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Digital voice assistants use wake word engines (WWEs) to monitor surrounding audio for detection of the voice assistant's name. There are two failed conditions for a WWE, false negative and false positive. Wake word false positives threaten a loss of personal privacy because, upon activation, the digital assistant records audio to the voice cloud service for processing.

This observational study attempted to identify which Amazon Alexa wake word and Amazon Echo smart speaker resulted in the fewest number of human voice false positives. During an eight-week period, false-positive data were collected from four different Amazon Echo smart speakers located in a small apartment with three female roommates.

Results from this study suggest the number of human voice false positives are related to wake word selection and Amazon Echo hardware. Results from this observational study determined that the wake word Alexa resulted in the fewest number of false positives.

This study suggests Amazon Alexa users can better protect their privacy by selecting Alexa as their wake word and selecting smart speakers with the highest number of microphones in the far-field array with 360-degree geometry.

The purpose of this paper is to show an efficient method for the detection of signs of early lung cancer. Various image processing algorithms are presented for different types of lesions, and a scheme is proposed for the combination of results.

A computer aided detection (CAD) scheme was developed for detection of lung cancer. It enables different lesion enhancer algorithms, sensitive to specific lesion subtypes, to be used simultaneously. Three image processing algorithms are presented for the detection of small nodules, large ones, and infiltrated areas. The outputs are merged, the false detection rate is reduced with four separated support vector machine (SVM) classifiers. The classifier input comes from a feature selection algorithm selecting from various textural and geometric features. A total of 761 images were used for testing, including the database of the Japanese Society of Radiological Technology (JSRT).

The fusion of algorithms reduced false positives on average by 0.6 per image, while the sensitivity remained 80 per cent. On the JSRT database the system managed to find 60.2 per cent of lesions at an average of 2.0 false positives per image. The effect of using different result evaluation criteria was tested and a difference as high as 4 percentage points in sensitivity was measured. The system was compared to other published methods.

The study described in the paper proves the usefulness of lesion enhancement decomposition, while proposing a scheme for the fusion of algorithms. Furthermore, a new algorithm is introduced for the detection of infiltrated areas, possible signs of lung cancer, neglected by previous solutions.

A report of research on a large, city‐wide US sample of black and white youths using official police records of juvenile delinquency status to predict adult criminality as revealed by FBI records of an adult arrest. Eight predictive criteria and six operationalised definitions of delinquency were related to three types of adult arrest record. Certain predictive criteria were found to be “best” for certain adult outcomes while others were “best” for different outcomes.

This chapter introduces a risk control framework on credit card fraud instead of providing a solely binary classifier model. The anomaly detection approach is adopted to identify fraud events as the outliers of the reconstruction error of a trained autoencoder (AE). The trained AE shows fitness and robustness on the normal transactions and heterogeneous behavior on fraud activities. The cost of false-positive normal transactions is controlled, and the loss of false-negative frauds can be evaluated by the thresholds from the percentiles of reconstruction error of trained AE on normal transactions. To align the risk assessment of the economic and financial situation, the risk manager can adjust the threshold to meet the risk control requirements. Using the 95th percentile as the threshold, the rate of wrongly detecting normal transactions is controlled at 5% and the true positive rate is 86%. For the 99th percentile threshold, the well-controlled false positive rate is around 1% and 83% for the truly detecting fraud activities. The performance of a false positive rate and the true positive rate is competitive with other supervised learning algorithms.

In the International Classification of Functioning, Disability and Health (ICF) 2001, the World Health Organization (WHO) defines disability as: ‘an umbrella term for impairments, activity limitations and participation restrictions. It denotes the negative aspects of the interaction between an individual (with a health condition) and that individual's contextual factors (environmental and personal factors)’, with environmental factors including assistance from other people, from equipment and from formal sources. WHO previously defined disability, in the context of health experience, as “any restriction or lack (resulting from impairment) of ability to perform an action in the manner or within the range considered normal for a human being” (World Health Organization, 1980, p. 28).

The purpose of this paper is to propose a self‐immune automated signature generation (SISG) for polymorphic worms which is able to work well, even while being attacked by any types of malicious adversary and produces global‐suited signatures other than local‐suited signatures for its distributed architecture. Through experimentations, the method is thereafter evaluated.

The ideal worm signature exist in each copy of the corresponding worm, but never in other worm categories and normal network traffic. SISG compares each worm copy and extract the same components, then produces the worm signature from the components which must achieve low‐false positive and low‐false negative. SISG is immune from the most attacks by filtering the harmful noise made by malicious adversaries before signature generation.

