Identification Of Behavior Patterns Research Articles

External Correlates of Adult Digital Problem-Solving Process

Abstract: Computerized assessments and interactive simulation tasks are increasingly popular and afford the collection of process data, i.e., an examinee’s sequence of actions (e.g., clickstreams, keystrokes) that arises from interactions with each task. Action sequence data contain rich information on the problem-solving process but are in a nonstandard, variable-length discrete sequence format. Two methods that directly extract features from the raw action sequences, namely multidimensional scaling and sequence-to-sequence autoencoders, produce multidimensional numerical features that summarize original sequence information. This study explores the utility of action sequence features in understanding how problem-solving behavior relates to cognitive proficiencies and demographic characteristics. This is empirically illustrated with the process data from the 2012 PIAAC PSTRE digital assessment. Regularized regression results showed that action sequence features are more predictive of examinees’ demographic and cognitive characteristics compared to final outcomes. Partial least squares analysis further aided the identification of behavioral patterns systematically associated with demographic/cognitive characteristics.

PATTERNS IN INTELLIGENT CONTROL SYSTEMS FOR AUTONOMOUS ENTITIES

Nowadays, the process of integrating autonomous robotic systems (ARS) and military personnel formation is actively underway. Such a new object in the armed forces (AF) should have the property of self-sufficient behavior that guarantees mission fulfillment. The problem is in the construction of information processes in control systems that ensure a predictable behavior of an autonomous artificial system within the Armed Force subdivisions similar to a human one. Currently, the solution for this problem is the development of artificial entities that use symbolic or neural network methods and models for acquiring and formalizing “knowledge”. Ontologies made it possible to make these methods and knowledge models interpretable in natural language semantics and to bring the problem of human-machine interaction to a new level, which was noted by a number of researchers. Nevertheless, it should be noted that as long as there is no cognition process model, the theoretical results obtained are not in demand in practice and are only of theoretical interest. There is a gap between primitive behavior models of artificial entities, their interaction models and expectations from practice. The purpose of this work is to propose an approach to the intellectualization of the artificial entities’ behavior by revising the logical and mathematical abstractions underlying the construction of their onboard control systems based on the pattern theory development. Such approach ensures the transfer of effective human experience to the control systems of artificial entities, as well as the compatibility of the theological and causal approach. It is shown that the choice under severe time constraints is based on behavioral patterns that reflect effective experience. Patterns form both the information structure of representations and a set of possible variants of representations. Subject’s assessments of satisfaction with the current choice situation lead to a change in a subject's interest structure, and he can choose it. There is a developed formal model of the behavioral pattern and a method of logical inference on patterns. The paper considers the problems of identification and construction of pattern models. It proposes to use four positions of information processing for these purposes. The paper proposes an approach to developing software for intelligent control systems for autonomous systems based on the pattern theory. There are the results of behavioral pattern identification solutions that use new generation training systems with voice control. The paper considers the issues of constructing the architecture of unmanned undersea vehicles with an onboard control system that uses pattern models and a pattern inference system. It also solves the problem of the behavior tactics of the node - autonomous unmanned vehicle based on the onboard sensor system data.

Identification of behaviour patterns in waste collection and disposal during the first wave of COVID-19 in Regina, Saskatchewan, Canada

The novel coronavirus (2019-nCov) has had significant impacts on almost every aspect of daily life. From ‘stay-at-home’ orders to the progressive lifting of restrictions, the COVID-19 pandemic has had unprecedented effects on consumer behaviours and waste disposal habits. The purpose of this short communication is to examine time series waste collection and disposal data in a mid-sized Canadian city to understand how behavioural changes have affected municipal waste management. The results suggest that private waste disposal increased during the pandemic. This may be due to people doing home renovations in order to accommodate working from home. Furthermore, it appears that changes in consumer habits destabilized the consistency of waste disposal tonnage when compared to the same time period in 2019. When considering curbside residential waste collection, there was also an increase in tonnage. This may be the result of more waste being generated at home due to changes in eating and cooking habits, and cleaning routine. Finally, the ratio of residential waste collection to total disposal is examined. More residential waste is being generated, which may have environmental and operational effects, especially related to collection and transportation. The results from this study are important from an operational perspective, and will help planners and policy makers to better prepare for changes in the waste stream due to pandemics or other emergencies.

