Reviewerly/exHarmony huggingface dataset:
SAP‐cGAN: Adversarial learning for breast mass segmentation in digital mammogram based on superpixel average pooling

Purpose Breast mass segmentation is a prerequisite step in the use of computer‐aided tools designed for breast cancer diagnosis and treatment planning. However, mass segmentation remains challenging due to the low contrast, irregular shapes, and fuzzy boundaries of masses. In this work, we propose a mammography mass segmentation model for improving segmentation performance. Methods We propose a mammography mass segmentation model called SAP‐cGAN, which is based on an improved conditional generative adversarial network (cGAN). We introduce a superpixel average pooling layer into the cGAN decoder, which utilizes superpixels as a pooling layout to improve boundary segmentation. In addition, we adopt a multiscale input strategy to enable the network to learn scale‐invariant features with increased robustness. The performance of the model is evaluated with two public datasets: CBIS‐DDSM and INbreast. Moreover, ablation analysis is conducted to evaluate further the individual contribution of each block to the performance of the network. Results Dice and Jaccard scores of 93.37% and 87.57%, respectively, are obtained for the CBIS‐DDSM dataset. The Dice and Jaccard scores for the INbreast dataset are 91.54% and 84.40%, respectively. These results indicate that our proposed model outperforms current state‐of‐the‐art breast mass segmentation methods. The superpixel average pooling layer and multiscale input strategy has improved the Dice and Jaccard scores of the original cGAN by 7.8% and 12.79%, respectively. Conclusions Adversarial learning with the addition of a superpixel average pooling layer and multiscale input strategy can encourage the Generator network to generate masks with increased realism and improve breast mass segmentation performance through the minimax game between the Generator network and Discriminator network.
A Geometrical Account to Explain the Fat-Face Illusion

Investigations of the "fat-face" illusion have unanimously agreed that the illusion is face-specific. Here, we offer several manipulations to highlight that the fat-face illusion is not restricted to the bottom image, isn't a property of internal features, facial contour/texture, and in general isn't even specific to faces. We propose the axis of horizontal asymmetry account to contextualize fat-face illusion as a geometry-led illusion.
Fall Detection Algorithm Based on MPU6050 and Long-Term Short-Term Memory network

Fall is the second cause of accidental death in the world and is the main cause of physical injuries, especially older. Development of fall detection systems can re-duce the injuries by falling. In this study, three-axis acceleration, three-axis angular acceleration, and Euler parameters that are obtained by MPU6050 sensor are adopted to collect standing, walking, and falling data. Two nrf52832 Bluetooth modules are the receiver and transmitter individual. One reads the data and calcu-lates behavior's information from MPU6050, then transmits data to another on the development board (Nordic nRF52832). Then the development board trans-mits data to the computer through UART. Long-Term Short-Term Memory net-work (LSTM) is used to identify the three movements and then distinguish the difference between falling and normal activities. The results show that this meth-od has 97% rate to determine when fall has occurred.
The impact of cyclical, multi-decadal to centennial climate variability on arsenic sequestration in lacustrine sediments

Examining paleoclimate-driven changes of elemental contaminants, such as Arsenic (As), increases the understanding of the mobility and fate of elements under a warming climate scenario. To characterize the variability in As sequestration in the sediments of a freshwater system in response to decadal- to centennial-scale climate oscillations, a freeze-core (CON01) was recovered from Control Lake, Northwest Territories. Radiocarbon dating of 13 bulk-organic samples provided temporal reference to core depth. Sediment geochemistry was determined using Itrax X-ray fluorescence core-scanning (Itrax-XRF). Elemental concentrations were measured on a sub-set of samples using ICP-MS after multi-acid (MA) digestion to assess the accuracy of Itrax-XRF results through a multivariate log-ratio (MLC) calibration. Comparison of Itrax-XRF to ICP-MS using the MLC in ItraXelerate software show Pearson's R2 values >0.75, with the exception of As (R2 = 0.44). MLC-calibrated Itrax-XRF elemental data were centered log-ratio (CLR) transformed to eliminate issues related to data closure. During the ca. 3300-yr sedimentary record, moderate-strength negative correlations between AsCLR and KCLR (Spearman's ρ = −0.38, p-value < 0.001, n = 785), and AsCLR and TiCLR, (Spearman's ρ = −0.52, p-value < 0.001, n = 785) suggest that As is primarily sequestered in sediments during intervals of warmer temperatures and higher productivity. Proxies for sediment particle size (TiCLR, KCLR) and As concentration (AsCLR) were examined for response to quasi-periodic climate oscillations using spectral analysis. Significant periodicities were observed with approximately 4–13, 30–60, 90–120, and 160–280 yr periods in TiCLR, KCLR, and AsCLR records. These frequencies are interpreted as corresponding to the North Atlantic Oscillation and/or 8–14-yr Schwabe sunspot cycles, 30–60-yr Pacific Decadal Oscillation, and centennial-scale solar cycles (e.g., 90-yr Gleissberg cycle; 205-yr Suess cycle). Coeval occurrence of these periodicities revealed through wavelet analysis of Control Lake geochemistry data suggests that these climate cycles only impact Control Lake when they occur concurrently.
Automatic Traffic Accident Detection System Using ResNet and SVM

