Reviewerly/exHarmony huggingface dataset:
Controller-Based Energy-Aware Wireless Sensor Network Routing Using Quantum Algorithms

Energy efficient routing in wireless sensor networks has attracted attention from researchers in both academia and industry, most recently motivated by the opportunity to use SDN (software defined network)-inspired approaches. These problems are NP-hard, with algorithms needing computation time which scales faster than polynomial in the problem size. Consequently, heuristic algorithms are used in practice, which are unable to guarantee optimally. In this paper, we show proof-of-principle for the use of a quantum annealing processor instead of a classical processor, to find optimal or near-optimal solutions very quickly. Our preliminary results for small networks show that this approach using quantum computing has great promise and may open the door for other significant improvements in the efficacy of network algorithms.
Applications of digital Medicine in oncology: Prospects and challenges

Abstract The current state of oncology medical services is not encouraging and is unable to fully meet the needs of patients with cancer. In recent years, rapidly developing artificial intelligence technology and gradual advancements in mobile phones, sensors, and wearable devices, which have made these more compact, affordable, and popular, have greatly expanded the development of digital medicine. Digital medicine refers to clinical evidence‐based technology and products with a direct impact on disease management and research. Integrating digital medicine into clinical practice has the advantages of broader applicability, greater cost‐effectiveness, better accessibility, and improved diagnostic and therapeutic performance. Digital medicine has emerged in different clinical application scenarios, including cancer prevention, screening, diagnosis, and treatment, as well as clinical trials. Additionally, big data generated from digital medicine can be used to improve levels of clinical diagnosis and treatment. However, digital medicine also faces many challenges, including security regulation and privacy protection, product usability, data management, and optimization of algorithms. In summary, the application and development of digital medicine in the field of cancer face numerous opportunities and challenges.
Graph-based code semantics learning for efficient semantic code clone detection

Recent studies have shown that high-quality code semantics learning can effectively improve the performance of code clone detection. However, existing approaches suffer from two major drawbacks: (a) insufficient utilization of code representations, leading to inefficient semantics learning, and (b) low efficiency of clone detection, resulting in massive detection time. Therefore, we are motivated to propose an efficient semantics learning method while speeding up the detection process. Specifically, to address the first one, we adopt either CFG (Control Flow Graph) or PDG (Program Dependency Graph) as our initial code representation because of their rich semantic information. Further, we propose a novel graph-based code semantics learning method, which can capture critical information at token, statement, edge, and graph levels. To address the second one, we design a Siamese graph-matching network based on attention mechanisms. It can uniformly generate graph embeddings for code fragments and facilitate parallel detection of semantic clones, thus significantly boosting the speed of semantic clone detection. We evaluated our approach on two Java benchmark datasets, Google Code Jam and BigCloneBench. The experimental results show that our model outperforms the SOTA (State-Of-The-Art) lightweight models and is over 20x faster in detection. In addition, our model performs on par with the large Bert-based models and is over 110x faster in detection. Our code and dataset are available online at: https://github.com/HduDBSI/CodeGraph4CCDetector.
POPDx: an automated framework for patient phenotyping across 392 246 individuals in the UK Biobank study

Objective For the UK Biobank standardized phenotype codes are associated with patients who have been hospitalized but are missing for many patients who have been treated exclusively in an outpatient setting. We describe a method for phenotype recognition that imputes phenotype codes for all UK Biobank participants. Materials and Methods POPDx (Population-based Objective Phenotyping by Deep Extrapolation) is a bilinear machine learning framework for simultaneously estimating the probabilities of 1,538 phenotype codes. We extracted phenotypic and health-related information of 392,246 individuals from the UK Biobank for POPDx development and evaluation. A total of 12,803 ICD-10 diagnosis codes of the patients were converted to 1,538 Phecodes as gold standard labels. The POPDx framework was evaluated and compared to other available methods on automated multi-phenotype recognition. Results POPDx can predict phenotypes that are rare or even unobserved in training. We demonstrate substantial improvement of automated multi-phenotype recognition across 22 disease categories, and its application in identifying key epidemiological features associated with each phenotype. Conclusions POPDx helps provide well-defined cohorts for downstream studies. It is a general purpose method that can be applied to other biobanks with diverse but incomplete data.
Black-box error diagnosis in Deep Neural Networks for computer vision: a survey of tools

