Reviewerly/exHarmony huggingface dataset:
Machine Learning Algorithms for Smart Data Analysis in Internet of Things Environment: Taxonomies and Research Trends

Machine learning techniques will contribution towards making Internet of Things (IoT) symmetric applications among the most significant sources of new data in the future. In this context, network systems are endowed with the capacity to access varieties of experimental symmetric data across a plethora of network devices, study the data information, obtain knowledge, and make informed decisions based on the dataset at its disposal. This study is limited to supervised and unsupervised machine learning (ML) techniques, regarded as the bedrock of the IoT smart data analysis. This study includes reviews and discussions of substantial issues related to supervised and unsupervised machine learning techniques, highlighting the advantages and limitations of each algorithm, and discusses the research trends and recommendations for further study.
A variant form of 3D-UNet for infant brain segmentation

Magnetic Resonance Imaging (MRI) is dominant modality for infant brain analysis. Segmenting the whole infant MRI brain into number of tissues such as Cerebrospinal fluid (CSF), White matter (WM), and Gray Matter (GM) are highly desirable in the clinical environment. However, traditional methods tend to be degrading due to low contrast between GM and WM in isointense phase (about 6–8 months of early life). Recently, Convolutional Neural Network (CNN) emerged as a robust intelligent approach to examine medical image. The UNet model is among the preferred CNN models that have been widely used for medical imaging applications and achieved excellent results. The UNet architecture is a combination of convolutional, pooling, and up-sampling layers. Recently, 3D-UNet architecture used to exploit 3D-contextual information of volumetric data in many applications. However, CNN faces challenge to distinguish the similar brain tissues. In this paper, we present a variant of 3D-UNet to extract the volumetric contextual information of medical data. We propose a novel combined architecture of dense connection, residual connection, and inception module. The proposed architecture contains three stages, namely the densely connected stage, a residual inception stage, and an up-sampling stage. Our proposed approach provides state-of-art results in comparison to other existing approaches. This suggested approach achieves dice scores of 0.95, 0.905, and 0.92 in CSF, WM, and GM tissues respectively.
"Person, Shoes, Tree. Is the Person Naked?" What People with Vision Impairments Want in Image Descriptions

Access to digital images is important to people who are blind or have low vision (BLV). Many contemporary image description efforts do not take into account this population's nuanced image description preferences. In this paper, we present a qualitative study that provides insight into 28 BLV people's experiences with descriptions of digital images from news websites, social networking sites/platforms, eCommerce websites, employment websites, online dating websites/platforms, productivity applications, and e-publications. Our findings reveal how image description preferences vary based on the source where digital images are encountered and the surrounding context. We provide recommendations for the development of next-generation image description technologies inspired by our empirical analysis.
A novel image encryption scheme using both pixel level and bit level permutation with chaotic map

In cryptography, chaotic cryptosystem is one of the methods to carry out encryption and decryption of images. This paper introduces a new symmetric key encryption technique based on chaotic map, scan method and cyclic shift operation. The confusion and diffusion techniques are implemented using Hilbert curve and Henon map. To ensure image scrambling, both pixel level and bit level permutations are performed. A novel method is adopted for bit level permutation using cyclic shift operation. The key streams for cyclic shift and diffusion operations are generated from the Henon map. Final encrypted image is generated from the double scrambled image. The performance of the proposed method has been analyzed using various analyses like statistical analysis, entropy analysis, differential attack analysis, key sensitivity analysis and known plain text attack analysis. Experimental results show that the proposed image encryption technique resists various attacks and ensures high security. It also provides better performance when compared with several traditional and state-of-the-art image encryption methods.
A hybrid energy–Aware virtual machine placement algorithm for cloud environments

The high energy consumption of cloud data centers presents a significant challenge from both economic and environmental perspectives. Server consolidation using virtualization technology is widely used to reduce the energy consumption rates of data centers. Efficient Virtual Machine Placement (VMP) plays an important role in server consolidation technology. VMP is an NP-hard problem for which optimal solutions are not possible, even for small-scale data centers. In this paper, a hybrid VMP algorithm is proposed based on another proposed improved permutation-based genetic algorithm and multidimensional resource-aware best fit allocation strategy. The proposed VMP algorithm aims to improve the energy consumption rate of cloud data centers through minimizing the number of active servers that host Virtual Machines (VMs). Additionally, the proposed VMP algorithm attempts to achieve balanced usage of the multidimensional resources (CPU, RAM, and Bandwidth) of active servers, which in turn, reduces resource wastage. The performance of both proposed algorithms are validated through intensive experiments. The obtained results show that the proposed improved permutation-based genetic algorithm outperforms several other permutation-based algorithms on two classical problems (the Traveling Salesman Problem and the Flow Shop Scheduling Problem) using various standard datasets. Additionally, this study shows that the proposed hybrid VMP algorithm has promising energy saving and resource wastage performance compared to other heuristics and metaheuristics. Moreover, this study reveals that the proposed VMP algorithm achieves a balanced usage of the multidimensional resources of active servers while others cannot.
Mind the Gap: The Underrepresentation of Female Participants and Authors in Virtual Reality Research

