Reviewerly/exHarmony huggingface dataset:
HLS-based High-throughput and Work-efficient Synthesizable Graph Processing Template Pipeline

Hardware systems composed of diverse execution resources are being deployed to cope with the complexity and performance requirements of Artificial Intelligence (AI) and Machine Learning (ML) applications. With the emergence of new hardware platforms, system-wide programming support has become much more important. While this is true for various devices ranging from CPUs to GPUs, it is especially critical for specific neural network accelerators implemented on FPGAs. For example, Intel’s recent HARP platform encompasses a Xeon CPU and an FPGA, which requires an intense software stack to be used effectively. Programming such a hybrid system will be a challenge for most of the non-expert users. High-level language solutions such as Intel OpenCL for FPGA try to address the problem. However, as the abstraction level increases, the efficiency of implementation decreases, depicting two opposing requirements. In this work, we propose a framework to generate HLS-based, FPGA-accelerated, high-throughput/work-efficient, synthesizable, and template-based graph-processing pipeline. While a fixed and clock-wise precisely designed deep-pipeline architecture, written in SystemC, is responsible for processing graph vertices, the user implements the intended iterative graph algorithm by implementing/modifying only a single module in C/C++. This way, efficiency and high performance can be achieved with better programmability and productivity. With similar programming efforts, it is shown that the proposed template outperforms a high-throughput OpenCL baseline by up to 50% in terms of edge throughput. Furthermore, the novel work-efficient design significantly improves execution time and power consumption by up to 100×.
Recognition Method of Wushu Human Complex Movement Based on Bone Point Feature

The existing recognition methods of complex human movements in Wushu have the problem of imperfect kinetic energy model, which leads to low recognition accuracy. A complex human motion recognition method based on bone point features is designed. Identify martial arts movement posture, combine the upward movement of human center of gravity trajectory, establish the kinetic energy model of joints according to the positioning results of extremity points, set the threshold of local spatial differences of human bones with the central node of hip joint as the center point, avoid overcalculation, and optimize the complex motion identification process by combining the characteristics of bone points. Experimental Results. The correct rate of different types of actions identified by this method is 90.1% and 92.7%, and the identification time is 1.2 s and 1.41 s, which shows that this method can identify actions quickly and effectively by combining the feature information of bone points.
Evolution of Modern Deep Image Inpainting Methods

Deep Image Inpainting is a field of research where damaged or deteriorated images are restored by adding the missing information or eliminating undesirable details using the assistance of deep learning techniques to make the final image relevant and visually appealing. Deep learning methods have evolved immensely over the past few years and now can work in extreme cases to produce semantically coherent images without any visible artifacts. This paper is an attempt to summarize some of the best algorithms and techniques with necessary comparisons by analyzing the pros and cons. In this paper, the previous works have been classified and arranged based on their network architecture thus presenting a comprehensive explanation for their approach and also highlights the issues that have a lot of room for improvement, such as training time, mask size and location, dealing with high resolution and diverse images, and so on.
Research and practice of ministry-level software process improvement based on SW − CMM model

With the continuous innovation of computer software technology, the e-government and other related information work of government departments has received increasing attention. Ministry-level information software projects are characterized by large scale, wide range of users, complex business logic, long project cycle, and large amount of post-operation and maintenance. In order to guarantee the quality of software projects, improve work efficiency, strengthen software process management capability, and effectively deal with software project risks, the article takes SW-CMM model as the basic framework, starts from three management models: project management process, engineering management process, and support activity process, and splits them into 12 sub-process domains according to the actual needs, and elaborates and analyzes the software process improvement of ministry-level information projects The main points of software process improvement for ministry-level information technology projects are elaborated and analyzed. Combining with the software engineering process improvement practices of actual software organizations, the corresponding implementation strategies are elaborated in six aspects, including controlling key links, covering the whole life cycle, promoting development standards and specifications, strengthening requirements management, enhancing project asset management and strengthening risk management, which provide an optimal solution for the process improvement of ministry-level software projects.
Comparative Analysis of Downsample Algorithms for the Method of Forming Images with a Virtual Pixel

