Reviewerly/exHarmony huggingface dataset:
An Overview of Blockchain Technology in Smart Grid

The increase in the use of renewable energy has opened up gates for new ideas and technologies to emerge. One such fresh concept to knock on the door for a while is blockchain technology. This technology is playing a significant role in changing the ethics of conventional transactions with its key features. Blockchain focuses on connecting information and data in such a way that any task or process performed on it becomes transparent as well as secure. With the implementation of blockchain in our conventional gird, consumers can buy and sell green energy directly from each other. This allows forming peer-to-peer network that results in the decentralization of electricity. Although the power of this technology is already known yet one of its crucial impacts is assumed to take place in the field of smart grid technology. This paper discusses the fundamental concepts and functionalities of a blockchain-based microgrid and provides information on peer-to-peer network for energy trade.
A Systematic Review on LiDAR-Based 3D Object Detection

Light Detection And Ranging (LiDAR) is a popular sensor for providing 3-Dimensional (3D) information about a point cloud to localize and characterize its shape. Also, object detection in point clouds is critical in several robotic and computer vision tasks. Object detection is gaining popularity in both industry and research because of its vast range of applications in a variety of sectors. We can either make a fixed number of grid forecasts (i.e., single-stage) or use a proposal network to discover items, then a second network to fine-tune these proposals and produce a final prediction (i.e., two-stage). The purpose of this paper is to review single-stage and two-stage approaches in terms of used datasets, advantages, limitations, hyperparameters, and performance results.
Feeding Longer Frames for Efficient Video Denoising Model

Recently, deep video denoising networks have shown substantially higher denoising performance with considerably lower computing times. However, due to the various characteristics of video motion, such models may not be able to denoise long-term frames. In this paper, we propose a method that takes longer input frames and feeds them to the existing architecture. In particular, the proposed method can effectively extract temporal information from frames that are dependent on each other over a long period of time. To demonstrate the performance of the proposed method, we implemented our method on the state-of-the-art video denoising model. Through the extensive experiments, the proposed method showed better performance in terms of quality metrics than the existing one, even with a higher noise level, resulting in considerably lower computing times.
Information processing with quantum photonic processors

In recent years, photonics has become one of the key contenders in the race to build large-scale quantum computers. The prominence of photonics as a quantum information technology is underscored by the fact that it is one of only a handful of technology platforms which has achieved a quantum advantage, i.e., a large-scale quantum system which outperforms a classical supercomputer at some well-defined computational task [1 2]. <br/> <br/> In this talk, I will highlight several aspects of the research that my team is doing in this field, focusing on the fundamental science that is enabled by quantum photonic processors, i.e. large-scale, tunable interferometers which are necessary to give these proof-of-principle systems a degree of programmability. We have recently demonstrated [3] the world’s largest quantum photonic processor, i.e. fully tunable multimode optical interferometer, using silicon nitride photonic waveguides, and I will briefly introduce these devices. <br/> <br/> Large-scale photonic systems are an interesting novel testbed with which to explore the fundamental concepts and problems of quantum mechanics. Specifically, I will focus on two results obtained in this experimental platform: first, a result in quantum thermodynamics [4], where we experimentally show how to reconcile the apparent contradiction between quantum mechanics, which is time-reversible and information-preserving, and thermodynamics, which is neither, and secondly, a semi-device independent entanglement witness [5] suitable specifically for linear-optical systems. <br/> <br/> [1] Zhong et al, Science 370, 6523 (2020). <br/> [2] Zhong et al, arXiv:2106.15534. <br/> [3] Taballione et al, arXiv:2012.05673. <br/> [4] Somhorst et al, arXiv:2201.00049. <br/> [5] v.d Meer et al, arXiv:2112.00067.
Light-AI interaction: the convergence of photonic AI and cross-layer circuit-architecture-algorithm co-design

