Reviewerly/exHarmony huggingface dataset:
A Combination Method for PV Output Prediction Using Artificial Neural Network

Photovoltaic power generation and grid-connected technologies are being used more and more widely. The accurate prediction of photovoltaic power generation can be beneficial to the stable operation of the power grid. An improved method for predicting the output characteristics of photovoltaic cells based on the combination of BP neural network and Elman neural network in different weather is proposed. Compared with the traditional BP network, a context layer is added to Elman, thereby reducing the number of iterations and reducing the amount of calculation. According to the method of using artificial intelligence in irradiance, the weather is divided into two categories. The first category is that the irradiance fluctuates more severely, and the second category is that the irradiance is relatively flat. According to the experiment, the prediction errors of the two neural networks are analyzed using the error function. In the weather with severe irradiance fluctuations, the prediction results of the BP neural network are more accurate, and in the weather with lower irradiance, using ELMAN neural network is more accurate. This paper proposes that the combined model based on different weather can be more reliable and accurate, and accurately predict the output characteristics of the photovoltaic system, which is very helpful for improving the security and stability of the power grid.
Intelligent mineral geological survey method: Daqiao-Yawan area in Gansu Province as an example

在矿产地质调查理论与实践的基础上，提出一种智能矿产地质调查方法，指出智能矿产地质调查生态系统是与智能矿产地质调查相关的智能数据采集设备、应用、用户、标准、规范、智能地质调查云平台等组成部分及相互关系构成的完整系统。智能矿产地质调查的主要步骤包括：智能数据分析、重点工作区圈定、矿产地质数据采集、重点区野外工作、智能找矿预测等。提出了数据驱动与知识驱动相结合的找矿预测方法，集成了采用深度学习技术进行特征匹配找矿预测的方法和基于知识图谱的找矿预测方法。设计和基本实现了智能矿产地质调查云平台的架构与功能。应用特征匹配找矿预测方法在甘肃大桥-崖湾地区圈定了5个找矿预测区。;Based on the theory and practice of intelligent mineral geological survey, an intelligent mineral geological survey method is proposed. It is interpreted that the new type of intelligent mineral geological survey ecosystem is a complete system composed of intelligent data acquisition equipment, application, user, standard, specification, intelligent geological survey cloud platform and other components related to intelligent mineral geological survey. The main steps of intelligent mineral geological survey include: intelligent data analysis, key workspace delineation, mineral geological data collection, key area field work, intelligent prospecting prediction and so on. The prospecting prediction method driven by data and knowledge is proposed, and the method of feature matching prospecting prediction using Deep Learning and the prospecting prediction method based on Knowledge Graph are integrated. The architecture and functions of the intelligent mineral geological survey cloud platform are designed and implemented. The feature matching method is applied to delineate five prospecting areas in Daqiao-Yawan area of Gansu Province. High precision magnetic survey is deployed in a prediction area, and the results show that the prediction area delineated by intelligent mineral geological survey method is effective.
Arithmetic Coding-Based 5-Bit Weight Encoding and Hardware Decoder for CNN Inference in Edge Devices

Convolutional neural networks (CNNs) have gained a huge attention for real-world artificial intelligence (AI) applications such as image classification and object detection. On the other hand, for better accuracy, the size of the CNNs’ parameters (weights) has been increasing, which in turn makes it difficult to enable on-device CNN inferences in resource-constrained edge devices. Though weight pruning and 5-bit quantization methods have shown promising results, it is still challenging to deploy large CNN models in edge devices. In this paper, we propose an encoding and hardware-based decoding technique which can be applied to 5-bit quantized weight data for on-device CNN inferences in resource-constrained edge devices. Given 5-bit quantized weight data, we employ arithmetic coding with range scaling for lossless weight compression, which is performed offline. When executing on-device inferences with underlying CNN accelerators, our hardware decoder enables a fast in-situ weight decompression with small latency overhead. According to our evaluation results with five widely used CNN models, our arithmetic coding-based encoding method applied to 5-bit quantized weights shows a better compression ratio by 9.6× while also reducing the memory data transfer energy consumption by 89.2%, on average, as compared to the case of uncompressed 32-bit floating-point weights. When applying our technique to pruned weights, we obtain better compression ratios by 57.5×–112.2× while reducing energy consumption by 98.3%–99.1% as compared to the case of 32-bit floating-point weights. In addition, by pipelining the weight decoding and transfer with the CNN execution, the latency overhead of our weight decoding with 16 decoding unit (DU) hardware is only 0.16%–5.48% and 0.16%–0.91% for non-pruned and pruned weights, respectively. Moreover, our proposed technique with 4-DU decoder hardware reduces system-level energy consumption by 1.1%–9.3%.
Car Pose Estimation Through Wheel Detection

