Reviewerly/exHarmony huggingface dataset:
A Hybrid Solution For The Cold Start Problem In Recommendation

Abstract Recommender systems are becoming more and more significant in today’s digital world and in the modern economy. They make a substantial contribution to company operations by offering tailored advice and decreasing overwhelm. Collaborative filtering, being popular in the domain of recommendation, is used to offer recommendations to attract the target audience based on the feedback of people with comparable interests. This method has some limitations, such as a cold-start issue, which makes the system less effective in anticipating unknown objects. We provide a hybrid deep-learning-based strategy centered on a method to enrich user and item profiles to address the cold-start issue in the recommendation process using a collaborative filtering approach. We employ pretrained deep learning models to produce rich user and item feature vectors that aid in the creation of useful suggestions and handling of user and item cold-start issues. The creation of more precise and tailored similarity matrices is made possible by adding metadata to the extracted features of the user and item. The results of the experiment demonstrate that in terms of precision and rate coverage, the proposed method performs better than the baseline techniques.
Duality of averaging of quantum states over arbitrary symmetry groups revealing Schur–Weyl duality

Abstract It is a well-established fact in quantum information theory, that uniform averaging over the collective action of a unitary group on a multipartite quantum state projects the state to a form equivalent to a permutation operator of the subsystems. Hence states equivalent to permutation operators are untouched by collective unitary noise. A trivial observation shows that uniform averaging over permutation operators projects the state into a form with block-diagonal structure equivalent to the one of the collective action of the unitary group. We introduce a name for this property: duality of averaging . The mathematical reason behind this duality is the fact that the collective action of the unitary group on the tensor product state space of a multipartite quantum system and the action of the permutation operations are mutual commutants when treated as matrix algebras. Such pairs of matrix algebras are known as dual reductive pairs . In this work we show, that in the case of finite dimensional quantum systems such duality of averaging holds for any pairs of symmetry groups being dual reductive pairs, regardless of whether they are compact or not, as long as the averaging operation is defined via iterated integral over the Cartan decomposition of the group action. Although our result is very general, we focus much attention on the concrete example of a dual reductive pair consisting of collective action of special linear matrices and permutation operations, which physically corresponds to averaging multipartite quantum states over non-unitary SLOCC-type (Stochastic Local Operations and Classical Communication) operations. In this context we show, that noiseless subsystems known from collective unitary averaging persist in the case of SLOCC averaging in a conditional way: upon postselection to specific invariant subspaces.
Blockchain Tokens for Enterprise Knowledge Sharing

Virtual knowledge communities have become essential platforms for enterprises to facilitate knowledge sharing. However, due to the lack of effective incentive mechanisms, the traditional virtual knowledge community is generally inefficient in attracting high-quality content and maintaining enthusiasm for users' long-term participation. To address these problems, we theoretically explore the characteristics and economic ecology of enterprise knowledge sharing based on blockchain tokens to promote its sustained development. Specifically, we examine the application scenarios of token-economy activities through system-dynamics simulation and discuss the development status and path of the economic ecology. In addition, we studied the blockchain token-based incentive and penalty mechanisms for virtual knowledge communities to better stimulate knowledge sharing. Finally, the research method is applied to the GienTech supply chain finance community website to validate the proposed model and analysis. The findings contribute to the existing literature and point enterprises in a new direction in designing more effective incentive mechanisms to improve knowledge-sharing in virtual knowledge communities.
DTrust: Toward Dynamic Trust Levels Assessment in Time-Varying Online Social Networks

The social trust assessment can spur extensive applications such as social recommendations, shopping, financial investment strategies, etc, but remain a challenging problem having limited exploration. Such explorations mainly limit their studies to static network topology or simplified dynamic networks, toward the social trust relationship prediction. In contrast, in this paper, we explore the social trust by taking into account the time-varying online social networks whereas the social trust relationship may vary over time. The DTrust, a dynamic graph neural network-based solution, will be proposed for accurate social trust prediction. In particular, DTrust is composed of a static aggregation unit and a dynamic unit, respectively responsible for capturing both the spatial dependence features and temporal dependence features. In the former unit, we stack multiple NNConv layers derived from the edge-conditioned convolution network for capturing the spatial dependence features correlated to the network topology and the observed social relationships. In the latter unit, a gated recurrent unit (GRU) is employed for learning the evolution law of social interaction and social trust relationships. Based on the extracted spatial and temporal features, we then employ a fully connected neural network for learning, able to predict the social trust relationships for both current and future time slots. Extensive experimental results exhibit that our DTrust can outperform the benchmark counterparts on two real-world datasets.
Communication-Efficient Federated Learning for Heterogeneous Edge Devices Based on Adaptive Gradient Quantization

