Reviewerly/exHarmony huggingface dataset:
ReID-DeePNet: A Hybrid Deep Learning System for Person Re-Identification

Person re-identification has become an essential application within computer vision due to its ability to match the same person over non-overlapping cameras. However, it is a challenging task because of the broad view of cameras with a large number of pedestrians appearing with various poses. As a result, various approaches of supervised model learning have been utilized to locate and identify a person based on the given input. Nevertheless, several of these approaches perform worse than expected in retrieving the right person in real-time over multiple CCTVs/camera views. This is due to inaccurate segmentation of the person, leading to incorrect classification. This paper proposes an efficient and real-time person re-identification system, named ReID-DeePNet system. It is based on fusing the matching scores generated by two different deep learning models, convolutional neural network and deep belief network, to extract discriminative feature representations from the pedestrian image. Initially, a segmentation procedure was developed based on merging the advantages of the Mask R-CNN and GrabCut algorithm to tackle the adverse effects caused by background clutter. Afterward, the two different deep learning models extracted discriminative feature representations from the pedestrian segmented image, and their matching scores were fused to make the final decision. Several extensive experiments were conducted, using three large-scale and challenging person re-identification datasets: Market-1501, CUHK03, and P-DESTRE. The ReID-DeePNet system achieved new state-of-the-art Rank-1 and mAP values on these three challenging ReID datasets.
Image sub-division and quadruple clipped adaptive histogram equalization (ISQCAHE) for low exposure image enhancement

In this paper, a novel image sub-division and quadruple clipped adaptive histogram equalization (ISQCAHE) technique is proposed for the enhancement of low exposure images. The proposed method involves, computation of the histogram which includes a new approach of image sub-division, enhancement controlling mechanism, modification of probability density function (PDF) and histogram equalization (HE). The original histogram is segmented into sub-histograms based on exposure threshold and mean, to preserve the brightness and entropy. Then, individual sub-histogram is clipped separately to control the enhancement rate. For enhancing the visual quality, HE is applied to individual sub-histogram using the modified PDF. The experimental results show that, the proposed ISQCAHE method avoids the unpleasant artifacts effectively and provide a natural appearance to the enhanced image. It is simple, adaptive and performs superior than other techniques in terms of visual quality, absolute mean brightness error, entropy, Natural image quality evaluation, brightness preservation, structure similarity index measure and feature similarity index measure.
A framework for the elderly first aid system by integrating vision-based fall detection and BIM-based indoor rescue routing

The occurrence of falls among older adults may result in life-threatening injuries and accidental deaths due to their vulnerability. As such, an advanced first aid system is significantly necessary to accurately detect falls and provide prompt assistance. However, current research primarily focused on fall prevention, fall detection, and first aid services after falling, thus lacking studies dealing with a systematic solution. To address this issue, the present research proposes an integrated framework for the elderly first aid system in an indoor environment using computer vision and building information model (BIM) techniques, which consists of three primary components: a vision-based module for fall detection, a cloud server (internet), and a BIM-based module for rescue routing. The experimental results showed that the proposed method could achieve 94.1% precision in identifying the fall status of older adults (i.e., falling or non-falling). Also, the proposed method enabled to automatically generate a rescue route in consideration of the routing accessibility for first aid in a BIM environment. The framework proposed in this study will improve the efficiency of the elderly first aid when falls occur, with shortening the rescue time to mitigate injury severity.
Add noise to remove noise: Local differential privacy for feature selection

Feature selection has become significantly important for data analysis. It selects the most informative features describing the data to filter out the noise, complexity, and over-fitting caused by less relevant features. Accordingly, feature selection improves the predictors' accuracy, enables them to be trained faster and more cost-effectively, and provides a better understanding of the underlying data. While plenty of practical solutions have been proposed in the literature to identify the most discriminating features describing a dataset, an understanding of feature selection over privacy-sensitive data in the absence of a trusted party is still missing. The design of such a framework is specifically important in our modern society, where each individual through accessing the Internet can play simultaneously the role of a data provider and a data-analysis beneficiary. In this study, we propose a novel feature selection framework based on Local Differential Privacy (LDP), named LDP-FS, which estimates the importance of features over securely protected data while protects the confidentiality of each individual data before leaving the user's device. The performance of LDP-FS in terms of scoring and ordering the features is assessed by investigating the impact of datasets properties, privacy mechanism, privacy levels, and feature selection techniques on this framework. The accuracy of classifiers trained on the selected subset of features by LDP-FS is also presented. Our experimental results demonstrate the effectiveness and efficiency of the proposed framework.
Deep-Learning to Predict BRCA Mutation and Survival from Digital H&amp;E Slides of Epithelial Ovarian Cancer