NOP sled, worm body and descriptor are not good to be signature because they can be confused intricately by polymorphic engines. Protocol frames may not suit to be signature for the anti‐automated signature generation attacks. Exploit bytes is the essential part of an ideal worm signature and it can be extracted by SISG exactly.

The paper proposes a SISG for polymorphic worms which is able to work well even while being attacked by any types of malicious adversary and produces global‐suited signatures other than local‐suited signatures for its distributed architecture.

The purpose of this paper is to improve the privacy in healthcare datasets that hold sensitive information. Putting a stop to privacy divulgence and bestowing relevant information to legitimate users are at the same time said to be of differing goals. Also, the swift evolution of big data has put forward considerable ease to all chores of life. As far as the big data era is concerned, propagation and information sharing are said to be the two main facets. Despite several research works performed on these aspects, with the incremental nature of data, the likelihood of privacy leakage is also substantially expanded through various benefits availed of big data. Hence, safeguarding data privacy in a complicated environment has become a major setback.

In this study, a method called deep restricted additive homomorphic ElGamal privacy preservation (DR-AHEPP) to preserve the privacy of data even in case of incremental data is proposed. An entropy-based differential privacy quasi identification and DR-AHEPP algorithms are designed, respectively, for obtaining privacy-preserved minimum falsified quasi-identifier set and computationally efficient privacy-preserved data.

Analysis results using Diabetes 130-US hospitals illustrate that the proposed DR-AHEPP method is more significant in preserving privacy on incremental data than existing methods. A comparative analysis of state-of-the-art works with the objective to minimize information loss, false positive rate and execution time with higher accuracy is calibrated.

The paper provides better performance using Diabetes 130-US hospitals for achieving high accuracy, low information loss and false positive rate. The result illustrates that the proposed method increases the accuracy by 4% and reduces the false positive rate and information loss by 25 and 35%, respectively, as compared to state-of-the-art works.

A quality management program was designed to eliminate false positive serological testing after administration of intravenous gamma globulin (IVIgG). The intervention and follow‐up chart review was performed in a tertiary care teaching hospital. After the index false positive test was reported, a multidisciplinary team sent memos to all affiliated physicians and devised a warning label to use in the patient charts after IVIgG administration. After the intervention, follow‐up of 28 patients who received a total of 162 doses of IVIgG revealed that five had at least one serologic test ordered which might have produced a false positive. However, chart review suggested that these serologic results did not influence patient treatment. Serological testing after IVIgG administration can generate erroneous results that can lead to costly delays in diagnosis and treatment. Process control actions can proactively foster proper laboratory ordering practices and interpretation.

This paper discusses data-collection strategies that use digitized historical newspaper archives to study social conflicts and social movements from a global and historical perspective focusing on nationalist movements. I present an analysis of State-Seeking Nationalist Movements (SSNMs) dataset I, which includes news articles reporting on state-seeking activities throughout the world from 1804 to 2013 using the New York Times and the Guardian/Observer. In discussing this new source of data and its relative value, I explain the various benefits and challenges involved with using digitized historical newspaper archives for world-historical analysis of social movements. I also introduce strategies that can be used to detect and minimize some potential sources of bias. I demonstrate the utility of the strategies introduced in this paper by assessing the reliability of the SSNM dataset I and by comparing it to alternative datasets. The analysis presented in the paper also compares the labor-intensive manual data-coding strategies to automated approaches. In doing so, it explains why labor-intensive manual coding strategies will continue to be an invaluable tool for world-historical sociologists in a world of big data.

The purpose of this paper is to create an assembly verification system that is capable of verifying complete assembly and torque for each individual fastener.

The 3D position of the tool used to torque the fastener and the assembly pallet will be tracked using an infrared (IR) tracking system. A set of retro‐reflective markers are attached to the tool and assembly while being tracked by multiple IR cameras. Software is used to triangulate the relative position of the tool in order to identify the fastener being torqued. The torque value is obtained from the tool controller device. By combining the location of the tool and the torque value from the tool controller, assembly of each individual fastener can be verified and its achieved torque recorded.

The IR tracking is capable of tracking within 2‐3 mm for each tracking ball, with a resulting practical resolution of 24 mm distance between fasteners while maintaining 99.9999 per cent reliability without false positive fastener identification.

This experiment was run under simulated assembly line lighting conditions.

By being able to verify assembly reliably, the need for manual torque check is eliminate and hence yield significant cost savings. This will also allow programming electric tools according in real time based on the fastener in proximity identification.

Currently, assembly verification is only done using the torque values. In automated assembly line, each process might involve fastening multiple fasteners. Using this system, a new level of assembly verification is achieved by recording the assembled fastener and its associated torque.