Reviewerly/exHarmony huggingface dataset:
Face Detection Algorithm Based on Double-Channel CNN with Occlusion Perceptron

Aiming at the problem of low accuracy of face detection under complex occlusion conditions, a double-channel occlusion perceptron neural network model was proposed. The area occlusion judgment unit is designed and integrated into the VGG16 network to form an occlusion perceptron neural network. Thereupon, the features of unoccluded regions and less occluded regions in facial images are extracted by the perceptual neural network. Transfer learning algorithm is utilized to pretrain parameters of the convolution layer to reduce the overfitting problem caused by insufficient training data samples. Face features of the whole face were extracted by optimizing the residual network, and then the face features of the occluding perceptron neural network and the residual network were weighted and fused. Experiments were carried out on two open data sets, AR and MAFA. The results demonstrate that the detection accuracy of this method is higher than that of other methods, and the detection speed is faster.
Improving Design of Enabling Collaborative Situation Based on Augmented Reality Devices

Industry 4.0 needs the help of technology 4.0 to improve production apparatus flexibility and productivity and decrease non-quality costs. However, these technologies aren’t well integrated in already existing processes because we try to correlate the technology to the norms and standards we have previously set, in a time when augmented reality systems were not part of the industry’s toolbox, by neglecting, the human factor. Some researchers in ergonomics develop concepts to create a symbiosis between Human and machine in these production tasks. This is notably the case for the paradigm of Enabling Collaborative Situation (ECS) where researchers investigate the collaboration between Human and machine over time. We relied on this paradigm to build a study where we have questioned multiple interactions between a user and an Augmented Reality (AR) device in order to find what kind of features might be considered as relevant to improve the HMIs and the AR devices in the future. This study has been conducted on a training workstation for automotive industry with a Hololens 1. We present in this paper the results of this study and envisage the technical solutions to move towards an ECS through the redesign of Human-Machine Interface. At the end of the paper, we draw the outlines of a future experience where we want to compare, the former HMI versus the new one redesigned thanks to the previous study in a first time, and the Hololens 1 against the Hololens 2 in a second time in order to understand how the new features move to an ECS.
Deep convolutional neural networks for regular texture recognition

Regular textures are frequently found in man-made environments and some biological and physical images. There are a wide range of applications for recognizing and locating regular textures. In this work, we used deep convolutional neural networks (CNNs) as a general method for modelling and classifying regular and irregular textures. We created a new regular texture database and investigated two sets of deep CNNs-based methods for regular and irregular texture classification. First, the classic CNN models (e.g. inception, residual network, etc.) were used in a standard way. These two-class CNN classifiers were trained by fine-tuning networks using our new regular texture database. Next, we transformed the trained filter features of the last convolutional layer into a vector representation using Fisher Vector pooling (FV). Such representations can be efficiently used for a wide range of machine learning tasks such as classification or clustering, thus more transferable from one domain to another. Our experiments show that the standard CNNs attained sufficient accuracy for regular texture recognition tasks. The Fisher representations combined with support vector machine (SVM) also showed high performance for regular and irregular texture classification. We also find CNNs performs sub-optimally for long-range patterns, despite the fact that their fully-connected layers pool local features into a global image representation.
Compound W-Net with Fully Accumulative Residual Connections for Liver Segmentation Using CT Images

Computed tomography (CT) is a common modality for liver diagnosis, treatment, and follow-up process. Providing accurate liver segmentation using CT images is a crucial step towards those tasks. In this paper, we propose a stacked 2-U-Nets model with three different types of skip connections. The proposed connections work to recover the loss of high-level features on the convolutional path of the first U-Net due to the pooling and the loss of low-level features during the upsampling path of the first U-Net. The skip connections concatenate all the features that are generated at the same level from the previous paths to the inputs of the convolutional layers in both paths of the second U-Net in a densely connected manner. We implement two versions of the model with different number of filters at each level of each U-Net by maximising the Dice similarity between the predicted liver region and that of the ground truth. The proposed models were trained with 3Dircadb public dataset that were released for Sliver and 3D liver and tumour segmentation challenges during MICCAI 2007-2008 challenge. The experimental results show that the proposed model outperformed the original U-Net and 2-U-Nets variants, and is comparable to the state-of-the-art mU-Net, DC U-Net, and Cascaded UNET.
Is Consciousness First in Virtual Reality?

