Reviewerly/exHarmony huggingface dataset:
Towards a More Reliable Interpretation of Machine Learning Outputs for Safety-Critical Systems Using Feature Importance Fusion

When machine learning supports decision-making in safety-critical systems, it is important to verify and understand the reasons why a particular output is produced. Although feature importance calculation approaches assist in interpretation, there is a lack of consensus regarding how features’ importance is quantified, which makes the explanations offered for the outcomes mostly unreliable. A possible solution to address the lack of agreement is to combine the results from multiple feature importance quantifiers to reduce the variance in estimates and to improve the quality of explanations. Our hypothesis is that this leads to more robust and trustworthy explanations of the contribution of each feature to machine learning predictions. To test this hypothesis, we propose an extensible model-agnostic framework divided in four main parts: (i) traditional data pre-processing and preparation for predictive machine learning models, (ii) predictive machine learning, (iii) feature importance quantification, and (iv) feature importance decision fusion using an ensemble strategy. Our approach is tested on synthetic data, where the ground truth is known. We compare different fusion approaches and their results for both training and test sets. We also investigate how different characteristics within the datasets affect the quality of the feature importance ensembles studied. The results show that, overall, our feature importance ensemble framework produces 15% less feature importance errors compared with existing methods. Additionally, the results reveal that different levels of noise in the datasets do not affect the feature importance ensembles’ ability to accurately quantify feature importance, whereas the feature importance quantification error increases with the number of features and number of orthogonal informative features. We also discuss the implications of our findings on the quality of explanations provided to safety-critical systems.
NeuroAED: Towards Efficient Abnormal Event Detection in Visual Surveillance With Neuromorphic Vision Sensor

Abnormal event detection is an important task in research and industrial applications, which has received considerable attention in recent years. Existing methods usually rely on standard frame-based cameras to record the data and process them with computer vision technologies. In contrast, this paper presents a novel neuromorphic vision based abnormal event detection system. Compared to the frame-based camera, neuromorphic vision sensors, such as Dynamic Vision Sensor (DVS), do not acquire full images at a fixed frame rate but rather have independent pixels that output intensity changes (called events) asynchronously at the time they occur. Thus, it avoids the design of the encryption scheme. Since events are triggered by moving edges on the scene, DVS is a natural motion detector for the abnormal objects and automatically filters out any temporally-redundant information. Based on this unique output, we first propose a highly efficient method based on the event density to select activated event cuboids and locate the foreground. We design a novel event-based multiscale spatio-temporal descriptor to extract features from the activated event cuboids for the abnormal event detection. Additionally, we build the NeuroAED dataset, the first public dataset dedicated to abnormal event detection with neuromorphic vision sensor. The NeuroAED dataset consists of four sub-datasets: Walking, Campus, Square, and Stair dataset. Experiments are conducted based on these datasets and demonstrate the high efficiency and accuracy of our method.
BugSum

During collaborative software development, bug reports are dynamically maintained and evolved as a part of a software project. For a historical bug report with complicated discussions, an accurate and concise summary can enable stakeholders to reduce the time effort perusing the entire content. Existing studies on bug report summarization, based on whether supervised or unsupervised techniques, are limited due to their lack of consideration of the redundant information and disapproved standpoints among developers' comments. Accordingly, in this paper, we propose a novel unsupervised approach based on deep learning network, called BugSum. Our approach integrates an auto-encoder network for feature extraction with a novel metric (believability) to measure the degree to which a sentence is approved or disapproved within discussions. In addition, a dynamic selection strategy is employed to optimize the comprehensiveness of the auto-generated summary represented by limited words. Extensive experiments show that our approach outperforms 8 comparative approaches over two public datasets. In particular, the probability of adding controversial sentences that are clearly disapproved by other developers during the discussion, into the summary is reduced by up to 69.6%.
Face Detection and Recognition, Face Emotion Recognition Through NVIDIA Jetson Nano

This paper focuses on implementing face detection, face recognition and face emotion recognition through NVIDIA’s state-of-the-art Jetson Nano. Face detection is implemented using OpenCV’s deep learning-based DNN face detector, supported by a ResNet architecture, for achieving better accuracy than the previously developed models. The result computed by framework libraries of OpenCV, with the support of the above-mentioned hardware, displayed reliable accuracy even with the change in lighting and angle. For face recognition, the approach of deep metric learning using OpenCV, supported by a ResNet-34 architecture, is used. Face emotion recognition is achieved by developing a system in which the areas of eyes and mouth are used to convey the analysis of the information into a merged new image, classifying the image into displaying any of the seven basic facial emotions. A powerful and a low-power platform, Jetson Nano carried out intensive computations of algorithms easily, contributing in high video processing frame.
An Improved Faster-RCNN Algorithm for Object Detection in Remote Sensing Images