The rate of road accidents has increased to a large extent over the last few years. This has eventually resulted in a huge loss of lives and property. Hence, the need of the hour has aroused to detect such accident spots as quickly as possible so that proper life-saving measures can be taken and the mishap prone areas can be put on alert. In order to address this problem, we propose a Machine Learning and Deep Learning based model on the concepts of Clustering and Classification that can be used to detect accidents from the traffic surveillance cameras. Firstly all the videos are split up into smaller shots according to scene changes. And then key frames are extracted from each shot based on histogram difference of consecutive frames. Then distance between the vehicles is determined to detect the potential accident. The obtained key frames are passed through a ResNet50 architecture for feature extraction. After obtaining the feature vectors of all videos, K-Means clustering has been applied to obtain Bag of Visual Words(BOVW). Finally, these bag of visual words is sent as input to a Support Vector Machine(SVM) classifier that outputs if a video contained an accident or not. The proposed method has an accuracy of 94.14%.
Adoption of Upcoming Blockchain Revolution in Higher Education: Its Potential in Validating Certificates

The important role of an educational certificate is very influential in playing professionalism in the ecosystem of a company. Because the certificate is very valuable, it is necessary to store the long term storage space available in a Tamper-proof ledger. The heavy Focus in the research conducted is to present a revolutionary Blockchain technology for education in response to an effective solution in validating the certificate by implementing a blockchain-owned advantage that is a system of Distributed and cryptography, the presence of blockchain technology will be able to eliminate the existence of false diplomas. The study adopted two methods namely, the method of descriptive analysis and the library study method. Blockchain is expected to contribute to success and improve the quality of national education.
Neural network pruning and hardware acceleration

Neural network pruning is a critical technique to efficiently deploy neural network models on edge devices with limited computing resources. Although many neural network pruning methods have been published, it is difficult to implement such algorithms due to their inherent complexity. In this work, we propose a functional pruning tool for neural network models. Our pruning procedure is simple and easy to be implemented, and efficient for deployment. Our pruning tool automatically detects redundancy inside neural network models and prunes the redundant channels. Doing so reduces the total number of model parameters and hence, compresses the size of the model. This approach significantly reduces the number of FLOPs needed for executing the neural network model and improves the inference runtime. To further improve the inference runtime of the pruned model, we leveraged Apache TVM to deploy the pruned model on the DPU FPGA-based hardware accelerator. To demonstrate our approach, we pruned the VGG-16 model on Flower dataset and reached 53-fold reduction in model size with only 7% drop in validation accuracy. The inference latency is reduced 4-fold on CPU and 16-fold on FPGA for the pruned models, compared with the latency of the base model on CPU.
Infrastructure-Aware TensorFlow for Heterogeneous Datacenters

Heterogeneous datacenters, with a variety of compute, memory, and network resources, are becoming increasingly popular to address the resource requirements of time-sensitive applications. One such application framework is the TensorFlow platform, which has become a platform of choice for running machine learning workloads. The state-of-the-art TensorFlow platform is oblivious to the availability and performance profiles of the underlying datacenter resources and does not incorporate resource requirements of the given workloads for distributed training. This leads to executing the training tasks on busy and resource-constrained worker nodes, which results in a significant increase in the overall training time. In this paper, we address this challenge and propose architectural improvements and new software modules in the default TensorFlow platform to make it aware of the availability and capabilities of the underlying datacenter resources. The proposed Infrastructure-Aware Tensor-Flow efficiently schedules the training tasks on the best possible resources for execution and reduces the overall training time. Our evaluation using the worker nodes with varying availability and performance profiles shows that the proposed enhancements yield up to 54 % reduced training time as compared to the default TensorFlow platform.
Fragments‐Expert: A graphical user interface MATLAB toolbox for classification of file fragments