The analysis of the problem of joint selection of image sensors with lenses is carried out. There is a trend towards the use of image sensors with an redundant number of pixels, for subsequent analog and digital processing to improve the quality of the acquisition image. A Comparative analysis of downsample algorithms for the method of forming images with a virtual pixel the virtual pixel method is carried out.
A Location-Triggered Augmented Reality Walking Tour Using Snap Spectacles 2021

We present an on-site 3D-animated audiovisual tour guide augmented reality application developed for Snap Spectacles 2021. The primary goal of this project is to explore how to use this experimental product to create an augmented reality tour guide. In addition, we present the design considerations for the user interface and the underlying system architecture. We illustrate the workflow of the tour application and discuss our experience working with Spectacles 2021 and its experimental API. We also present our design choices and directions for future work.
MienCap: Performance-based Facial Animation with Live Mood Dynamics

Our purpose is to improve performance-based animation which can drive believable <tex>$3\mathrm{D}$</tex> stylized characters that are truly perceptual. By combining traditional blendshape animation techniques with machine learning models, we present a real time motion capture system, called MienCap, which drive character expressions in a geometrically consistent and perceptually valid way. We demon-strate the effectiveness of our system by comparing to a commercial product Faceware. Results reveal that ratings of the recognition of expressions depicted for animated characters via our systems are statistically higher than Faceware. Our results may be implemented into the VR filmmaking and animation pipeline, and provide animators with a system for creating the expressions they wish to use more quickly and accurately.
Research on secure communication module based on subscriber identity module as root of trust

Based on the analysis of the SIM card based Internet of Things security service, this paper proposes a secure communication module solution based on SIM card as root of trust. The solution can provide Internet of Things users a transparent policy management, transparent secure communication service which can help device users to realize security service transparently. The solution provides Internet of Things users with a more intelligent, more convenient management of secure communication.
Research on the Current Situation and Strategies of College Students' Online Learning Interaction and Satisfaction During the COVID-19 Pandemic

With the introduction of the Educational Informationization 2.0 Action Plan, major colleges and universities have also launched online learning along with this educational wave. In order to analyze the interaction and satisfaction of college students' online learning, and to understand the current situation of college online learning, this paper uses a questionnaire survey research method. Based on the existing research results, this paper sorts out the factors that affect the interaction and satisfaction of college students' online learning. These include the basic characteristics of the survey object, teacher-student interaction, student-student interaction, interactive platform, learning resources, learning efficiency, learning literacy, learning platform, and satisfaction. This study found that the students of China West Normal University are quite satisfied with the online learning method, and also proposed strategies to improve the interaction and satisfaction of college students' online learning, such as setting teaching goals, guiding learners to learn, improving students' quality, and enhancing learners' information literacy. At the same time, the result has a certain reference effect for some international universities in online learning interaction and satisfaction.
Research on Food Safety Data Sharing and Exchange Mechanism

With the development of the economy and the improvement of people's quality of life, the public demand for food taste has gradually changed to the demand for food safety. In order to better facilitate the government to strengthen food safety supervision and protect people's food safety, it is necessary for the government to realize information interaction with enterprises related to the food supply chain and ensure the traceability of food flowing into the market by exchanging and sharing the data of food safety information. With the rapid promotion and popularization of various mobile terminals in the Internet era, the data analysis technology based on artificial intelligence technology is more accurate, which makes the value contained in the data more and more important to people. At present, many fields need the opening and sharing of big data, but there is no reliable data sharing environment in the field of food supervision, and it is still difficult to ensure the traceability of data related to food safety. Blockchain has unique advantages of decentralization and distribution, which can help break the current obstacles of big data sharing and exchange and achieve a high degree of data sharing, interconnection and exchange. Based on the blockchain technology, this paper studies the food safety data sharing and exchange mechanism, combines the blockchain with the distributed file system, constructs the data connection model, stores the shared information on the blockchain, then introduces IPFs and zigzag coding, designs the corresponding control method, and establishes a reliable data sharing and exchange mechanism. The analysis shows that the data sharing and exchange mechanism proposed in this paper can meet the needs of food safety data sharing and exchange.
A Survey on Text-Based Topic Summarization Techniques

