Reviewerly/exHarmony huggingface dataset:
Authentication System by using HOG Face Recognition Technique and Web-Based for Medical Dispenser Machine

An infectious and contagious disease affects human life significantly. Unexpectedly, monthly check-up of patients is inconvenient to receive a medical supply. Conveniently receiving medical supplies is a solution to hospital patients, eliminating crowded environments, and reducing contamination of the surface area. Therefore, this paper proposed a medical dispenser machine composed of two parts which are Web-based and face recognition in real-time. The medical profession can adjust the quantity of medicine. After the pharmacist arranged medicine, the email is distributed to the patients with the date and time for receiving packages. An automated system with a machine-learning algorithm assists them in receiving medical supply by using face recognition with Histogram Orientated Gradients (HOG) embedded in Raspberry Pi 4B. Face recognition is the ultimate technology to improve and make an impact in people's lives by diminishing contamination by viruses by not touching any surface area. The result of 50 image inputs showed an impressive recognition of authorized individuals with an accuracy of 80.0 %. This work combines hardware set up with PHP Web-based to distribute the medical supply efficiently. Subsequently, the implementation performed a satisfying performance that was suitable for the hospital.
Gender and Age Recognition Using Audio Data—Artificial Neural Networks

Human voice is a result of movement of air in our vocal cords, and as we grow, different voice features tend to vary according to age and gender. In many criminal cases, the evidence is sometimes collected as voice samples which we can use to identify the speaker’s features like age and gender. Hence, in our model, we are focusing on recognizing age and gender from audio samples using different machine learning algorithms and deep learning models. In the existing models, mostly work has been done using machine learning algorithms, but here we have tried to implement the same along with different dimensionality reduction techniques and deep learning models to figure the best one out of all. The main objective of this project is to identify age and gender by producing a robust feature and to design a good classifier that will improve the accuracy and performance of our system.
Hierarchical image classification with a literally toy dataset

Unsupervised Domain Adaptation (UDA) in image classification remains a big challenge. In existing UDA image dataset, classes are usually organized in a flattened way, where a plain classifier can be trained. Yet in some scenarios, the flat categories originate from some base classes. For example, buggies belong to the class bird. We define the classification task where classes have characteristics above and the flat classes and the base classes are organized hierarchically as hierarchical image classification. Intuitively, leveraging such hierarchical structure will benefit hierarchical image classification, e.g., two easily confusing classes may belong to entirely different base classes. In this paper, we improve the performance of classification by fusing features learned from a hierarchy of labels. Specifically, we train feature extractors supervised by hierarchical labels and with UDA technology, which will output multiple features for an input image. The features are subsequently concatenated to predict the finest-grained class. This study is conducted with a new dataset named Lego-15. Consisting of synthetic images and real images of the Lego bricks, the Lego-15 dataset contains 15 classes of bricks. Each class originates from a coarse-level label and a middle-level label. For example, class “85080” is associated with bricks (coarse) and bricks round (middle). In this dataset, we demonstrate that our method brings about consistent improvement over the baseline in UDA in hierarchical image classification. Extensive ablation and variant studies provide insights into the new dataset and the investigated algorithm.
Analysis of the Zhang Neural Network and its Application for the control of Nonlinear Dynamical Systems

This research article presents a development of the Zhang Recurrent Neural Network (Z-RNN) model to estimate the on-line solution to linear time-varying matrix equations. This Z-RNN model is further used to develop a control strategy for nonlinear systems by estimating the solution to a Lyapunov equation on-line, without approximations and linearization. The proposed concept is validated using a 4th order Inverted Pendulum on a Cart model and it has been compared with the Linear Quadratic Regulator. The proposed technique offers a significantly lower cost and better performance. The issues with stability of the Z-RNN and sampling rate is also discussed in detail, along with the choice of parameters involved.
Feature Cloud: Improving Deep Visual Recognition With Probabilistic Feature Augmentation