Summary The classification of file fragments of various file formats is an essential task in various applications such as firewalls, intrusion detection systems, antiviruses, web content filtering, and digital forensics. However, the community lacks a suitable software tool that can integrate major methods for feature extraction from file fragments and classification among various file formats. In this article, we present Fragments‐Expert that is a graphical user interface MATLAB toolbox for the classification of file fragments. It provides users with 23 categories of features extracted from file fragments. These features can be employed by seven categories of machine learning algorithms for the task of classification among various file formats.
Steering Kernel-Based Guided Image Filter for Single Image Dehazing

The guided image filter (GIF) technique is used for haze removal. It reduces the gradient reversal artifact as well as preserves the edge information precisely in smooth (flat) region only. However, it fails to avoid halo artifact and edge-smoothing effect in sharp regions. So, to mitigate this problem, we propose an adaptive haze removal algorithm using a steering kernel-based guided image filter (SKGIF). Steering kernel determines the edge-direction in guidance image more adequately. The edge-direction is an essential feature of guidance image, and it helps to determine more edge-preserving information in flat as well as sharp regions. Experimental outcomes on different hazy images prove the effectiveness of the proposed algorithm.
Fruit Maturity Classification Using Convolutional Neural Networks Method

Abstract Difficulty in finding information about levels maturity based on the type of fruit using data textual, make a search system using image as query needed. The concept of Content-Based Image Retrieval (CBIR) will search and display images again which are relevant based on the visual features query image. In this study an application was made to classify 5 classes of fruit, Star fruit, Mango, Melon, Banana and Tomato. Which in each class divided again into 52 subclasses consisting of the type and level of fruit maturity with a total of 5030 training data images. The method used to classify and extract images features is Convolutional Neural Network (CNN). After the image is classified, the search process is carried out to determine the fruit that is similar to the classified image. The results of the classification accuracy of 1294 images are 61%. While the retrieval of 50 images has a precision value of 88.93%.
Employing entropy measures to identify visitors in multi-occupancy environments

Abstract Human activity recognition (HAR) is used to support older adults to live independently in their own homes. Once activities of daily living (ADL) are recognised, gathered information will be used to identify abnormalities in comparison with the routine activities. Ambient sensors, including occupancy sensors and door entry sensors, are often used to monitor and identify different activities. Most of the current research in HAR focuses on a single-occupant environment when only one person is monitored, and their activities are categorised. The assumption that home environments are occupied by one person all the time is often not true. It is common for a resident to receive visits from family members or health care workers, representing a multi-occupancy environment. Entropy analysis is an established method for irregularity detection in many applications; however, it has been rarely applied in the context of ADL and HAR. In this paper, a novel method based on different entropy measures, including Shannon Entropy, Permutation Entropy, and Multiscale-Permutation Entropy, is employed to investigate the effectiveness of these entropy measures in identifying visitors in a home environment. This research aims to investigate whether entropy measures can be utilised to identify a visitor in a home environment, solely based on the information collected from motion detectors [e.g., passive infra-red] and door entry sensors. The entropy measures are tested and evaluated based on a dataset gathered from a real home environment. Experimental results are presented to show the effectiveness of entropy measures to identify visitors and the time of their visits without the need for employing extra wearable sensors to tag the visitors. The results obtained from the experiments show that the proposed entropy measures could be used to detect and identify a visitor in a home environment with a high degree of accuracy.
Information Theoretical Clustering Is Hard to Approximate

An impurity measures I : R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">d</sup> → R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">+</sup> is a function that assigns a d-dimensional vector v to a non-negative value I(v) so that the more homogeneous v, with respect to the values of its coordinates, the larger its impurity. A well known example of impurity measures is the entropy impurity. We study the problem of clustering based on the entropy impurity measures. Let V be a collection of n many d-dimensional vectors with non-negative components. Given V and an impurity measure I, the goal is to find a partition n of V into k groups V <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> , . . . , V <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</sub> so as to minimize the sum of the impurities of the groups in P, i.e., I(P) = Σ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i=1</sub> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</sup> I (Σ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">v∈Vi</sub> v). Impurity minimization has been widely used as quality assessment measure in probability distribution clustering (KL-divergence) as well as in categorical clustering. However, in contrast to the case of metric based clustering, the current knowledge of impurity measure based clustering in terms of approximation and in approximability results is very limited. Here, we contribute to change this scenario by proving that the problem of finding a clustering that minimizes the Entropy impurity measure is APX-hard, i.e., there exists a constant ε > 0 such that no polynomial time algorithm can guarantee (1 + ε)-approximation under the standard complexity hypothesis P ≠ N P . The in approximability holds even when all vectors have the same 11 norm. This result provides theoretical limitations on the computational efficiency that can be achievable in the quantization of discrete memoryless channels, a problem that has recently attracted significant attention in the signal processing community. In addition, it also solve a question that remained open in previous work on this topic [Chaudhuri and McGregor COLT 08; Ackermann et. al. ECCC 11].
Blockchain-based cooperative autonomous detection of suspicious vehicles

