Toward Taming the Overhead Monster for Data-flow Integrity

Data-Flow Integrity (DFI) is a well-known approach to effectively detecting a wide range of software attacks. However, its real-world application has been quite limited so far because of the prohibitive performance overhead it incurs. Moreover, the overhead is enormously difficult to overcome without substantially lowering the DFI criterion. In this work, an analysis is performed to understand the main factors contributing to the overhead. Accordingly, a hardware-assisted parallel approach is proposed to tackle the overhead challenge. Simulations on SPEC CPU 2006 benchmark show that the proposed approach can completely enforce the DFI defined in the original seminal work while reducing performance overhead by 4×, on average.

Enhancing Intelligent Anemia Detection via Unifying Global and Local Views of Conjunctiva Image with Two-Branch Neural Networks

Anemia is one of the most widespread clinical symptoms all over the world, which could bring adverse effects on people's daily life and work. Considering the universality of anemia detection and the inconvenience of traditional blood testing methods, many deep learning detection methods based on image recognition have been developed in recent years, including the methods of anemia detection with individuals’ images of conjunctiva. However, existing methods using one single conjunctiva image could not reach comparable accuracy in anemia detection in many real-world application scenarios. To enhance intelligent anemia detection using conjunctiva images, we proposed a new algorithmic framework which could make full use of the data information contained in the image. To be concrete, we proposed to fully explore the global and local information in the image, and adopted a two-branch neural network architecture to unify the information of these two aspects. Compared with the existing methods, our method can fully explore the information contained in a single conjunctiva image and achieve more reliable anemia detection effect. Compared with other existing methods, the experimental results verified the effectiveness of the new algorithm.

Review on Gold Nanoparticles-Based Biosensors in Clinical and Non-Clinical Applications

Gold nanoparticles (GNP) acquire unique properties that have made significant contributions to clinical and non-clinical fields, specifically in the application of GNP’s for designing biosensor devices in which exhibit novel functional properties. Many properties of GNP’s are reviewed in this literature including optical properties, biocompatibility, conductivity, catalytic properties, high surface-to-volume ratio, and high density of the GNPs, that make them excellent in the application of constructing GNP-based biosensors. This literature review covers a specific comparison between the optical, electrochemical, and piezoelectric biosensors, as these are the three most common GNP-based biosensors. Optical biosensors are optimal due to their ability to cater to surface modification, which then leads to the ability for selective bonding. Furthermore, with the use of GNP and the sensor's non-invasive and non-toxic method of use, high-resolution images and signals can be formed. The sensitivity and specificity of electrochemical biosensors with the conductivity of GNPs, the electrodes of this stable biosensor can detect tumour markers in the human body. Piezoelectric biosensors are mass sensitive sensors and with the use of GNP, it amplifies the changes in mass. Through this, these sensors progress to be immunosensors which determine microorganisms and macromolecular compounds. As well, this review will conclude with an outline of present and future research recommendations for real-world application of the three GNP-based biosensors discussed.

Estimating the true arrival, balking, and reneging processes from censored transactional data: a simulation-based approach

The transactional data typically collected/available on queueing systems are often subject to censoring as unsuccessful arrivals due to balking and/or unserved entities due to reneging are not recorded. In fact, in many situations, the true arrival, balking, and reneging events are unobservable, making it virtually impossible to collect data on these stochastic processes—information that is crucial for capacity planning and process improvement decisions. The objective of this paper is to estimate the true (latent) external arrival, balking, and reneging processes in queueing systems from such censored transactional data. The estimation problem is formulated as an optimization model and an iterative simulation-based inference approach is proposed to find appropriate input models for these stochastic processes. The proposed method is applicable in any complex queueing situation as long as it can be simulated. The problem is investigated under both known and unknown reneging distribution. Through extensive simulation experiments, general guidelines are provided for specifying the parameters of the proposed approach, namely, sample size and number of replications. The proposed approach is also validated through a real-world application in a call center, where it successfully estimates the underlying arrival, balking, and reneging distributions. Finally, to enable reproducibility and technology transfer, a working example, including all codes and sample data, are made available in an open online data repository associated with this paper.

A novel interaction-based methodology towards explainable AI with better understanding of Pneumonia Chest X-ray Images

In the field of eXplainable AI (XAI), robust “blackbox” algorithms such as Convolutional Neural Networks (CNNs) are known for making high prediction performance. However, the ability to explain and interpret these algorithms still require innovation in the understanding of influential and, more importantly, explainable features that directly or indirectly impact the performance of predictivity. A number of methods existing in literature focus on visualization techniques but the concepts of explainability and interpretability still require rigorous definition. In view of the above needs, this paper proposes an interaction-based methodology–Influence score (I-score)—to screen out the noisy and non-informative variables in the images hence it nourishes an environment with explainable and interpretable features that are directly associated to feature predictivity. The selected features with high I-score values can be considered as a group of variables with interactive effect, hence the proposed name interaction-based methodology. We apply the proposed method on a real world application in Pneumonia Chest X-ray Image data set and produced state-of-the-art results. We demonstrate how to apply the proposed approach for more general big data problems by improving the explainability and interpretability without sacrificing the prediction performance. The contribution of this paper opens a novel angle that moves the community closer to the future pipelines of XAI problems. In investigation of Pneumonia Chest X-ray Image data, the proposed method achieves 99.7% Area-Under-Curve (AUC) using less than 20,000 parameters while its peers such as VGG16 and its upgraded versions require at least millions of parameters to achieve on-par performance. Using I-score selected explainable features allows reduction of over 98% of parameters while delivering same or even better prediction results.

On the use of neural networks for energy reconstruction in high-granularity calorimeters

We contrasted the performance of deep neural networks — Convolutional Neural Network (CNN) and Graph Neural Network (GNN) — to current state of the art energy regression methods in a finely 3D-segmented calorimeter simulated by GEANT4. This comparative benchmark gives us some insight to assess the particular latent signals neural network methods exploit to achieve superior resolution. A CNN trained solely on a pure sample of pions achieved substantial improvement in the energy resolution for both single pions and jets over the conventional approaches. It maintained good performance for electron and photon reconstruction. We also used the Graph Neural Network (GNN) with edge convolution to assess the importance of timing information in the shower development for improved energy reconstruction. We implement a simple simulation based correction to the energy sum derived from the fraction of energy deposited in the electromagnetic shower component. This serves as an approximate dual-readout analogue for our benchmark comparison. Although this study does not include the simulation of detector effects, such as electronic noise, the margin of improvement seems robust enough to suggest these benefits will endure in real-world application. We also find reason to infer that the CNN/GNN methods leverage latent features that concur with our current understanding of the physics of calorimeter measurement.

House Hunters, Gerontology Style: A Unique Classroom Activity for Undergraduates

Small group discussion activities that capitalize on students’ interest in technology may generate enthusiasm for course content in gerontology. We describe a unique classroom activity that supports discussion about retirement issues in older adulthood by leveraging student dexterity in utilizing web applications. In this activity, students act as real estate agents for a retired older adult couple who is relocating to be closer to family. Students are presented with details about the couple, including demographic information (e.g., age, functional limitations, hobbies) and the couple’s “wish list” for features and amenities of their future home. Then, students use these details to choose a home for the couple on Zillow and prepare a “pitch” of the home that is presented to the class and judged by the course instructor. Feedback from students suggests that this activity offers a “real world application to course material” and facilitates enthusiasm about course content.