A Cost-Quality Beneficial Cell Selection Approach for Sparse Mobile Crowdsensing with Diverse Sensing Costs

The Internet of Things (IoT) and mobile techniques enable real-time sensing for urban computing systems. By recruiting only a small number of users to sense data from selected subareas (namely cells), Sparse Mobile Crowdsensing (MCS) emerges as an effective paradigm to reduce sensing costs for monitoring the overall status of a large-scale area. The current Sparse MCS solutions reduce the sensing subareas (by selecting the most informative cells) based on the assumption that each sample has the same cost, which is not always realistic in real-world, as the cost of sensing in a subarea can be diverse due to many factors, e.g. condition of the device, location, and routing distance. To address this issue, we proposed a new cell selection approach consisting of three steps (information modeling, cost estimation, and cost-quality beneficial cell selection) to further reduce the total costs and improve the task quality. Specifically, we discussed the properties of the optimization goals and modeled the cell selection problem as a solvable bi-objective optimization problem under certain assumptions and approximation. Then, we presented two selection strategies, i.e. Pareto Optimization Selection (POS) and Generalized Cost-Benefit Greedy (GCB-GREEDY) Selection along with our proposed cell selection algorithm. Finally, the superiority of our cell selection approach is assessed through four real-life urban monitoring datasets (Parking, Flow, Traffic, and Humidity) and three cost maps (i.i.d with dynamic cost map, monotonic with dynamic cost map and spatial correlated cost map). Results show that our proposed selection strategies POS and GCB-GREEDY can save up to 15.2% and 15.02% sample costs and reduce the inference errors to a maximum of 16.8% (15.5%) compared to the baseline-Query by Committee (QBC) in a sensing cycle. The findings show important implications in Sparse Mobile Crowdsensing for urban context properties

Forensic Ancestry Analysis with Autosomal Polymorphisms

The inference of ancestry from biological material left at a crimescene has been a longstanding but specialised forensic technique, often lacking sufficient detail to make a reliable inference of ancestr y. This thesis describes the key steps in developing a forensic ancestry test that can be adopted by any laboratory using capillary electrophoresis equipment: optimisation of a PCR multiplex to detect DNA markers from contact traces; compilation of population data from which to infer the likely pop ulation of origin of the person; detection of coancestry patterns in an individual with admixed backgr ounds; and development of online statistical tools that calculate the probability of an individual’s an cestry from a submitted SNP profile. Additional types of autosomal markers were compiled from Ind el polymorphisms; short tandem repeats (STRs); multiple allele SNPs; and Microhaplotype markers

Quality of Information in Mobile Crowdsensing: Survey and Research Challenges

Smartphones have become the most pervasive devices in people's lives, and are clearly transforming the way we live and perceive technology. Today's smartphones benefit from almost ubiquitous Internet connectivity and come equipped with a plethora of inexpensive yet powerful embedded sensors, such as accelerometer, gyroscope, microphone, and camera. This unique combination has enabled revolutionary applications based on the mobile crowdsensing paradigm, such as real-time road traffic monitoring, air and noise pollution, crime control, and wildlife monitoring, just to name a few. Differently from prior sensing paradigms, humans are now the primary actors of the sensing process, since they become fundamental in retrieving reliable and up-to-date information about the event being monitored. As humans may behave unreliably or maliciously, assessing and guaranteeing Quality of Information (QoI) becomes more important than ever. In this paper, we provide a new framework for defining and enforcing the QoI in mobile crowdsensing, and analyze in depth the current state-of-the-art on the topic. We also outline novel research challenges, along with possible directions of future work.Comment: To appear in ACM Transactions on Sensor Networks (TOSN

Earnings Management and Board Structure: Evidence from Nigeria

The board structure of an organization gives an overview of the standard of such organization, which also influences its public image. This study attempts to evaluate the role board structure plays in curtailing earnings management practices in Nigerian companies. This study sampled the data of 137 quoted companies in Nigeria for a period of 8 years (2003-2010). Earnings management was measured using the magnitude of the discretionary accruals as estimated by the performance matched modified Jones model. The ordinary least squares (OLS) regression technique was used to measure the research model as well as the Pearson moment correlation coefficient. The study shows that there is a significant relationship between board structure and earnings management practices in Nigeria. The study shows that there is a negative significant relationship between board size, gender, and board composition with earnings management; also, there is a positive significant relationship between board meeting and earnings management practices in Nigeria. There is a positive nonsignificant relationship between the presence of a remuneration committee and the dualization of CEO and chairman positions with earnings management practices in Nigeria. This study recommends that regulators at all levels should enforce the preparation and publication of financial reports by companies operating in Nigeria. Keywords: earnings management, board composition, board size, gender equality, board meeting

AI-Enhanced Intensive Care Unit: Revolutionizing Patient Care with Pervasive Sensing

The intensive care unit (ICU) is a specialized hospital space where critically ill patients receive intensive care and monitoring. Comprehensive monitoring is imperative in assessing patients conditions, in particular acuity, and ultimately the quality of care. However, the extent of patient monitoring in the ICU is limited due to time constraints and the workload on healthcare providers. Currently, visual assessments for acuity, including fine details such as facial expressions, posture, and mobility, are sporadically captured, or not captured at all. These manual observations are subjective to the individual, prone to documentation errors, and overburden care providers with the additional workload. Artificial Intelligence (AI) enabled systems has the potential to augment the patient visual monitoring and assessment due to their exceptional learning capabilities. Such systems require robust annotated data to train. To this end, we have developed pervasive sensing and data processing system which collects data from multiple modalities depth images, color RGB images, accelerometry, electromyography, sound pressure, and light levels in ICU for developing intelligent monitoring systems for continuous and granular acuity, delirium risk, pain, and mobility assessment. This paper presents the Intelligent Intensive Care Unit (I2CU) system architecture we developed for real-time patient monitoring and visual assessment

Gradual Certified Programming in Coq

Expressive static typing disciplines are a powerful way to achieve high-quality software. However, the adoption cost of such techniques should not be under-estimated. Just like gradual typing allows for a smooth transition from dynamically-typed to statically-typed programs, it seems desirable to support a gradual path to certified programming. We explore gradual certified programming in Coq, providing the possibility to postpone the proofs of selected properties, and to check "at runtime" whether the properties actually hold. Casts can be integrated with the implicit coercion mechanism of Coq to support implicit cast insertion a la gradual typing. Additionally, when extracting Coq functions to mainstream languages, our encoding of casts supports lifting assumed properties into runtime checks. Much to our surprise, it is not necessary to extend Coq in any way to support gradual certified programming. A simple mix of type classes and axioms makes it possible to bring gradual certified programming to Coq in a straightforward manner.Comment: DLS'15 final version, Proceedings of the ACM Dynamic Languages Symposium (DLS 2015

An investigation of customer order flow In the norwegian foreign exchange market

This thesis aimes at examining customer order flow in the Norwegian currency market (NOK/EUR). The key findings suggest heterogeneity among market participants, where non–financial customers’ order flow is the primary information source that drives price movements and foreign banks’ transaction flow provide liquidity in the market. However, the segments’ effect on price is non-permanent. Further evidence indicates that the transaction flow is complimentary to the traditional fundamentals when modeling the exchange rate. The out of sample findings indicate that order flow based models perform better than a random walk and a traditional model for statistical forecasts