Compared with conventional object detection, remote sensing images are taken from the air. The angle of view is not fixed and the object direction, scale which compared with conventional object detection algorithm are quite different. These factors lead to the object detection in remote sensing images difficult. To solve the above problems, this paper proposes an improved remote sensing object detection method based on Faster-RCNN algorithm. Using online difficult example mining technology, feature pyramid structure, Soft-NMS technology, and RoI-Align technology to enhance the capabilities of Faster-RCNN in small object detection task in remote sensing images. The algorithm in this paper was evaluated on the RSOD-Dataset, compared with the original Faster-RCNN algorithm, the proposed algorithm improves the detection accuracy and training convergence speed, which shows that these improvements are of great significance to the object detection algorithm of remote sensing images.
The Capacity of Quantum Neural Networks

Quantum neural networks (QNN) are a promising application of near-term quantum computers. We present an information theory of QNN’s expressive power, which we apply to an example optical QNN based on a Gaussian Boson Sampler.
A deep learning algorithm with high sensitivity for the detection of basal cell carcinoma in Mohs micrographic surgery frozen sections

To the Editor: Advances in digital pathology with rapid slide scanning of whole slide images (WSIs)1,2 and artificial intelligence (AI) offer opportunities for improved accuracy and real-time quality assurance of histopathologic interpretations, which could be used for intraoperative frozen section margin assessment of skin cancer. A recent study explored an AI algorithm for detecting basal cell carcinoma (BCC) in digital images of Mohs micrographic surgery slides using a total of 100 cases: 60 for training, 20 for validation, and 20 for testing.
Laplacian state transfer in total graphs

The total graph of a graph G, denoted by T(G), is defined to be the graph whose vertices are the vertices and edges of G, with two vertices of T(G) adjacent if and only if the corresponding elements of G are adjacent or incident. In this paper, we investigate the existence of Laplacian perfect state transfer and Laplacian pretty good state transfer in the total graph of an r-regular graph, where r≥2. We prove that if r+1 is not a Laplacian eigenvalue of an r-regular graph G, then there is no Laplacian perfect state transfer in T(G). In contrast, we give a sufficient condition for total graphs of regular graphs to have Laplacian pretty good state transfer.
Eyes-free Target Acquisition During Walking in Immersive Mixed Reality

Reaching towards out-of-sight objects during walking is a common task in daily life, however the same task can be challenging when wearing immersive Head-Mounted Displays (HMD). In this paper, we investigate the effects of spatial reference frame, walking path curvature, and target placement relative to the body on user performance of manually acquiring out-of-sight targets located around their bodies, as they walk in a spatial-mapping Mixed Reality (MR) environment wearing an immersive HMD. We found that walking and increased path curvature negatively affected the overall spatial accuracy of the performance, and that the performance benefited more from using the torso as the reference frame than the head. We also found that targets placed at maximum reaching distance yielded less error in angular rotation and depth of the reaching arm. We discuss our findings with regard to human walking kinesthetics and the sensory integration in the peripersonal space during locomotion in immersive MR. We provide design guidelines for future immersive MR experience featuring spatial mapping and full-body motion tracking to provide better embodied experience.
A smartphone camera and built-in gyroscope based application for non-contact yet accurate off-axis structural displacement measurements

Image-based optical methods have been widely used for noncontact structural displacement measurements, due to their prominent advantages over conventional contact sensors. However, existing optical methods usually require complicated and expensive imaging systems, and have difficulties to accurately measure in-plane displacements when the optical axis is not perpendicular to surface of the object (i.e., off-axis measurement). In this work, we develop a low-cost and portable smartphone-based optical method for accurately measuring off-axis structural displacements. The theoretical equations of the in-plane physical displacements on the object surface are derived based on the smartphone gyroscope data that can be used to determine the rotation matrix between the defined world coordinate system and the camera coordinate system. Simple calibration tests are performed to validate the accuracy of the smartphone gyroscope in detecting the rotation angles. The effectiveness and accuracy of this method in off-axis structural displacement measurement are then verified by two in-plane translation tests in laboratory condition. Finally, the noise from the smartphone camera is evaluated, and the effectiveness of the proposed method in continuous displacement measurement is further confirmed the vibration measurement of a hanging light under random wind load.
Pyramid graph cut: Integrating intensity and gradient information for grayscale medical image segmentation