A common goal of human-subject experiments in virtual reality (VR) research is evaluating VR hardware and software for use by the general public. A core principle of human-subject research is that the sample included in a given study should be representative of the target population; otherwise, the conclusions drawn from the findings may be biased and may not generalize to the population of interest. In order to assess whether characteristics of participants in VR research are representative of the general public, we investigated participant demographic characteristics from human-subject experiments in the Proceedings of the IEEE Virtual Reality Conferences from 2015-2019. We also assessed the representation of female authors. In the 325 eligible manuscripts, which presented results from 365 human-subject experiments, we found evidence of significant underrepresentation of women as both participants and authors. To investigate whether this underrepresentation may bias researchers' findings, we then conducted a meta-analysis and meta-regression to assess whether demographic characteristics of study participants were associated with a common outcome evaluated in VR research: the change in simulator sickness following head-mounted display VR exposure. As expected, participants in VR studies using HMDs experienced small but significant increases in simulator sickness. However, across the included studies, the change in simulator sickness was systematically associated with the proportion of female participants. We discuss the negative implications of conducting experiments on non-representative samples and provide methodological recommendations for mitigating bias in future VR research.
A deep learning image-based intrinsic molecular subtype classifier of breast tumors reveals tumor heterogeneity that may affect survival

Breast cancer intrinsic molecular subtype (IMS) as classified by the expression-based PAM50 assay is considered a strong prognostic feature, even when controlled for by standard clinicopathological features such as age, grade, and nodal status, yet the molecular testing required to elucidate these subtypes is not routinely performed. Furthermore, when such bulk assays as RNA sequencing are performed, intratumoral heterogeneity that may affect prognosis and therapeutic decision-making can be missed.As a more facile and readily available method for determining IMS in breast cancer, we developed a deep learning approach for approximating PAM50 intrinsic subtyping using only whole-slide images of H&E-stained breast biopsy tissue sections. This algorithm was trained on images from 443 tumors that had previously undergone PAM50 subtyping to classify small patches of the images into four major molecular subtypes-Basal-like, HER2-enriched, Luminal A, and Luminal B-as well as Basal vs. non-Basal. The algorithm was subsequently used for subtype classification of a held-out set of 222 tumors.This deep learning image-based classifier correctly subtyped the majority of samples in the held-out set of tumors. However, in many cases, significant heterogeneity was observed in assigned subtypes across patches from within a single whole-slide image. We performed further analysis of heterogeneity, focusing on contrasting Luminal A and Basal-like subtypes because classifications from our deep learning algorithm-similar to PAM50-are associated with significant differences in survival between these two subtypes. Patients with tumors classified as heterogeneous were found to have survival intermediate between Luminal A and Basal patients, as well as more varied levels of hormone receptor expression patterns.Here, we present a method for minimizing manual work required to identify cancer-rich patches among all multiscale patches in H&E-stained WSIs that can be generalized to any indication. These results suggest that advanced deep machine learning methods that use only routinely collected whole-slide images can approximate RNA-seq-based molecular tests such as PAM50 and, importantly, may increase detection of heterogeneous tumors that may require more detailed subtype analysis.
Violence Recognition from Videos using Deep Learning Techniques

Automatic recognition of violence between individuals or crowds in videos has a broad interest. In this work, an end-to-end deep neural network model for the purpose of recognizing violence in videos is proposed. The proposed model uses a pre-trained VGG-16 on ImageNet as spatial feature extractor followed by Long Short-Term Memory (LSTM) as temporal feature extractor and sequence of fully connected layers for classification purpose. The achieved accuracy is near state-of-the-art. Also, we contribute by introducing a new benchmark called Real- Life Violence Situations which contains 2000 short videos divided into 1000 violence videos and 1000 non-violence videos. The new benchmark is used for fine-tuning the proposed models achieving a best accuracy of 88.2%.
A competitive chain-based Harris Hawks Optimizer for global optimization and multi-level image thresholding problems