Car pose estimation is an essential part of different applications, including traffic surveillance, Augmented Reality (AR) guides or inductive charging assistance systems. For many systems, the accuracy of the determined pose is important. When displaying AR guides, a small estimation error can result in a different visualization, which will be directly visible to the user. Inductive charging assistance systems have to guide the driver as precise as possible, as small deviations in the alignment of the charging coils can decrease charging efficiency significantly. For accurate pose estimation, matches between image coordinates and 3d real-world points have to be determined. Since wheels are a common feature of cars, we use the wheelbase and rim radius to compute those real-world points. The matching image coordinates are obtained by three different approaches, namely the circular Hough-Transform, ellipse-detection and a neural network. To evaluate the presented algorithms, we perform different experiments: First, we compare their accuracy and time performance regarding wheel-detection in a subset of the images of The Comprehensive Cars (CompCars) dataset []. Second, we capture images of a car at known positions, and run the algorithms on these images to estimate the pose of the car. Our experiments show that the neural network based approach is the best in terms of accuracy and speed. However, if training of a neural network is not feasible, both other approaches are accurate alternatives.
Computer Eye-Tracking Model to Investigate Influence of the Viewer’s Perception of the Graphic Information

The goal is to study the visual perception of graphic composition of various styles. An original author's method of conducting an experiment has been developed, which includes the preparation of stimulus material, collecting data, and statistical algorithms to analyze parametric data. The stimulus materials were based on graphic images in the cubism and abstractionism styles as well as on photorealistic images. An eye-tracking equipment was used to record eye movement activity and collect experimental data. The statistical analysis of the parametric data of the observer's viewing pattern has revealed that the viewer’s perception of visual information is more effective by observers with art education. The results are of importance for developing effective training and test systems for operators, users, GUI developers, etc.
Sentence-Permuted Paragraph Generation

Generating paragraphs of diverse contents is important in many applications.Existing generation models produce similar contents from homogenized contexts due to the fixed left-toright sentence order.Our idea is permuting the sentence orders to improve the content diversity of multi-sentence paragraph.We propose a novel framework PermGen whose objective is to maximize the expected log-likelihood of output paragraph distributions with respect to all possible sentence orders.PermGen uses hierarchical positional embedding and designs new procedures for both training phase and inference phase.Experiments on three paragraph generation benchmarks demonstrate Per-mGen generates more diverse outputs with a higher quality than existing models.
Modeling and topology analysis of S-SCDN supply area based on graph database

In order to realize the mutual promotion between the energy management system and the traditional monitoring system under the development of active distribution network and ensure the high flexibility and reliability of urban distribution network under normal operation and fault operation, active distribution network needs new grid graph model, algorithm and parallel software computing platform. This paper proposes a modeling method of 10kV distribution network based on the combination of graph database and S-SCDN concept. Firstly, the PMIS data of the actual power grid are processed and converted into S-SCDN data. Then, the S-SCDN data are modeled in a reasonable graph database, and a generic switching station model is designed to complete the dynamic topology analysis of the S-SCDN supply area. The S-SCDN modeling based on graph database is compared with relational database modeling of traditional distribution network through case simulation data import, and the superiority of the model is verified.
Survey of video object detection algorithms based on deep learning

Video object detection is based on the static image detection, and combines the time dimension characteristics to further improve the performance of object detection. As an extension of the field of object detection, video object detection has gradually become more and more popular in recent years, and the video object detection algorithm based on deep learning is expanding. In this paper, the main video object detection algorithms are summarized. Firstly, based on the relevant principles of the algorithm, the main algorithms' motivation, core ideas, network architecture and innovation points are classified. Based on the main structure, design ideas and implementation effect, the advantages and disadvantages of the algorithms are compared. Based on the detection accuracy of the algorithm on ImageNet VID data set, the performance of the algorithms are analyzed. Finally, the problems and challenges in the field of video object detection are proposed, and research direction in the future is prospected.
A Systematic Evaluation of Large Language Models of Code

Large language models (LMs) of code have recently shown tremendous promise in completing code and synthesizing code from natural language descriptions. However, the current state-of-the-art code LMs (e.g., Codex (Chen et al., 2021)) are not publicly available, leaving many questions about their model and data design decisions. We aim to fill in some of these blanks through a systematic evaluation of the largest existing models: Codex, GPT-J, GPT-Neo, GPT-NeoX-20B, and CodeParrot, across various programming languages. Although Codex itself is not open-source, we find that existing open-source models do achieve close results in some programming languages, although targeted mainly for natural language modeling. We further identify an important missing piece in the form of a large open-source model trained exclusively on a multi-lingual corpus of code. We release a new model, PolyCoder, with 2.7B parameters based on the GPT-2 architecture, which was trained on 249GB of code across 12 programming languages on a single machine. In the C programming language, PolyCoder outperforms all models including Codex. Our trained models are open-source and publicly available at https://github.com/VHellendoorn/Code-LMs, which enables future research and application in this area.
Multi-View Graph Representation for Programming Language Processing: An Investigation into Algorithm Detection