The application of Deep Neural Networks (DNNs) to a broad variety of tasks demands methods for coping with the complex and opaque nature of these architectures. When a gold standard is available, performance assessment treats the DNN as a black box and computes standard metrics based on the comparison of the predictions with the ground truth. A deeper understanding of performances requires going beyond such evaluation metrics to diagnose the model behavior and the prediction errors. This goal can be pursued in two complementary ways. On one side, model interpretation techniques “open the box” and assess the relationship between the input, the inner layers and the output, so as to identify the architecture modules most likely to cause the performance loss. On the other hand, black-box error diagnosis techniques study the correlation between the model response and some properties of the input not used for training, so as to identify the features of the inputs that make the model fail. Both approaches give hints on how to improve the architecture and/or the training process. This paper focuses on the application of DNNs to computer vision (CV) tasks and presents a survey of the tools that support the black-box performance diagnosis paradigm. It illustrates the features and gaps of the current proposals, discusses the relevant research directions and provides a brief overview of the diagnosis tools in sectors other than CV.
Automated multi-modal Transformer network (AMTNet) for 3D medical images segmentation

Abstract Objective. Over the past years, convolutional neural networks based methods have dominated the field of medical image segmentation. But the main drawback of these methods is that they have difficulty representing long-range dependencies. Recently, the Transformer has demonstrated super performance in computer vision and has also been successfully applied to medical image segmentation because of the self-attention mechanism and long-range dependencies encoding on images. To the best of our knowledge, only a few works focus on cross-modalities of image segmentation using the Transformer. Hence, the main objective of this study was to design, propose and validate a deep learning method to extend the application of Transformer to multi-modality medical image segmentation. Approach. This paper proposes a novel automated multi-modal Transformer network termed AMTNet for 3D medical image segmentation. Especially, the network is a well-modeled U-shaped network architecture where many effective and significant changes have been made in the feature encoding, fusion, and decoding parts. The encoding part comprises 3D embedding, 3D multi-modal Transformer, and 3D Co-learn down-sampling blocks. Symmetrically, the 3D Transformer block, upsampling block, and 3D-expanding blocks are included in the decoding part. In addition, a Transformer-based adaptive channel interleaved Transformer feature fusion module is designed to fully fuse features of different modalities. Main results. We provide a comprehensive experimental analysis of the Prostate and BraTS2021 datasets. The results show that our method achieves an average DSC of 0.907 and 0.851 (0.734 for ET, 0.895 for TC, and 0.924 for WT) on these two datasets, respectively. These values show that AMTNet yielded significant improvements over the state-of-the-art segmentation networks. Significance. The proposed 3D segmentation network exploits complementary features of different modalities during the feature extraction process at multiple scales to increase the 3D feature representations and improve the segmentation efficiency. This powerful network enriches the research of the Transformer to multi-modal medical image segmentation.
An Enhanced Hyper-Parameter Optimization of a Convolutional Neural Network Model for Leukemia Cancer Diagnosis in a Smart Healthcare System

Healthcare systems in recent times have witnessed timely diagnoses with a high level of accuracy. Internet of Medical Things (IoMT)-enabled deep learning (DL) models have been used to support medical diagnostics in real time, thus resolving the issue of late-stage diagnosis of various diseases and increasing performance accuracy. The current approach for the diagnosis of leukemia uses traditional procedures, and in most cases, fails in the initial period. Hence, several patients suffering from cancer have died prematurely due to the late discovery of cancerous cells in blood tissue. Therefore, this study proposes an IoMT-enabled convolutional neural network (CNN) model to detect malignant and benign cancer cells in the patient’s blood tissue. In particular, the hyper-parameter optimization through radial basis function and dynamic coordinate search (HORD) optimization algorithm was used to search for optimal values of CNN hyper-parameters. Utilizing the HORD algorithm significantly increased the effectiveness of finding the best solution for the CNN model by searching multidimensional hyper-parameters. This implies that the HORD method successfully found the values of hyper-parameters for precise leukemia features. Additionally, the HORD method increased the performance of the model by optimizing and searching for the best set of hyper-parameters for the CNN model. Leukemia datasets were used to evaluate the performance of the proposed model using standard performance indicators. The proposed model revealed significant classification accuracy compared to other state-of-the-art models.
Lossless Image Steganography Based on Invertible Neural Networks

Image steganography is a scheme that hides secret information in a cover image without being perceived. Most of the existing steganography methods are more concerned about the visual similarity between the stego image and the cover image, and they ignore the recovery accuracy of secret information. In this paper, the steganography method based on invertible neural networks is proposed, which can generate stego images with high invisibility and security and can achieve lossless recovery for secret information. In addition, this paper introduces a mapping module that can compress information actually embedded to improve the quality of the stego image and its antidetection ability. In order to restore message and prevent loss, the secret information is converted into a binary sequence and then embedded in the cover image through the forward operation of the invertible neural networks. This information will then be recovered from the stego image through the inverse operation of the invertible neural networks. Experimental results show that the proposed method in this paper has achieved competitive results in the visual quality and safety of stego images and achieved 100% accuracy in information extraction.
Petrochemical Equipment Detection by Improved Yolov5 with Multiscale Deep Feature Fusion and Attention Mechanism

