Reviewerly/exHarmony huggingface dataset:
INT: Towards Infinite-Frames 3D Detection with an Efficient Framework

It is natural to construct a multi-frame instead of a single-frame 3D detector for a continuous-time stream. Although increasing the number of frames might improve performance, previous multi-frame studies only used very limited frames to build their systems due to the dramatically increased computational and memory cost. To address these issues, we propose a novel on-stream training and prediction framework that, in theory, can employ an infinite number of frames while keeping the same amount of computation as a single-frame detector. This infinite framework (INT), which can be used with most existing detectors, is utilized, for example, on the popular CenterPoint, with significant latency reductions and performance improvements. We’ve also conducted extensive experiments on two large-scale datasets, nuScenes and Waymo Open Dataset, to demonstrate the scheme’s effectiveness and efficiency. By employing INT on CenterPoint, we can get around 7% (Waymo) and 15% (nuScenes) performance boost with only 2 $${}^{\tilde{}}$$ 4 ms latency overhead, and currently SOTA on the Waymo 3D Detection leaderboard.
HPH: Hybrid Parallelism on Heterogeneous Clusters for Accelerating Large-scale DNNs Training

As the deep learning model grows larger, training model with a single computational resource becomes impractical. To solve this, hybrid parallelism, which combines data and pipeline parallelism emerges to train large models with multiple GPUs. In practice, using heterogeneous GPU clusters to train large models is a common need due to the upgrade of a part of hardware. However, existing hybrid parallelism approaches in the heterogeneous environment do not work well in communication efficacy, workload balance among GPUs and utilizing the memory constrained GPU. To address these problems, we present a parallel DNN training approach, Hybrid Parallelism on Heterogeneous clusters (HPH). In HPH, we propose a topology designer that minimizes the communication time cost. Furthermore, HPH uses a partition algorithm that automatically partitions DNN layers among workers to maximize throughput. Besides, HPH adopts recomputation-aware scheduling to reduce memory consumption and further reschedule the pipeline to eliminate the extra time overhead of recomputation. Our experimental results on a 32-GPU heterogeneous cluster show that HPH achieves up to 1.42x training speed-ups compared with the state-of-the-art approach.
Semantic-Guided Multi-mask Image Harmonization

Previous harmonization methods focus on adjusting one inharmonious region in an image based on an input mask. They may face problems when dealing with different perturbations on different semantic regions without available input masks. To deal with the problem that one image has been pasted with several foregrounds coming from different images and needs to harmonize them towards different domain directions without any mask as input, we propose a new semantic-guided multi-mask image harmonization task. Different from the previous single-mask image harmonization task, each inharmonious image is perturbed with different methods according to the semantic segmentation masks. Two challenging benchmarks, HScene and HLIP, are constructed based on 150 and 19 semantic classes, respectively. Furthermore, previous baselines focus on regressing the exact value for each pixel of the harmonized images. The generated results are in the ‘black box’ and cannot be edited. In this work, we propose a novel way to edit the inharmonious images by predicting a series of operator masks. The masks indicate the level and the position to apply a certain image editing operation, which could be the brightness, the saturation, and the color in a specific dimension. The operator masks provide more flexibility for users to edit the image further. Extensive experiments verify that the operator mask-based network can further improve those state-of-the-art methods which directly regress RGB images when the perturbations are structural. Experiments have been conducted on our constructed benchmarks to verify that our proposed operator mask-based framework can locate and modify the inharmonious regions in more complex scenes. Our code and models are available at https://github.com/XuqianRen/Semantic-guided-Multi-mask-Image-Harmonization.git .
How things fit together: a general model of the information behaviour field

Anyone new to a research field may easily feel overwhelmed by the sheer number of new concepts and must struggle with how things fit together. The aim of this paper is to initiate a discussion about a new general model for information behaviour as a field, and to serve both as an introduction and as a tool to highlight understudied areas within the field. The paper compares previous general models, textbooks, and a subject index with standard definitions of information behaviour. Based on the analysis, a new general model of the information behaviour field is developed. A comparative analysis was used. The paper argues that the existing general models of information behaviour do not reflect the breadth of the field as seen in community-accepted definitions of information behaviour. The analysis also shows that topics like information discovery and information seeking are overrepresented in textbooks and subject indices. The new model illustrates the breadth of the field, explains how topics fit together and highlights research areas that are in special need of attention.
Privacy-Preserving Intelligent Resource Allocation for Federated Edge Learning in Quantum Internet