Segmentation of grayscale medical images is challenging because of the similarity of pixel intensities and poor gradient strength between adjacent regions. The existing image segmentation approaches based on either intensity or gradient information alone often fail to produce accurate segmentation results. Previous approaches in the literature have approached the problem by embedded or sequential integration of different information types to improve the performance of the image segmentation on specific tasks. However, an effective combination or integration of such information is difficult to implement and not sufficiently generic for closely related tasks. Integration of the two information sources in a single graph structure is a potentially more effective way to solve the problem. In this paper we introduce a novel technique for grayscale medical image segmentation called pyramid graph cut, which combines intensity and gradient sources of information in a pyramid-shaped graph structure using a single source node and multiple sink nodes. The source node, which is the top of the pyramid graph, embeds intensity information into its linked edges. The sink nodes, which are the base of the pyramid graph, embed gradient information into their linked edges. The min-cut uses intensity information and gradient information, depending on which one is more useful or has a higher influence in each cutting location of each iteration. The experimental results demonstrate the effectiveness of the proposed method over intensity-based segmentation alone (i.e. Gaussian mixture model) and gradient-based segmentation alone (i.e. distance regularized level set evolution) on grayscale medical image datasets, including the public 3DIRCADb-01 dataset. The proposed method archives excellent segmentation results on the sample CT of abdominal aortic aneurysm, MRI of liver tumor and US of liver tumor, with dice scores of 90.49±5.23%, 88.86±11.77%, 90.68±2.45%, respectively.
Blockchain technology applications to postmarket surveillance of medical devices

The amount of mandatory data that needs to be analyzed as part of a medical device postmarket surveillance (PMS) system has grown exponentially in recent times. This is a consequence of increasingly demanding and complex regulatory requirements from Health Authorities, aimed at a better understanding of the medical device safety evaluation. Proactive approaches to PMS processes are becoming more necessary as regulators increase the scrutiny of device safety. New technologies have been explored to address some of the challenges associated with this changing regulatory environment.This paper focuses on the different technical aspects of blockchain and how this new technology has the potential to support the ongoing efforts to improve the PMS system for medical devices.To address these challenges, we suggest to generate a private PMS data permissioned blockchain with a proof-of-authority consensus mechanism, to which only a restricted number of designated and audited participants have authorization to validate transactions and add them to the PMS data blockchain ledger. Blockchain has the potential to support a more efficient approach, which could offer many advantages to the different stakeholders involved in the PMS process, such as supporting with new regulatory initiatives.
Theory-Based Model and Prediction Analysis of Information Security Compliance Behavior in the Saudi Healthcare Sector

The adoption of health information systems provides many potential healthcare benefits. The government of the Kingdom of Saudi Arabia has subsidized this field. However, like those of other less developed countries, organizations in the Kingdom of Saudi Arabia struggle to secure their health information systems. This issue may stem from a lack of awareness regarding information security. To date, most related studies have not considered all of the factors affecting information security compliance behavior (ISCB), which include psychological traits, cultural and religious beliefs, and legal concerns. This paper aims to investigate the usefulness of a theory-based model and determine the predictors of ISCB among healthcare workers at government hospitals in the Kingdom of Saudi Arabia. The study investigated 433 health workers in Arar, the capital of the Northern Borders Province in the Kingdom of Saudi Arabia. Two phases involved in this study were the hypothetical model formulation and identification of ISCB predictors. The results suggest that moderating and non-common factors (e.g., religion and morality) impact ISCB, while demographic characteristics (e.g., age, marital status, and work experience) do not. All published instruments and theories were embedded to determine the most acceptable theories for Saudi culture. The theory-based model of ISCB establishes the main domains of theory for this study, which were religion/morality, self-efficacy, legal/punishment, personality traits, cost of compliance/noncompliance, subjective norms, information security policy, general information security, and technology awareness. Predictors of ISCB indicate that general information security, followed by self-efficacy and religion/morality, is the most influential factor on ISCB among healthcare workers in the Kingdom of Saudi Arabia. This study is considered as the first to present the symmetry between theory and actual descriptive results, which were not investigated before.
Surfacing knowledge mobilities in higher education: reconfiguring the teacher function through automation