This paper considers deep visual recognition on long-tailed data. Under the long-tailed distribution, a small portion of the classes (head classes) occupy most training samples and the most classes (tail classes) only occupy relatively few samples. We observe that such long-tailed distribution significantly distorts the deeply-learned feature space, which consequentially compromises the deep visual recognition. Specifically, during training, each head class is prone to a relatively wide spatial distribution in the deep feature space, while each tail class is prone to a relatively small spatial distribution. In another word, the tail classes usually have much smaller spatial distribution than the head classes, distorting the overall feature space. In response, we propose to explicitly inflate the distribution of each tail class in the deep feature space, so that the tail classes will have comparable distribution range as the head classes. To this end, we replace each tail feature vector with a set of feature vectors on the fly. These feature vectors follow a probabilistic distribution learned from the head classes and yield a “feature cloud” surrounding the original tail feature. We show that the feature cloud effectively transfers the within-class diversity from the head classes onto the tail classes, maintaining an effect of probabilistic feature augmentation. An important advantage of the proposed feature cloud is that it is capable to bring general improvement to long-tailed visual recognition on two fundamental tasks, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e.</i> , deep classification and deep representation learning, in spite of the significant differences between them. Extensive experiments on both deep metric learning benchmarks and deep image classification benchmarks validate the effectiveness of the proposed feature cloud.
A Video Axle Counting and Type Recognition Method Based on Improved YOLOv5S

The number of axles of the vehicle and the type of tires can reflect the information of the vehicle to a certain extent, and the load capacity can be calculated according to the number of axles of the truck and the type of axles. Therefore, the identification of the axle is of great significance for judging whether the truck is overweight. At present, the method of calculating the axles is carried out by the method of Laser Radar or grating for axle counting. In the prior art, the method of axle counting is complicated to deploy and the cost is high. Some computer vision-based axle statistics methods have emerged in recent years, but complete vehicle sideways pictures are required. However, due to the long body of the truck and the limited space factor, it is difficult to obtain the complete vehicle in an original image. Although image stitching can solve this problem, the current video image stitching methods have a relatively high time cost. To solve this issue, we propose an object detection and tracking method based on YOLOv5s for axle counting and tire type identification. Experimental results show that the proposed method has extremely high accuracy and can meet real-time requirements even without GPU.
MIMO Modeling by Learning Explicitly the Projection Space: The Maximum Correlation Ratio Cost Function

Maximal correlation measures the statistical dependency between two random variables and has been broadly used by statisticians. In this paper, we present a novel regression framework based on the rich theory of maximal correlation to train a Multiple-input Multiple-output (MIMO) model called the Bank of Wiener models (BWM). Nonlinear time series modeling under maximal correlation unifies the cost and the mapping function under the same mathematical optimization framework. This provides direct optimality (maximal statistical dependence) between BWM model outputs and the desired response, without the restriction of constructing an error signal as conventionally done in regression. Based on the idea of maximal correlation, we propose the Maximal Correlation Algorithm (MCA), which approximates directly the maximal correlation ratio between BWM outputs and the desired response. As a result, MCA modularizes the training of models with hidden layers and avoids the end-to-end training of backpropagation (BP), while improving the equivalent mapping capability. We prove that MCA is a pseudo-concave function, which yields better-behaved parameter optimization. We demonstrate experimentally that MCA is very competitive when compared to a single-hidden-layer MLP trained with BP and the Mean Squared Error (MSE). We also show that the system identification capabilities of MCA are superior to MLPs trained with BP. Hence, MCA has great promise in both nonlinear system identification and machine learning.
Students Attention Monitoring and Alert System for Online Classes using Face Landmarks

With the advantage of the web and present-day innovation, it has been made conceivable to direct and conduct daily classes and regular assessments for students at educational institutions remotely. This has become all the more important with the recent shift of working life for students and teachers to the online realm. Nonetheless, this sort of web-based learning does not have the same advantage of intuitive human interaction and correspondence that physical homeroom learning has. In order to improve the experience of web-based learning, instructors may find that it is useful to have some system to alert them when a student appears to stop focusing during online sessions. Facial recognition and facial expression recognition have seen great strides forward in the recent years, with various methods being developed to facilitate the detection of human faces as well as the classification of facial expressions. Various techniques have seen success in the past, such as the use of machine learning techniques that use Histogram of Oriented Gradients (HOG), Local Binary Patterns (LBP), etc., and deep learning methods such as Residual Neural Network (RESNET), Visual Group Geometry 16 (VGG16), LeNet, to name but a few. To solve this problem of waning student concentration due to lack of human supervision during online classes, this paper aims to develop a Students Attention Monitoring and Alert Model (S-AMAM).
Interoperability Among Heterogeneous Blockchains: A Systematic Literature Review

