Person-specific Information Research Articles

Poverty and Intellectual Development in Childhood.

Objectives and ApproachThe effects of the timing and duration of economic deprivation and maternal mental health on intellectual development is researched using unique linkable databases following almost 90 thousand children born in the Canadian province of Manitoba. This paper studies such questions as: How important are differences in measurement of poverty in assessing intellectual development? How do various differences change over childhood? ResultsConcentrating on those born in 2000-2002 generated a total of 7,424 children having scores on all six measures of intellectual development. Such person-specific information controlled for many individual factors and extended from ages 5 through 17. Major differences were found in the scores associated with exposure to the two kinds of poverty and to poor maternal mental health. In summary, differences emerge by age 5, with administration of the Early Development Index. These differences are largely the same at age 8, even though another measure (the Grade 3 Competencies Index) is used. ConclusionsIf household poverty is taken as a definition of poverty, poverty seems much more important than maternal mental health in affecting a child’s intellectual development. However, if neighborhood poverty is used to define poverty, maternal mental health and poverty have effects on childhood intellectual development which are more similar. Definition plays a critical role in interpretation. ImplicationsThese population-based data, with multiple measures and multiple time points, suggest many analytical possibilities. If childhood conditions are included, the list of medical conditions which might correlate with intellectual development multiplies.

Quantifying the Effects of Anonymization Techniques Over Micro-Databases

Micro-databases are unique datasets that contain person-specific information about individuals. Preserving the privacy of such datasets has become a cause for serious concern since this massive repository of personalized data regularly gets published in the public domain. Sanitization mechanisms are specialized techniques that provide the required privacy guarantees to the published data. The work in this article establishes an efficient framework for quantitatively estimating the effectiveness of any privacy-preservation scheme which employs the anonymization principle. In our study, we have introduced an information-theoretic metric termed as <i>Sanitization Degree</i> <inline-formula><tex-math notation="LaTeX">$(\eta)$</tex-math></inline-formula> which assigns a cumulative score in the range [0,1] for a generic anonymization process. The design of our proposed metric is based on the fundamental fact that any sanitization mechanism attempts to reduce the amount of correlated information within the database attributes while simultaneously preserving the utility of the original dataset. Furthermore, we have characterized the <i>privacy-utility</i> tradeoff associated with our model by establishing a working relationship between these two fundamental quantities. We have empirically computed the value of <inline-formula><tex-math notation="LaTeX">$\eta$</tex-math></inline-formula> for three popular anonymization models ( <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula> -anonymity, <inline-formula><tex-math notation="LaTeX">$l$</tex-math></inline-formula> -diversity, and <inline-formula><tex-math notation="LaTeX">$t$</tex-math></inline-formula> -closeness) over a couple of real-life micro-databases, thereby demonstrating the practicability of our study.

Authentic volumetric avatars from a phone scan

Creating photorealistic avatars of existing people currently requires extensive person-specific data capture, which is usually only accessible to the VFX industry and not the general public. Our work aims to address this drawback by relying only on a short mobile phone capture to obtain a drivable 3D head avatar that matches a person's likeness faithfully. In contrast to existing approaches, our architecture avoids the complex task of directly modeling the entire manifold of human appearance, aiming instead to generate an avatar model that can be specialized to novel identities using only small amounts of data. The model dispenses with low-dimensional latent spaces that are commonly employed for hallucinating novel identities, and instead, uses a conditional representation that can extract person-specific information at multiple scales from a high resolution registered neutral phone scan. We achieve high quality results through the use of a novel universal avatar prior that has been trained on high resolution multi-view video captures of facial performances of hundreds of human subjects. By fine-tuning the model using inverse rendering we achieve increased realism and personalize its range of motion. The output of our approach is not only a high-fidelity 3D head avatar that matches the person's facial shape and appearance, but one that can also be driven using a jointly discovered shared global expression space with disentangled controls for gaze direction. Via a series of experiments we demonstrate that our avatars are faithful representations of the subject's likeness. Compared to other state-of-the-art methods for lightweight avatar creation, our approach exhibits superior visual quality and animateability.

