Reviewerly/exHarmony huggingface dataset:
Real Market Basket Analysis using Apriori and Frequent Pattern Tree Algorithm

Recently, data mining has been implemented in various fields, including business and telecommunications. Data mining is a technique for extracting and detecting patterns in massive data sets that combines machine learning, statistics, and database systems. One of the most important use-cases in data mining is finding the high-frequency patterns between the set of itemset called association rules. Association rule mining is a well-researched technique for finding some relations between variables in large databases. This paper aims to measure the performance of the Apriori and Frequent Pattern Tree algorithms by comparing them using several points of comparison. Then we compared the outputs, whether they produce the same or different rules, to find out whether the way the two algorithms work is similar or not. After that, we looked for the itemsets that best match the reality in the market by giving them to a user who had transaction data from his spare parts shop.
Skeleton-Based Typing Style Learning For Person Identification

We present a novel approach for person identification based on typing-style, using a novel architecture constructed of adaptive non-local spatio-temporal graph convolutional network. Since type style dynamics convey meaningful information that can be useful for person identification, we extract the joints positions and then learn their movements’ dynamics. Our non-local approach increases our model’s robustness to noisy input data while analyzing joints locations instead of RGB data provides remarkable robustness to alternating environmental conditions, e.g., lighting, noise, etc.. We further present two new datasets for typing style based person identification task and extensive evaluation that displays our model’s superior discriminative and generalization abilities, when compared with state-of-the-art skeleton-based models.
Research and analysis of wing assembly tracking registration method based on Hololens2

In the intelligent auxiliary assembly system based on mixed reality technology, three-dimensional tracking registration technology is one of its core technologies. At the same time, this technology is also the basis for the stable operation of the intelligent auxiliary assembly system. When mixed reality technology is applied to the production and assembly of wings, it is accurate and rapid. The tracking registration technology is one of the important links of the system, because it often means the accuracy and stability of the virtual information superimposed on the real environment. Based on the Hololens2 equipment, this paper develops a virtual and real combination auxiliary wing assembly system on the unity3D virtual simulation platform. Through the simulation of the real assembly environment, It compares the performance effects of today's mainstream tracking registration technology in the wing assembly process, and finds out a tracking and registration technology most suitable for the wing assembly process, thereby further improving the assembly efficiency and learning efficiency of assembly operators and trainers.
Raycasting method using hand gesture for target selection on the occluded object in handheld Augmented Reality

Selecting a target object that is obscured by another virtual object is one of the remaining issues that are difficult to be precisely performed in Augmented Reality (AR). The conventional way of interaction using handheld devices which is by the touchscreen metaphor, often comes with issue of user's fat finger, leading to incorrect selection for interaction. Other than that, to perform selection on occluded target object is difficult as the available surface area for selection is smaller. Therefore to overcome the issues, we propose an to implement raycasting method for selection on occluded object by using hand gesture in handheld AR. We describes the process of designing the propose interaction for selection on occluded target object in handheld AR. This paper ends with a conclusion with the future works recommendation.
Design and implementation of parallel self-adaptive differential evolution for global optimization

Abstract The results of evolutionary algorithms depend on population diversity that normally decreases by increasing the selection pressure from generation to generation. Usually, this can lead the evolution process to get stuck in local optima. This study is focused on mechanisms to avoid this undesired phenomenon by introducing parallel self-adapted differential evolution that decomposes a monolithic population into more variable-sized sub-populations and combining this with the characteristics of evolutionary multi-agent systems into a hybrid algorithm. The proposed hybrid algorithm operates with individuals having some characteristics of agents, e.g. they act autonomously by selecting actions, with which they affect the state of the environment. Additionally, this algorithm incorporates two additional mechanisms: ageing and adaptive population growth, which help the individuals by decision-making. The proposed parallel differential evolution was applied to the CEC’18 benchmark function suite, while the produced results were compared with some traditional stochastic nature-inspired population-based and state-of-the-art algorithms.
Bringing any medical 3D scan onto augmented reality hardware: a streamlined data pipeline