Federated learning (FL) is an emerging technology for empowering various applications that generate large amounts of data in intelligent cyber-physical systems (ICPS). Though FL can address users' concerns about data privacy, its maintenance still depends on efficient incentive mechanisms. For long-term incentivization to participants in data federation under dynamic environments, deep reinforcement learning as a promising technology has been extensively studied. However, the non-stationary problem caused by the heterogeneity of ICPS devices results in a serious effect on the convergence rate of existing single-agent reinforcement learning. In this paper, we propose a multi-agent learning-based incentive mechanism to capture the stationarity approximation in FL with heterogeneous ICPS. First, we formulate the secure communication and data resource allocation problem as a Stackelberg game in FL with multiple participants. Then, to tackle the heterogeneous problem, we model this multi-agent game as a partially observable Markov decision process. Particularly, a multi-agent federated reinforcement learning algorithm is proposed to learn the allocation policies efficiently by dwindling variances in policy evaluation caused by interaction among multiple devices without sharing privacy information. Moreover, the proposed algorithm is proved to attain convergence at an expected rate. Lastly, extensive experimental results demonstrate that our proposed algorithm significantly outperforms baselines.
Sentiment Analysis of Amazon Smartphone Reviews Using Machine Learning &amp; Deep Learning

The past few years have been marked by quite a few developments in e-commerce and online shopping with the biggest of them being in the smartphone segment. India is now the world’s largest market for smartphones with its share having increased to 45% in 2020 by registering a mammoth 7% growth during the pandemic year. Some of the major smartphone brands here are Xiaomi, Samsung, and OnePlus. These brands have often partnered exclusively with e-commerce platforms like Amazon and Flipkart with sweet deals and offers for buyers. For smartphones of all price segments, reviews on these sites can be an important indicator of how satisfied customers are with the product and can also be an important factor for decision making that helps customers choose whether a product is worth purchasing or not. In this paper, we will be exploring algorithms and techniques used for sentiment analysis and text classification of smartphone reviews on Amazon. The dataset we used for research is available on Kaggle and contains 6S,000 reviews of 720 smartphones of numerous brands. We have used a combination of machine learning and deep learning algorithms for the same, starting with baseline logistic regression and naive Bayes models and then moving on to complex support vector machines and Recurrent Neural Networks such as LSTM using the FastAI library.
Compliance Challenges in Forensic Image Analysis Under the Artificial Intelligence Act

In many applications of forensic image analysis, state-of-the-art results are nowadays achieved with machine learning methods. However, concerns about their reliability and opacity raise the question whether such methods can be used in criminal investigations. So far, this question of legal compliance has hardly been discussed, also because legal regulations for machine learning methods were not defined explicitly. To this end, the European Commission recently proposed the artificial intelligence (AI) act, a regulatory framework for the trustworthy use of AI. Under the draft AI act, high-risk AI systems for use in law enforcement are permitted but subject to compliance with mandatory requirements. In this paper, we review why the use of machine learning in forensic image analysis is classified as high-risk. We then summarize the mandatory requirements for high-risk AI systems and discuss these requirements in light of two forensic applications, license plate recognition and deep fake detection. The goal of this paper is to raise awareness of the upcoming legal requirements and to point out avenues for future research.
Bi-level Feature Alignment for Versatile Image Translation and Manipulation

Generative adversarial networks (GANs) have achieved great success in image translation and manipulation. However, high-fidelity image generation with faithful style control remains a grand challenge in computer vision. This paper presents a versatile image translation and manipulation framework that achieves accurate semantic and style guidance in image generation by explicitly building a correspondence. To handle the quadratic complexity incurred by building the dense correspondences, we introduce a bi-level feature alignment strategy that adopts a top-k operation to rank block-wise features followed by dense attention between block features which reduces memory cost substantially. As the top-k operation involves index swapping which precludes the gradient propagation, we approximate the non-differentiable top-k operation with a regularized earth mover’s problem so that its gradient can be effectively back-propagated. In addition, we design a novel semantic position encoding mechanism that builds up coordinate for each individual semantic region to preserve texture structures while building correspondences. Further, we design a novel confidence feature injection module which mitigates mismatch problem by fusing features adaptively according to the reliability of built correspondences. Extensive experiments show that our method achieves superior performance qualitatively and quantitatively as compared with the state-of-the-art.
Empathetic Dialogue Generation with Pre-trained RoBERTa-GPT2 and External Knowledge