How does the delivery of paid home care compare to the care plan for clients living with dementia?

Many people living with dementia choose to remain in their own homes, supported by home‐care workers, who provide care that is specified in care plans. We explored how care plans of clients living with dementia, compared with ethnographic observations of home care they received. In a secondary, reflexive thematic analysis, we reviewed care plans for 17 clients living with dementia and transcripts from 100 h of observations with 16 home‐care workers delivering care to them. Our overarching theme was: Care plans as a starting point but incomplete repository. Clients’ care plans provided useful background information but did not reflect a wealth of knowledge home‐care workers built through practice. Two sub‐themes described: (a) Person‐centred care planning: whether and how the care plan supported tailoring of care to clients’ needs and (b) Filling in the gaps: home‐care workers often worked beyond the scope of vague, incomplete or out‐of‐date care plans. We found considerable inconsistencies between care plans and the care that was delivered. Care plans that were comprehensive about care needs, and rich in person‐specific information aided the delivery of person‐centred care. Lack of documentation was sometimes associated with observed failures in person‐centred care, as helpful information and strategies were not shared. Including information in care plans about how, as well as what care tasks, should be completed, and frequently discussing and updating care plans can create more person‐centred plans that reflect changing needs. Electronic care planning systems may support this.

Capturing rich person-centred discharge information: exploring the challenges in developing a new model

PurposeCapture, consumption and use of person-centred information presents challenges for hospitals when operating within the scope of limited resources and the push for organisational routines and efficiencies. This paper explores these challenges for patients with Acute Coronary Syndrome (ACS) and the examination of information that supports successful hospital discharge. It aims to determine how the likelihood of readmission may be prevented through the capturing of rich, person-specific information during in-patient care to improve the process for discharge to home.Design/methodology/approachThe authors combine four research data collection and analysis techniques: one, an analysis of the patient record; two, semi-structured longitudinal interviews; three, an analysis of the patient's journey using process mining to provide analytics about the discharge process, and four, a focus group with nurses to validate and confirm our findings.FindingsThe authors’ contribution is to show that information systems which support discharge need to consider models focused on individual patient stressors. The authors find that current discharge information capture does not provide the required person-centred information to support a successful discharge. Data indicate that rich, detailed information about the person acquired through additional nursing assessments are required to complement data provided about the patient's journey in order to support the patients’ post-discharge recovery at home.Originality/valuePrior research has focused on information collection constrained by pre-determined limitations and barriers of system design. This work has not considered the information provided by multiple sources during the whole patient journey as a mechanism to reshape the discharge process to become more person-centred. Using a novel combination of research techniques and theory, the authors have shown that patient information collected through multiple channels across the patient care journey may significantly extend the quality of patient care beyond hospital discharge. Although not assessed in this study, rich, person-centred discharge information may also decrease the likelihood of patient readmission.

Depth as Attention for Face Representation Learning

Face representation learning solutions have recently achieved great success for various applications such as verification and identification. However, face recognition approaches that are based purely on RGB images rely solely on intensity information, and therefore are more sensitive to facial variations, notably pose, occlusions, and environmental changes such as illumination and background. A novel depth-guided attention mechanism is proposed for deep multi-modal face recognition using low-cost RGB-D sensors. Our novel attention mechanism directs the deep network "where to look" for visual features in the RGB image by focusing the attention of the network using depth features extracted by a Convolution Neural Network (CNN). The depth features help the network focus on regions of the face in the RGB image that contains more prominent person-specific information. Our attention mechanism then uses this correlation to generate an attention map for RGB images from the depth features extracted by CNN. We test our network on four public datasets, showing that the features obtained by our proposed solution yield better results on the Lock3DFace, CurtinFaces, IIIT-D RGB-D, and KaspAROV datasets which include challenging variations in pose, occlusion, illumination, expression, and time-lapse. Our solution achieves average (increased) accuracies of 87.3\% (+5.0\%), 99.1\% (+0.9\%), 99.7\% (+0.6\%) and 95.3\%(+0.5\%) for the four datasets respectively, thereby improving the state-of-the-art. We also perform additional experiments with thermal images, instead of depth images, showing the high generalization ability of our solution when adopting other modalities for guiding the attention mechanism instead of depth information