This contribution presents a streamlined data pipeline to bring medical 3D scans onto Augmented Reality (AR) hardware. When a 3D scan is visualized on a 2D screen, depth information is lost and doctors have to rely on their experience to map the displayed data to the patient. Showing such a scan in AR addresses this problem, as one can view that scan in real 3D. To achieve this, the scan produced by a medical scanner has to be preprocessed by the user and brought onto the AR hardware. Usually, many manual steps are involved in achieving this, which require technical knowledge about the underlying software and hardware components and impede acceptance of this new technology by the target group, medical personnel. This work presents a streamlined pipeline for this process, leading to an enhanced user experience. The core component of the pipeline is a web application, to which a user can upload the direct output of a medical scanner. The scan can be interactively segmented by the user, after which both the scan and segment are stored on a server. Additionally, this paper introduces an AR application, which can be used to browse through patients and view their scans and previously created segments. We evaluate our streamlined data pipeline and AR application in a user study, reporting the results of a system usability questionnaire and a Thinking Aloud test.
Navigating Emergency Remote Teaching During the COVID-19 Pandemic

The COVID-19 pandemic began in the late months of 2019, and by Spring of 2020, in an effort to limit transmission of the virus, schools across the globe closed and transitioned to emergency online teaching. This disrupted the schooling for over 80% of the students worldwide. While the move to online teaching and learning was inevitable, many learners, especially in rural and remote areas, found that online schooling had certain challenges due to lack of access, lack of resources, lack of infrastructure, unavailability of devices, and a lack of qualified teachers who can assist with online learning. To be able to transition to online teaching, teachers too had to adjust their instructional strategies and pedagogies. How did teachers and students navigate this sudden shift to online teaching and learning?
Enhancing Sybil Detection via Social-Activity Networks: A Random Walk Approach

Detecting fake accounts (sybils) in online social networks (OSNs) is vital to protect OSN operators and their users from various malicious activities. Typical graph-based sybil detection (a mainstream methodology) assumes that sybils can make friends with only a limited (or small) number of honest users. However, recent evidences showed that this assumption does not hold in real-world OSNs, leading to low detection accuracy. To address this challenge, we explore users’ activities to assist sybil detection. The intuition is that honest users are much more selective in choosing who to interact with than to befriend with. We first develop the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">social and activity network</i> (SAN), a two-layer hyper-graph that unifies users’ friendships and their activities, to fully utilize users’ activities. We also propose a more practical sybil attack model, where sybils can launch both friendship attacks and activity attacks. We then design Sybil_SAN to detect sybils via coupling three random walk-based algorithms on the SAN, and prove the convergence of Sybil_SAN. We develop an efficient iterative algorithm to compute the detection metric for Sybil_SAN, and derive the number of rounds needed to guarantee the convergence. We use <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">“matrix perturbation theory”</i> to bound the detection error when sybils launch many friendship attacks and activity attacks. Extensive experiments on both synthetic and real-world datasets show that Sybil_SAN is highly robust against sybil attacks, and can detect sybils accurately under practical scenarios, where current state-of-art sybil defenses have low accuracy. Lastly, we present two extensions of Sybil_SAN to further improve its accuracy.
Transferable 3D Adversarial Textures using End-to-end Optimization

Deep visual models are known to be vulnerable to adversarial attacks. The last few years have seen numerous techniques to compute adversarial inputs for these models. However, there are still under-explored avenues in this critical research direction. Among those is the estimation of adversarial textures for 3D models in an end-to-end optimization scheme. In this paper, we propose such a scheme to generate adversarial textures for 3D models that are highly transferable and invariant to different camera views and lighting conditions. Our method makes use of neural rendering with explicit control over the model texture and background. We ensure transferability of the adversarial textures by employing an ensemble of robust and non-robust models. Our technique utilizes 3D models as a proxy to simulate closer to real-life conditions, in contrast to conventional use of 2D images for adversarial attacks. We show the efficacy of our method with extensive experiments.
CPU and GPU real-time filtering methods for dense surface metrology using general matrix to matrix multiplications

Abstract Filtering is a required task in surface metrology for the identification of the components relevant for automated quality control. The calculation of real-time features about the surface is crucial to determining the mechanical and physical properties of the inspected product. The computation efficiency of the filtering operations is a major challenge in surface metrology, as current sensors provide massive volumes of data at very high acquisition rates. To overcome the challenges, this work presents different real-time filtering solutions comparing the performance on the CPU and on the GPU, using modern hardware. The proposed framework is focused on filtering techniques that can be expressed using a finite impulse response (FIR) kernel that includes the Gaussian kernel, the most common filtering technique recommended by ISO and ASME standards. This research work proposes variations of the double FIFO and double circular filters. The filters are transformed into a series of general matrix to matrix multiplications, which can be run extremely efficiently on different architectures. The proposed filtering approach provides superior performance compared with previous works. Additionally, tests are carried out to quantify the performance of the GPU in terms of data transfer and computation capabilities in order to diminish the penalty imposed by data transfer from main memory to the GPU in real-time operations. Based on the results, an efficient batch filtering technique is proposed that can be run on the GPU faster than the CPU even for small profile and kernel sizes, offloading this task from the host CPU for optimal system and application response.
The Impact of the Development of the Internet of Things on the Robotics Engineering Profession