The prevailing scientific paradigm is that matter is primary and everything, including consciousness can be derived from the laws governing matter. Although the scientific explanation of consciousness on these lines has not been realized, in this view it is only a matter of time before consciousness will be explained through neurobiological activity in the brain, and nothing else. There is an alternative view that holds that it is fundamentally impossible to explain how subjectivity can arise solely out of material processes-"the hard problem of consciousness"-and instead consciousness should be regarded in itself as a primary force in nature. This view attempts to derive, for example, the laws of physics from models of consciousness, instead of the other way around. While as scientists we can understand and have an intuition for the first paradigm, it is very difficult to understand what "consciousness is primary" might mean since it has no intuitive scientific grounding. Here we show that worlds experienced through virtual reality (VR) are such that consciousness is a first order phenomenon. We discuss the Interface Theory of Perception which claims that in physical reality perceptions are not veridical and that we do not see the "truth" but that perception is based on evolutionary payoffs. We show that this theory may provide an accurate description of perception and consciousness within VR, and we put forward an experimental study that could throw light on this. We conclude that VR does offer an experimental frame that provides intuition with respect to the idea that "consciousness is first" and what this might mean regarding the perceived world. However, we do not draw any conclusions about the veracity of this notion with respect to physical reality or question the emergence of consciousness from brain function.
Big Data in Higher Education for Student Behavior Analytics (Big Data-HE-SBA System Architecture)

Big data is an important part of innovation that has recently attracted a lot of interest from academics and practitioners alike. Given the importance of the education industry, there is a growing trend to investigate the role of big data in this field. Much research has been undertaken to date in order to better understand the use of big data in many sectors for diverse reasons. Big data in higher education, however, still lacks a complete examination. Thus, the purposes of the research were (1) to design the system architecture of big data in higher education for student behavior analytics and (2) to evaluate the system architecture of big data in higher education for student behavior analytics. The research procedure was divided into two phases. The first phase is designing a system architecture for big data in higher education for student behavior analytics, and the second phase is the architecture evaluation by experts. Purposive sampling was used to select ten experts in big data and student behavior analytics. Data collection tools were the system and the assessment of an appropriate model with a five-level rating scale. The statistics used in the data analysis were means and standard deviation. The results showed that the system architecture of big data in higher education for student behavior analytics consists of four elements: a) Big Data Sources for Behavioral Analytics; b) Big Data Sources for Behavioral Analytics Sub-Domains; c) Big data capture and storage for behavioral analytics; and d) big data behavioral analysis. The experts&amp;#39; opinions on the system architecture were at the most appropriate level.
A Comparison of K-Means and Mean Shift Algorithms

Clustering, also known as cluster analysis, is a learning problem that occurs without the intervention of a human.This technique is frequently used very efficiently in data analysis to observe and identify interesting, useful, or desirable patterns in data.The clustering technique operates by dividing the data involved into similar objects based on their identified properties.This process results in the formation of groups, and each formed group is referred to as a cluster.A single said cluster consists of objects from the data that share similarities with other objects found in the same cluster and differ from objects identified from the data that now exist in other clusters.Clustering is an important process in many aspects of data analysis because it determines and presents the intrinsic grouping of objects in the data based on their attributes in a batch of unlabeled raw data.This method of cluster analysis lacks a textbook or, to put it another way, good criteria.This is due to the fact that this process is unique and customizable for each user who requires it for a variety of reasons.There is no single best clustering algorithm because it is so dependent on the user's scenario and needs.The purpose of this paper is to compare and contrast two different clustering algorithms.The algorithms under consideration are the k-mean and the mean shift.These algorithms are compared based on the following criteria: time complexity, training, prediction performance, and clustering algorithm accuracy.
Research on Object Detection of PCB Assembly Scene Based on Effective Receptive Field Anchor Allocation

Vision-based object detection of PCB (printed circuit board) assembly scenes is essential in accelerating the intelligent production of electronic products. In particular, it is necessary to improve the detection accuracy as much as possible to ensure the quality of assembly products. However, the lack of object detection datasets in PCB assembly scenes is the key to restricting intellectual PCB assembly research development. As an excellent representative of the one-stage object detection model, YOLOv3 (you only look once version 3) mainly relies on placing predefined anchors on the three feature pyramid layers and realizes recognition and positioning using regression. However, the number of anchors distributed in each grid cell of different scale feature layers is usually the same. The ERF (effective receptive field) corresponding to the grid cell at different locations varies. The contradiction between the uniform distribution of fixed-size anchors and the ERF size range in different feature layers will reduce the effectiveness of object detection. Few people use ERF as a standard for assigning anchors to improve detection accuracy. To address this issue, firstly, we constructed a PCB assembly scene object detection dataset, which includes 21 classes of detection objects in three scenes before assembly, during assembly, and after assembly. Secondly, we performed a refined ERF analysis on each grid of the three output layers of YOLOv3, determined the ERF range of each layer, and proposed an anchor allocation rule based on the ERF. Finally, for the small and difficult-to-detect TH (through-holes), we increased the context information and designed improved-ASPP (Atrous spatial pyramid pooling) and channel attention joint module. Through a series of experiments on the object detection dataset of the PCB assembly scene, we found that under the framework of YOLOv3, anchor allocation based on ERF can increase mAP (mean average precision) from 79.32% to 89.86%. At the same time, our proposed method is superior to Faster R-CNN (region convolution neural network), SSD (single shot multibox detector), and YOLOv4 (you only look once version 4) in the balance of high detection accuracy and low computational complexity.
Cryptomarkets and the returns to criminal experience

