Reviewerly/exHarmony huggingface dataset:
Robust Computing for Machine Learning-Based Systems

Abstract The drive for automation and constant monitoring has led to rapid development in the field of Machine Learning (ML). The high accuracy offered by the state-of-the-art ML algorithms like Deep Neural Networks (DNNs) has paved the way for these algorithms to being used even in the emerging safety-critical applications, e.g., autonomous driving and smart healthcare. However, these applications require assurance about the functionality of the underlying systems/algorithms. Therefore, the robustness of these ML algorithms to different reliability and security threats has to be thoroughly studied and mechanisms/methodologies have to be designed which result in increased inherent resilience of these ML algorithms. Since traditional reliability measures like spatial and temporal redundancy are costly, they may not be feasible for DNN-based ML systems which are already super computer and memory intensive. Hence, new robustness methods for ML systems are required. Towards this, in this chapter, we present our analyses illustrating the impact of different reliability and security vulnerabilities on the accuracy of DNNs. We also discuss techniques that can be employed to design ML algorithms such that they are inherently resilient to reliability and security threats. Towards the end, the chapter provides open research challenges and further research opportunities.
An Development of Web-based Customer Relationship Management (CRM) system for Beauty Clinic

With the rapid development of beauty clinics that are everywhere, it can be concluded that beauty and women are two things that are interrelated. Almeera Skin Care is a beauty clinic that focuses on serving beauty treatments with beauty treatments and medicines formulated by doctors. The difficulty of administrators in managing patient data, drug data, and treatment data, this has an impact on services to the clinic. the problem is that all regular customers are not regulated systemically. Another problem that arises is that all beauty products have not been recorded in a computerized database, this is what causes difficulties in searching product data. The purpose of this research is to design an information system, so that it can help services at Almeera Skin Care and can determine the effect of service quality, product, type of care, and registration on customer satisfaction and its effect on consumer loyalty at Almeera Almeera Skin Care to provide satisfactory service to customers through the CRM (Customer Relationship Management) strategy by looking at the existing problems. The results of the development of the beauty clinic service system at Almeera Skin Care are able to make it easier for admins to record customer data, products, types of care, registration, examinations, patient medical records and reduce errors in recapitulating income report data which means simplifying admin tasks.
Novel Objects Detection for Robotics Grasp Planning

Object detection and segmentation have seen significant advances in robotics grasp planning in the last few years. But many algorithms and methods are still in low accuracy and unsuitable for real-time performance. This paper presents a data-driven approach to perform objects and grasp point detection from camera images for novel objects in cluttered environments. To achieve this, it introduces a model with two parallel loops of image encoders and decoders, to perform object detection, segmentation, and object grasping points detection between raw mages and robotics grasping process simultaneously. The experimental results demonstrate that our model and algorithm achieve efficiently grasping performance for various types of novel objects with high success rates in a cluttered environment. We improved the detection accuracy compared to the basic ResNet and DeepLab model by more than 11 % rate and have better performance in grasping detection than other comparable methods.
Deep Learning Methods in Medical Imaging for the Recognition of Breast Cancer

Breast Cancer is one of the most common cancers among women that affects about 10% of women worldwide. Although there are available treatments for bread cancer, the real challenge is to be properly detected it in early stages, a challenge that doctors and patients encounter constantly. In this study, we examine the performance of different deep learning models and depth-wise convolutional neural networks in medical imaging and assess their performance on breast cancer detection from ultrasounds and breast histopathology images. Experimental results suggest that the proposed deep learning models can effectively recognize breast cancer from ultrasound and histopathology images. The performance of the Convolutional Neural Network models reached 96.82% accuracy on ultrasounds, 88.23% on breast histology with cases of Invasive Ductal Carcinoma (IDC) and 91.04% on cancer-free tissue. The results are very promising and point out that deep-learning methods and depth-wise convolutional neural networks are very assistive in the diagnosis of breast cancer from ultrasound and histopathology images.
An image stitching method for blades of wind turbine based on background removal preprocessing

