Efficient duration and hierarchical modeling for human activity recognition

A challenge in building pervasive and smart spaces is to learn and recognize human activities of daily living (ADLs). In this paper, we address this problem and argue that in dealing with ADLs, it is beneficial to exploit both their typical duration patterns and inherent hierarchical structures. We exploit efficient duration modeling using the novel Coxian distribution to form the Coxian hidden semi-Markov model (CxHSMM) and apply it to the problem of learning and recognizing ADLs with complex temporal dependencies. The Coxian duration model has several advantages over existing duration parameterization using multinomial or exponential family distributions, including its denseness in the space of nonnegative distributions, low number of parameters, computational efficiency and the existence of closed-form estimation solutions. Further we combine both hierarchical and duration extensions of the hidden Markov model (HMM) to form the novel switching hidden semi-Markov model (SHSMM), and empirically compare its performance with existing models. The model can learn what an occupant normally does during the day from unsegmented training data and then perform online activity classification, segmentation and abnormality detection. Experimental results show that Coxian modeling outperforms a range of baseline models for the task of activity segmentation. We also achieve a recognition accuracy competitive to the current state-of-the-art multinomial duration model, while gaining a significant reduction in computation. Furthermore, cross-validation model selection on the number of phases K in the Coxian indicates that only a small K is required to achieve the optimal performance. Finally, our models are further tested in a more challenging setting in which the tracking is often lost and the activities considerably overlap. With a small amount of labels supplied during training in a partially supervised learning mode, our models are again able to deliver reliable performance, again with a small number of phases, making our proposed framework an attractive choice for activity modeling.     

Fabricating Strong and Stiff Bioplastics from Whole Spirulina Cells

Since the 1950s, 8.3 billion tonnes (Bt) of virgin plastics have been produced, of which around 5 Bt have accumulated as waste in oceans and other natural environments, posing severe threats to entire ecosystems. The need for sustainable bio-based alternatives to traditional petroleum-derived plastics is evident. Bioplastics produced from unprocessed biological materials have thus far suffered from heterogeneous and non-cohesive morphologies, which lead to weak mechanical properties and lack of processability, hindering their industrial integration. Here, a fast, simple, and scalable process is presented to transform raw microalgae into a self-bonded, recyclable, and backyard-compostable bioplastic with attractive mechanical properties surpassing those of other biobased plastics such as thermoplastic starch. Upon hot-pressing, the abundant and photosynthetic algae spirulina forms cohesive bioplastics with flexural modulus and strength in the range 3–5 GPa and 25.5–57 MPa, respectively, depending on pre-processing conditions and the addition of nanofillers. The machinability of these bioplastics, along with self-extinguishing properties, make them promising candidates for consumer plastics. Mechanical recycling and fast biodegradation in soil are demonstrated as end-of-life options. Finally, the environmental impacts are discussed in terms of global warming potential, highlighting the benefits of using a carbon-negative feedstock such as spirulina to fabricate plastics.     

Extending the two‐stage information systems continuance model: incorporating UTAUT predictors and the role of context

This study presents two extensions to the two‐stage expectation‐confirmation theory of information systems (IS) continuance. First, we expand the belief set from perceived usefulness in the original IS continuance model to include three additional predictors identified in the unified theory of acceptance and use of technology, namely effort expectancy, social influence and facilitating conditions. Second, we ground the IS continuance model in the context of transactional systems that involve transmission of personal and sensitive information and include trust as a key contextual belief in the model. To test the expanded IS continuance model, we conducted a longitudinal field study of 3159 Hong Kong citizens across two electronic government (e‐government) technologies that enable citizens' access to government services. In general, the results support the expanded model that provides a rich understanding of the changes in the pre‐usage beliefs and attitudes through the emergent constructs of disconfirmation and satisfaction, ultimately influencing IS continuance intention. Finally, we discuss the theoretical and practical implications of the expanded model.     

Fire Endurance of High Strength Concrete Columns

In buildings, fire represents one of the most severe environmental conditions, and therefore, should be properly accounted for in the design of high strength concrete (HSC) structural members. The increased use of HSC in buildings has raised concerns regarding the behavior of such concrete in fire. In particular, spalling at elevated temperatures, as identified in studies by a number of laboratories, is a main concern.In this paper, results from an experimental program on the fire resistance of HSC columns are presented. The factors that influence the thermal and structural behavior of HSC concrete columns under fire conditions are discussed. Data from this study indicate that the type of aggregate, concrete strength, load intensity, and detailing and spacing of ties have an influence on the fire resistance performance of HSC columns. Further, the test results show that tie configuration (bending of ties at 135°, ties and provision of cross ties) and closer tie spacing has a significantly beneficial effect on the fire resistance of HSC columns. The results presented will generate data on the fire resistance of HSC columns, and contribute to identifying the factors that influence the behavior of HSC columns.     