Enabling Secure Trustworthiness Assessment and Privacy Protection in Integrating Data for Trading Person-Specific Information

With increasing adoption of cloud services in the e-market, collaboration between stakeholders is easier than ever. Consumer stakeholders demand data from various sources to analyze trends and improve customer services. Data-as-a-service enables data integration to serve the demands of data consumers. However, the data must be of good quality and trustful for accurate analysis and effective decision making. In addition, a data custodian or provider must conform to privacy policies to avoid potential penalties for privacy breaches. To address these challenges, we propose a twofold solution: 1) we present the first information entropy-based trust computation algorithm, IEB_Trust, that allows a semitrusted arbitrator to detect the covert behavior of a dishonest data provider and chooses the qualified providers for a data mashup and 2) we incorporate the Vickrey-Clarke-Groves (VCG) auction mechanism for the valuation of data providers' attributes into the data mashup process. Experiments on real-life data demonstrate the robustness of our approach in restricting dishonest providers from participation in the data mashup and improving the efficiency in comparison to provenance-based approaches. Furthermore, we derive the monetary shares for the chosen providers from their information utility and trust scores over the differentially private release of the integrated dataset under their joint privacy requirements.

Personalized-medicine on carotid endarterectomy and stenting.

Evidence based medicine (EBM) is the core of current clinical guidelines and is considered as the gold standard of clinical practice. Despite this, a number of limitations and criticisms are moved to EBM. The major one is that this method privileges randomized controlled trials (RCTs), in which the selection of patients is often based on rigid inclusion criteria. The lack of “pragmatism” of some RCTs sometimes makes it difficult to apply guidelines that derive from them to patients observed in clinical practice, who are often affected by comorbidities and disabilities. The new paradigm to overcome this limitation is personalized medicine (PM), which aims to take into account the particular characteristics displayed by the individual. In order to tailor the best treatment for the patient, PM uses EBM but emphasizes the person's specific information from the assessment of the clinic, lifestyle and risk/benefit scores. This narrative review tries to find the best evidence by analysing subgroups and risk scores of patients from meta-analysis and RCTs in order to try to apply PM and to provide good practice points (GPP) on grey aspects and open questions not fully covered by current guidelines on carotid endarterectomy (CEA) and stenting for stroke prevention.

Culture as a Sensor? A Novel Perspective on Human Activity Recognition

Human Activity Recognition (HAR) systems are devoted to identifying, amidst the sensory stream provided by one or more sensors located so that they can monitor the actions of a person, portions related to the execution of a number of a-priori defined activities of interest. Improving the performance of systems for Human Activity Recognition is a long-standing research goal: solutions include more accurate sensors, more sophisticated algorithms for the extraction and analysis of relevant information from the sensory data, and the enhancement of the sensory analysis with general or person-specific knowledge about the execution of the activities of interest. Following the latter trend, in this article we propose the association and enhancement of the sensory data analysis with cultural information, that can be seen as an estimate of person-specific information, relieved of the burden of a long/complex setup phase. We propose a culture-aware Human Activity Recognition system which associates the recognition response provided by a state-of-the-art, culture-unaware HAR system with culture-specific information about where and when activities are most likely performed in different cultures, encoded in an ontology. The merging of the cultural information with the culture-unaware responses is done by a Bayesian Network, whose probabilistic approach allows for avoiding stereotypical representations. Experiments performed offline and online, using images acquired by a mobile robot in an apartment, show that the culture-aware HAR system consistently outperforms the culture-unaware HAR system.

Perceptual and Semantic Phases of Face Identification Processing: A Multivariate Electroencephalography Study.