In the post Moore's era, conventional electronic digital computers have encountered escalating challenges in supporting massively parallel and energy-hungry artificial intelligence (AI) workloads, which raises a high demand for a revolutionary AI computing solution. Optical neural network (ONN) is a promising hardware platform that could represent a paradigm shift in efficient AI with its ultra-fast speed, high parallelism, and low energy consumption. In recent years, efforts have been made to facilitate the ONN design stack and push forward the practical application of optical neural accelerators. In this paper, we present a holistic solution with state-of-the-art cross-layer co-design methodologies towards scalable, robust, and self-learnable integrated photonic neural accelerator designs across the circuit, architecture, and algorithm levels. We will introduce (1) an area-efficient butterfly-style ONN architecture design beyond traditional general tensor units, (2) model-circuit co-optimization that boosts variation-tolerance and endurance of photonic in-memory computing, (3) efficient ONN on-chip training algorithms that enable self-learnable photonic AI engines, and (4) AI-assisted automated photonic integrated circuit (PIC) design methodology beyond manual PIC designs in footprint, expressivity, and noise-tolerance. Our proposed ONN design stack is integrated into our open-source PyTorch-centric ONN library TorchONN to construct customized photonic AI engine designs and perform high-performance ONN training and optimization.
Emojination Facilitates Inclusive Emoji Design Through Technical Writing: Fitting Tactical Technical Communication Inside Institutional Structures

Creating new emojis is predicated on a system of technical writing that lobbies for new emojis to the Unicode Consortium. Emojination, an activist collective working for cultural inclusivity, helps everyday people write proposals for inclusive and culturally sensitive emojis. Through a case study of Emojination, this article describes ways that Tactical Technical Communication can work toward cultural inclusivity within regulatory frameworks.
Correlation-Concealing Adversarial Noise Injection for Improved Disentanglement in Label-Based Image Translation

Deep learning models in image synthesis have proven their applicability in various image translation areas. However, although the synthesized image may reflect the user’s intention, some of its properties may be different from those of real images. In this study, we introduce an undesirable property that we discovered in the multi-domain label-based image translation techniques: Once the image is translated to one domain, the translated image cannot be adequately translated again to another domain. We refer to this problem as the failure of recursive translation, and analyze this phenomenon from the viewpoint of attribute disentanglement and establish a hypothesis: Unlabeled or unknown attributes that are correlated with the direction of translation hinder the network from learning the correct direction of translation. Based on our hypothesis, we also devise a solution that endows the generator with the power of recursive translation, which is achieved by injecting additive perturbations during model training. Our method is simple and easy to implement on various translation models without requiring much hyperparameter adjustment. Beyond enabling recursive translation, it is worth noting that solving the recursive translation problem improves the disentanglement of single translations, which eventually strengthens its practicability.
A Shapley value-enhanced evaluation technique for effective aggregation in Federated Learning

5G networks make it possible to transfer real-time sensory data between millions of devices, forming the internet of things. A typical method to utilize these data is to train a machine learning algorithm to extract the features. Federated learning (FL) is a platform for a coalition of clients to train a model collaboratively without sharing their data to preserve data privacy. Data and model poisoning attacks, free-riding attacks, and model divergence due to clients' non-independent and identically distributed (non-IID) datasets are some challenges in conventional federated learning. The lack of an evaluation method in federated averaging (FedAvg) in FL makes it impossible to identify malicious users or amend the divergence of the global model. In this study, we propose a Shapley-based aggregation algorithm called Shapley averaging (ShapAvg) to aggregate the global model more effectively by evaluating the clients' models. In this algorithm, each client's weight in the weighted average will be proportional to its contribution to the global model performance. The results show that the proposed method outperforms FedAvg when using non-IID datasets and in case of data poisoning or free-riding attacks.
Music Distance Education Resource Sharing Method Based on Big Data Platform

In order to promote the rapid transmission of music distance education resources, so that students can obtain more shared data information in unit time. This paper proposes a resource sharing method for music distance education based on the big data platform. According to the connection form of the big data platform architecture, determine the function capability of the virtualization sharing technology. Then, by calculating the shared weight of educational resources, the farthest transmission distance of music distance education resources in the network environment is constrained. Complete the relevant application technology analysis based on the big data platform. On this basis, construct the IaaS resource sharing structure. Combined with the established OpenStack scheduling policy, the statistical numerical indicators of shared access are calculated. To realize the smooth application of the music distance education resource sharing method based on the big data platform. The experimental results show that under the action of this new sharing method, the total value of shared data information obtained by students in unit time increases significantly. The method can promote the rapid transmission of music distance education resources, and meet the needs of practical applications.
Innovative Application of Big Data Technology in Network Teaching Model of University Courses

