Reviewerly/exHarmony huggingface dataset:
Vehicle Kinematics-Based Image Augmentation against Motion Blur for Object Detectors

&lt;div class="section abstract"&gt;&lt;div class="htmlview paragraph"&gt;High-speed vehicles in low illumination environments severely blur the images used in object detectors, which poses a potential threat to object detector-based advanced driver assistance systems (ADAS) and autonomous driving systems. Augmenting the training images for object detectors is an efficient way to mitigate the threat from motion blur. However, little attention has been paid to the motion of the vehicle and the position of objects in the traffic scene, which limits the consistence between the resulting augmented images and traffic scenes. In this paper, we present a vehicle kinematics-based image augmentation algorithm by modeling and analyzing the traffic scenes to generate more realistic augmented images and achieve higher robustness improvement on object detectors against motion blur. Firstly, we propose a traffic scene model considering vehicle motion and the relationship between the vehicle and the object in the traffic scene. Simulations based on typical ADAS test scenes show that the high vehicle speed and near object position is the key factor in generating motion blur. Second, we propose the vehicle-motion-based image augmentation algorithm. The proposed method applies the motion blur on the clear object based on the vehicle's speed and the relative position of the object. Subjective evaluation and multiple objective evaluation indexes including structural similarity index measure (SSIM), perceptual hash, normalized mutual information, and cosine similarity demonstrates that the proposed image augmentation can produce images more consistent with the traffic scenes. Thirdly, we apply the proposed method to the training of object detectors. Experiments on the KITTI dataset as well as real-world driving tests show that the proposed image augmentation achieves a higher robustness improvement than existing image augmentation algorithms on multiple object detectors including CenterNet, YOLOv3, and Faster R-CNN.&lt;/div&gt;&lt;/div&gt;
Mixed reality in threatened environments

This article considers the ecoscenographer’s use of technologies to ‘timeshift’ performances. Timeshifting refers to watching locally recorded content after its originally scheduled broadcast. It explores the evolution, refinement, and emergent issues related to applying this approach to performing relationships within threatened environments, including how they relate to notions of ‘hauntology’ in highlighting and collapsing time in reference to climate change. The article reviews the cultivation of new technologies to provide located media in remote locations, such as: Dance Exchange’s 500 Miles/ 500 Stories, Out of Box Productions’ Rallentando, and more recent projects including Toasterlab’s Groundworks and park-based projects, Swim Pony’s TrailOff, The Only Animal’s 1000-Year Theatre, and Nakai Theatre’s Landscape Theatre. These projects establish a trajectory of emplaced mixed reality practice. This includes practical production topics such as asymmetrical data infrastructure and the trade-offs of accessible technology. It considers how gaming (i.e. open world games) and 360VR provide tools for scenographers to stage spatial performances that examine the deep performativity of space over time despite the limitations resulting from the phenomenology of archive.
Suction Cup Quality Predication by Digital Image Correlation

&lt;div class="section abstract"&gt;&lt;div class="htmlview paragraph"&gt;Vacuum suction cups are used as transforming handles in stamping lines, which are essential in developing automation and mechanization. However, the vacuum suction cup will crack due to fatigue or long-term operation or installation angle, which directly affects production productivity and safety. The better design will help increase the cups' service life. If the location of stress concentration can be predicted, this can prevent the occurrence of cracks in advance and effectively increase the service life. However, the traditional strain measurement technology cannot meet the requirements of tracking large-field stains and precise point tracking simultaneously in the same area, especially for stacking or narrow parts of the suction cups. The application must allow multiple measurements of hidden component strain information in different fields of view, which would add cost. In this study, a unique multi-camera three-dimensional digital image correlation (3D-DIC) system was designed and applied to measure the strain concentration of the suction cups while the cups were running the pulling progress. In this technique, a multiplexed quad-cameras DIC system which contains two sets of 3D-DIC system (4 cameras) with different field of view or different measurement directions enables simultaneous measurement of full-filed and hidden parts under the same calibration progress. The first two cameras built a sub-group of the 3D-DIC system, which was used to measure the local strain of the narrow or stacked prats. The other system was used to acquire the strain fields of the entire suction cup. In addition to the experimental test, the fatigue test to see the cracks appeared location. The results of DIC were compared to the fatigue data, and the DIC experimental data validated the crack location. This project aims to help designers and operators thoroughly understand the performance of vacuum cups by investigating the strain concentration and crack location.&lt;/div&gt;&lt;/div&gt;
Assessing Enterprise Level, Augmented Reality Solutions for Electronics Manufacturing