Spatial Indexing for Video Databases

Spatial reasoning techniques based on 2D string systems have been successfully applied to indexing pictorial databases. We demonstrate how similar techniques may be extended to represent video sequences, and how this representation may be compressed. Query schemes for both single frame queries, and full subsequence queries are presented. The strength of these query schemes is that the query process does not require the expansion of the compressed notation during query evaluation.     

Modulation of stability properties of bovine trypsin after in vitro structural changes with a variety of chemical modifiers

Controlled chemical modification of enzymes, targeting groups not involvedin the active site, can lead to modified catalysts that are intrinsicallymore efficient and resistant to heat and denaturing agents. Bovinepancreatic trypsin was covalently modified up to 75-85% with monomericglutaraldehyde (MGA), polymeric glutaraldehyde (PGA), oxidized sucrose andoxidized sucrose polymers (OSP 70 and OSP 400). Virtually no loss inactivity occurred upon modification. Temperature optima of trypsin shiftsfrom 45-76 degrees C and T50 from 54-76 degrees C for the best modifiedsample made with OSP. The efficiency of the modifiers in stabilization wasranked in the order: OSP 400-T > OSP 70-T > PGA-T > MGA-T >Sucrose-T. Half-life of modified enzymes also followed the same trend. Bothstabilization factor and t1/2 decreased with increasing temperatures. Thefree energy of activation for inactivation delta(deltaG*) varies from 12-20kJ/mol and the activation enthalpy delta(deltaH*) of the modified trypsinby 80-120 kJ/mol indicating stabilization. Inactivation of modified trypsinby urea is less noticeable. The character of the two-step inactivationprocess of trypsin changes with the degree of stabilization in that theduration of phase I one increased noticeably as stabilization increases.Native trypsin fluoresces less intensely showing a red shift under theinfluence of denaturation. Such a fluorescence change is not so obvious forthe modified enzymes indicating conformational stability acquired bymodification.     

Mood sensing from social media texts and its applications

We present a large-scale mood analysis in social media texts. We organise the paper in three parts: (1) addressing the problem of feature selection and classification of mood in blogosphere, (2) we extract global mood patterns at different level of aggregation from a large-scale data set of approximately 18 millions documents (3) and finally, we extract mood trajectory for an egocentric user and study how it can be used to detect subtle emotion signals in a user-centric manner, supporting discovery of hyper-groups of communities based on sentiment information. For mood classification, two feature sets proposed in psychology are used, showing that these features are efficient, do not require a training phase and yield classification results comparable to state of the art, supervised feature selection schemes; on mood patterns, empirical results for mood organisation in the blogosphere are provided, analogous to the structure of human emotion proposed independently in the psychology literature; and on community structure discovery, sentiment-based approach can yield useful insights into community formation.     

Selecting Agricultural Best Management Practices for Water Conservation and Quality Improvements Using Atanassov’s Intuitionistic Fuzzy Sets

Improper agricultural practices can affect ground water through leaching, surface water through runoff, algae infestations, deforestation, and air quality through burning operations and ammonia emissions. These effects may be mitigated through the institution of best management practices. The utility of best management practices (BMPs) is recognized and being actively promoted by agricultural agencies; however, identifying a set of mandatory BMPs is inappropriate given variations between climactic, demographic and geographic regions as well as differences in farming practices. In this study, a multi-criteria decision making model based on Attanassov’s Intuitionistic Fuzzy Set (A-IFS) theory is introduced and its utility to rank agricultural best management practices is illustrated using a case-study from South Texas. Implementation of the A-IFS MCDM method to the South Texas region resulted in “irrigation scheduling” being ranked as the most preferred alternative, while “brush control/management” was the least preferred. The A-IFS MCDM approach was particularly suitable for prioritizing and ranking agricultural best management practices because decision makers often tend to have both likes and dislikes with regards to specific BMPs and for a given evaluation attribute. Not only does the A-IFS MCDM method provide a single composite score to rank the BMP alternatives, but the output of the A-IFS MCDM method also includes upper and lower bounds that help identify the uncertainties in the decision making process.