Violencia televisiva: análisis de la figura abusiva como manifestación social en una reconocida serie de dibujos animados

The following scientific article presents the review of the plot, physical aspects and dialogue of the various characters associated with the chapter entitled The Bully, an episode of Nickelodeon’s American animated series Spongebob SquarePants; Television product created by Stephen Hillenburg, broadcast since 1999 in the USA and subsequently serialized in different countries over the next decades. The series featured various marine figures who imitated rational behaviors and suffered events that compromised their lives, presenting themselves under the guise of black humor. The content analysis performed at The Bully confirms that this animation in question transmitted to the children’s audience a strong message focused on the identification of behavioral patterns that makes up the identity of an abuser, being present in the longevity of the episode an emotional charge represented through the mood of the protagonist and the real suppression of the consequences of the acts, aspects that made it impossible for the audience to fully understand the argument of the chapter.

Industry 4.0 and Society 5.0 through Lens of Condition Based Maintenance (CBM) and Machine Learning of Artificial Intelligence (MLAI)

This paper provides preliminary discourse on buzz words about Industry 4.0 and Society 5.0. This discourse focuses on the lens of Condition Based Maintenance (CBM) and Machine Learning of Artificial Intelligence (MLAI). To some extent several companies have embarked Industry 4.0 and Society 5.0 within Internet of Things (IoT) technology. Through the wave of IoT Technology, Industries are adopting automated machinery. Predictive maintenance (PM) is indispensable not only toward the machines” vitality and longevity purpose, but also toward the human error reduction. This paper elaborates its discourse of Industry 4.0 and Society through the lens of CBM and MLAI. The mentioned Machine Learning, in this paper, refers to research methodology, as methodological frameworks. Those frameworks comprise several phases, which are: 1. Equipment Analysis; 2. Data Evaluation; 3. Data Selection and Process; 4. Modeling; 5. Decision Support Model Evaluation. The MLAI techniques are based upon the identification of behaviour patterns. This identification comprises datasets that exclude mathematical models or prior historical knowledge. The discourse in this paper intertwines CBM process and MLAI through data cleaning and processing, features stratification and extraction, model stratification and validation. This paper elaborates two renowned maintenance approaches which are preventive and corrective maintenance. Discourse in this paper focuses on corrective action, known as predictive maintenance (PM), or condition based maintenance (CBM) within Reliability Centered Maintenance (RCM). CBM is chosen as the most desirable strategy, as it involves the intervention as the consequence of the machine breakdown. It also provides cost savings toward spare parts consumption, and optimizes production.

Cybersecurity best practices among Polish students

The major purpose of this article is to analyze state of knowledge within the cybersecurity area accompanied with identification of behavior patterns represented by students (seen as future users and managers of modern technology). The research was divided into: source of knowledge used as so called cybersecurity best practices and password protection aspects. The research was performed using the CAWI method in February 2017. The process included following steps: prepared questionnaire was verified and validated by cybersecurity experts, after expert assessment questionnaire was placed at servers of Faculty of Management (University of Warsaw). The following structure was applied within the article: short introduction and literature review of the researched subject, definition of research conditions and thesis, and used research method, analysis and discussion of research findings, and the last part was dedicated to conclusions related to the research and cybersecurity awareness represented by respondents. Author used 3 working thesis to support the research that fully were proved by collected answers. The research showed that Internet and Friends and Colleagues are the leading sources of knowledge about cybersecurity within the examined group. Based on that author got an evidence that those practices are faulty and lead to wrong behavior patterns both in internet and password protection. It also showed that people indicate lacking of reliable sources of information about cybersecurity where professional training was treated marginally comparing to the leading sources of information. Received results indicate that either employer or university was not interested in promoting good behavior patterns nor the training was the target for respondents. And it can be a reason that users of modern technology put more trust in Internet than in qualified sources of knowledge.

Unveiling Spatial Epidemiology of HIV with Mobile Phone Data.

An increasing amount of geo-referenced mobile phone data enables the identification of behavioral patterns, habits and movements of people. With this data, we can extract the knowledge potentially useful for many applications including the one tackled in this study - understanding spatial variation of epidemics. We explored the datasets collected by a cell phone service provider and linked them to spatial HIV prevalence rates estimated from publicly available surveys. For that purpose, 224 features were extracted from mobility and connectivity traces and related to the level of HIV epidemic in 50 Ivory Coast departments. By means of regression models, we evaluated predictive ability of extracted features. Several models predicted HIV prevalence that are highly correlated (>0.7) with actual values. Through contribution analysis we identified key elements that correlate with the rate of infections and could serve as a proxy for epidemic monitoring. Our findings indicate that night connectivity and activity, spatial area covered by users and overall migrations are strongly linked to HIV. By visualizing the communication and mobility flows, we strived to explain the spatial structure of epidemics. We discovered that strong ties and hubs in communication and mobility align with HIV hot spots.

SEAT CHOICE AND DISTANCE JUDGMENT IN PUBLIC SPACES (1).