This paper presents an enhanced Harris Hawks Optimizer (HHO) to tackle global optimization and determine the optimal threshold values for multi-level image segmentation problems. HHO is a new swarm-based metaheuristic technique that simulates the behaviors of Harris hawks during the process of catching the rabbits. The HHO established its strong performance as a swarm-based optimization technique. However, population-based HHO still may face some limitations in dealing with more multi-modal and composition problems. For example, this optimizer may be stagnated to local optima and turned to immature convergence when performing phases of exploration and exploitation. To mitigate these drawbacks, an improved HHO is proposed that considers the salp swarm algorithm (SSA) as a competitive method to enhance the balance between its exploration and exploitation trends. Firstly, a set of solutions is generated. Then, we divide those solutions into two halves, where the exploratory and exploitative phases of HHO will be applied to the first half, and the searching stages of SSA will be used to update the solutions in the second half. Thereafter, the best solutions from the union sub-populations are selected to continue the iterative process. According to the improved HHO, which is called HHOSSA, an effective multi-level image segmentation approach is also developed in this research. A comprehensive set of experiments are performed using 36 IEEE CEC 2005 benchmark functions and 11 natural gray-scale images. Extensive results and comparisons show the high ability of the SSA to improve the HHO’s performance since the proposed HHOSSA achieves a more stable performance compared to HHO, SSA, and many other well-known methods.
Application of Internet of Things and Virtual Reality Technology in College Physical Education

College physical education is an important component of the higher education system and national health plans. Promoting the scientific and modern construction of the physical education teaching system in colleges and universities is conducive to enhancing the science and effectiveness of higher education. Aiming at the problems of single teaching methods and insufficient long-distance teaching ability in the current physical education teaching process of colleges and universities, based on virtual reality technology, this paper designs and proposes a college physical education virtual reality system consisting of the Internet of Things, cloud platform and mobile client. This system collects relevant data from the Internet of Things and interacts with the virtual reality scene in real time, rendering the scene through the cloud and experiencing virtual reality through the mobile terminal. After the requirements analysis and system framework design of the virtual reality system for physical education in colleges and universities, the system's main functions and data warehouse, software architecture design and system testing process were carried out. Through the analysis of specific trial cases and user feedback information of a college, it shows that the designed virtual reality system of college physical education has good application and promotion effect, and provides a scientific reference for deepening reform of college physical education Suggest.
Wearable Inertial Sensors for Daily Activity Analysis Based on Adam Optimization and the Maximum Entropy Markov Model

Advancements in wearable sensors technologies provide prominent effects in the daily life activities of humans. These wearable sensors are gaining more awareness in healthcare for the elderly to ensure their independent living and to improve their comfort. In this paper, we present a human activity recognition model that acquires signal data from motion node sensors including inertial sensors, i.e., gyroscopes and accelerometers. First, the inertial data is processed via multiple filters such as Savitzky-Golay, median and hampel filters to examine lower/upper cutoff frequency behaviors. Second, it extracts a multifused model for statistical, wavelet and binary features to maximize the occurrence of optimal feature values. Then, adaptive moment estimation (Adam) and AdaDelta are introduced in a feature optimization phase to adopt learning rate patterns. These optimized patterns are further processed by the maximum entropy Markov model (MEMM) for empirical expectation and highest entropy, which measure signal variances for outperformed accuracy results. Our model was experimentally evaluated on University of Southern California Human Activity Dataset (USC-HAD) as a benchmark dataset and on an Intelligent Mediasporting behavior (IMSB), which is a new self-annotated sports dataset. For evaluation, we used the "leave-one-out" cross validation scheme and the results outperformed existing well-known statistical state-of-the-art methods by achieving an improved recognition accuracy of 91.25%, 93.66% and 90.91% when compared with USC-HAD, IMSB, and Mhealth datasets, respectively. The proposed system should be applicable to man-machine interface domains, such as health exercises, robot learning, interactive games and pattern-based surveillance.
YAGO 4: A Reason-able Knowledge Base

YAGO is one of the large knowledge bases in the Linked Open Data cloud. In this resource paper, we present its latest version, YAGO 4, which reconciles the rigorous typing and constraints of schema.org with the rich instance data of Wikidata. The resulting resource contains 2 billion type-consistent triples for 64 Million entities, and has a consistent ontology that allows semantic reasoning with OWL 2 description logics.
ShipDeNet-20: An Only 20 Convolution Layers and &lt;1-MB Lightweight SAR Ship Detector