Rapid identification of a familiar face requires an image-invariant representation of person identity. A varying sample of familiar faces is necessary to disentangle image-level from person-level processing. We investigated the time course of face identity processing using a multivariate electroencephalography analysis. Participants saw ambient exemplars of celebrity faces that differed in pose, lighting, hairstyle, and so forth. A name prime preceded a face on half of the trials to preactivate person-specific information, whereas a neutral prime was used on the remaining half. This manipulation helped dissociate perceptual- and semantic-based identification. Two time intervals within the post-face onset electroencephalography epoch were sensitive to person identity. The early perceptual phase spanned 110-228 msec and was not modulated by the name prime. The late semantic phase spanned 252-1000 msec and was sensitive to person knowledge activated by the name prime. Within this late phase, the identity response occurred earlier in time (300-600 msec) for the name prime with a scalp topography similar to the FN400 ERP. This may reflect a matching of the person primed in memory with the face on the screen. Following a neutral prime, the identity response occurred later in time (500-800 msec) with a scalp topography similar to the P600f ERP. This may reflect activation of semantic knowledge associated with the identity. Our results suggest that processing of identity begins early (110 msec), with some tolerance to image-level variations, and then progresses in stages sensitive to perceptual and then to semantic features.

Knowledge Representation for Culturally Competent Personal Robots: Requirements, Design Principles, Implementation, and Assessment

Culture, intended as the set of beliefs, values, ideas, language, norms and customs which compose a person’s life, is an essential element to know by any robot for personal assistance. Culture, intended as that person’s background, can be an invaluable source of information to drive and speed up the process of discovering and adapting to the person’s habits, preferences and needs. This article discusses the requirements posed by cultural competence on the knowledge management system of a robot. We propose a framework for cultural knowledge representation that relies on (i) a three-layer ontology for storing concepts of relevance, culture-specific information and statistics, person-specific information and preferences; (ii) an algorithm for the acquisition of person-specific knowledge, which uses culture-specific knowledge to drive the search; (iii) a Bayesian Network for speeding up the adaptation to the person by propagating the effects of acquiring one specific information onto interconnected concepts. We have conducted a preliminary evaluation of the framework involving 159 Italian and German volunteers and considering 122 among habits, attitudes and social norms.

What maximizes the effectiveness and implementation of technology-based interventions to support healthcare professional practice? A systematic literature review

BackgroundTechnological support may be crucial in optimizing healthcare professional practice and improving patient outcomes. A focus on electronic health records has left other technological supports relatively neglected. Additionally, there has been no comparison between different types of technology-based interventions, and the importance of delivery setting on the implementation of technology-based interventions to change professional practice. Consequently, there is a need to synthesise and examine intervention characteristics using a methodology suited to identifying important features of effective interventions, and the barriers and facilitators to implementation. Three aims were addressed: to identify interventions with a technological component that are successful at changing professional practice, to determine if and how such interventions are theory-based, and to examine barriers and facilitators to successful implementation.MethodsA literature review informed by realist review methods was conducted involving a systematic search of studies reporting either: (1) behavior change interventions that included technology to support professional practice change; or (2) barriers and facilitators to implementation of technological interventions. Extracted data was quantitative and qualitative, and included setting, target professionals, and use of Behaviour Change Techniques (BCTs). The primary outcome was a change in professional practice. A thematic analysis was conducted on studies reporting barriers and facilitators of implementation.ResultsSixty-nine studies met the inclusion criteria; 48 (27 randomized controlled trials) reported behavior change interventions and 21 reported practicalities of implementation. The most successful technological intervention was decision support providing healthcare professionals with knowledge and/or person-specific information to assist with patient management. Successful technologies were more likely to operationalise BCTs, particularly “instruction on how to perform the behavior”. Facilitators of implementation included aligning studies with organisational initiatives, ensuring senior peer endorsement, and integration into clinical workload. Barriers included organisational challenges, and design, content and technical issues of technology-based interventions.ConclusionsTechnological interventions must focus on providing decision support for clinical practice using recognized behavior change techniques. Interventions must consider organizational context, clinical workload, and have clearly defined benefits for improving practice and patient outcomes.