Petrochemical equipment detection technology plays important role in petrochemical industry security monitoring systems, equipment working status analysis systems, and other applications. In complex scenes, the accuracy and speed of petrochemical equipment detection would be limited because of the missing and false detection of equipment with extreme sizes, due to image quality, equipment scale, light, and other factors. In this paper, a one-stage attention mechanism-enhanced Yolov5 network is proposed to detect typical types of petrochemical equipment in industry scene images. The model considers the advantages of the channel and spatial attention mechanism and incorporates it into the three mainframes. Furthermore, the multiscale deep feature is fused with a bottom-up feature pyramid structure to learn the features of equipment with extreme sizes. Moreover, an adaptive anchor generation algorithm is proposed to handle objects with extreme sizes in a complex background. In addition, the data augmentation strategy is also introduced to handle the relatively small and extremely large sample and to enhance the robustness of the fused model. The proposed model was validated on the self-built petrochemical equipment image data set, and the experimental results show that it achieves a competitive performance in comparison with the related state-of-the-art detectors.
Autoencoders for sample size estimation for fully connected neural network classifiers

Abstract Sample size estimation is a crucial step in experimental design but is understudied in the context of deep learning. Currently, estimating the quantity of labeled data needed to train a classifier to a desired performance, is largely based on prior experience with similar models and problems or on untested heuristics. In many supervised machine learning applications, data labeling can be expensive and time-consuming and would benefit from a more rigorous means of estimating labeling requirements. Here, we study the problem of estimating the minimum sample size of labeled training data necessary for training computer vision models as an exemplar for other deep learning problems. We consider the problem of identifying the minimal number of labeled data points to achieve a generalizable representation of the data, a minimum converging sample (MCS). We use autoencoder loss to estimate the MCS for fully connected neural network classifiers. At sample sizes smaller than the MCS estimate, fully connected networks fail to distinguish classes, and at sample sizes above the MCS estimate, generalizability strongly correlates with the loss function of the autoencoder. We provide an easily accessible, code-free, and dataset-agnostic tool to estimate sample sizes for fully connected networks. Taken together, our findings suggest that MCS and convergence estimation are promising methods to guide sample size estimates for data collection and labeling prior to training deep learning models in computer vision.
Optimal Geometries for AOA Localization in the Bayesian Sense

This paper considers the optimal sensor placement problem for angle-of-arrival (AOA) target localization in the 2D plane with a Gaussian prior. Optimal sensor locations are analytically determined for a single AOA sensor using the D- and A-optimality criteria and an approximation of the Bayesian Fisher information matrix (BFIM). Optimal sensor placement is shown to align with the minor axis of the prior covariance error ellipse for both optimality criteria. The approximate BFIM is argued to be valid for a sufficiently small prior covariance compared with the target range. Optimal sensor placement results obtained for Bayesian target localization are extended to manoeuvring target tracking. For sensor trajectory optimization subject to turn-rate constraints, numerical search methods based on the D- and A-optimality criteria as well as a new closed-form projection algorithm that aims to achieve alignment with the minor axis of the prior error ellipse are proposed. It is observed that the two optimality criteria generate significantly different optimal sensor trajectories despite having the same optimal sensor placement for the localization of a stationary target. Analysis results and the performance of the sensor trajectory optimization methods are demonstrated with simulation examples. It is observed that the new closed-form projection algorithm achieves superior tracking performance compared with the two numerical search methods.
Using cascade CNN-LSTM-FCNs to identify AI-altered video based on eye state sequence