Criminal capital theory suggests more experienced offenders receive higher returns from crime. Offenders who accrue skills over their criminal career are better able to minimise detection, increase profits, and navigate illegal markets. Yet shifts in the offending landscape to technologically-dependent crimes have led some to suggest that the skills necessary to be successful in conventional crimes no longer apply, meaning ‘traditional’ criminals may be left behind. The recent turn of drug vendors to online markets provides an opportunity to investigate whether ‘street smarts’ translate to success in technologically-dependent crimes. This study surveys 51 drug vendors on online drug markets to compare individuals who began their drug-selling career in physical drug markets with vendors whose onset began on digital platforms. The findings suggest vendors who broker between online and offline drug markets are better positioned to maximize profits. The results inform potential spillover effects from offline drug-selling into online marketplaces.
Leveraging electronic health record data for clinical trial planning by assessing eligibility criteria’s impact on patient count and safety

To present an approach on using electronic health record (EHR) data that assesses how different eligibility criteria, either individually or in combination, can impact patient count and safety (exemplified by all-cause hospitalization risk) and further assist with criteria selection for prospective clinical trials.Trials in three disease domains - relapsed/refractory (r/r) lymphoma/leukemia; hepatitis C virus (HCV); stages 3 and 4 chronic kidney disease (CKD) - were analyzed as case studies for this approach. For each disease domain, criteria were identified and all criteria combinations were used to create EHR cohorts. Per combination, two values were derived: (1) number of eligible patients meeting the selected criteria; (2) hospitalization risk, measured as the hazard ratio between those that qualified and those that did not. From these values, k-means clustering was applied to derive which criteria combinations maximized patient counts but minimized hospitalization risk.Criteria combinations that reduced hospitalization risk without substantial reductions on patient counts were as follows: for r/r lymphoma/leukemia (23 trials; 9 criteria; 623 patients), applying no infection and adequate absolute neutrophil count while forgoing no prior malignancy; for HCV (15; 7; 751), applying no human immunodeficiency virus and no hepatocellular carcinoma while forgoing no decompensated liver disease/cirrhosis; for CKD (10; 9; 23893), applying no congestive heart failure.Within each disease domain, the more drastic effects were generally driven by a few criteria. Similar criteria across different disease domains introduce different changes. Although results are contingent on the trial sample and the EHR data used, this approach demonstrates how EHR data can inform the impact on safety and available patients when exploring different criteria combinations for designing clinical trials.
Analysis and Design Multiple Layer Adaptive Kalman Filter Applied to Electro-Optical Infrared Payload Vision System

This study designs a trigger to determine the number of layers of a multi-layer adaptive Kalman filter and applies it to optoelectronic infrared payload system vision. This feature reduces the number of mechanically stabilized motors, equipment weight, CPU resources, and power for an electro-optical infrared payload system. The goal is to reduce the traditional use of multiple gyroscopes to perform calibration measurements on different gimbal frames by this design. In this study, mathematical modeling was carried out for the three-axis, three-frame camera stabilizer system, and the system foundation without motor and gimbal frame was established to achieve aperture-type camera mode. The exposure of the drone’s payload structure outside the aircraft can be reduced. This study provides the adaptive Kalman filter with the offset parameters of the camera image Minimum Output Sum of Squared Error and the three-axis degrees of freedom vector and angle data on the gyroscope. By using the image processing unit, the offset was corrected at each frame per second. The experimental results show that under the same hardware, failure limit and camera field of view constraints. The processing time by this method was compared to the traditional frame correction and full image stabilization methods. The results show that the proposed method can shorten 6 microseconds under the traditional method and can be used to provide lower power consumption, lower image delay, and a larger viewing angle range.
A Survey of Toulmin Argumentation Approach for Medical Applications