&lt;div class="section abstract"&gt;&lt;div class="htmlview paragraph"&gt;With the growth of Industry 4.0 in recent years, Augmented Reality (AR) technologies are changing the way operators work by increasing their efficiency and operational performance. A common use of AR is providing operators helpful work instructions for assembly by presenting relevant digital information in the context of the physical environment. These AR experiences can be viewed via several devices such as mobile, wearable, and stationary devices, each being useful for different applications. While in the experience, instructions are provided by means of 3D animation, text, images, and interactive buttons, all of which are directly overlaid onto the physical product or equipment being worked on. This work presents a closed-loop, enterprise connected, AR system for post end Printed Circuit Board (PCB) assembly work instructions. The system is designed to work with a stationary device, allows for varying types of PCB”s, provides overlaid instruction, and logs important information to an enterprise system, such as overall cycle time, step cycle time, number of errors, type of error, and who performed the assembly. A comparison was made for single cell manual assembly PCB work instructions using both an Industry 4.0 driven system and a more traditional manufacturing system which used packets, tracers, Manufacturing Execution System (MES), and PDF instructions. Discovered benefits of an enterprise connected AR system included increased throughput and utilization, improved communication between operators and support, reduced overtime costs, reduced defects, reduced non-value-added secondary inspections, nearly 50% increase in ergonomics, reduced labor due to rework, and improved corrective actions due to granularity between steps compared to total operation. This AR driven solution transformed the single cell manual assembly to be more informed, make better decisions, and help operator productivity.&lt;/div&gt;&lt;/div&gt;
Efficient online migration mechanism for memory write-intensive virtual machines

Abstract Online migration of virtual machines (VMs) is indispensable for system maintenance as it helps to achieve several resource management objectives such as load balancing, proactive fault tolerance, green operation, and resource management of data centers. The migration efficiency and reliability are two major challenges in the online migration of memory write-intensive VMs. For example, pre-copy migration transfers a large amount of data and takes a long time to migrate. This study proposes an efficient and reliable adaptive hybrid migration mechanism for memory write-intensive VMs. The mechanism optimizes the data transfer mode of the common migration method and improves the performance of conventional hybrid migration. First, the virtual machine (VM) memory data to be migrated are divided into dynamic and static data based on the bitmap marking method, and the migration efficiency is improved through parallel transmission based on different networks. Second, to accelerate the migration reliability, an iterative convergence factor is proposed to evaluate the current system load state and adaptively calculate the switching time of the migration mode for adaptive hybrid migration based on the convergence factor. Through adaptive hybrid migration can achieve migration completed successfully, shorten the post-copy migration duration, and minimize the impact on the performance of VMs. Finally, this paper implements the system prototype based on a kernel-based virtual machine (KVM), and experiments are performed using multiple memory write-intensive load VMs. The results show that the proposed migration algorithm can significantly improve migration performance and complete migration quickly to solve the pre-copy migration failure problem with a memory write-intensive load. Compared with the traditional hybrid migration with only one round of pre-copy, the proposed migration algorithm reduces the total migration time and transmits data by 23.2% and 26.7%, respectively.
Optimizing smart contract vulnerability detection via multi-modality code and entropy embedding

Smart contracts have been widely used in the blockchain world these years, and simultaneously vulnerability detection has gained more and more attention due to the staggering economic losses caused by the attacker. Existing tools that analyze vulnerabilities for smart contracts heavily rely on rules predefined by experts, which are labour-intense and require domain knowledge. Moreover, predefined rules tend to be misconceptions and increase the risk of crafty potential back-doors in the future. Recently, researchers mainly used static and dynamic execution analysis to detect the vulnerabilities of smart contracts and have achieved acceptable results. However, the dynamic method cannot cover all the program inputs and execution paths, which leads to some vulnerabilities that are hard to detect. The static analysis method commonly includes symbolic execution and theorem proving, which requires using constraints to detect vulnerability. These shortcomings show that traditional methods are challenging to apply and expand on a large scale. This paper aims to detect vulnerabilities via the Bug Injection framework and transfer learning techniques. First, we train a Transformer encoder using multi-modality code, which contains source code, intermediate representation, and assembly code. The input code consists separately of Solidity source code, intermediate representation, and assembly code. Specifically, we translate source code into the intermediate representation and decompile the byte code into assembly code by the EVM compiler. Then, we propose a novel entropy embedding technique, which combines token embedding, segment embedding, and positional embedding of the Transformer encoder in our approach. After that, we utilize the Bug Injection framework to automatically generate specific types of buggy code for fine-tuning and evaluating the performance of vulnerability detection. The experimental results show that our proposed approach improves the performance in detecting reentrancy vulnerabilities and timestamp dependence. Moreover, our approach is more flexible and scalable than static and dynamic analysis approaches in detecting smart contract vulnerabilities. Our approach improves the baseline approaches by an average of 11.89% in term of F1 score.
Analyzing track management strategies for multi object tracking in cooperative autonomous driving scenarios