The damage detection of the wind turbine (WT) blade is an important aspect of its health situation monitoring. In order to catch small damages, it is necessary to stitch the multi segment images of the blade to obtain a high resolution image. In image stitching, a blurring of the image is carried out to reduce the influence of noise. However, the preprocessing may decrease the amount of feature points and cannot avoid the interaction of feature points in the background and the target parts. To overcome these problems, a preprocessing method based on background removal is proposed. The idea of the method is to carry out image segmentation to get the background part, then the intensities of pixels in background part are replaced by a same value, such as average intensity. Employing the proposed preprocessing method, the image stitching is implemented on several groups of WT blade images. Feature points extraction result are compared between the proposed and the blurring preprocessing, which shows that the proposed method is more robust to the parameters in image stitching process. Moreover, the capability about image stitching and multi-image stitching of the proposed method is well verified by the experiment results.
A Practice-Oriented, Control-Flow-Based Anomaly Detection Approach for Internal Process Audits

Internal auditing tries to identify anomalies, weaknesses and manipulations in business processes in order to protect the company from risks. Due to the digitalization of processes, auditors also have to check the associated data volumes. Already existing IT-systems focus on process-related data where the control flow, i.e. the actual sequence of process events, is not considered. This paper examines how the control flow and the process-related data can be analyzed in combination to support auditors in process auditing. To realize this, audit requirements were collected in the literature and evaluated by auditors from industry. On this basis, a concept with five indicators was developed, then transferred into a prototype and evaluated using real-life data as well as two auditors. The results show that the requirements can be technically realized and the developed indicators enable auditors to identify and interpret abnormal process executions.
Multi-Service Electronic Library of Scientific and Technical Articles

In our world, there are a huge number of smart, talented people who can potentially be compared with scientists, scientific researchers in various fields without any framework and restrictions. In most cases, these people are junior and senior students. The works of many bright minds go unnoticed over time for a number of offensive, but logical reasons. A scientist will never become a scientist if the results of his research remain unknown to the public. Without publications, without proper design, without constructive criticism and discussion, all the works and discoveries of students disappear in huge amounts of archival information. Works simply do not lose their current value here and now. Based on this, the problem of popularization of science and scientific activity among students is very relevant. Creating an electronic library of scientific and technical articles can help in solving this problem. In this article, I will demonstrate how to create an information resource not only in providing students with the opportunity to publish, read, discuss scientific papers, but also in creating a community of students who are interested in scientific activities.
Dynamic interaction between robot and UAV in aerial manipulation

Aerial manipulation is an emerging field of research, and important applications can be envisaged, such as inspection and maintenance, search and rescue, structure assembly, and logistics. The manipulator transfers forces and torques to the UAV, which affect the UAV position and attitude. This may cause failure or loss of precision in pick and place and assembly operations. In this paper, first, the effect of simple impulsive force and torque disturbances on UAV dynamics is studied. Then, a coupled dynamic model of the UAV and a 1-DOF (Degree of Freedom) manipulator is developed and compared to a simplified decoupled model. Finally, a decoupled dynamic model is proposed for a 3-DOFs manipulator, and the simulation results for a real pick and place operation are presented and discussed. It is evidenced that, in all of the considered scenarios, a lateral displacement of the system is generated during the manipulation in hovering flight, which could jeopardize the manipulator precision.
Robust Hashing Based on Quaternion Gyrator Transform for Image Authentication

Image hashing is one of the most effective methods in many image processing applications including image recognition or authentication, tampering detection, image retrieval, etc. In this paper, we propose a novel image hash method based on quaternion gyrator transform, which is more secure and compact in addition to its robustness and discriminative properties. In this proposed method, the quaternion gyrator transform (QGT), like the traditional quaternion Fourier transform which is a linear regular integral ratio transform is applied to effectively extract image features. Firstly, our hash function needs to scale all pictures to a fixed size. And the quaternion gyrator transform is used to transform each non-overlapping block in the original image for extracting the feature map of the source image. For each block, the inner product of the feature and a random weight is used to be one bit of the final image hash. Experiments with different image databases are conducted to test and verify the efficiency of our method. The result has proved that our method of constructing hash is robust to most of the content-preserving operations with a good distinction. Compared with some state-of-the-art hash algorithms, our algorithm is more excellent and has better performance in robustness and distinguishability.
A Mobile Game-Based Learning Approach for Motivating Preschoolers and Primary Students in Learning Mathematics