Artificial intelligence is the development direction of contemporary society. At present, China's industry, medicine, and other fields are developing rapidly, which has also created new opportunities for China's manufacturing reform. Especially, as an important achievement of artificial intelligence, robots have achieved obvious application value in the fields of industry and medicine. Especially in the context of artificial intelligence, the advantages of the talent training model for the integration of production and education are constantly being highlighted. Here, we will discuss the training mode of the integration of industry and education for robot engineering major under the background of artificial intelligence.
Towards an Integrated Micro-services Architecture for Campus environments

This paper posits the need for an integrated micro services framework for handling all business and student services at our local University. We present the research question, &#x201C;to what extent do the functionalities of the micro service frameworks provide beneficial considerations for the implementation of a micro service system within the University campus environment?&#x201D; We discuss the response to this question regarding using a use case implementation now in progress from our enterprise systems solution - ISAS (Integrated Student Assessment System). The assumption is to implement functional micro-services to support our student and staff environments like never before. As such, the pivot from traditionally monolithic legacy systems to one that is component-based and service-driven is urgently necessary to allow our University to support all its application layered services continuously. The adaption of scalable micro services architectures focuses on Universities like ours to keep delivering a sustainable virtual presence. We use the summary perspectives presented in this paper to inform other institutions seeking to make these changes part of driving workable virtualized infrastructure, both containerized and serverless in design.
A non-interactive verifiable computation model of perceptual layer data based on CP-ABE

The computing of smart devices at the perception layer of the power Internet of Things is often insufficient, and complex computing can be outsourced to server resources such as the cloud computing, but the allocation process is not safe and controllable. Under special constraints of the power Internet of Things such as multi-users and heterogeneous terminals, we propose a CP-ABE-based non-interactive verifiable computation model of perceptual layer data. This model is based on CP-ABE, NPOT, FHE and other relevant safety and verifiable theories, and designs a new multi-user non-interactive secure verifiable computing scheme to ensure that only users with the decryption key can participate in the execution of NPOT Scheme. In terms of the calculation process design of the model, we gave a detailed description of the system model, security model, plan. Based on the definition given, the correctness and safety of the non-interactive safety verifiable model design in the power Internet of Things environment are proved, and the interaction cost of the model is analyzed. Finally, it proves that the CP-ABE-based non-interactive verifiable computation model for the perceptual layer proposed in this paper has greatly improved security, applicability, and verifiability, and is able to meet the security outsourcing of computing in the power Internet of Things environment.
Research and Design of Virtual Driving Platform of Simulated Train Based on Python

With the rapid development of urban rail, the number of fully automatic driverless system lines is increasing, and system functions have become more and more complex. This will put more and more stringent requirements on the rapid operation and safe operation of rail transit systems. The functional requirements of the verification system are becoming more and more extensive. Preliminary product testing in a laboratory environment is of great significance for ensuring product quality and shortening the development cycle [1]. The python-based simulation train virtual Driving platform, as the interface interaction software between the signal profession and the vehicle profession, needs to be highly integrated with the functions of the real vehicle Driving platform, integrated driving platform input and output switch code positions, traction handle acceleration analog data, and man-machine interface. It has good interactivity and can manage the operation of all trains on the test line at the same time. It can manage up to 255 vehicles. The driving platform can realize the flying car function. The online vehicle can be accurately moved to the required position on the line, which is convenient for the test work of the test verifier [2]. The driving platform has achieved good results in the test application.
Finding Discriminative Regions with More Subtle Granularities for Fine-grained Image Classification

Abstract Classifying subcategories of the same category (such as birds, cars, airplanes) is mainly to find discriminative features and accuracy regional positions in the fine-grained visual classification (FGVC). In this article, we propose to use global average pooling slice feature maps to find significant discriminative regions without complicated network designs or training operations and use Drop-Block mechanisms to solve the problem of network overfitting. Specially, we use the feature maps of the multi-branches as inputs, and average-pooling them with different sizes of convolution kernels to obtain feature maps that containing deeper and shallow information for getting more accuracy granularities. Our methods are called as More Accurate Multi-Granular Convolutional Neural Network (MAG-CNN). Compared with other networks with more complex designs, the network only requires common operations such as pooling and convolution to achieve higher accuracy. The MAG-CNN can be trained end-to-end, without any bounding-box, and our method has reached the most advanced performance on three common fine-grained image classification benchmark datasets (CUB-Birds, FGVC-Aircraft, and Stanford-Cars).
ABE-AC4DDS: An Access Control Scheme Based on Attribute-Based Encryption for Data Distribution Service

