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Behavioral pattern mining for intelligent system such as SmEs sensor data are vitally important in many applications and performance optimizations. Sensor pattern mining (SPM) is also dynamic and a hot research issue to pervasive and ubiquitous of smart technologies toward improving human life. However, in large-scale sensor data, exploring and mining pattern, which leads to detect the abnormal behavior is challenging. The paper aims to discuss these issues.

Sensor data are complex and multivariate, for example, which data captured by the sensors, how it is precise, what properties are recorded or measured, are important research issues. Therefore, the method, the authors proposed Sequential Data Mining (SDM) approach to explore pattern behaviors toward detecting abnormal patterns for smart space fault diagnosis and performance optimization in the intelligent world. Sensor data types, modeling, descriptions and SPM techniques are discussed in depth using real sensor data sets.

The outcome of the paper is measured as introducing a novel idea how SDM technique’s scale-up to sensor data pattern mining. In the paper, the approach and technicality of the sensor data pattern analyzed, and finally the pattern behaviors detected or segmented as normal and abnormal patterns.

The paper is focussed on sensor data behavioral patterns for fault diagnosis and performance optimizations. It is other ways of knowledge extraction from the anomaly of sensor data (observation records), which is pertinent to adopt in many intelligent systems applications, including safety and security, efficiency, and other advantages as the consideration of the real-world problems.

Traditional crime linkage studies on serial sexual assaults have relied predominantly on a binary crime linkage approach that has yielded successful results in terms of linkage accuracy. Such an approach is a coarse reflection of reality by focussing mainly on the outcome of an offence, neglecting the forceful differences due to the intricate offender-victim interaction. Only few researchers have examined sexual assaults through the lens of a sequence analysis framework. This paper aims to present the first empirical test of offence sequence-based crime linkage, moving beyond exploratory analyses.

Offence accounts from 90 serial sexual assault and rape victims from the UK were analysed and sequentially coded. Sequence analysis allowed to compare all offences combinations regarding their underlying sequence of events. The resulting comparison was transformed and plotted in two-dimensional space by multidimensional scaling analysis for a visual inspection of linkage potential. The transformed proximities of all offences were used as predictors in a receiver operating characteristic analysis to actually test their discriminatory accuracy for crime linkage purpose.

Sequence analysis shows significant discriminatory accuracy for crime linkage purpose. However, the method does perform less well than previous binary crime linkage studies.

Several limitations due to the nature of the data will be discussed.

The practical limitations are as follows: the study is a potential practical value for crime analysts; it is a complimentary methodology for statistical crime linkage packages; it requires automated coding to be useful; and it is very dependent on crime recoding standards.

The exploratory part of this study has been published in a book chapter in 2015. However, to the best of the authors’ knowledge, the succinct test of crime linkage accuracy is the first of its kind.

This paper outlines 4 assumptions behind attempts to explain the sequential organization of communication behavior during conflict. These assumptions were supported by an analysis of behavioral sequences coded from 9 hostage negotiations and 20 divorce mediations. Analyses showed that negotiators use only a small proportion of available responses to other party's behavior, and that this proportion rapidly decreases as sequence length increases. Critical to this channeling in behavior was the triple‐interact (i.e., cue‐response‐cue‐response), which represents the maximum sequence length required to enable accurate prediction of negotiators' future behavior. More detailed analysis showed that the triple‐interact reduced uncertainty in behavior by over 70%, which compares to less than 1% from knowledge of negotiation context and approximately 10% from knowledge of individual differences.

While behaviour sequence analysis (BSA) is popular, it is not without limitations, namely, the level of detail required and time taken to run analyses; therefore, this paper aims to outline a novel method, using 30 serial homicide cases as a worked example.

Temporal analysis methods are becoming increasingly popular in applied forensic and criminological research. In recent years, BSA has become a widely used approach.

Waypoint sequencing provides a streamlined version of the traditional BSA approach, allowing for fewer behaviours to be included and providing a clearer overview of the main behaviours of interest.

Waypoint sequencing is shown in the current paper through serial killer research to show how to conduct the analyses and how it is effective in current investigations by expediting the process and allowing quicker analysis to facilitate current investigations.

The current research provides a novel approach to sequence analysis that is more useful in applied settings as it requires fewer behaviours or events than traditional BSA.

Understanding when an individual is being deceptive is an important part of police and criminal investigations. While investigators have developed multiple methods, the research literature has yet to fully explore some of the newer applied techniques. This study aims to investigate statement analysis, a recent approach in forensic linguistic analysis that has been applied to criminal investigations.

Real-world statements of individuals exposed as deceptive or truthful were used in the analyses. A behaviour sequence analysis approach is used to provide a timeline analysis of the individuals’ statements.

Results indicate that sequential patterns are different in deceptive statements compared to truthful statements. For example, deceptive statements were more likely to include vague words and temporal lacunas, to convince investigators into believing that the suspect was not present when the crime occurred. The sample in this research did not use one deceptive indicator, instead, electing to frequently change the order of deceptive indicators. Gaps in deception were also noted, and there was common repetition found in both the deceptive and truthful statements. While gaps are predicted to occur in truthful statements to reflect an absence of deception, gaps occurring in the deceptive statements are likely due to cognitive load.