Existing most deep learning-based synthetic aperture radar (SAR) ship detectors have huge network scale and big model size. Thus, to solve these defects, we propose a lightweight SAR ship detector “ShipDeNet-20” with 20 convolution layers and <; 1 MB (0.82 MB) model size. We use fewer layers and kernels, and depthwise separable convolution (DS-Conv) to ensure ShipDeNet-20's lightweight attribute. Moreover, we also propose a feature fusion module (FF-Module), a feature enhance module (FE-Module), and a scale share feature pyramid module (SSFP-Module) to compensate for the raw ShipDeNet-20's accuracy loss. Experimental results on the open SAR ship detection data set (SSDD) reveal that the accuracy and speed of ShipDeNet-20 are both superior to the other nine state-of-the-art object detectors. Finally, detection results on another two wide-region SAR images show ShipDeNet-20's strong migration ability. ShipDeNet-20 is a novel SAR ship detector, built from scratch, lighter than others by tens even hundreds of times, helpful for real-time SAR application and future hardware transplantation.
Breast ultrasound region of interest detection and lesion localisation

In current breast ultrasound computer aided diagnosis systems, the radiologist preselects a region of interest (ROI) as an input for computerised breast ultrasound image analysis. This task is time consuming and there is inconsistency among human experts. Researchers attempting to automate the process of obtaining the ROIs have been relying on image processing and conventional machine learning methods. We propose the use of a deep learning method for breast ultrasound ROI detection and lesion localisation. We use the most accurate object detection deep learning framework – Faster-RCNN with Inception-ResNet-v2 – as our deep learning network. Due to the lack of datasets, we use transfer learning and propose a new 3-channel artificial RGB method to improve the overall performance. We evaluate and compare the performance of our proposed methods on two datasets (namely, Dataset A and Dataset B), i.e. within individual datasets and composite dataset. We report the lesion detection results with two types of analysis: (1) detected point (centre of the segmented region or the detected bounding box) and (2) Intersection over Union (IoU). Our results demonstrate that the proposed methods achieved comparable results on detected point but with notable improvement on IoU. In addition, our proposed 3-channel artificial RGB method improves the recall of Dataset A. Finally, we outline some future directions for the research.
Silq: a high-level quantum language with safe uncomputation and intuitive semantics

Existing quantum languages force the programmer to work at a low level of abstraction leading to unintuitive and cluttered code. A fundamental reason is that dropping temporary values from the program state requires explicitly applying quantum operations that safely uncompute these values.
Connecting user and item perspectives in popularity debiasing for collaborative recommendation

Recommender systems learn from historical users' feedback that is often non-uniformly distributed across items. As a consequence, these systems may end up suggesting popular items more than niche items progressively, even when the latter would be of interest for users. This can hamper several core qualities of the recommended lists (e.g., novelty, coverage, diversity), impacting on the future success of the underlying platform itself. In this paper, we formalize two novel metrics that quantify how much a recommender system equally treats items along the popularity tail. The first one encourages equal probability of being recommended across items, while the second one encourages true positive rates for items to be equal. We characterize the recommendations of representative algorithms by means of the proposed metrics, and we show that the item probability of being recommended and the item true positive rate are biased against the item popularity. To promote a more equal treatment of items along the popularity tail, we propose an in-processing approach aimed at minimizing the biased correlation between user-item relevance and item popularity. Extensive experiments show that, with small losses in accuracy, our popularity-mitigation approach leads to important gains in beyond-accuracy recommendation quality.
Efficient Dynamic Scene Deblurring Using Spatially Variant Deconvolution Network With Optical Flow Guided Training

In order to remove the non-uniform blur of images captured from dynamic scenes, many deep learning based methods design deep networks for large receptive fields and strong fitting capabilities, or use multi-scale strategy to deblur image on different scales gradually. Restricted by the fixed structures and parameters, these methods are always huge in model size to handle complex blurs. In this paper, we start from the deblurring deconvolution operation, then design an effective and real-time deblurring network. The main contributions are three folded, 1) we construct a spatially variant deconvolution network using modulated deformable convolutions, which can adjust receptive fields adaptively according to the blur features. 2) our analysis shows the sampling points of deformable convolution can be used to approximate the blur kernel, which can be simplified to bi-directional optical flows. So the position learning of sampling points can be supervised by bi-directional optical flows. 3) we build a light-weighted backbone for image restoration problem, which can balance the calculations and effectiveness well. Experimental results show that the proposed method achieves state-of-the-art deblurring performance, but with less parameters and shorter running time.