In response to the security threats faced by distributed real-time applications based on DDS, a fine-grained data access control scheme is proposed, which is based on attribute-based encryption theory and suitable for topic-based publish/subscribe communication model. The scheme takes the topic as the unit of data access control and integrates the access control process with the DDS communication process, In the discovery phase of DDS, the digital signature is used to verify the publication permission for a topic, and in the publish/subscribe phase of DDS, the CP-ABE is used to verify the subscription permission for a topic. The scheme ensures not only the privacy of users but also the confidentiality and authenticity of data. Theoretical analysis shows that this scheme can resist security threats such as unauthorized publication and unauthorized subscription. Moreover, the performance test of the prototype system shows that it matches the loose coupling and one to many characteristics of the publish/subscribe communication model and has good scalability in multi-subscriber scenarios while adjusting key parameters.
Assessment of the Possibility of Using ROV in the Mode of Joint Traffic with the Support Vessel

Abstract The correspondence of the characteristics of the propulsion and steering complex (PSC) to the values required to compensate for the response of the communication cable determines the efficiency of using tethered underwater vehicles. The report assesses the requirements for the thrust characteristics of the PSC, which ensures the maneuvering of the vehicle relative to the carrier or garage in the mode of movement of the complex along an extended bottom object. The assessment is based on the results of calculating the tension of the communication cable in the stream, due to the joint movement of the tethered system “carrier-garage-vehicle”. This tension, together with the hydrodynamic resistance of the apparatus body, determine the requirements for the thrust characteristics of the PSC, as well as for the power supply system of the complex as a whole. An algorithm is proposed for calculating the tension of the communication cable between the vehicle and carrier in a stationary flow, based on the equation of a chain line and numerical integration of the equations of an inextensible flexible thread. An example of using the proposed methodology for calculating the variants of a tethered system is given.
A Robust Disparity Map Estimation for Handling Outliers in Stereo Images

The paper focuses on estimating disparity map that can handle outliers. Outliers are the major challenging problem in stereo matching. The results obtained from depth maps are significantly affected by outliers regions that are present in the image. The stereo images are pre-processed using homomorphic filter. The Semi-global block matching method is used to estimate disparity information. Left to right consistency check method is applied to identify outliers. Outliers identified are classified as occlusion and mismatches and then they are refined. The proposed method is robust against illumination differences and recovers disparities at the object edges, occluded regions. The performance of the technique used is analyzed by percentage of bad matching pixel and root mean square error. Experimental results generated determine that the proposed algorithm gives more stable disparity maps.
PUMP: Profiling-free Unified Memory Prefetcher for Large DNN Model Support

Modern DNNs are going deeper and wider to achieve higher accuracy. However, existing deep learning frameworks require the whole DNN model to fit into the GPU memory when training with GPUs, which puts an unwanted limitation on training large models. Utilizing NVIDIA Unified Memory (UM) could inherently support training DNN models beyond GPU memory capacity. However, naively adopting UM would suffer a significant performance penalty due to the delay of data transfer. In this paper, we propose PUMP, a Profiling-free Unified Memory Prefetcher. PUMP exploits GPU asynchronous execution for prefetch; that is, there exists a delay between the time that CPU launches a kernel and the time the kernel executes in GPU. PUMP extracts memory blocks accessed by the kernel when launching and swaps these blocks into GPU memory. Experimental results show PUMP achieves about 2x speedup on the average compared to the baseline that naively enables UM.
A Double-Color Image Watermarking Algorithm Based on Quaternion Schur Decomposition

In view of the fact that many watermarking algorithms used for copyright protection have ignored the correlation and synchronization between the color channels, a new quaternion Schur (QSchur) decomposition algorithm is presented in this paper. Firstly, the watermark image is encrypted by the compound chaotic map; then, the QSchur decomposition is used to associate and transform multiple color channels; finally, the watermark is embedded and extracted blindly by using the high correlation between the coefficient pairs in the block. Due to the high time complexity of the quaternion, a real structure-preserving algorithm is used to reduce the running time of the proposed algorithm. Simulation experiments and analysis demonstrate that this algorithm has stronger robustness and higher security when the actual embedding rate and invisibility are the same as those of other algorithms given.