Blockchain technology is evolving rapidly; it has proved to be capable of solving many of the issues encountered by industries such as banking, supply chain management, and healthcare. However, several challenges must be overcome for it to reach its full potential and be adopted on a large scale. In a blockchain context, interoperability is the ability to connect multiple networks, thus enabling the exchange of assets, the invocation of smart contracts, and the verification of data, all while ensuring consistency between systems. In reality, most of the existing blockchain networks operate in a stand-alone environment, isolated from other blockchain networks. This causes a lack of communication that further leads to restrictions imposed on data, i.e., preventing it from transmitting freely to and from various blockchains regardless of the underlying infrastructure. Given the potential of blockchain interoperability, researchers have proposed many protocols over the last few years, and the solutions being offered are on the rise. In this chapter, we will discuss the methodologies used in the available solutions and compare several of their characteristics, including throughput, average block confirmation time, and consensus mechanism. Furthermore, we will present the projects currently implementing these protocols and their future directions.
Artificial Intelligence for Futuristic Banking

Artificial Intelligence (AI) has become an essential resource for large banks that deal with regulatory changes, new Anti-Money Laundering (AML) obligations and vulnerable fraud-prone clients. Cybersecurity has thus become a hot topic due to security failures using traditional methods and concerns about how companies use the personal data collected from clients or their regular users. The most obvious apparent reason why cybersecurity is critical in banking sector transactions is to protect client assets with a high level of data privacy. The main approaches in the front office conventional banking such as AI chatbots, smart virtual assistants and biometric user authentication are discovered to answer security challenges and to enhance prosperity in the field. Concurrently, advanced AI applications in fraud detection, fraud risk monitoring, anti-money laundering techniques and cross-border payments handling are observed under the back-office operations. The paper reviews the conceptualizations of privacy concerns and the antecedents and consequences of using AI-power in the banking sector. Moreover, overlooked limitations of AI such as scarcity of quality data, a rise of hidden-bias in suggestions and obliviousness of lacking knowledge are discussed with several thriving solutions.
The Object at Hand: Automated Editing for Mixed Reality Video Guidance from Hand-Object Interactions

In this paper, we concern with the problem of how to automatically extract the steps that compose real-life hand activities. This is a key competence towards processing, monitoring and providing video guidance in Mixed Reality systems. We use egocentric vision to observe hand-object interactions in real-world tasks and automatically decompose a video into its constituent steps. Our approach combines hand-object interaction (HOI) detection, object similarity measurement and a finite state machine (FSM) representation to automatically edit videos into steps. We use a combination of Convolutional Neural Networks (CNNs) and the FSM to discover, edit cuts and merge segments while observing real hand activities. We evaluate quantitatively and qualitatively our algorithm on two datasets: the GTEA [19], and a new dataset we introduce for Chinese Tea making. Results show our method is able to segment hand-object interaction videos into key step segments with high levels of precision.
PEAR: A Product Expiry-Aware and Revenue-Conscious Itemset Placement Scheme

Placement of items on the shelf space of retail stores significantly impacts the revenue of the retailer. Since customers typically tend to buy sets of items (i.e., itemsets) together, several research efforts have been undertaken towards facilitating itemset placement in retail stores for improving retailer revenue. However, they fail to consider that the time-period of expiry can vary across items i.e., some items expire sooner than others. This leads to loss of opportunity towards improving retailer revenue. Hence, we propose PEAR, which is a Product Expiry-Aware and Revenue-conscious itemset placement scheme for improving retailer revenue. Our key contributions are three-fold. First, we introduce the problem of addressing retail itemset placement when the items can be associated with different time-periods of expiry. Second, we propose the expiry-aware PEAR scheme for efficiently identifying and placing high-revenue itemsets for improving retailer revenue. Third, we conduct a performance study with two real datasets to demonstrate that PEAR is indeed effective in improving retailer revenue w.r.t. a reference scheme.
A Data-Driven Approach based on Tensor Completion for Replacing “Physical Sensors” with “Virtual Sensors”

Sensors are being used in many industrial applications for equipment health monitoring and anomaly detection. However, sometimes operation and maintenance of these sensors are costly. Thus companies are interested in reducing the number of required sensors as much as possible. The straightforward solution is to check the prediction power of sensors and eliminate those sensors with limited prediction capabilities. However, this is not an optimal solution because if we discard the identified sensors. As a result, their historical data also will not be utilized anymore. However, typically such historical data can help improve the remaining sensors' signal power, and abolishing them does not seem the right solution. Therefore, we propose the first data-driven approach based on tensor completion for re-utilizing data of removed sensors and the remaining sensors to create virtual sensors. We applied the proposed method on vibration sensors of high-speed separators, operating with five sensors. The producer company was interested in reducing the sensors to two. But with the aid of tensor completion-based virtual sensors, we show that we can safely keep only one sensor and use four virtual sensors that give almost equal detection power when we keep only two physical sensors.
Super-resolution for in situ Plankton Images