Federated learning (FL) enables geographically dispersed edge devices (i.e., clients) to learn a global model without sharing the local datasets, where each client performs gradient descent with its local data and uploads the gradients to a central server to update the global model. However, FL faces massive communication overhead resulted from uploading the gradients in each training round. To address this problem, most existing research compresses the gradients with fixed and unified quantization for all the clients, which neither seeks adaptive quantization due to the varying gradient norms at different rounds, nor exploits the heterogeneity of the clients to accelerate FL. In this paper, we propose a novel adaptive and heterogeneous gradient quantization algorithm (AdaGQ) for FL to minimize the wall-clock training time from two aspects: i) adaptive quantization which exploits the change of gradient norm to adjust the quantization resolution in each training round; and ii) heterogeneous quantization which assigns lower quantization resolution to slow clients to align their training time with other clients to mitigate the communication bottleneck, and higher quantization resolution to fast clients to achieve a better communication efficiency and accuracy tradeoff. Evaluations based on various models and datasets validate the benefits of AdaGQ, reducing the total training time by up to 52.1% compared to baseline algorithms (e.g., FedAvg, QSGD).
Hyperspectral Imaging for Vehicle Traction Effort Prediction: ISEAUTO case study

Modern self-driving platforms require new solutions for the more complex scene understanding to develop the driving assistance systems further. This paper presents a new approach for the vehicle traction effort calculation based on the information obtained from a hyperspectral camera. In this case study, the scenarios for different road surfaces are considered to predict the road load affecting the (ISEAUTO) self-driving electric vehicle (EV) designed at Tallinn University of Technology (TalTech). The collected dataset contains the hyperspectral images acquired mainly in the urban environment of the vehicle pathway. Combining the knowledge of the platform parameters and information regarding the road surface material derived from hyperspectral images using a deep learning technique provides a new layer of data that may be used in more accurate and safe vehicle control.
Network Adaptive Federated Learning: Congestion and Lossy Compression

In order to achieve the dual goals of privacy and learning across distributed data, Federated Learning (FL) systems rely on frequent exchanges of large files (model updates) between a set of clients and the server. As such FL systems are exposed to, or indeed the cause of, congestion across a wide set of network resources. Lossy compression can be used to reduce the size of exchanged files and associated delays, at the cost of adding noise to model updates. By judiciously adapting clients’ compression to varying network congestion, an FL application can reduce wall clock training time. To that end, we propose a Network Adaptive Compression (NAC-FL) policy, which dynamically varies the client’s lossy compression choices to network congestion variations. We prove, under appropriate assumptions, that NAC-FL is asymptotically optimal in terms of directly minimizing the expected wall clock training time. Further, we show via simulation that NAC-FL achieves robust performance improvements with higher gains in settings with positively correlated delays across time.
Implementing STEAM Projects in Schools in Nepal Through Participatory Action Research Approach

The first author conducted participatory action research (PAR) with schoolteachers to design STEAM-based pedagogical approaches in two of the schools in a rural part of Nepal. This study's initial objective was to investigate innovative pedagogy contributing to improved teacher and student performance. The authors collaborated with schoolteachers and to develop integrated projects for students. The entire endeavor went on for three cycles and took approximately three years on the research site to complete. The fieldwork was initiated prior to COVID-19, in February 2019, and proceeded throughout COVID-19 and beyond. In the first cycle, inquiry-based approaches were used, in the second cycle, integrated projects were developed and implemented, and in the third cycle, the STEAM approach was initiated through several real-world projects (such as save the species). This book chapter discusses how the authors devised and implemented the ‘Save the Species' project in the third cycle with a short synopsis of the first and second cycles.
Online Container Scheduling for Data-intensive Applications in Serverless Edge Computing