Modern times have brought about increased criminal activity, and in an ever-connected world, numerous crimes go un-rectified. Car theft, while a crime, is also the basis for other crimes to be committed. In the world of smart things and ubiquitous computing surveillance cameras with small onboard computers are everywhere, from car dashboard cameras to street cameras. These devices or networks of devices are mainly referenced after a crime was committed supporting after-the-fact events rather than supporting proactive handling of potential crimes. In this paper, we present a proactive blockchain-based distributed platform to detect, verify, and track suspicious vehicles that can be a base for a heinous crime. The proposed platform takes advantage of the under-utilized resources in already-present surveillance equipment. The platform allows remote and local processing video feeds in search of suspicious vehicles, allowing police to act quickly following the identification and verification of a stolen car being detected depending on the computational power of the user's device. The platform will verify the findings by group voting using a blockchain base shared between all devices, and the subsequent tracking of the suspicious vehicle by tasking other cameras running the platform to actively scan for the vehicle. This will free up some of the limited resources our police possess, a big resource being time, and allowing these now-freed resources to be reallocated into other areas. Our system evaluation and performance show indications of prompt responses in detecting stolen cars, helping police catch the stolen cars sooner.
Optimized Diffuse Scattering Selection for Large Area Real-Time Geometry-Based Stochastic Modeling of Vehicular Communication Links

In this paper we present a geometry-based stochastic channel model (GSCM) for mobile urban scenarios. GSCMs are very use case specific and high effort is needed to transfer existing ones to other scenarios. We propose to use OpenStreetMap data bootstrapping the geometry including the automatic placement of static, mobile and diffuse scatterers. We propose a method to distribute diffuse scatterers along buildings and roads. Our goal is to model complete city areas having the use case of real-time emulation of the wireless channel for vehicle-to-everything (V2X) communication in mind. In order to achieve this ambitious goal we propose a novel active scattering set selection based on locality-sensitive hashing (LSH). We show the trade-off between the number of selected diffuse scattering points by comparing the resulting power delay profile and Doppler spectral density to a measurement campaign.
An auxiliary system discretization approach to Takagi-Sugeno fuzzy models

This paper proposes a new procedure for discretizing nonlinear systems described by Takagi-Sugeno fuzzy models. The discretization procedure consists of obtaining a linear auxiliary system that approximates the Takagi-Sugeno model over a sampling instant. By discretizing this auxiliary system, a norm bounded uncertain linear discrete-time system is found, which is capable of representing the fuzzy model. This auxiliary system, as well as the norm bounded uncertainty, is found by solving an optimization problem with Linear Matrix Inequality (LMI) constraints. To illustrate the discretization procedure, a constant state observer is synthesized based on simple LMI conditions and then applied to a real nonlinear Chua's circuit. Additionally, a state-feedback controller based on our discretization approach is synthesized and we obtain larger maximum sampling periods than other tested strategies from the literature.
Cluster Analysis of Malware Family Relationships

In this chapter, we use K-means clustering to analyze various relationships between malware samples. We consider a dataset comprising 20 malware families with 1000 samples per family. These families can be categorized into seven different types of malware. We perform clustering based on pairs of families and use the results to determine relationships between families. We perform a similar cluster analysis based on malware type. Our results indicate that K-means clustering can be a powerful tool for data exploration of malware family relationships.
Physical Distancing Monitoring with Background Subtraction Methods

World Health Organization (WHO) has confirmed that the spreading of coronavirus disease 2019 (COVID-19) could be avoided by keeping the physical distance at least 3 feet (1 meter). Then, we have a motivation to employ computer vision techniques to monitor social distancing violations. The principle of the works are to detect persons, then to assess the physical distancing violation from their distance. Most of the researchers have tried to utilize object detection methods such as faster RCNN, Yolo, and SSD to detect persons from the frame. Those methods rely on, the support of Graphics Processing Unit (GPU) to execute their heavy computation. In this works, we propose social distancing monitoring by applying background subtraction methods based on Gaussian Mixture Models (GMM) i.e. Geo-metric Multigrid (GMG), k-Nearest Neighbor (KNN), Mixture of Gaussian (MOG), and Mixture of Gaussian 2 (MOG2). These methods have been used to filter persons from the frame with computational process. Some parameters evaluation measures have been determined to check the best method suitable for this works. In terms of performance, better methods are ranked as KNN, MOG, MOG2, and GMG.