Being inherently limited by the wave properties of light, underwater plankton cameras compromise between their imaging resolution and field of view (FOV) for in situ observations. In order to enlarge the sampling volume in single frame acquisition, lower magnifications are usually adopted to enable larger FOV but sacrifice the resolution. In this paper, we build a real-underwater image dataset called IsPlanktonSR for in situ plankton im-age super-resolution (SR), in which paired low resolution (LR) and high resolution (HR) images of the same individual live planktonic organisms are captured by a customized dual-channel darkfield imaging system. An image registration algorithmic pipeline is also proposed to preprocess and align the image pairs at different scaling factors of 2× and 4×. The IsPlanktonSR dataset is used to train an enhanced deep residual network for SR through the L2, the perceptual and the contextual losses, respectively. Our extensive experimental results demonstrate that the deep learning model trained on real data through the contextual loss has de-livered better visual and quantitative SR performance than those trained on simulated data or through other loss functions. The trained SR model is also proved to generalize well to images of various plankton species or captured by different instruments. The proposed SR technology is anticipated to enhance the existing darkfield plankton imageries and enable the future in situ plankton imaging instruments for better observation capability and hence deepen under-standing of the plankton ecology.
DANCE: Distributed Generative Adversarial Networks with Communication Compression

Generative adversarial networks (GANs) have shown great success in deep representations learning, data generation, and security enhancement. With the development of the Internet of Things, 5th generation wireless systems (5G), and other technologies, the large volume of data collected at the edge of networks provides a new way to improve the capabilities of GANs. Due to privacy, bandwidth, and legal constraints, it is not appropriate to upload all the data to the cloud or servers for processing. Therefore, this article focuses on deploying and training GANs at the edge rather than converging edge data to the central node. To address this problem, we designed a novel distributed learning architecture for GANs, called DANCE. DANCE can adaptively perform communication compression based on the available bandwidth, while supporting both data and model parallelism training of GANs. In addition, inspired by the gossip mechanism and Stackelberg game, a compatible algorithm, AC-GAN is proposed. The theoretical analysis guarantees the convergence of the model and the existence of approximate equilibrium in AC-GAN. Both simulation and prototype system experiments show that AC-GAN can achieve better training effectiveness with less communication overhead than the SOTA algorithms, i.e., FL-GAN and MD-GAN.
Cutoff Threshold Decisions for Classification Algorithms with Risk Aversion

Classification algorithms predict the class membership of an unknown record. Methods such as logistic regression or the naïve Bayes algorithm produce a score related to the likelihood that a record belongs to a particular class. A cutoff threshold is then defined to delineate the prediction of one class over another. This paper derives analytic results for the selection of an optimal cutoff threshold for a classification algorithm that is used to inform a two-action decision in the cases of risk aversion and risk neutrality. The results provide insight to how the optimal cutoff thresholds relate to the associated costs and the sensitivity and specificity of the algorithm for both the risk neutral and risk averse decision makers. The optimal risk averse threshold is not reliably above or below the optimal risk neutral threshold, but the relation depends on the parameters of a particular application. The results further show the risk averse optimal threshold is insensitive to the size of the data set or the magnitude of the costs, but instead is sensitive to the proportion of positive records in the data and the ratio of costs. Numeric examples and sensitivity analysis derive further insight. Results show the percent value gap from a misspecified risk attitude increases as the specificity of the classification algorithm decreases.
Comparison of sentinel 2A MSI and Landsat 8 OLI for soil organic matter inversion in southwestern Shandong province, China

It remains to be explored whether some new multispectral satellite images with more available bands and higher spatial resolution have better inversion capability for soil organic matter (SOM). Taking southwestern Shandong Province of China as the research area, this article compared the performance of Landsat 8 Operational Land Imager (OLI) and Sentinel 2A Multi-Spectral Instrument (MSI) data for SOM quantitative inversion. The results indicate that, the model including red-edge 1 band of Sentinel 2A MSI was determined as the best model, as the validation R2 increased by 0.106, the RMSE decreased by 0.565 g/kg, and RPD increased by 0.163 compared with the model on Landsat 8 OLI image. The SOM distribution characteristics inversed on the best model were consistent with that on the measured samples. Therefore, the Sentinel 2A MSI can obtain better results than Landsat 8 OLI for the regional SOM inversion in the southwestern Shandong Province of China.