BRCA 1/2 genes mutation status can already determine the therapeutic algorithm of high grade serous ovarian cancer patients. Nevertheless, its assessment is not sufficient to identify all patients with genomic instability, since BRCA 1/2 mutations are only the most well-known mechanisms of homologous recombination deficiency (HR-d) pathway, and patients displaying HR-d behave similarly to BRCA mutated patients. HRd assessment can be challenging and is progressively overcoming BRCA testing not only for prognostic information but more importantly for drugs prescriptions. However, HR testing is not already integrated in clinical practice, it is quite expensive and it is not refundable in many countries. Selecting patients who are more likely to benefit from this assessment (BRCA 1/2 WT patients) at an early stage of the diagnostic process, would allow an optimization of genomic profiling resources. In this study, we sought to explore whether somatic BRCA1/2 genes status can be predicted using computational pathology from standard hematoxylin and eosin histology. In detail, we adopted a publicly available, deep-learning-based weakly supervised method that uses attention-based learning to automatically identify sub regions of high diagnostic value to accurately classify the whole slide (CLAM). The same model was also tested for progression free survival (PFS) prediction. The model was tested on a cohort of 664 (training set: n = 464, testing set: n = 132) ovarian cancer patients, of whom 233 (35.1%) had a somatic BRCA 1/2 mutation. An area under the curve of 0.7 and 0.55 was achieved in the training and testing set respectively. The model was then further refined by manually identifying areas of interest in half of the cases. 198 images were used for training (126/72) and 87 images for validation (55/32). The model reached a zero classification error on the training set, but the performance was 0.59 in terms of validation ROC AUC, with a 0.57 validation accuracy. Finally, when applied to predict PFS, the model achieved an AUC of 0.71, with a negative predictive value of 0.69, and a positive predictive value of 0.75. Based on these analyses, we have planned further steps of development such as proving a reference classification performance, exploring the hyperparameters space for training optimization, eventually tweaking the learning algorithms and the neural networks architecture for better suiting this specific task. These actions may allow the model to improve performances for all the considered outcomes.
PheValuator 2.0: Methodological improvements for the PheValuator approach to semi-automated phenotype algorithm evaluation

Phenotype algorithms are central to performing analyses using observational data. These algorithms translate the clinical idea of a health condition into an executable set of rules allowing for queries of data elements from a database. PheValuator, a software package in the Observational Health Data Sciences and Informatics (OHDSI) tool stack, provides a method to assess the performance characteristics of these algorithms, namely, sensitivity, specificity, and positive and negative predictive value. It uses machine learning to develop predictive models for determining a probabilistic gold standard of subjects for assessment of cases and non-cases of health conditions. PheValuator was developed to complement or even replace the traditional approach of algorithm validation, i.e., by expert assessment of subject records through chart review. Results in our first PheValuator paper suggest a systematic underestimation of the PPV compared to previous results using chart review. In this paper we evaluate modifications made to the method designed to improve its performance.The major changes to PheValuator included allowing all diagnostic conditions, clinical observations, drug prescriptions, and laboratory measurements to be included as predictors within the modeling process whereas in the prior version there were significant restrictions on the included predictors. We also have allowed for the inclusion of the temporal relationships of the predictors in the model. To evaluate the performance of the new method, we compared the results from the new and original methods against results found from the literature using traditional validation of algorithms for 19 phenotypes. We performed these tests using data from five commercial databases.In the assessment aggregating all phenotype algorithms, the median difference between the PheValuator estimate and the gold standard estimate for PPV was reduced from -21 (IQR -34, -3) in Version 1.0 to 4 (IQR -3, 15) using Version 2.0. We found a median difference in specificity of 3 (IQR 1, 4.25) for Version 1.0 and 3 (IQR 1, 4) for Version 2.0. The median difference between the two versions of PheValuator and the gold standard for estimates of sensitivity was reduced from -39 (-51, -20) to -16 (-34, -6).PheValuator 2.0 produces estimates for the performance characteristics for phenotype algorithms that are significantly closer to estimates from traditional validation through chart review compared to version 1.0. With this tool in researcher's toolkits, methods, such as quantitative bias analysis, may now be used to improve the reliability and reproducibility of research studies using observational data.
Efficient comparison of sentence embeddings