The argumentation field in Artificial Intelligence has increases significantly resulting in a practical body of work. This survey explores the techniques that are used to accomplish the foundations for argumentation. It also provides a review of the recent advances in the argumentation techniques that specialize in using Toulmin’s model when dealing with conflicting problems in the medicine domain. It also displays a discussion of the challenges faced in the automatic extraction of a deeper understanding of inference techniques. The drug conflict problem is considered to be one of most challenging aspects in medicine filed in the world. This survey also reviews Toulmin's model of argumentation in terms of solving conflicting problems in different domains that are specialized in solving drug conflicts in medicine.
Multi-Target Multi-Camera Tracking of Vehicles by Graph Auto-Encoder and Self-Supervised Camera Link Model

Multi-Target Multi-Camera Tracking (MTMCT) of vehicles is a challenging task in smart city related applications. The main challenge of MTMCT is how to accurately match the single-camera trajectories generated from different cameras and establish a complete global cross-camera trajectory for each target, i.e., the multi-camera trajectory matching problem. In this paper, we propose a novel framework to solve this problem using the self-supervised trajectory-based camera link model (CLM) with both appearance and topological features systematically extracted from a graph auto-encoder (GAE) network. Unlike most related works that represent the spatio-temporal relationships of multiple cameras with the laborious human-annotated CLM, we introduce a self-supervised CLM (SCLM) generation method that extracts the crucial multi-camera relationships among the vehicle trajectories passing through different cameras robustly and automatically. Moreover, we apply a GAE to encode topological information and appearance features to generate the topological embeddings. According to our experimental results, the proposed method achieves a new state-of-the-art on both CityFlow 2019 and CityFlow 2020 benchmarks with IDF1 of 77.21% and 55.56%, respectively.
Human Fall Down Recognition Using Coordinates Key Points Skeleton

Falls pose a substantial threat to human safety and can quickly result in disastrous repercussions. This threat is particularly true for the elderly, where falls are the leading cause of hospitalization and injury-related death. A fall that is detected and responded to quickly has a lower danger and long-term impact. Many real-time fall detection solutions are available; however, these solutions have specific privacy, maintenance, and proper use issues. Vision-based fall event detection has the benefit of being completely private and straightforward to use and maintain. However, in real-world scenarios, falls are diverse and result in high detection instability. This study proposes a novel vision-based technique for fall detection and analyzes an extracted skeleton to define human postures. OpenPose can be used to get skeletal information about the human body. It identifies a fall using three critical parameters: the center of the value of the head and shoulder coordinates, the critical points of the shoulder coordinates, and the distance between the center of the skeleton's head and the floor with the angle between the center of the shoulders and the ground. Our proposed methodology was effective, with a classification accuracy of 97.7%.
E-MDAV: A Framework for Developing Data-Intensive Web Applications

The ever-increasing adoption of innovative technologies, such as big data and cloud computing, provides significant opportunities for organizations operating in the IT domain, but also introduces considerable challenges. Such innovations call for development processes that better align with stakeholders needs and expectations. In this respect, this paper introduces a development framework based on the OMG’s Model Driven Architecture (MDA) that aims to support the development lifecycle of data-intensive web applications. The proposed framework, named E-MDAV (Extended MDA-VIEW), defines a methodology that exploits a chain of model transformations to effectively cope with both forward- and reverse-engineering aspects. In addition, E-MDAV includes the specification of a reference architecture for driving the implementation of a tool that supports the various professional roles involved in the development and maintenance of data-intensive web applications. In order to evaluate the effectiveness of the proposed E-MDAV framework, a tool prototype has been developed. E-MDAV has then been applied to two different application scenarios and the obtained results have been compared with historical data related to the implementation of similar development projects, in order to measure and discuss the benefits of the proposed approach.
Flow Feature-Based Network Traffic Classification Using Machine Learning

Reliable network traffic classification is essential to management and security tasks. Therefore, it is beneficial to analyze and improve existing techniques. Some of the most traditional methodologies for traffic classification are based on port number and packet payload, each of which presents an increasing set of problems. Port number-based classification techniques suffer from the misuse of port numbers and tunneling. This is primarily due to their reliance on the proper use of IANA (Internet Assigned Numbers Authority) assigned numbers. On the other hand, packet payload-based classification has difficulty dealing with encrypted data and legal restrictions to accessing user data. Flow feature-based classification canovercome these challenges by creating profiles based on the traffic patterns of applications. Furthermore, machine learning techniques have shown to be a good match for traffic classification. Thus, the goal of this paper is to explore the combination of these fields and to develop a set of machine learning models capable of classifying network traffic based on flow features. This was achieved by using a ready to use dataset to train two supervised and one unsupervised clustering model.The results for the supervised classifiers were considered comparable to similar studies, while the performance of the clustering model was found to be not satisfactory.