For autonomous driving to operate safely it is crucial to perceive surrounding objects correctly. Not only detection but also state estimation (track) of a perceived object is urgent. The state is required to enable a safe motion planning, since it allows to predict the future position of an object. To include only valid information, the state estimations must be maintained to determine which track is active and which is not. Mostly, a simple count-based approach is used. For this, we present an investigation of two common approaches from non-cooperative track management in comparison to two new management strategies to maintain tracks in a cooperative scenario. We evaluate them using three simulated scenarios with a varying rate of cooperative vehicles. A confidence-based approach was able to increase the average precision by up to 9 percentage points.
The black box aesthetics: performative dynamics between artificial intelligence and human interactive staging

The present paper addresses a theoretical model for human-machine interaction in intermedia performance taking as reference the author’s perspective. It is discussed the role of artificial intelligence as an autonomous, pervasive, and real-time evolving entity in dialectical relation with the performer and/or audience. The convergence of physical and virtual elements suggests the emergence of hybrid systems, insofar as digital instances manifest their own agency and humans show mechanical actions linked to information processing. Three typologies of human-machine interaction are inferred: 1) the algorithm elaborates the output through self-referential data, as in Δnfang; 2) the algorithm gathers information from performed actions, as in Convergence; 3) the algorithm processes data from the audience’s reaction, as in DoPPioGioco. As digital and analogue devices are employed, programming environments are hidden from human agents and filtered through the author’s gaze. Comparing the underlying black box rules with stage setting, it is observed to what extent algorithms are mediated and enacted. The article considers the information bias commonly attributed to artificial intelligence as rooted in the aesthetic domain, with significant dramaturgical implications.
Soft multimodal style transfer via optimal transport

The goal of arbitrary Neural Style Transfer (NST) is to transform a content image into a different style. Based on the assumption that styles can be represented by global statistics, the majority of NST methods seek to match global statistics for style transfer. These global statistics disregard the multimodal property of the style because distinct semantic parts of a style image may comprise various styles. Multimodal style transfer addresses this issue by extracting semantic patterns of style images through image clustering, obtaining multiple different sub-style components. The matching of the sub-style components and content features in this case is crucial because neighboring content features may be given various sub-style components. In this paper, we propose a novel assignment based on optimal transport to solve this issue. The proposed assignment theoretically guarantees that the distributions between the stylized image and the style image are similar, and it also permits more than one sub-style component to be assigned to each content feature. Based on this soft assignment, a weighted style transfer method is proposed to blend different stylized results, each of which is stylized by one sub-style component. With a more precise description of the image style, the proposed method achieves promising stylized results. Extensive experiments are conducted to demonstrate the superior effectiveness of the proposed method.
impact of students’ attitudes about distance learning on their creativity

Introduction. Since 2020, education systems around the world had to shift from traditional classroom-based methods of teaching and learning to different types of remote or distance learning. Therefore, the shift to distance learning is able to provide some understanding on how this model of learning influences student motivation and creativity. Purpose. The study aimed to reveal how student positive or negative attitudes about distance learning influenced their creativity. Methods. The intervention took place at V.O. Sukhomlynskyi National University of Mykolaiv in Ukraine. The research was based on such methodology as a document analysis of student works, conducting a survey, and the correlation analysis. The Torrance Tests of Creative Thinking, a Likert scale questionnaire, the correlation analysis in Statistical Package for the Social Sciences (SPSS) were utilized as the research procedure. Results. The research findings revealed that while studying remotely on the elective course of Marketing during 2020-2021 academic year at V.O. Sukhomlynskyi National University of Mykolaiv, students’ attitudes toward distance learning in terms of their attitude to the platform used by the teacher affected their creativity. Conclusion. The main implication of the study is that educators should pay more attention to the platform of distance learning they use to teach students remotely. Understanding that students’ attitudes toward the necessity to study remotely may decrease their creativity, teachers can redirect remote learning toward blended learning and combining traditional and distance forms of learning to support the high level of student creativity.
Weighted Cause-Reward Analysis-based Reinforcement Learning Method for Optimizing the Sentiment Prediction