Learning in an interesting and funny way is a successful key for little kids to absorb new concepts. In Hong Kong, kids are studying core subjects through "assessment" as they have to prepare for their standardized tests and examinations. Most of them are studying under high pressure to obtain excellence in the assessment. If they did not get good results, they will be frustrated and will lose their interest in learning eventually. This paper introduces the use of "game-based learning" for little kids to learn simple arithmetic calculations in a funny way. By using artificial intelligence (AI) techniques, the application can provide training exercises casually to motivate kids to learn simple arithmetic during the gameplay. Kids are focusing on how to defeat the monsters to satisfy the game goal that they will work hard to solve the simple mathematical questions. Also, the application can enhance the parent-child relationship when the kids achieve the goal in each stage with their parents together.
A Reduced Network Traffic Method for IoT Data Clustering

Internet of Things (IoT) systems usually involve interconnected, low processing capacity, and low memory sensor nodes (devices) that collect data in several sorts of applications that interconnect people and things. In this scenario, mining tasks, such as clustering, have been commonly deployed to detect behavioral patterns from the collected data. The centralized clustering of IoT data demands high network traffic to transmit the data from the devices to a central node, where a clustering algorithm must be applied. This approach does not scale as the number of devices increases, and the amount of data grows. However, distributing the clustering process through the devices may not be a feasible approach as well, since the devices are usually simple and may not have the ability to execute complex procedures. This work proposes a centralized IoT data clustering method that demands reduced network traffic and low processing power in the devices. The proposed method uses a data grid to summarize the information at the devices before transmitting it to the central node, reducing network traffic. After the data transfer, the proposed method applies a clustering algorithm that was developed to process data in the summarized representation. Tests with seven datasets provided experimental evidence that the proposed method reduces network traffic and produces results comparable to the ones generated by DBSCAN and HDBSCAN, two robust centralized clustering algorithms.
Evaluation of Scrum Practice Maturity in Software Development of Mobile Communication Application

Focusing on the big topic of information technology related to software engineering, Scrum is one of the frameworks on the agile methodology to develop innovative products and services. Implementing the Scrum framework can be challenging. Changes in teamwork culture, both vertically and horizontally in a software development organization, cannot be avoided. In software development process improvement, evaluation of the scrum implementation using the Scrum Maturity Model. Scrum Maturity Model also assesses the extent of the implementation of the scrum such as the implementation of roles, events, and artifacts following the Scrum Guide. This research occurred to the representative of the product owner, scrum master, and development team. There are seven sub-categories which do not implement the best scrum practices. These practices have an impact on the avoided risk. By understanding the impact of these risks, the team can make plans to avoid those risks. The sub-categories are the product owner, development team, sprint, sprint planning, daily Scrum, product backlog, and sprint backlog.
Anonymous Person Tracking Across Multiple Camera Using Color Histogram and Body Pose Estimation

Tracking anonymous persons is a necessary domain of the computer vision sector for security purposes. Nowadays, most of the important areas are covered with video surveillance cameras to tackle unwanted occurrences. Previous learning-based studies overlooked the checking of similarities of features of a person among multiple cameras for person detection and tracking. Only color feature-based studies may fail to recognize the same dressed different persons. In this paper, we are proposing a method for tracking and re-identification of a person in multiple cameras using color-based features with posture estimation in real-time scenarios. For this, we have used YOLOv3 for person tracking and OpenCV and OpenPose libraries for feature collection []. Model accuracy is tested on experiment video of single person multi-camera environmental scenario. In our proposal, we have taken HSV values of the most dominant color (DMC) for a targeted person on a camera as well as the fast Fourier transformed spectrum magnitude (FSM) as our feature vector to compare with another subject on different cameras. Our experiment shows that the re-identification of a person in different camera locations can be done successfully using cosine similarity and one ID is to be assigned to the same person and different IDs to different persons among the cameras to make the security issue more reliable, accurate, and faster.
Poisoning finite-horizon Markov decision processes at design time