Introducing the emerging serverless paradigm into edge computing could avoid over- and under-provisioning of limited edge resources and make complex edge resource management transparent to application developers, which largely facilitates the cost-effectiveness, portability, and short time-to-market of edge applications. However, the computation/data dispersion and device/network heterogeneity of edge environments prevent current serverless computing platforms from acclimating to the network edge. In this paper, we address such challenges by formulating a container placement and data flow routing problem, which fully considers the heterogeneity of edge networks and the overhead of operating serverless platforms on resource-limited edge servers. We design an online algorithm to solve the problem. We further show its local optimum for each arriving container and prove its theoretical guarantee to the optimal offline solution. We also conduct extensive simulations based on practical experiment results to show the advantages of the proposed algorithm over existing baselines.
HyperMODEST: Self-Supervised 3D Object Detection with Confidence Score Filtering

Current LiDAR-based 3D object detectors for autonomous driving are almost entirely trained on human-annotated data collected in specific geographical domains with specific sensor setups, making it difficult to adapt to a different domain. MODEST [1] is the first work to train 3D object detectors without any labels. Our work, HyperMODEST, proposes a universal method implemented on top of MODEST that can largely accelerate the self-training process and does not require tuning on a specific dataset. We filter intermediate pseudo-labels used for data augmentation with low confidence scores. On the nuScenes dataset, we observe a significant improvement of 1.6% in AP_BEV in 0-80m range at IoU=0.25 and an improvement of 1.7% in AP BEV in 0-80m range at IoU=0.5 while only using one-fifth of the training time in the original approach by MODEST [1]. On the Lyft dataset, we also observe an improvement over the baseline during the first round of iterative self-training. We explore the trade-off between high precision and high recall in the early stage of the self-training process by comparing our proposed method with two other score filtering methods: confidence score filtering for pseudo-labels with and without static label retention. The code and models of this work are available at https://github.com/TRAILab/HyperMODEST
Research on Hot Operation of a Petrochemical Plant Based on Compound Edge Operator

In the operation of petrochemical plants, safety operation is the top priority, especially for the control of fire operation. In this paper, based on the real hot work in petrochemical plants, based on the extraction methods of different edge operators, the compound analysis of different edge operators can be carried out to visually detect the hot work of petrochemical workers in petrochemical plants, and based on the neural network algorithm, the convolution operation of edge data is carried out, and the data is input into the hot work database to build the hot work database and identify the flame. Remind the hot operation, according to the size of the flame to make a preliminary judgment on whether the danger, combined with the characteristics of the petrochemical device itself to add smoke identification. This method is of great significance to improve the thermal safety monitoring level of petrochemical plant.
You Are Not Alone: Towards Cleaning Robot Navigation in Shared Environments through Deep Reinforcement Learning

For mobile cleaning robot navigation, it is crucial to not only base the motion decisions on the ego agent’s capabilities but also to take into account other agents in the shared environment. Therefore, in this paper, we propose a deep reinforcement learning (DRL)-based approach for learning motion policy conditioned not only on ego observations of the environment, but also on incoming information about other agents. First, we extend a replay buffer to collect state observations on all agents at the scene and create a simulation setting from which to gather the training samples for DRL policy. Next, we express the incoming agent information in each agent’s frame of reference, thus making it translation and rotation invariant. We propose a neural network architecture with edge embedding layers that allows for the extraction of incoming information from a dynamic range of agents. This allows for generalization of the proposed approach to various settings with a variable number of agents at the scene. Through simulation results, we show that the introduction of edge layers improves the navigation policies in shared environments and performs better than other state-of-the-art DRL motion policy methods.
Research Progress on the Aesthetic Quality Assessment of Complex Layout Images Based on Deep Learning

With the development of the information age, the layout image is no longer a simple combination of text and graphics, but covers the complex layout image obtained from text, graphics, images and other layout elements through the process of artistic design, pre-press processing, typesetting, and so on. At present, the field of aesthetic-quality assessment mainly focuses on photographic images, and the aesthetic-quality assessment of complex layout images is rarely reported. However, the design of complex layout images such as posters, packaging labels, advertisements, etc., cannot be separated from the evaluation of aesthetic quality. In this paper, layout analysis is performed on complex layout images. Traditional and deep-learning-based methods for image layout analysis and aesthetic-quality assessment are reviewed and analyzed. Finally, the features, advantages and applications of common image aesthetic-quality assessment datasets and layout analysis datasets are compared and analyzed. Limitations and future perspectives of aesthetic assessment of complex layout images are discussed in relation to layout analysis and aesthetic characteristics.
A latent topic‐aware network for dense video captioning