In online markets or shops, the products are purchased by people based on their visual appearance and textual description. These ecommerce systems also collect user views or feedback about the sold product or services. The user also submits his complaints, reaction, and opinion including his sentiments. These reviews can be processed by ecommerce sites to rank their products and identify the features and flaws of products. In this paper, a weighted cause-reward analysis-based reinforcement learning method is proposed to optimize the sentiment classification. The model first transformed the input reviews to the weighted features by identifying the frequency and boundness with product. These weighted features are processed by the reinforcement learning method to classify the sentiments. The model is applied on the reviews collected from amazon reviews dataset. The analysis results are compared against machine learning and deep learning methods. The results shows that the proposed model achieved the average accuracy rate of 94.60% which is significantly better than existing methods. The performance and reliability of the system are improved by the proposed model.
Computer Technology Based on the Intelligentization of College English Cross-cultural Practice

With the reform and development of education, colleges and universities have adopted cross-cultural teaching methods in English teaching, which has effectively improved the quality of college English teaching. This article mainly analyzes the significance of early English teaching in cross-cultural teaching. It also studies the cultivation of cross-cultural competence of English talents based on computer technology, and it also studies the design of computer-based cross-cultural practice and intelligent college English teaching mode. The cross-cultural English teaching model has played an important role in the reform of English teaching.
Law Enforcement Companion

Identifying absconding criminals after committing an offence or illegal act is a time consuming and tedious task. Seeing the current growing population density and considering the vastness of the land area any country has, it is very difficult for law enforcement agencies alone to do this task. So public involvement becomes extremely significant, game-changer and helpful. According to many reports, finding lawbreakers by causing different individuals from the office to sit with PCs and PCs to look through the CCTV film to find and follow the blameworthy, as they don't have the robotized framework for doing this errand with them. This cycle is both times and works seriously. In this paper, we have attempted to review the current advancements as well as propose another framework for criminal Distinguishing & Recognition using Deep learning and Heroku Cloud i.e Cloud Computing, which assuming utilized by our Crime control Organizations would assist them with tracking down crooks from the pictures of CCTV or images uploaded by the public if seen anywhere. This system if implemented helps find criminals as well as any person can upload the information that he has seen the required person in a particular place and time. Existing arrangements utilize conventional face acknowledgement calculations which can be problematic in changing Indian conditions, particularly factors like light, climate and particular direction and there is no open public contribution. All the workload and pressure will be upon the Law Enforcing Agencies only. This research paper proposes to use Deep Learning and the Heroku Cloud systems for the implementation of the proposed system.
Sentiment Analysis with NLP on Twitter Data

Sentiment analysis has been widely used in a variety of fields in the previous decade, including business, social media, and education. The usage of sentiment analysis is rising, but it remains problematic, particularly in the education area, where, dealing with and processing students' thoughts is a difficult task. Several studies of the literature illustrate the current state of sentiment analysis application in this sector from various perspectives and settings. To address classification difficulties, traditional machine learning methods are commonly utilized. These machine learning techniques are frequently biased toward a specific class. The problem of sentiment categorization on the Twitter dataset is addressed in this research. In this research, we look into the problem of sentiment categorization using a Twitter dataset. We present a By including semantics in feature vectors and utilizing ensemble methods, we use Natural Language Processing (NLP) to improve sentiment classification. Increasing the accuracy of prediction by includes semantically comparable terms and context-sense identities in the feature vectors to accomplish sentiment analysis, we employ a range of text preprocessing and Naive Bayes classifier approaches. Finally, we utilize an accuracy classifying technique of diverse text preprocessing to get a classification accuracy of 77.26 percent.
Decentralized Image Sharing App using Blockchain

While the rise of social media swept the globe, in recent years, a discussion concerning the technology's ramifications has gained traction. Two key concerns, in particular, are concerns about user privacy and data, as well as concerns about centralized authority, including censorship. Because these two areas are directly related to the technical issues that decentralization addresses, there is a growing interest in blockchain-based data-sharing multimedia applications such as Instagram. This application prototypes a clone of Instagram, where the users can earn tokens for likes, shares comments on their posts. Their posts are copyright protected and similar duplicate posts will be detected.