The contemporary decision making environment is becoming increasingly more automated. Developments in artificial intelligence, machine learning, and operations research have increased the prevalence of computer systems in decision making tasks across a myriad of applications. Markov decision processes (MDPs) are utilized in a variety of system controllers, and attacks against them are of particular interest, even though this problem structure is relatively understudied in the adversarial learning literature. Therefore, in this research, we consider the finite-horizon MDP poisoning problem wherein an adversary perturbs a decision maker’s baseline MDP formulation to induce desired behavior while balancing the risk of attack detection. We formally define the associated mathematical programming formulation as a mixed-integer bilevel programming problem. We provide a single-level representation that can be handled by some commercial global solvers, but, since their performance is frequently inadequate, we develop gradient-based, gradient-free, and bifurcation heuristic solution methodologies that include self-tuning extensions. The performance of these algorithms is explored on a wide array of sample problem instances to determine their relative efficacy in terms of solution quality and computational effort for different finite-horizon MDP structures.
Benign: An Automatic Optimization Framework for the Logic of Swarm Behaviors

In the field of swarm intelligence, it is usually complicated to express the logic of swarm behaviors. Behavior tree has drawn a lot of attention to be a practical approach to solving this problem in recent years. However, how to automatically design the logic of swarm behaviors according to the target of a task is the focus of swarm intelligence. Hence, we propose an automatic optimizing framework named Benign which is capable of using gene expression programming (GEP) to optimize the logic of swarm behaviors. In Benign, the basic swarm behaviors and the relationships among those behaviors are mapped to nodes of behavior tree by the method named Matt firstly. With these nodes, we design an artificial behavior tree. After that, the artificial behavior tree is transformed into an expression tree in GEP according to the method named Meet. Finally, GEP is used for optimization to generate the expected logic of swarm behaviors. We conduct simulation experiments to validate the efficiency of Benign. The experimental results show the superiority of Benign. Compared with the logic of the artificial behavior tree before optimization, the conduction of the optimized logic of swarm behaviors increases efficiency by more than 50%.
Cost-based recommendation of parameters for local differentially private data aggregation

The ability to analyze personal data for a group of individuals without compromising their respective privacy has been a focus of significant research in recent years. For such analyses, data analysts need to acquire data from individuals without revealing their Individually Identifiable Data (IID). Well established Differentially Private techniques, characterized by privacy parameters (ϵ,δ), transform the data to protect the IID. However, such transformations adversely affect the usefulness of data leading to a trade-off between usefulness and privacy. Therefore, negotiating appropriate values of privacy parameters before data acquisition is a challenging task for data analysts. Most of the work, in selecting values of privacy parameters, is either based on constraining all other parameters or they provide a set of acceptable values. Here also the problem of selecting the best value from the set of acceptable values is left to the analyst. A major contribution of this paper is the method of identifying the best value of privacy parameters in a trade-off between usefulness and privacy by introducing a cost-based model, thereby addressing the issue. To enable estimation of usefulness and its cost before data acquisition, we have mathematically modeled utility in terms of data and privacy parameters. We have considered standard statistical aggregates such as Sum, Mean and Standard Deviation as compared to most of the existing works that consider only Count query as aggregate analysis. The correctness of our mathematical estimation has been validated on a diverse set of synthetic and real-world datasets spanning popular data distributions.
SARANIQA: Self-Attention Restorative Adversarial Network for No-Reference Image Quality Assessment

In this work, we propose a self-attention restorative adversarial net (SARAN), a GAN-based model for no-reference image quality assessment (NR-IQA). Consistent with the human visual system, we add the self-attention module and improve the model structure to enhance the intensity of the against learning, the generated reference image for evaluation also could be more reliable. SARAN is composed of three parts: a generative network, a discriminative network and a quality evaluation network. The generative network performs feature extraction and reconstruction of the input distored image patches, while the discriminative network distinguishes the reconstructed patches from the pristine distortion-free patches. In order to capture the long range dependence which is beneficial to enhance the key texture and structure features of an image, the self-attention module is added into network. After restoration, the perceived distance between the restored and distorted patches is defined as appreciation of restoration (AoR). In the quality evaluation network, features vectors are concatenated from the distorted and restored patches, AoR. Eventually, the quality score of an input image is estimated by weighted sum of the patches scores. Experimental results are carried out on the LIVE and TID2013 datasets, SARAN is superior to the mainstream methods in the no-reference image quality assessment and has good performance for different distortion types. Meanwhile, it is more consistent with the human visual system (HVS) for no-reference image quality assessment.