The text summing method is obsolete due to recent advances in news articles, official documents, textual interpretation in scientific studies, manual text extraction, and many archives. Dealing with large amounts of text data requires the deployment of effective solutions. It is also impossible to capture text material due to high cost and labor. As a result, the academic community is increasingly interested in developing new ways to capture text automatically. Researchers have been working to improve the process of creating summaries since the invention of text summaries with the aim of creating machine summary matches with man-made summaries. Meaningful sentences are selected from the input document and added to the summaries using the hybrid technique. As a result, researchers are increasingly focusing on concise summaries to provide more coherent and relevant summaries. They use an artificial text summary to gather knowledge and information about recent research. A complete overview of abstraction methods is provided by a recent text summary created over the past decade.
Supporting novice designers design of digital touch

Digitally mediated touch is an emerging and significant area for technology and therefore for design and design education. However, the design of digital touch is a challenge, especially for novice designers, compounded by low awareness and understanding of the sociality of touch and the complexity of communicating felt sensations. This paper presents a qualitative study of a two-part educational intervention on the design of digital touch using a design-based research methodology. Findings are presented and discussed on the design challenges faced by novice designers in relation to touch and digital touch (the focus of part one of the intervention) and the development and piloting of the Designing Digital Touch (DDT) toolkit (the outcome of part two). The paper discusses how the toolkit can be used to foster and support novice designers to respond to the future facing complexity of digital touch design.
Effectiveness of solar panel positioning and grid tie inverter (GTI) in generating solar energy in Indonesian houses

Abstract In Indonesia, most electricity powers are based on non-renewable resources, which is not good for climate change. Therefore, the use of solar panels based on rooftops is an excellent alternative way to reduce electricity use. Thus, the objective of this paper is to analyze the effectiveness of solar panel positioning and Grid Tie Inverter (GTI) in generating solar energy for small- to medium-sized houses in Indonesia. Solar panel usage by residents living in the city may be limited, as the rooftop space in city houses is limited. GTIs can generate different types of power as they have different resources, this is good for climate change as no battery is needed In this research, two different solar panels with different positioning and angles, and connected to different types of GTI, were analyzed. The period of study was from 23rd July 2020 to 5th May 2021. The analysis was based on index comparison and movement of the sun during the said period. The result of the study indicates that the position and angle of solar panels affect the electricity generated by them. Solar panels placed in a horizontal position were observed to generate more power, and imported model inverters were observed to provide better electricity supply to homes.
Pathologies of Pre-trained Language Models in Few-shot Fine-tuning

Although adapting pre-trained language models with few examples has shown promising performance on text classification, there is a lack of understanding of where the performance gain comes from.In this work, we propose to answer this question by interpreting the adaptation behavior using post-hoc explanations from model predictions.By modeling feature statistics of explanations, we discover that (1) without fine-tuning, pre-trained models (e.g.BERT and RoBERTa) show strong prediction bias across labels; (2) although few-shot fine-tuning can mitigate the prediction bias and demonstrate promising prediction performance, our analysis shows models gain performance improvement by capturing non-task-related features (e.g.stop words) or shallow data patterns (e.g.lexical overlaps).These observations alert that pursuing model performance with fewer examples may incur pathological prediction behavior, which requires further sanity check on model predictions and careful design in model evaluations in few-shot fine-tuning.
Analyzing the impact of Augmented Reality based Digital Environment on Students' Learning

Augmented Reality (AR) is an immersive technology that provides a realistic and authentic teaching learning experience. It is clear that the today&#x0027;s generation favors technology-assisted learning over traditional learning. Although past research on AR has offered some light on how it can improve learning outcomes in different fields, studies on science experiment methods are still sparse. Experimental knowledge is essential for students to have a deeper grasp of the phenomena. The process of creating and developing a Smartphone app for a physics experiment employing AR technology is discussed in this article. This is an additional learning tool for upper secondary school. The ultimate goal of this mobile based AR app for Physics Experiment is to deliver meticulous knowledge in addition to traditional procedures and learning resources. A number of experiments have been chosen for inclusion in ElectroAR. To gauge feedback, an expert review is done. The user evaluation of ElectroAR in real classroom settings is something that will be done in the future.
An Evaluation of Large-scale Information Network Embedding based on Latent Space Model Generating Links