The current research provides more support for using statement analysis in real-world criminal cases.

The purpose of this paper is to address the complexity of missing persons cases and highlight the linguistic differences that arise in this type of crime. Missing persons cases are typically very complex investigations. Without a body, crime scene forensics is not possible, and police are often left only with witness and suspect statements. Forensic linguistics methods may help investigators to prioritise or remove suspects. There are many competing approaches in forensic linguistic analysis; however, there is limited empirical research available on emerging methods.

This research investigates Statement Analysis, a recent development in linguistic analysis that has practical applications in criminal investigations. Real-world statements of individuals convicted of or found to be not guilty of their involvement in missing persons cases were used in the analyses. In addition, Behaviour Sequence Analysis was used to map the progressions of language in the suspects' statements.

Results indicated differences between the guilty and innocent individuals based on their language choices, for example, guilty suspects in missing [alive] cases were found more likely to use passive language and vague words because of high levels of cognitive load associated with the several types of guilty knowledge suspects in missing persons cases possess. Of particular interest is the use of untruthful words in the innocent suspects’ statements in missing [murdered] cases. While typically seen in deceptive statements, untruthful words in innocent statements may result because of false acquittals.

This research provides some support for Statement Analysis as a suitable approach to analysing linguistic statements in missing persons cases.

Aggressive behaviour is a problem for services providing care for people with intellectual disabilities, affecting the quality of life of the individual and the quality of care provided. Current research trends, which focus on risk factors and mental health problems, are discussed. Other factors that could contribute to aggression in people with intellectual disability (PWID), such as lifestyle and environmental issues are examined. A methodology that would allow for the integration of all these factors, Behavioural Sequence Analysis, is a suitable investigative approach to this problem.

When investigating missing persons cases, witness and suspect statements are often the only available evidence to investigators. As such, forensic linguistics can be useful to police when seeking information from individuals during investigative interviews. The presumption of innocence must be maintained in an investigation, and therefore this study aims to use a method focused on veracity detection, rather than the majority of studies using deception detection approaches.

The current research uses criteria-based content analysis (CBCA), a method that has been applied to a variety of criminal cases. Real-world statements of individuals convicted or found innocent of their involvement in missing persons cases were used in the analyses. Additionally, behaviour sequence analysis (BSA) was used to map language patterns within individuals’ statements.

Results indicated that two individual markers occurred at a high frequency across all four groups (the guilty and innocent statements of both case types); however, differences were noted in the sequences based on an individual’s ability to provide experiential details.

The current research contributes to the growing literature that aims to test CBCA in adult samples across crimes that do not pertain to sexual abuse, in addition to aiding researchers and practitioners to understand better the linguistic differences that occur in missing persons cases.

To the best of the authors’ knowledge, this type of research, using CBCA to assist with missing person's cases with a temporal method (BSA), has never before been tested. BSA has previously been used in forensic linguistics, and shown support for the method. The current research builds on this in terms of missing persons cases.

This study explores the factors that promote university teachers' switching intention from a traditional classroom to a smart classroom based on the push–pull–mooring (PPM) framework to enrich the theoretical research on the smart classroom and provide a reference for smart classroom promotion.

The proposed conceptual framework was developed from a comprehensive review of the related literature. This study tested and validated the proposed framework using a partial least square structural equation model based on 269 valid questionnaires.

(1) Perceived inefficiency, inquiry-based learning, future expectation and technical self-efficacy had significant effects on switching intention, while low participation, perceived usefulness and habit had no significant effects on university teachers’ switching intention in the smart classroom. (2) In the process of decision-making, the course category significantly moderates the impact of perceived inefficiency and technical self-efficacy on switching intention, while the user experience of smart classrooms significantly moderates the impact of perceived inefficiency on switching intention.

This study explains university teachers' switching intention from a traditional classroom to the smart classroom, which enriches the application area of the PPM framework.

The purpose of this paper is to study the modelling mechanisms of several grey incidence analysis models with great influence, including Deng’s grey incidence model, absolute degree of grey incidence model, slope degree of incidence model, similitude degree of incidence model and closeness degree of incidence model; then analyse the problems to be solved in grey incidence analysis models; and clarify the applicable ranges of commonly used grey incidence models.

The paper comes to conclusions by means of comparable analysis. The authors compare several commonly used grey incidence analysis models, including Deng’s grey incidence model, absolute degree of grey incidence model, slope degree of incidence model, similitude degree of incidence model and closeness degree of incidence model and give several examples to clarify the reasons why quantitative analysis results of different models are not exactly the same.

As the intension of each kind of incidence model is clear and the extension is relatively obscure, grey incidence orders calculated by different incidence models are often different. When making actual decisions, incompatible results may appear. According to different characteristics of extraction, grey incidence analysis models can be divided into three types: incidence model based on closeness perspective, incidence model based on similarity perspective and incidence model based on comprehensive perspective.

The conclusions obtained in this paper can help people avoid some defects in the process of actual selection and choose the better incidence analysis model.

The conclusions can be used as a reference and basis for the selection of grey incidence analysis models, it can help to overcome the defects and shortcomings of models caused by themselves and screen out more excellent analytical models.