Program representation, which aims at converting program source code into vectors with automatically extracted features, is a fundamental problem in programming language processing (PLP). Recent work tries to represent programs with neural networks based on source code structures. However, such methods often focus on the syntax and consider only one single perspective of programs, limiting the representation power of models. This paper proposes a multi-view graph (MVG) program representation method. MVG pays more attention to code semantics and simultaneously includes both data flow and control flow as multiple views. These views are then combined and processed by a graph neural network (GNN) to obtain a comprehensive program representation that covers various aspects. We thoroughly evaluate our proposed MVG approach in the context of algorithm detection, an important and challenging subfield of PLP. Specifically, we use a public dataset POJ-104 and also construct a new challenging dataset ALG-109 to test our method. In experiments, MVG outperforms previous methods significantly, demonstrating our model's strong capability of representing source code.
Another Combination of Classical and Intuitionistic Conditionals

On the one hand, classical logic is an extremely successful theory, even if not being perfect. On the other hand, intuitionistic logic is, without a doubt, one of the most important non-classical logics. But, how can proponents of one logic view the other logic? In this paper, we focus on one of the directions, namely how classicists can view intuitionistic logic. To this end, we introduce an expansion of positive intuitionistic logic, both semantically and proof-theoretically, and establish soundness and strong completeness. Moreover, we discuss the interesting status of disjunction, and the possibility of combining classical logic and minimal logic. We also compare our system with the system of Caleiro and Ramos.
An Ising machine based on networks of subharmonic electrical resonators

Abstract Combinatorial optimization problems are difficult to solve with conventional algorithms. Here we explore networks of nonlinear electronic oscillators evolving dynamically towards the solution to such problems. We show that when driven into subharmonic response, such oscillator networks can minimize the Ising Hamiltonian on non-trivial antiferromagnetically-coupled 3-regular graphs. In this context, the spin-up and spin-down states of the Ising machine are represented by the oscillators’ response at the even or odd driving cycles. Our experimental setting of driven nonlinear oscillators coupled via a programmable switch matrix leads to a unique energy minimizer when one exists, and probes frustration where appropriate. Theoretical modeling of the electronic oscillators and their couplings allows us to accurately reproduce the qualitative features of the experimental results and extends the results to larger graphs. This suggests the promise of this setup as a prototypical one for exploring the capabilities of such an unconventional computing platform.
Algorithm forming randomized system of iterative functions by based cantor structure

This paper has been considered the results of the development of the randomized system of iterated functions (RSIF) formation algorithm from the existing fractal image of the “Fractal Dust” type (the Cantor set). The mathematical formulas and patterns for calculating the RSIF coefficients have been derived. This algorithm is to find the formulas of functions relative to the center of the first iteration of the fractal structure. This makes it possible to determine a randomized system of iterative functions from an existing fractal image. The construction algorithm does not use recursive functions and the entry of the loop into the loop, which allows without spending a lot of computing power, and is quite optimized. The algorithm will allow you to make direct and inverse transformations without involving additional software and hardware resources. The use of forward and inverse transformations will allow in the future to form a source data set for neural networks, which will form the basis of object recognition systems.
Design and Evaluation of a Distributed Security Framework for the Internet of Things

The adopters of IoT face challenges with the surging Internet-based attacks on their IoT assets and inefficiencies within the technology.Unfortunately, IoT is overly distributed, still evolving and facing implementation and security challenges.Given the above scenario, we argue that the IoT network should always be decentralized design, and security should be built by design.The paper is the design and construction of a decentralized IoT security framework, with the goal of making emerging IoT systems more resilient to attacks and supporting complex communication and resource sharing.The framework improves efficiency and scalability in IoT, exposes vulnerable subsystems and components as possible weak links to system compromise, and meets the requirements of a heterogeneous computing environment.Other features of the framework including efficient resource sharing, fault tolerance, and distributed storage support the Internet of Things.We discuss the design requirements and carry out the implementation of Proof of Concept and evaluation of our framework.Two underlying technologies: the actor model and the blockchain were used for the implementation.Our reason for choosing the actor model and blockchain is to compare its suitability for IoT integration in parallel.Hence, evaluation of the system is performed based on computational and memory efficiency, security, and scalability.We conclude from the evaluations that the actorbased implementation has better scalability than the block-chain-based implementation.Also, the blockchain seems to be computationally more intensive than the actors and less suitable for IoT systems.
The Taming of the Stack: Isolating Stack Data from Memory Errors