One challenge for dialogue agents is to recognize the feelings of the conversation partner and respond accordingly. In this work, RoBERTa-GPT2 is proposed for empathetic dialogue generation, where the pre-trained auto-encoding RoBERTa is utilized as encoder and the pre-trained auto-regressive GPT-2 as decoder. With the combination of the pre-trained RoBERTa and GPT-2, our model realizes a new state-of-the-art emotion accuracy. To enable the empathetic ability of RoBERTa-GPT2 model, we propose a commonsense knowledge and emotional concepts extractor, in which the commonsensible and emotional concepts of dialogue context are extracted for the GPT-2 decoder. The experiment results demonstrate that the empathetic dialogue generation benefits from both pre-trained encoder-decoder architecture and external knowledge.
FLoBC: A Decentralized Blockchain-Based Federated Learning Framework

The rapid expansion of data worldwide invites the need for more distributed solutions in order to apply machine learning on a much wider scale. The resultant distributed learning systems can have various degrees of centralization. In this work, we demonstrate our solution FLoBC for building a generic decentralized federated learning system using the blockchain technology, accommodating any machine learning model that is compatible with gradient descent optimization. We present our system design comprising the two decentralized actors: trainer and validator, alongside our methodology for ensuring reliable and efficient operation of said system. Finally, we utilize FLoBC as an experimental sandbox to compare and contrast the effects of trainer-to-validator ratio, reward-penalty policy, and model synchronization schemes on the overall system performance, ultimately showing by example that a decentralized federated learning system is indeed a feasible alternative to more centralized architectures.
Challenges of Adopting DevOps Culture on the Internet of Things Applications - A Solution Model

DevOps is a culture that emphasizes team cooperation and encourages enterprises to deliver software product features through automated processes. The Internet of Things paradigm is impulsively and continuously reforming industrial endeavours. DevOps combines continuous integration followed by continuous testing which is then succeeded by continuous deployment and continuous delivery. Continuous monitoring is a nerve ending once the product is delivered. This entire combination helps to produce faster deployments with quality. The DevOps-related challenges in IoT projects are discussed in this paper. The DevOps methodology and its recommended practices are frequently used in combination with version control, monitoring, and alerting at the device level. It has outlined the software architecture for IoT devices. This study reviewed research articles to investigate challenges with the Internet of Things applications related to DevOps framework. This study also tried to explore solutions for those challenges in form of guidelines, practices to adhere to, and models suggested in various available literature. The proposed article identifies important issues that must be resolved to implement the DevOps culture in IoT applications. Additionally, the organizations' cultural difficulties confronted while placing DevOps into practice are studied and a model is developed to provide guidelines. Effectiveness measures under consideration are collaboration, sharing, efficiency, and quality (Business value). So, this study emphasizes the challenges related to these effectiveness measures. According to research, there are several obstacles to implementing a DevOps culture in IoT applications, including poor communication, less no of DevOps experts, weak collaboration, and less management support. This research suggested a new model, Industrial DevOps Maturity Model (IDMM) to provide usage guidelines that can be the solution to existing challenges related to the effectiveness measures faced while using DevOps framework in IoT applications.
The Investment Value of Metaverse in the Media and Entertainment Industry

With the launch of the film Ready Player One, more investors start to notice the potential of the metaverse. This will lead to a revolution in consumer goods, technology, entertainment and almost all industries. The entertainment metaverse is growing at a fast speed with the production of many new economies. Although people are worried about the stalled development of metaverse and crimes derived from virtuality, many enterprises have brought a surprise to the public. The author gives many examples of potential companies to illustrate technology and application developments. Shortly, it is expected that people can interact with many sub-metaverses and more investment opportunities will exist. While the entertainment metaverse will be established with a new revolution of industries, many companies are competing with each other extremely to capture the market. Investors can keep an eye on leading companies in internet applications, software platforms and hardware platforms, cryptocurrency and NFTs.
Investigating the dimensions of students’ privacy concern in the collection, use and sharing of data for learning analytics