Abstract Multiple events in a long untrimmed video possess the characteristics of similarity and continuity. These characteristics can be considered as a kind of topic semantic information, which probably behaves as same sports, similar scenes, same objects etc. Inspired by this, a novel latent topic‐aware network (LTNet) is proposed in this article. The LTNet explores potential themes within videos and generates more continuous captions. Firstly, a global visual topic finder is employed to detect the similarity among events and obtain latent topic‐level features. Secondly, a latent topic‐oriented relation learner is designed to further enhance the topic‐level representations by capturing the relationship between each event and the video themes. Benefiting from the finder and the learner, the caption generator is capable of predicting more accurate and coherent descriptions. The effectiveness of our proposed method is demonstrated on ActivityNet Captions and YouCook2 datasets, where LTNet shows a relative performance of over 3.03% and 0.50% in CIDEr score respectively.
A Neural Inference of User Social Interest for Item Recommendation

Abstract User-generated content is daily produced in social media, as such user interest summarization is critical to distill salient information from massive information for recommendation tasks. While the interested messages (e.g., tags or posts) from a single user are usually sparse becoming a bottleneck for existing methods, we propose a neural inference method ( NIGraphNet ) by mining user social interest for item recommendation. It can unearth user latent topics combined with user relation learning. Specifically, we exploit a neural variational inference approach to learn the distributions between user interests and hidden topics. (We denote it as interest-topic distributions in the following.) Then, we adopt a unified graph-based training loss that jointly learns the hidden topics and user relations for item recommendation. Experiments on two datasets collected from well-known social media platforms demonstrate the superior performance of our model in the tasks of user interest summarization and item recommendation. Further discussions also show that exploiting the latent topic representations and user relations is conducive to the user’s automatic language understanding.
Analysis And Design Of Online Based Plastic Sales Information System With User Acceptance Testing Method

The purpose of this research is to design applications that can be directly used by customers to help speed up the selectionof plastic products according to the criteria and desires of customers and to expand the sales area of plastic shops thathave not been reached. The method used in this study is UAT is one of the most innovative methodologies to preventIT project failure. The UAT process begins by providing documentation of business requirements, then continues withbusiness processes (workflows) or scenarios and finally tests using data. Effectiveness in testing is needed in thedevelopment of an application or information system so that the product can arrive at the user on time and according tothe user's needs. Tests carried out on the built system are divided into two mechanism tests, namely alpha testing usingthe black box method and beta testing using a Likert scale. The results obtained from this research are that this ecommerce website can be used directly by customers to help speed up the selection of rice products according to thecriteria and wishes of customers, this online sales website is also very helpful in marketing plastic products in a wide areacoverage and test results with user acceptance testing getting an average result of 94.67% it can be concluded that ECommerce is made for UD customers. Various Plastics are in accordance with user needs.
A Comparative Study on Selecting Acoustic Modeling Units for WFST-based Mongolian Speech Recognition

Traditional weighted finite-state transducer– (WFST) based Mongolian automatic speech recognition (ASR) systems use phonemes as pronunciation lexicon modeling units. However, Mongolian is an agglutinative, low-resource language, and building an ASR system based on the phoneme pronunciation lexicon remains a challenge for various reasons. First, the phoneme pronunciation lexicon manually constructed by Mongolian linguists is finite, which is usually used to build a grapheme-to-phoneme conversion (G2P) model to frequently expand new words. However, the data sparsity decreases the robustness of the G2P model and affects the performance of the final ASR system. Second, homophones and polysyllabic words are common in Mongolian, which has a certain impact on the construction of the Mongolian acoustic model. To address these problems, in this work, we first propose a grapheme-to-phoneme alignment model to obtain the mapping relationship between phonemes and subword units. Then, we construct an acoustic subword segmentation set to segment words directly instead of using the traditional G2P method to predict phoneme sequences to expand the pronunciation lexicon. Further, by analyzing the Mongolian encoding form, we also propose an acoustic subword modeling units construction method that removes control characters. Finally, we investigate various acoustic subword modeling units for pronunciation lexicon construction for the Mongolian ASR system. Experiments on a Mongolian dataset with 325 hours of training show that the pronunciation lexicon based on the acoustic subword modeling unit can effectively construct the WFST-based Mongolian ASR system. Further, removing the control characters when building the acoustic subword modeling unit can further improve the ASR system performance.