Despite vast research on defenses to protect stack objects from the exploitation of memory errors, much stack data remains at risk.Historically, stack defenses focus on the protection of code pointers, such as return addresses, but emerging techniques to exploit memory errors motivate the need for practical solutions to protect stack data objects as well.However, recent approaches provide an incomplete view of security by not accounting for memory errors comprehensively and by limiting the set of objects that can be protected unnecessarily.In this paper, we present the DATAGUARD system that identifies which stack objects are safe statically from spatial, type, and temporal memory errors to protect those objects efficiently.DATAGUARD improves security through a more comprehensive and accurate safety analysis that proves a larger number of stack objects are safe from memory errors, while ensuring that no unsafe stack objects are mistakenly classified as safe.DATAGUARD's analysis of server programs and the SPEC CPU2006 benchmark suite shows that DATAGUARD improves security by: (1) ensuring that no memory safety violations are possible for any stack objects classified as safe, removing 6.3% of the stack objects previously classified safe by the Safe Stack method, and (2) blocking exploit of all 118 stack vulnerabilities in the CGC Binaries.DATAGUARD extends the scope of stack protection by validating as safe over 70% of the stack objects classified as unsafe by the Safe Stack method, leading to an average of 91.45% of all stack objects that can only be referenced safely.By identifying more functions with only safe stack objects, DATAGUARD reduces the overhead of using Clang's Safe Stack defense for protection of the SPEC CPU2006 benchmarks from 11.3% to 4.3%.Thus, DATAGUARD shows that a comprehensive and accurate analysis can both increase the scope of stack data protection and reduce overheads.
Research on Path Planning Algorithm of Service Robot in Dynamic Environment

With the popularity of service robots in dynamic environments such as hospitals and shopping malls, traditional path planning algorithms have problems such as inflection points of planning paths and difficulty in effectively avoiding dynamic obstacles. In response to these problems, this paper proposes an optimization algorithm based on A* and dynamic window method (DWA). First, in the global path planning, the number of turning points is reduced by deleting redundant turning points. Secondly, by establishing the motion model of the obstacle, the range of the dynamic obstacle is updated to the static environment, and the dynamic obstacle avoidance ability of the DWA algorithm is improved. Finally, the optimized A* algorithm is combined with the DWA algorithm to realize the path planning of the service robot in a dynamic environment. Experimental results show that the algorithm reduces the global path length without disturbing dynamic obstacles, the path is smoother, and the dynamic obstacle avoidance ability is more reliable.
Intelligent big data service for meteorological cloud platform

With the acceleration of meteorological informationisation, meteorological data has gradually become a typical industry big data.In view of the challenge of storage, index and processing, cloud computing technology provides the technical support for meteorological big data.We design a framework of meteorological big data service in the cloud computing environment which contains meteorological services, meteorological scientific research services and public meteorological services.As the most popular distributed processing technology, MapReduce is effectively used for distributed processing of meteorological big data.Finally, based on MapReduce, the daily meteorological data of Baoshan Station in Shanghai is analysed, and the statistical results and corresponding examples are provided.The application research of meteorological big data in the cloud environment can not only improve the overall meteorological service level, but also play a significant role in accelerating the meteorological informatisation process in the big data era.
MoTransFrame: Model Transfer Framework for CNNs on Low-Resource Edge Computing Node

Deep learning technology has been widely used in computer vision, speech recognition, natural language processing, and other related fields. The deep learning algorithm has high precision and high reliability. However, the lack of resources in the edge terminal equipment makes it difficult to run deep learning algorithms that require more memory and computing power. In this paper, we propose MoTransFrame, a general model processing framework for deep learning models. Instead of designing a model compression algorithm with a high compression ratio, MoTransFrame can transplant popular convolutional neural networks models to resources-starved edge devices promptly and accurately. By the integration method, Deep learning models can be converted into portable projects for Arduino, a typical edge device with limited resources. Our experiments show that MoTransFrame has good adaptability in edge devices with limited memories. It is more flexible than other model transplantation methods. It can keep a small loss of model accuracy when the number of parameters is compressed by tens of times. At the same time, the computational resources needed in the reasoning process are less than what the edge node could handle.
Asian CHI Symposium

This symposium showcases the latest work from Asia on interactive systems and user interfaces that address under-explored problems and demonstrate unique approaches. In addition to circulating ideas and sharing a vision of future research in human-computer interaction, this symposium aims to foster social networks among academics (researchers and students) and practitioners and create a fresh research community from Asian region.