Automation is becoming increasingly common in higher education. This is generally posed around accepting large number of students while seeking to keep faculty numbers static and casualising an already fragmented academic labour market. Much of this automation is positioned around perceived gains (efficiency, time, cost) and learning personalisation (personal assistants as a means of providing support). Yet there has been little work in exploring how human and student agency, code, algorithms, and further digital instruments might be reconfigured through automation to service the teacher function. In this paper, we draw on a combination of speculative events and qualitative work conducted with students, faculty, and staff across the three colleges at the University of Edinburgh to explore automation as a component of the teacher function assemblage. Through participatory student and teacher-led research and development, narratives of teaching practice and the role of automation in performing aspects of the teacher function emerged. Consequently, this paper offer insights into debates about sociomaterial dimensions of higher education and demonstrates what mobilities are being engendered as a result of the reconfigured teacher function.
A polymatroid approach to generalized weights of rank metric codes

Abstract We consider the notion of a ( q , m )-polymatroid, due to Shiromoto, and the more general notion of ( q , m )-demi-polymatroid, and show how generalized weights can be defined for them. Further, we establish a duality for these weights analogous to Wei duality for generalized Hamming weights of linear codes. The corresponding results of Ravagnani for Delsarte rank metric codes, and Martínez-Peñas and Matsumoto for relative generalized rank weights are derived as a consequence.
Embedding information reversibly in medical images for e-health

Embedding information in medical images is considered as one of the significant methods for safeguarding the integrity and authenticity of medical images besides providing security to electronic patient records (EPR). The conventional embedding methods deteriorate the perceptual quality of medical images making them unsuitable for proper diagnosis. To preserve the perceptual quality of medical images reversible embedding is used. The reversible embedding schemes, however, have less embedding capacity. In this work, a reversible scheme based on histogram bin shifting and RGB plane concatenation has been proposed which offers high embedding capacity as well. We have exploited the fact that medical images, unlike general images, consist of a large number of peaks and zero points that can be employed for reversibly embedding the data. Reversibility ensures that original image restoration takes place after the extraction of embedded data, which is of great importance in medical images for proper diagnosis and treatment. We have used various subjective and objective image quality metrics for analyzing the scheme. The proposed scheme has been shown to provide a Peak Signal to Noise Ratio (PSNR) value of above 56 dB for an embedding capacity of 0.58 bits per pixel (bpp). The results obtained show that the performance of scheme presented is far better in comparison to the state-of-the-art.
Efficient Group ID-Based Encryption With Equality Test Against Insider Attack

Abstract ID-based encryption with equality test (IBEET) allows a tester to compare ciphertexts encrypted under different public keys for checking whether they contain the same message. In this paper, we first introduce group mechanism into IBEET and propose a new primitive, namely group ID-based encryption with equality test (G-IBEET). With the group mechanism: (1) group administrator can authorize a tester to make comparison between ciphertexts of group users, but it cannot compare their ciphertexts with any ciphertext of any user who is not in the group. Such group granularity authorization can make IBEET that adapts to group scenario; (2) for the group granularity authorization, only one trapdoor, named group trapdoor, should be issued to the tester, which can greatly reduce the cost of computation, transmission and storage of trapdoors in traditional IBEET schemes; (3) G-IBEET can resist the insider attack launched by the authorized tester, which is an open problem in IBEET. We give definitions for G-IBEET and propose a concrete construction with an efficient test algorithm. We then give its security analysis in the random oracle model.
What are the practices used by software practitioners on technical debt payment

Context: Technical debt (TD) is a metaphor used to describe technical decisions that can give the company a benefit in the short term but possibly hurting the overall quality of the software in the long term. Objective: This study aims to characterize the current state of practices related to TD payment from the point of view of software practitioners. Method: We used a survey research method to collect and analyze - both quantitatively and qualitatively - a corpus of responses from a survey of 432 software practitioners from Colombia, Chile, Brazil, and the United States, as a part of the InsighTD project. Results: We were able to identify that refactoring (24.3%) was the main practice related to TD payment, along with improving testing (6.2%) and improve design (5.8%). Also, we identify that small-sized systems and big-sized systems, along with young systems (less than one year) tend to use more refactoring. As a part of these results, we also could identify that some practices do not eliminate the debt by itself, but support a favorable scenario for TD payment or prevention. Additionally, after comparing the three major TD types cited (code debt, test debt and design debt) we could discover an important similarity of TD payment practices between code debt and design debt. Lastly, we identified that no matter the cause leading to TD occurrence, refactoring remained the most common practice. Conclusion: Definition of practices related to TD payment is an essential activity for software development teams. Developing healthy software systems that can be maintained in the future requires that companies find the right approaches for TD payment.