphasis is caused, on the one hand, by the fact that humanity is aware of the limited natural (natural) resources of its habitat, and on the other hand, by the emergence of global problems (for example, energy, the environment), a solution that is impossible with previous methods. Information is becoming the main resource for the development of the world community and significantly affects the development of other industries and spheres of life: science, technology, social spheres (cultural communication between people, education). The core values of the information society are:- knowledge; -qualification; - independent thinking; -the ability to work with information and make informed decisions based on it; - awareness not only in a narrow professional field, but also in related fields.

The international market requires the strengthening of the competitive advantages of all participants in it. The formulation of a positioning strategy requires the investment of resources in acquiring additional knowledge. This resource enables the avoidance of “strategic failures”, which often characterized the attempts of domestic business systems to access the international market. Managing international development and market positioning of business systems within the domestic and international environment requires additional management energy and additional knowledge. Digitalization of business significantly reduces business costs, thus eliminating numerous obstacles for poorer and less developed countries, which need and must use their chance to effectively integrate into international economic flows. Informing about censored demand, as well as its forecasting within the selected business area, represents an opportunity that enables safe and efficient access to the global market. Censored demand is a part of demand within the business scope that is not recorded by the bidders, because it arose after the bidders in the selected market segment, made the most of their own production capacity. Predicting censored demand is an area of quantitative analysis with the task of predicting the value of a variable, which is a special challenge for the researcher, given that the history of the variable is not known. Diversification of business activities into the global market requires knowladge of the scope and structure of damand for products that bussines sistems are able to offer. Sometimes, bussines systems avoid applaying those products that, in full capacity, are offerd by local comapnies on the selected target market segment, becouse the fact that a certain company places a total offer does not mean that the offer is satisfied. The business systems who are on the top a certain market segment, although aware of the existance of additional demand are no able to expand the scope of activities for objective reasons. All of the above leaves space for market positioning business systems that are not able to do busines locally. The special interest may have small and medium companys from contrys such as ours, who have all the necessary infrastructure to sell in a foreign market. Doing business in a distance market must be based on careful nalaysis of the factors that affests the success of such a business strategy. Specioal atention shoud be paid to the analysis of censored becouse it is one of the most important factors of success in foreign markets. Adequate prediction of censored demand, which is effectively solved by mathematical simulation models, whose application methodology will be presented in this research project. The possibilities of mathematical simulation in the function of prognostics are very effective becouse they are adjusetd individually to each management problem. The application of the mathematical simulation model doese not require a real system and can also be applied to abstract systems. In relation to how similar the model is to the real system it affects to credibility obtained information as a solution of the model.

This article is a brief survey of topics of mutual interest in hydrology and meteorology intended primarily for the members of these two professions whose special interests are somewhat removed from the common ground. The last decade has seen substantial progress in our understanding of the water balance and in our ability to put this knowledge to use. A rapidly expanding world population with a correspondingly rapid increase in water requirements for human, industrial, and agricultural uses during the next few decades will require still further efforts to increase our knowledge and develop solutions to new water problems.

RGB-D-based object recognition has been enthusiastically investigated in the past few years. RGB and depth images provide useful and complementary information. Fusing RGB and depth features can significantly increase the accuracy of object recognition. However, previous works just simply take the depth image as the fourth channel of the RGB image and concatenate the RGB and depth features, ignoring the different power of RGB and depth information for different objects. In this paper, a new method which contains three different classifiers is proposed to fuse features extracted from RGB image and depth image for RGB-D-based object recognition. Firstly, a RGB classifier and a depth classifier are trained by cross-validation to get the accuracy difference between RGB and depth features for each object. Then a variant RGB-D classifier is trained with different initialization parameters for each class according to the accuracy difference. The variant RGB-D-classifier can result in a more robust classification performance. The proposed method is evaluated on two benchmark RGB-D datasets. Compared with previous methods, ours achieves comparable performance with the state-of-the-art method.