The evolution of natural language processing (NLP) has drastically improved numerous applications in terms of quality of results and speed, like the use of semantic search in modern search engines. NLP has highly benefited from the recent developments in word and sentence embeddings which enable the transformation of complex NLP tasks, such as semantic similarity or Question and Answering (Q&A), into much simpler to perform vector comparisons. However, the new problems resulting from such transformations have also challenging tasks to address like the efficient comparison of embeddings and their manipulation. In this work, we will discuss about various word and sentence embeddings algorithms, we will select a sentence embedding algorithm, BERT, as our algorithm of choice and we will evaluate the performance of two vector comparison approaches, FAISS and Elasticsearch, in the specific problem of sentence embeddings. According to the results, FAISS outperforms Elasticsearch when used in a centralized environment with only one node, especially when big datasets are included.
Unconditionally secure digital signatures implemented in an eight-user quantum network*

The ability to know and verifiably demonstrate the origins of messages can often be as important as encrypting the message itself. Here we present an experimental demonstration of an unconditionally secure digital signature (USS) protocol implemented for the first time, to the best of our knowledge, on a fully connected quantum network without trusted nodes. Our USS protocol is secure against forging, repudiation and messages are transferrable. We show the feasibility of unconditionally secure signatures using only bi-partite entangled states distributed throughout the network and experimentally evaluate the performance of the protocol in real world scenarios with varying message lengths.
Risk Assessment and Analysis in Supply Chain Finance Based on Blockchain Technology

With the development of the times, the financial industry is constantly changing. Blockchain technology continues to play an important role in supply chain finance. The security of the financial industry is very important. Blockchain technology can better protect the security of the financial industry, but there are also some risks. We have made a detailed investigation and research on the risk assessment and analysis of blockchain technology in supply chain finance. The research is as follows: (1) the risks under blockchain technology are introduced in detail, and the risks in many aspects are detailed. It is convenient for the public to do a good job in risk control in the use of blockchain finance. (2) The algorithm of the blockchain is used for mining. In finance, the security of privacy is the most important. The differential privacy in the algorithm and the Bc-ppkCa algorithm makes the financial privacy under the blockchain better. It is beneficial for the public to use blockchain technology more efficiently and safely. (3) The financial industry under the blockchain is very complex. Let us take the financial situation under industrial enterprises as an example. Investigate the company’s supply chain financial situation data, and compare the advantages of supply chain finance and the company’s financial situation. It also evaluates the risks generated by supply chain finance and makes corresponding analysis.
Automated Dam Data Acquisition and Analysis in Real-Time

The Mechanized Dam Information Procurement and Panic Reportage System was configuration to get, screen, and dissect, in real-time, basic wellbeing boundaries, for example, inflows, surges, door openings, pressure gages and level of the water kept up in the dam. This framework permits dams to be worked all the more securely and crisis intends to be all the more successfully organized and actualized with the crisis the executive's organizations. The model portrayed in this paper is the initial move toward our vision of improving wellbeing on dams by having a system of sensors sending defensive rigging and checking framework. The health care monitoring system also processed with the self-evaluation module which helps to identify the process and movement of the object. The real time received data also discussed in this research work. The wireless sensor plays an important role to locate the data and identify the drop in the transmission line. Overall, the dam data information is fetched and processed the IoT operation. The reports are analyzed and tabulated in this research work which identify the level of prediction and operation of the dam data. The energy consumption, delay, and percentage of reading performances are improved in the proposed method than the conventional works.
The Study of Mathematical Models and Algorithms for Face Recognition in Images Using Python in Proctoring System

The article analyzes the possibility and rationality of using proctoring technology in remote monitoring of the progress of university students as a tool for identifying a student. Proctoring technology includes face recognition technology. Face recognition belongs to the field of artificial intelligence and biometric recognition. It is a very successful application of image analysis and understanding. To implement the task of determining a person’s face in a video stream, the Python programming language was used with the OpenCV code. Mathematical models of face recognition are also described. These mathematical models are processed during data generation, face analysis and image classification. We considered methods that allow the processes of data generation, image analysis and image classification. We have presented algorithms for solving computer vision problems. We placed 400 photographs of 40 students on the base. The photographs were taken at different angles and used different lighting conditions; there were also interferences such as the presence of a beard, mustache, glasses, hats, etc. When analyzing certain cases of errors, it can be concluded that accuracy decreases primarily due to images with noise and poor lighting quality.
A Medical Semantic-Assisted Transformer for Radiographic Report Generation