Graph representation learning encodes vertices as low-dimensional vectors that summarize their graph position and the structure of their local graph neighborhood. These methods give us beneficial representation in continuous space from big relational data. However, the algorithms are usually evaluated indirectly from the accuracy of applying the learning results to classification tasks because of not giving the correct answer when graph representation learning is applied. Therefore, this study proposes a method to evaluate graph representation learning algorithms by preparing correct learning results for the data by distributing objects in the latent space in advance and probabilistically generating relational graph data from the distributions in the latent space. Using this method, we evaluated LINE: Large-scale information network embedding, one of the most popular algorithms for learning graph representations. LINE consists of two algorithms optimizing two objective functions defined by first-order proximity and second-order proximity. We prepared two link-generating models suitable for these two objective functions and clarified that the corresponding LINE algorithm performed well for the link data generated by each model.
An initial step toward a quantum annealing approach to the discrete logarithm problem

Abstract The discrete logarithm problem over a multiplicative group of integer modulo n is the key ingredient in the ElGamal encryption system. This problem has been proven to be tractable in polynomial time on a quantum computer by Shor's algorithm. This algorithm makes use of basic quantum gates, circuits, measurements and it has been experimented in the circuit‐gate framework of quantum computing. On the other hand, the adiabatic framework of quantum computing with the quantum annealers such as the D‐Wave machine that simulates the adiabatic process has become very popular, showing its potential for making a practical pathway to achieve quantum speedup. Furthermore, much progress has been reported recently on the tractability of the integer factoring problem, which is the other widely‐used encryption method, on quantum annealers. In this context, it is important to explore the tractability of the discrete logarithm problem too using quantum annealers. In this work we present a first step made in this direction, by converting the discrete logarithm problem over a multiplicative group into the problem of minimizing a binary quadratic form, a standard problem format acceptable to a quantum annealer. Our formulation was tested for small‐scale problem instances using the quantum‐classical hybrid platform PyQUBO and we discuss the computational challenges and propose several potential improvements to the formulation.
Knowledgebra: An Algebraic Learning Framework for Knowledge Graph

Knowledge graph (KG) representation learning aims to encode entities and relations into dense continuous vector spaces such that knowledge contained in a dataset could be consistently represented. Dense embeddings trained from KG datasets benefit a variety of downstream tasks such as KG completion and link prediction. However, existing KG embedding methods fell short to provide a systematic solution for the global consistency of knowledge representation. We developed a mathematical language for KG based on an observation of their inherent algebraic structure, which we termed as Knowledgebra. By analyzing five distinct algebraic properties, we proved that the semigroup is the most reasonable algebraic structure for the relation embedding of a general knowledge graph. We implemented an instantiation model, SemE, using simple matrix semigroups, which exhibits state-of-the-art performance on standard datasets. Moreover, we proposed a regularization-based method to integrate chain-like logic rules derived from human knowledge into embedding training, which further demonstrates the power of the developed language. As far as we know, by applying abstract algebra in statistical learning, this work develops the first formal language for general knowledge graphs, and also sheds light on the problem of neural-symbolic integration from an algebraic perspective.
ARTFM: Augmented Reality Visualization of Tool Functionality Manuals in Operating Rooms

Error-free surgical procedures are crucial for a patient&#x0027;s health. However, with the increasing complexity and variety of surgical instruments, it is difficult for clinical staff to acquire detailed assembly and usage knowledge leading to errors in process and preparation steps. Yet, the gold standard in retrieving necessary information when problems occur is to get the paperbased manual. Reading through the necessary instructions is time-consuming and decreases care quality. We propose ARTFM, a process integrated manual, highlighting the correct parts needed, their location, and step-by-step instructions to combine the instrument using an augmented reality head-mounted display.
Improving Few-Shot Learning with Vision Transformer

Abstract Vision Transformer (ViT) is emerging as an alternative to convolutional neural network (CNN) for visual recognition and has achieved impressive results, however, due to its data-hungry nature, ViT encoder in few-shot setting remains rarely explored. In this paper, we first propose a simple yet effective baseline that exploits the complementarity of self-supervised learning (SSL) and ViT, enabling the use of standard ViT model as the few-shot learner. Second, based on the baseline, we introduce a novel regularized fine-tuning framework, where the Parametric Instance discrimination (PID) and Base-Novel (BN) regularizations are proposed to reduce the intra-class variance and calibrate the biased distribution of novel classes to further enhance few-shot recognition. We conduct extensive experiments and show that our method can achieve new state-of-the-art performances on two widely used benchmarks.