Deep learning is notably successful in data analysis, computer vision, and human control. Nevertheless, this approach has inevitably allowed the development of DeepFake video sequences and images that could be altered so that the changes are not easily or explicitly detectable. Such alterations have been recently used to spread false news or disinformation. This study aims to identify Deepfaked videos and images and alert viewers to the possible falsity of the information. The current work presented a novel means of revealing fake face videos by cascading the convolution network with recurrent neural networks and fully connected network (FCN) models. The system detection approach utilizes the eye-blinking state in temporal video frames. Notwithstanding, it is deemed challenging to precisely depict (i) artificiality in fake videos and (ii) spatial information within the individual frame through this physiological signal. Spatial features were extracted using the VGG16 network and trained with the ImageNet dataset. The temporal features were then extracted in every 20 sequences through the LSTM network. On another note, the pre-processed eye-blinking state served as a probability to generate a novel BPD dataset. This newly-acquired dataset was fed to three models for training purposes with each entailing four, three, and six hidden layers, respectively. Every model constitutes a unique architecture and specific dropout value. Resultantly, the model optimally and accurately identified tampered videos within the dataset. The study model was assessed using the current BPD dataset based on one of the most complex datasets (FaceForensic++) with 90.8% accuracy. Such precision was successfully maintained in datasets that were not used in the training process. The training process was also accelerated by lowering the computation prerequisites.
Person Re-Identification with Improved Performance by Incorporating Focal Tversky Loss in AGW Baseline

Person re-identification (re-ID) is one of the essential tasks for modern visual intelligent systems to identify a person from images or videos captured at different times, viewpoints, and spatial positions. In fact, it is easy to make an incorrect estimate for person re-ID in the presence of illumination change, low resolution, and pose differences. To provide a robust and accurate prediction, machine learning techniques are extensively used nowadays. However, learning-based approaches often face difficulties in data imbalance and distinguishing a person from others having strong appearance similarity. To improve the overall re-ID performance, false positives and false negatives should be part of the integral factors in the design of the loss function. In this work, we refine the well-known AGW baseline by incorporating a focal Tversky loss to address the data imbalance issue and facilitate the model to learn effectively from the hard examples. Experimental results show that the proposed re-ID method reaches rank-1 accuracy of 96.2% (with mAP: 94.5) and rank-1 accuracy of 93% (with mAP: 91.4) on Market1501 and DukeMTMC datasets, respectively, outperforming the state-of-the-art approaches.
ECONOMIC FREEDOM, HUMAN CAPITAL AND ECONOMIC GROWTH IN THE MAGHREB COUNTRIES

This article's goal is to judge impacts or effects of economic freedom and human capital on economic growth, in the three Maghreb countries, which are Tunisia, Algeria and Morocco covering the period of 1996-2018.Economic growth refers to the positive change in the production of goods and services in an economy over a given period, usually a long period.In practice, the most used indicator to measure it is gross domestic product (GDP).The results show that there is an interaction between the variable of economic freedom and human capital, which implies that there is a relationship of complementarily between economic freedom and human capital on economic growth.In this study, the factors economic freedom and human capital are critically important in attracting more foreign investments which increase the economic growth.Human capital is a significant determinant of economic growth in the Maghreb countries.The economic freedom is not enough to attract foreign investments, but in conjunction with the existence of human capital well qualified can attract more investments, which are contributed to the economic growth.
A seer knows best

Serverless platforms offer high resource elasticity and pay-as-you-go billing, making them a compelling choice for data analytics. To craft a "pure" serverless solution, the common practice is to transfer intermediate data between serverless functions via serverless object storage (IBM COS; AWS S3). However, prior works have led to inconclusive results about the performance of object storage, since they have left large margin for optimization. To verify that object storage has been underrated, we design a novel shuffle manager for serverless data analytics termed Seer. Specifically, Seer dynamically chooses between two shuffle algorithms to maximize performance. The algorithm choice is based on some predictive models, and very importantly, without users having to specify intermediate data sizes at the time of the job submission. We integrate Seer with PyWren-IBM [31], a serverless analytics framework, and evaluate it against both serverful (e.g., Spark) and serverless systems (e.g., Google BigQuery). Our results certify that our new shuffle manager can deliver performance improvements over them.
User Perspective Bangla Sentiment Analysis for Online Gaming Addiction using Machine Learning

Sentiment analysis is the modern Natural Language Processing (NLP) technique for determining the sentiment of a user. The recent COVID-19 pandemic has pushed people of all ages, particularly the youth to get directly or indirectly involved in internet activities, one of which is online gaming. People have become increasingly involved in online gaming since they have easy access to the internet via smartphones. This research study has attempted to investigate online gaming addiction using different machine learning classification algorithms from over 401 data points. People of all ages, particularly students in high school, college, and university, are considered for data collection. After preprocessing and feature engineering the collected data, six state-of-the-art machine learning classification algorithms viz. Decision Tree, Random Forest, Multinomial Naive Bayes, Extreme Gradient Boosting, Support Vector Machine and K Nearest Neighbor are used to train the model. All six classifiers predict with high accuracy, with Multinomial Naive Bayes (MNB) having the highest accuracy of 73.27%.