Automated radiographic report generation is a challenging cross-domain task that aims to automatically generate accurate and semantic-coherence reports to describe medical images. Despite the recent progress in this field, there are still many challenges at least in the following aspects. First, radiographic images are very similar to each other, and thus it is difficult to capture the fine-grained visual differences using CNN as the visual feature extractor like many existing methods. Further, semantic information has been widely applied to boost the performance of generation tasks (e.g. image captioning), but existing methods often fail to provide effective medical semantic features. Toward solving those problems, in this paper, we propose a memory-augmented sparse attention block utilizing bilinear pooling to capture the higher-order interactions between the input fine-grained image features while producing sparse attention. Moreover, we introduce a novel Medical Concepts Generation Network (MCGN) to predict fine-grained semantic concepts and incorporate them into the report generation process as guidance. Our proposed method shows promising performance on the recently released largest benchmark MIMIC-CXR. It outperforms multiple state-of-the-art methods in image captioning and medical report generation.
OLP++: An online local classifier for high dimensional data

Ensemble diversity is an important characteristic of Multiple Classifier Systems (MCS), which aim at improving the overall performance of a classification system by combining the response of several models. While diversity may be introduced through various manipulations at the data level and the model level, some MCSs incorporate local information in order to increase it and/or take advantage of it, based on the idea that the different classifiers in the ensemble may have expertise in distinct areas of the feature space. 1 1 Code available at: https://github.com/marianaasouza/olp_plusplus . Following a similar reasoning, we introduced in a previous work an ensemble method which produces in test time a few experts in the local region where each given query sample is located. These local experts, which are generated with slightly differing views of the target area, are then used to label the corresponding unknown instance. While the framework was shown to perform well especially over imbalanced problems, the locality definition in the method is based on the nearest neighbors rule and Euclidian distance, as is the case of various local-based ensembles, which may suffer from the effects of the curse of dimensionality over high dimensional problems. Thus, in this work, we propose a local ensemble method in which we leverage the data partitions given by decision trees for locality definition. More specifically, the partitions defined at different levels of the decision path that a given query instance traverses in the tree(s) are used as the regions over which the local experts are produced. By using different node levels from the path, each classifier in the local pool has a moderately distinct view of the target region without resorting to a dissimilarity metric, which might be susceptible to high dimensional spaces. Experimental results over 39 high dimensional problems showed that the proposed approach was significantly superior to our previous, distance-based framework in balanced accuracy rate. Compared to other six local-based ensemble methods, including dynamic selection and weighting schemes, the proposed method achieved competitive results, outperforming the random forest baseline and two state-of-the-art dynamic ensemble selection techniques. • Local-based ensemble methods produce and/or exploit models specialized in each area. • Locality is often achieved via distance metrics which degrade in high dimensionality. • We propose a method that produces local experts on the fly for high dimensional data. • The models are fit over multiple partitions recursively defined by decision trees. • The proposed method outperformed several distance-based local ensemble methods.
Short-Term Prediction Method of Solar Photovoltaic Power Generation Based on Machine Learning in Smart Grid

In order to improve the accuracy of ultra short-term power prediction of the photovoltaic power generation system, a short-term photovoltaic power prediction method based on an adaptive k-means and Gru machine learning model is proposed. This method first introduces the construction process of the model and then builds a short-term photovoltaic power generation prediction model based on an adaptive k-means and Gru machine learning models. Then, the network structure and key parameters are determined through experiments, and the initial training set of the prediction model is selected according to the short-term photovoltaic power generation characteristics. And the adaptive k-means is used to cluster the initial training set and the photovoltaic power on the forecast day. The Gru model is trained on the initial training set data of each category, and the generated power is predicted in combination with the trained Gru model. Finally, considering three typical weather types, the proposed method is used for simulation analysis and compared with the other three traditional photovoltaic power generation single prediction models. The comparison results show that the proposed short-term photovoltaic power generation prediction method based on an adaptive k-means and Gru network has better effect, better robustness, and less error.
Can Visual Motion Presented in a VR Headset Reduce Motion Sickness for Vehicle Passengers?

To make the rise of automated vehicles possible and to allow for their mass adoption, one major problem still needs to be solved: Motion sickness. Automated vehicles lead to increased motion sickness partly caused by an occlusion of the outside world (conflict between visual and vestibular system). In this study, we propose the usage of Virtual Reality (VR) headsets for productivity tasks while traveling as well as a motion sickness mitigation strategy. Car motion is simulated using a rotating chair while a reading task is presented in VR with or without visual motion cues being presented in the background. Visual motion cues showed a somewhat beneficial effect on motion sickness in this study without being perceived as too distracting from the primary reading task or affecting reading performance further highlighting the potential of VR usage in transport.