Facing the huge amount of learning resources, how to push the appropriate learning resources to the learners has become a major problem. Therefore, in this context, the application of big data technology has obvious advantages. Based on the above analysis, we will study the big data technology in the university curriculum network teaching model innovation application, to achieve the big data technology expansion network teaching model function goal. After analyzing the current situation of networked teaching model of university courses, the data mining technology is used to obtain user preferences. Through clustering algorithm to build a user image, using collaborative filtering algorithm personalized recommended teaching resources, model optimization is completed. Compared with the teaching models before and after optimization, the accuracy rate of the recommended resources is higher than 95%, the score is higher and the teaching effect is better.
College Library Assisted Online Teaching Model Based on Cloud Service

Due to the low suitability between online teaching and learners, the teaching effect of online teaching is not ideal. Therefore, this paper puts forward a college library assisted online teaching model based on cloud service. On the analysis of the cloud services on the basis of the auxiliary in the teaching characteristics, constructs the contains learner characteristics analysis module, design module, the task group composition design module and learning resource design module of the online teaching model, using cloud services for university library resources characteristics were extracted, combining the resources of the university library association rules matching features and characteristics of learners, Realize the design of teaching resources. The results show that the degree of fitness, teaching resource satisfaction, teaching activity satisfaction and communication mode satisfaction of design students are 86%, 80%, 87% and 83% respectively, and it is very helpful for the improvement of comprehensive quality.
Continuous Self-study: Scene Graph Generation with Self-knowledge Distillation and Spatial Augmentation

As an extension of visual detection tasks, scene graph generation (SGG) has drawn increasing attention with the achievement of complex image understanding. However, it still faces two challenges: one is the distinguishing of objects with high visual similarity, the other is the discriminating of relationships with long-tailed bias. In this paper, we propose a Continuous Self-Study model (CSS) with self-knowledge distillation and spatial augmentation to refine the detection of hard samples. We design a long-term memory structure for CSS to learn its own behavior with the context feature, which can perceive the hard sample of itself and focus more on similar targets in different scenes. Meanwhile, a fine-grained relative position encoding method is adopted to augment spatial features and supplement relationship information. On the Visual Genome benchmark, experiments show that the proposed CSS achieves obvious improvements over the previous state-of-the-art methods. Our code is available at https://github.com/LINYE1998/Continuous_Self_Study .
Process Improvement Required for Software Requirement

In a period of one year, we study software requirement as a subject over couple of semester. We do experiment in different software houses in Lahore Pakistan. Our Four team member goes to the different software houses for our team project .we noticed that our 80% of software houses don’t get requirement from their employee if they get requirement they don’t known about the processes for requirement. After some time we does some experiments on software houses with our team, we noticed that 90 percent project are not completed because of incorrect requirement and required processes for the project and 80 percent software houses is in loss .client will change their requirement when they want to change .Many software houses want improvement that will only happened to improve their process improvement. Different software want different process improvement and process models. In this paper we identifies some improvement in requirement and in processes. We also noticed continues improvement in the processes are required to achieve a good result. The supportive culture of any organization is helpful to achieve the goals of the organization .we study in this paper about the continuous improvement and how to get requirement from our client.
Sample-efficient Real-time Planning with Curiosity Cross-Entropy Method and Contrastive Learning

Model-based reinforcement learning (MBRL) with real-time planning has shown great potential in locomotion and manipulation control tasks. However, the existing planning methods, such as the Cross-Entropy Method (CEM), do not scale well to complex high-dimensional environments. One of the key reasons for underperformance is the lack of exploration, as these planning methods only aim to maximize the cumulative extrinsic reward over the planning horizon. Furthermore, planning inside the compact latent space in the absence of observations makes it challenging to use curiosity-based intrinsic motivation. We propose Curiosity CEM (CCEM), an improved version of the CEM algorithm for encouraging exploration via curiosity. Our proposed method maximizes the sum of state-action Q values over the planning horizon, in which these Q values estimate the future extrinsic and intrinsic reward, hence encouraging reaching novel observations. In addition, our model uses contrastive representation learning to efficiently learn latent representations. Experiments on image-based continuous control tasks from the DeepMind Control suite show that CCEM is by a large margin more sample-efficient than previous MBRL algorithms and compares favorably with the best model-free RL methods.
Data Portraits: Recording Foundation Model Training Data