Electron emission induced by slow highly charged ions on a clean metal surface

The Registry of the International Society for Heart and Lung Transplantation: Thirty-third Adult Lung and Heart–Lung Transplant Report—2016; Focus Theme: Primary Diagnostic Indications for Transplant

Training machines to understand natural language and interact with humans is one of the major goals of artificial intelligence. Recent years have witnessed an evolution from matching networks to pretrained language models (PrLMs). In contrast to the plain-text modeling as the focus of the PrLMs, dialog texts involve multiple speakers and reflect special characteristics, such as topic transitions and structure dependencies, between distant utterances. However, the related PrLM models commonly represent dialogs sequentially by processing the pairwise dialog history as a whole. Thus, the hierarchical information on either utterance interrelation or speaker roles coupled in such representations is not well addressed. In this work, we propose compositional learning for holistic interaction across the utterances beyond the sequential contextualization from PrLMs, in order to capture the utterance-aware and speaker-aware representations entailed in a dialog history. We decouple the contextualized word representations by masking mechanisms in transformer-based PrLM, making each word only focus on the words in the current utterance, other utterances, and two speaker roles (i.e., utterances of the sender and utterances of the receiver), respectively. In addition, we employ domain-adaptive training strategies to help the model adapt to the dialog domains. Experimental results show that our method substantially boosts the strong PrLM baselines in four public benchmark datasets, achieving new state-of-the-art performance over previous methods.

In this chapter, we will review the current epidemiological, clinical, and biological evidence on the relationships between hallucinations and dementing illnesses, particularly Lewy Body Dementia (LBD) and Parkinson's disease dementia (PDD) where visual hallucinations are common (see also Fénelon, this volume). We will compare dementia-specific models for their genesis with models derived in other disorders. We will then evaluate assessment, treatment, and support options, and finally, look to unresolved issues and the potential for further developments.Dementia can be neatly defined as an acquired, generally progressive global impairment of cognition in the absence of impaired consciousness. As with all neat definitions, its simplicity is clinically useful but may also be misleading. In most cases, impairments are not equal in all cognitive areas (the exact nature of cognitive change varies by the stage and type of dementing illness), and consciousness may be variably affected. The focus on cognition alone distracts attention from the non-cognitive effects of dementia—emotional, behavioural, psychological, and psychiatric—including hallucinations. There is only a difference of degree between cognitive impairments which are, or are not, severe enough to be classed as dementia. It may be more helpful to think of a dementing process, rather than a state of dementia. The reader with a special interest in the assessment and management of dementia may find more detailed information in specific textbooks or in up-to-date assessment guidelines, for example, the European Federation of Neurological Societies Guidelines (Waldemar et al., 2007).The overall prevalence of dementia in Western Europe is about 5.4% for those over the age of 60 years, and the estimated annual incidence is 8.8 per 1,000 inhabitants. The prevalence increases exponentially with age: 1% (60–64 years), 1.5 % (65–69 years), 3.6% (70–74 years), 6% (75–79 years), 12.2% (80–84 years), and 24.8% (85 years). Global prevalence rates will double in the next 20 years, mainly due to an increased life expectancy in the third world (Ferri et al., 2005). The economical cost of dementia in Europe exceeds 55 billion Euros per year with the major costs emerging in institutional care (Johnson et al., 2005). If the unpaid work of family and other carers is included, the costs rise threefold.

News data is one of the most abundant and familiar data sources. News data can be systematically utilized and ex- plored by database, data mining, NLP and information re- trieval researchers to demonstrate to the general public the power of advanced information technology. In our view, news data contains rich, inter-related and multi-typed data objects, forming one or a set of gigantic, interconnected, het- erogeneous information networks. Much knowledge can be derived and explored with such an information network if we systematically develop effective and scalable data-intensive information network analysis technologies. By further developing a set of information extraction, in- formation network construction, and information network mining methods, we extract types, topical hierarchies and other semantic structures from news data, construct a semi- structured news information network NewsNet. Further, we develop a set of news information network exploration and mining mechanisms that explore news in multi-dimensional space, which include (i) OLAP-based operations on the hierarchical dimensional and topical structures and rich-text, such as cell summary, single dimension analysis, and promo- tion analysis, (ii) a set of network-based operations, such as similarity search and ranking-based clustering, and (iii) a set of hybrid operations or network-OLAP operations, such as entity ranking at different granularity levels. These form the basis of our proposed NewsNetExplorer system. Although some of these functions have been studied in recent research, effective and scalable realization of such functions in large networks still poses multiple challenging research problems. Moreover, some functions are our on-going research tasks. By integrating these functions, NewsNetExplorer not only provides with us insightful recommendations in NewsNet exploration system but also helps us gain insight on how to perform effective information extraction, integration and mining in large unstructured datasets.

Multi-sensor data fusion in the nondestructive measurement of kiwifruit texture

The global martian volcanic evolutionary history