An improved privacy preservation technique in health-cloud

Abstract In today’s cloud computing environment, health-cloud preserves the person specific sensitive information for several purposes such as bio-medical research, health insurance companies, medical data analysis, etc. When any authorized person accesses these clouds, the released data should not compromise any individuals’ privacy and it remains useful as well. In the health-cloud system, the data must be released in such a way that any individuals’ identity cannot be revealed. The database management system alone cannot ensure any individual’s privacy. The Access Control (AC) models are also not able to protect the data from indirect access or multiple queries. To remove such issues inference control is one of the techniques which ensures the data confidentiality from indirect data access. In this paper, we have proposed a hybrid technique which includes two different inference control techniques, query set size restriction and k-anonymity to ensure individuals’ privacy. A query set size restriction is used to prevent the sensitive data from inference attacks, whereas k-anonymity is implemented to protect the data from linking attacks. Both these techniques reach a certain privacy level with satisfactory data utilization. We have also generated a rule set to increase the privacy of healthcare data.

The Effects of Sampling Frame Designs on Nonresponse and Coverage Error: Evidence from the Netherlands

Abstract Survey researchers sometimes face several options to formally define and draw random probability samples from their target population of individuals. Relatively little is known about the consequences for the quality of survey estimates when different sampling frames are used, for example frames that list addresses versus individuals While the initial choice for a sampling frame design may be based on diverse criteria, we argue that any decision has consequences for the quality of survey estimates. We hypothesize that knowing respondents’ names upfront decreases nonresponse and coverage error. This can be accomplished by either using person-based sampling frames or augmenting nonperson-based sampling frames with names. We systematically compare sampling frame designs in the context of face-to-face surveys in connection with the European Social Survey (ESS) with the help of three quasi-experimental datasets from a single country. Even the most conservative measures support our hypothesis that the presence of names in the sampling frames could improve response rates, noncontact rates, cooperation rates, and ineligibility rates by between 2.5 and 6 percentage points. Additionally, the accuracy of population estimates could increase. These results suggest that survey researchers collecting individual-level data would be best advised to use well-maintained sampling frame designs (augmented) with person-specific information.

Personalized Saliency and Its Prediction.

Nearly all existing visual saliency models by far have focused on predicting a universal saliency map across all observers. Yet psychology studies suggest that visual attention of different observers can vary significantly under specific circumstances, especially a scene is composed of multiple salient objects. To study such heterogenous visual attention pattern across observers, we first construct a personalized saliency dataset and explore correlations between visual attention, personal preferences, and image contents. Specifically, we propose to decompose a personalized saliency map (referred to as PSM) into a universal saliency map (referred to as USM) predictable by existing saliency detection models and a new discrepancy map across users that characterizes personalized saliency. We then present two solutions towards predicting such discrepancy maps, i.e., a multi-task convolutional neural network (CNN) framework and an extended CNN with Person-specific Information Encoded Filters (CNN-PIEF). Extensive experimental results demonstrate the effectiveness of our models for PSM prediction as well their generalization capability for unseen observers.

Truth disclosure on prognosis: Is it ethical not to communicate personalised risk of death?

Predicting risk of death based on personalised and objective clinical indicators is an improvement over intuition and clinical judgement. Risk assessment can benefit clinicians by improving prognostic certainty, and truth disclosure helps patients and families by preventing futile management. Some argue that consent should be obtained before a patient is given an estimate of their prognosis as disclosure of bad news can overburden patients. In this article, we argue that it is unethical not to use existing person-specific information to guide diagnosis and shared decision making on management in partnership with well-informed patients. Disclosure of a poor prognosis should be normalised in personalised medicine, performed incrementally and with sensitivity so that it is acceptable to patients, and only occur if patients want to know it. However, a requirement of consent for truth disclosure should not be mandatory. Despite some level of imprecision, personalised risk estimations can be used to tailor management to the patient's informed wishes and ensure that healthcare providers and families are acting ethically in the patient's best interest.