Foundation models are trained on increasingly immense and opaque datasets. Even while these models are now key in AI system building, it can be difficult to answer the straightforward question: has the model already encountered a given example during training? We therefore propose a widespread adoption of Data Portraits: artifacts that record training data and allow for downstream inspection. First we outline the properties of such an artifact and discuss how existing solutions can be used to increase transparency. We then propose and implement a solution based on data sketching, stressing fast and space efficient querying. Using our tools, we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack). We show that our solution enables answering questions about test set leakage and model plagiarism. Our tool is lightweight and fast, costing only 3% of the dataset size in overhead. We release a live interface of our tools at https://dataportraits.org/ and call on dataset and model creators to release Data Portraits as a complement to current documentation practices.
A Two-Step Rule for Backpropagation

We present a simplified computational rule for the back-propagation formulas for artificial neural networks. In this work, we provide a generic two-step rule for the back-propagation algorithm in matrix notation. Moreover, this rule incorporates both the forward and backward phases of the computations involved in the learning process. Specifically, this recursively computing rule permits the propagation of the changes to all synaptic weights in the network, layer by layer, efficiently. In particular, we use this rule to compute both the up and down partial derivatives of the cost function of all the connections feeding into the output layer.
Relationship Between Self-Efficacy and Life Satisfaction of University Students

This study examined the effect of self-efficacy on life satisfaction for college students living in the Chungcheong area for four years. Multiple regression analysis was conducted to analyze the frequency of basic variables, correlation analysis to confirm the correlation between variables, and to analyze the effect between variables. First, it was found that life satisfaction and self-efficacy had a positive relationship with each other. Second, as a result of multiple regression analysis with control variables to examine the relationship between life satisfaction and self-efficacy of college students, the value representing the explanatory power of the regression model was 0.335 and the variables were about 33.5% of the change in life satisfaction. Self-efficacy was found to have a positive effect in a direction consistent with theoretical expectations, and was statistically significant. The beta value of self-efficacy was 0.496, and it was found to be statistically significant at the significance level 0.001. In terms of control variables, gender (beta=-0.120, p=0.016), allowance (beta=0.135, p=0.004), and academic performance (beta=0.095, p=0.047) were found to be statistically significant. Through this, it is intended to improve college students' satisfaction with school life adaptation, reduce their dropout in the middle of school, increase college students' self-esteem, and use it as basic data and develop programs that can help them pursue and get jobs in the future.
Multiple object tracking based on quadratic graph matching

Abstract Recently, with the development of deep‐learning, the performance of multiple object tracking (MOT) algorithm based on deep neural networks has been greatly improved. However, it is still a difficult problem to successfully solve the tracking misalignment caused by occlusion and complex tracking scenes. Most of the work focusses on designing a sophisticated network, only little work focusses on data association. Actually, data association is very helpful in MOT. In this study, data association in tracking is taken as the main research task, and the authors introduce quadratic graph matching into MOT. Considering the objects in each frame as a graph, we can model the data association between two frames as a quadratic graph matching problem. And then it is transformed into a convex quadratic optimisation problem. Introducing high‐order structure features into the matching function effectively solves some tracking problems. The data association is designed into the tracking model as an overall solution. Experiments show that the proposed data association algorithm performs favourably against several state‐of‐the‐art data association methods and can be used in any tracking‐by‐detection method. Our code is publicly available from https://github.com/gjy0514/G_Model .
Content-based Filtering and Web Scraping in Website for Recommended Anime

Aim: This research aim is to determine the level of user satisfaction using the Delone and Mclean models obtained from the implementation of the content-based filtering method in the anime recommendation system.&#x0D; Study Design: This study was designed with Delone and Mclean and with a Content-based Filtering method and web-scrapping to build an anime recommendation system.&#x0D; Place and Duration of Study: Department of Informatic Universitas Multimedia Nusantara, between July 2022 and December 2022.&#x0D; Methodology: The initial step in this research was collecting data using web scrapping and questionnaires, then followed by a literature study, and after that continued with system design and application development. After the application is made the next step is to get the level of user satisfaction with Delone and Mclean, and the final step is writing a report from this research.&#x0D; Results: The design and development of a system by implementing a content-based filtering method to the website-based have been successfully created, and the results of calculating the level of user satisfaction calculated from 43 respondents using the Delone and Mclean methods show, an anime recommendation system with content-based filtering methods has good result with a user satisfaction percentage of 74.23%.&#x0D; Conclusion: The anime system recommendation application has been successfully made and the results of user satisfaction are 74.23%.