The datafication of learning has created vast amounts of digital data which may contribute to enhancing teaching and learning. While researchers have successfully used learning analytics, for instance, to improve student retention and learning design, the topic of privacy in learning analytics from students' perspectives requires further investigation. Specifically, there are mixed results in the literature as to whether students are concerned about privacy in learning analytics. Understanding students' privacy concern, or lack of privacy concern, can contribute to successful implementation of learning analytics applications in higher education institutions. This paper reports on a study carried out to understand whether students are concerned about the collection, use and sharing of their data for learning analytics, and what contributes to their perspectives. Students in a laboratory session (n = 111) were shown vignettes describing data use in a university and an e-commerce company. The aim was to determine students' concern about their data being collected, used and shared with third parties, and whether their concern differed between the two contexts. Students' general privacy concerns and behaviours were also examined and compared to their privacy concern specific to learning analytics. We found that students in the study were more comfortable with the collection, use and sharing of their data in the university context than in the e-commerce context. Furthermore, these students were more concerned about their data being shared with third parties in the e-commerce context than in the university context. Thus, the study findings contribute to deepening our understanding about what raises students’ privacy concern in the collection, use and sharing of their data for learning analytics. We discuss the implications of these findings for research on and the practice of ethical learning analytics.
Academic and Socioсultural Adaptation of Students in Russian Universities

The article presents the results of the All-Russian sociological study of academic and sociocultural adaptation of students in Russian universities. The purpose of the article was to identify and interpret the problems of students’ adaptation to the educational process, research activities, project training, sociocultural environment of the university. The subject of the study was the selfassessment of the adaptability of junior students, as well as the factors influencing it. The novelty of the research consists in a comprehensive consideration of students’ adaptation to key activities at the university, including a new type of project activity. The empirical study was implemented through a massive online survey of students in May-July 2022. The respondents were first and second-year students enrolled in bachelor’s and full-time specialty programs in various areas of training. The total volume of the sample was 15,902 people, the maximum sampling error didn’t exceed 1%.Based on empirical data, the article shows the main problems of students’ adaptation to learning, namely, a significant amount of new information, the pace of the educational process, the format of practical classes and coursework that require more independence and activity than in school education. The authors reveal the connection between the level of students’ educational activity and initiative and the degree of their integration into the university community. The negative trend of decreasing student activity in the research field compared to the school period of study , as well as the preservation of a high proportion of those who are prone to academic fraud due to the lack of adaptive resources, is shown. It is concluded that the adaptation of students to project activities is complicated by their unwillingness to responsibility and independence, an attitude towards individualism and autonomy, an insufficiently high level of professional culture and professional knowledge, which is at odds with the expectations of project customers. At the same time, positive effects have been recorded in the field of sociocultural adaptation of nonresident and foreign students. Getting used to the specifics of Russian culture, the academic environment, and the new urban society occurs without much difficulty. The conclusion is made about the effectiveness of university programs of adaptation and support of foreign students and students with disabilities, reasoned by the absence of significant differences in adaptation problems in all groups of students.
Intermediate data fault-tolerant method of cloud computing accounting service platform supporting cost-benefit analysis

Abstract This study mainly aims at the intermediate data fault-tolerant method of cloud computing accounting service platform supporting cost-benefit analysis, which aims at providing cost-benefit analysis function for the platform and strengthening the fault-tolerant ability of the intermediate data of the platform. The invention discloses a method for constructing an enterprise cloud computing accounting service platform. Collect the internal and external accounting service system data of an enterprise by using an accounting service platform network environment provided by a cloud service provider. Transmit the data to a data processing and storage layer data warehouse for storage after data cleaning, extraction and processing. Then, call the collected data through the data processing and storage layer to analyze the transaction cost-benefit of the enterprise. The intermediate data fault-tolerant model is constructed. After being solved by the ant colony algorithm, the intermediate data generated in the process of cost-benefit analysis and other accounting services are fault-tolerant processed. Finally, the platform accounting service results are output to the interactive interface through the data output display layer. The highest data availability probability of the method proposed in this study is 0. 98, which indicates that the method has high data availability after fault-tolerant processing, and can effectively realize the interaction with users. The experimental analysis shows that the method proposed in this study can effectively analyze the transaction costs and benefits of enterprises. The probability of data availability after fault-tolerant processing is higher, and a load of reading and writing is lower.