K-Anonymity Versus L-Diversity: A Comparative Analysis on Data Anonymization Techniques

The main aim of data publishing is to make the data utilized by the researchers, scientists and data analysts to process the data by analytics and statistics which in turn useful for decision making. This data in its original form may contain some person-specific information, which should not be disclosed while publishing the data. So, privacy of such individuals should be preserved. Hence, privacy preserving data publishing plays a major role in providing privacy for person-specific data. The data should be published in such a way that, there should not be any technical way for adversary to infer the information of specific individuals. This paper provides overview on popular privacy preserving techniques. In this study, a honest effort shows that, concepts behind these techniques are analyzed and justified with suitable examples, drawbacks and vulnerability of these techniques towards privacy attacks are narrated.

Item consistency in retrieving person-specific semantic information from faces and voices: An exploratory study in healthy subjects

ABSTRACTControversies exist over the format of person-specific semantic representations in healthy subjects and the loss of part of these representations in conditions of brain pathology. Some authors have suggested that in brain-damaged patients item-specific consistency of errors through different recognition modalities might indicate a loss of person-specific information. This view is hindered by the fact that item consistency might also be variable in healthy subjects, because names, faces and voices have different degrees of effectiveness in the recognition of familiar people. The aim of the present research was to try to clarify this issue by assessing the value of consistency of semantic retrieval in a large sample of Italian healthy subjects who had judged as familiar the faces and voices of 40 Italian celebrities. The effect of fame level on item consistency was also evaluated. Results showed that the degree of item consistency between faces and voices was rather variable, for example it was influenced by fame level when the latter was based on an integrated (non-verbal) face/voice familiarity score, but not when it was based on the celebrities’ names in a (verbal) questionnaire. The consistency between non-verbal assessment of fame and item consistency in retrieving information from faces and voices suggests a different specialization of the left anterior temporal lobe (ATL) for the verbally coded aspects of personal semantics and of the right ATL for the visual (face) and auditory (voice) aspects of person representation.

Applying multi-label and multi-class classification to enhance K-anonymity in sequential releases

Privacy-preserving data mining is gaining prominence due to increased accumulation of data containing personal information. Data holders in healthcare, finance and other sectors collecting person-specific information are challenged to publish useful data, while meeting ever-increasing demands of privacy protection for data subjects. K-anonymity is a popular technique used to preserve data privacy for data publishing by anonymizing quasi identifiers (QI) (e.g., race, gender, age). However, K-anonymized data can be at risk of temporal attacks that target multiple versions of released data, also called sequential releases. The objective of this study is to develop a model that uses multi-class and multi-label classifiers to evaluate risk in re-identifying QI information in previous data releases through learning from current data release. In our empirical study, we use five healthcare and financial data sets to compare performance of binary relationship and label powerset problem transformations and Naive Bayes, C4.5, random tree and kNN learning algorithms. Our empirical results show that multi-label classification is a powerful tool in enhancing K-anonymity of sequential data release. Statistical analysis of the classification results shows that RAkEL outperforms other transformation methods in predicting demographics information, hence, can be useful in assessing risks of QI re-identification.

IPV: A system for identifying privacy vulnerabilities in datasets

The automated discovery of privacy vulnerabilities in large datasets containing person-specific information is an important first step in the privacy-preserving data publishing process and an area of increased interest for commercial data masking offerings. In this paper, we describe Identification of Privacy Vulnerabilities (IPV), a scalable system for automatically analyzing datasets to expose privacy vulnerabilities. IPV provides data owners with a wealth of methods for analyzing their data by offering state-of-the-art algorithms for 1) computing the direct identifiers and the quasi-identifiers of a dataset, as the single attributes and the minimal combinations of attributes, respectively, that lead to few records; 2) calculating the vulnerability index associated with a dataset, by reporting the cardinality of the smallest group of records that share the same values for each combination of attributes; and 3) reporting the specific records in a dataset that contain a combination of unique or rare values. All of these algorithms operate in a parallel, massively multi-threaded fashion and support various hardware configurations, spanning from commodity machines to multi-CPU multi-core nodes in cluster environments. After describing the system, we discuss the algorithms that are currently supported by IPV and provide some examples of their workings. We conclude this paper with a discussion on promising directions for future research in this area that will lead to the improvement of IPV.