This research assessed whether public space users will adopt a least-effort approach and choose a less suitable seat nearby or seek the most suitable seat, even if it is farther away from them when the decision is made. How distance assessment affects seat choice was investigated through an observational survey, which allowed the identification of behavioral patterns. Those behavioral patterns were then tested in a paired comparison experiment with 40 participants. The results showed that the effect of distance on seat choice is related to the difference in distance between the options and that a sufficient difference can cause trade-offs between distance and seat properties. The necessary difference in distance is conditioned by the activity and the seat properties.

Identification of behaviour patterns of viticultural regions according to their agroclimatic fingerprint and grape characteristics

Background and Aims Each viticultural region has its own oenological tradition, heritage culture and history. Consequently, in wine production, the human factor may be significant in differentiating the final product of one viticultural region from another. Then, the question remains as to whether such differentiation is caused by winemakers or whether they simply amplify the differences present already in grapes and shaped by the agroclimatic fingerprint. The present research aims to provide a methodology, based on a multimetric approach to both grape characteristics and agroclimatic conditions of the vineyard, that is able to evaluate the appropriateness of the Protected Geographical Indications (PGIs) of a wine region. The Balearic Islands (Spain) were chosen for the study. Methods and Results Twenty-eight Merlot and 28 Cabernet Sauvignon vineyards were selected from the four vine-growing subregions of the Balearic Islands. Grapes and soils were sampled evenly over the whole surface of each vineyard. Climatic and landscape conditions were also recorded at each site. Principal component analysis (PCA) was applied to grape attributes, soil texture and fertility, climate features and landscape conditions of the vineyards. The PCA results revealed that 70% of the total variance of the dataset in Merlot and 68% in Cabernet Sauvignon could be explained by the first three components. For both cultivars, two of these three principal components are related to agroclimatic features and the third, to grape attributes. Independent of the cultivar, the PCA distribution of the samples resulted in four clusters, where the maximum temperature and silts content of the vineyard were the most important descriptors. Conclusion The proposed methodology contributes to an understanding of the behaviour patterns of the four PGIs present in the Balearic Islands because they matched the four clusters obtained by the PCA distribution of the sample dataset. It thus corroborates their historical existence and explains it, in a first approach, on a more geographical basis. Significance of the Study This work provides a methodology for evaluating the appropriateness of the PGIs recognition based on both the grape and the agroclimatic attributes of the region.

Maximum entropy, fractal dimension and lacunarity in quantification of cellular rejection in myocardial biopsy of patients submitted to heart transplantation

This paper presents a method for the quantification of cellular rejection in endomyocardial biopsies of patients submitted to heart transplant. The model is based on automatic multilevel thresholding, which employs histogram quantification techniques, histogram slope percentage analysis and the calculation of maximum entropy. The structures were quantified with the aid of the multi-scale fractal dimension and lacunarity for the identification of behavior patterns in myocardial cellular rejection in order to determine the most adequate treatment for each case.

Statistical Mechanics of Complex Systems for Pattern Identification

This paper presents a statistical mechanics concept for identification of behavioral patterns in complex systems based on measurements (e.g., time series data) of macroscopically observable parameters and their operational characteristics. The tools of statistical mechanics, which provide a link between the microscopic (i.e., detailed) and macroscopic (i.e., aggregated) properties of a complex system are used to capture the emerging information and to identify the quasi-stationary evolution of behavioral patterns. The underlying theory is built upon thermodynamic formalism of symbol sequences in the setting of a generalized Ising model (GIM) of lattice-spin systems. In this context, transfer matrix analysis facilitates construction of pattern vectors from observed sequences. The proposed concept is experimentally validated on a richly instrumented laboratory apparatus that is operated under oscillating load for identification of evolving microstructural changes in polycrystalline alloys.

Identification of behavior patterns in household solid waste generation in Mexicali's city: Study case

A strategy of household waste data analysis is proposed in this work, based on the WEKA workbench, developed on the Pattern Recognition community. The analysis was conducted on data collected from homes at residential areas in the city of Mexicali, México. The data included information about solid waste produced and also the householder's commitment to the environment, assessed in a Likert's scale based on a questionnaire. A cluster analysis and a tree classifier constructed using the clustered data are presented. An analysis of the decision tree allowed to translate the resulting tree in a set of production rules. After the interpretation of these rules, we were able to predict an environmental behavior, based on the information about waste generation and the questionnaire answers. The rules showed a tendency to bring together members of the same family, concurring in all cases the reference to the same period of waste generation. The elements identified on each rule indicate that the socioeconomic stratum is an important factor, related to behavioral attributes and consumption habits, the main relationship is based on attributes of waste generation as glass, organics, paper, inert, mixer containers and sanitary. The discovered relationships between the cluster, socio-demographic, behavioral and waste attributes are presented and discussed.