Miracle-dz/UniFAR-FTUs huggingface dataset:
Overview of Trading Models for New Energy Participation in The Electricity Market

For saving energy and reduce emissions, countries are vigorously developing new energy sources, but at the same time, there is also the problem that the consumption of new energy is constrained. We may start from the electricity market to find a solution. In order to achieve the goal of promoting new energy consumption while safeguarding the interests of each market player, this paper focuses on the trading mode of new energy, summarizes the new energy's typical mode of trading in electricity market at home and abroad, and outlines the existing research results in five aspects, including the design of the electricity market model and mechanism, the trading of power generation rights, multi-energy complementarity, auxiliary service products, quota system and green certificate market and proposes issues for further research.
Real-Time Forecasting with Vector Autoregressions: Spurious Drift, Structural Change, and Intercept Correction

A recent literature, dominated by David Hendry and Michael Clements, has emerged which emphasises the importance of the estimated constant term in Vector Autoregressive (VAR) forecasting models using nonstationary time series. A part of this research agenda has resulted in the resurrection of intercept-corrections, once popular with the large econometric models of the 1970s, in the VAR context owing to the common occurrence of intermittent structural breaks and the importance of the constant term in determining medium and longer-term forecasting trajectories. This procedure is further developed in this paper and a new Bayesian estimator is proposed. It is shown that, in many commonly occurring situations, the proposed estimator produces a substantial gain in mean squared forecast error over the alternatives of not intercept-correcting, or always correcting in the classical fashion.
RDIDI: Recognition of Defect Image Detection in Industry

Electronic consumer products are closely related to life. With the increase of demand, various consumer electronic products have emerged as the times require. For some high-precision equipment, such as in all aspects of the chip manufacturing process, the requirements for PCB are relatively high. In the PCB manufacturing process, it is often due to various problems caused by improper operation of certain links in the process. Many defects may appear on the PCB, such as bubbles appearing when the film is not firmly attached to the ground during the film attachment process, and bubbles appear during the exposure process. Negative film scratches, excessive pressure during etching, unevenness during plating, etc. Printed board surface defects vary in size. This inconsistency of multi-scale features will cause the pooling operation of the network model to lose some fine-grained spatial features. In the field of object detection, in the past, the detection effect in the field of PCB defect detection was poor, and the natural defects were few and small, and faced some bottlenecks in engineering. Aiming at this problem, a PCB defect detection method based on RDIDet is proposed. The experimental results prove that the improved network has obvious performance advantages over the previous classic model, with an accuracy rate of 98.3%, and a better detection effect on PCB defects.
A Secure Cloud-Based Infrastructure for Virtual Sensors in IoT Environments

Recent work has proven the effort of researchers to integrate small sensors and a cloud environment, delivering the Internet of Things (IoT). Sensors as a service are one of the leading research concerns in this context. Nevertheless, security is becoming one of the most significant attributes of the IoT as sensors become more human-independent and are being extensively used to monitor human lives. That way, IoT brings many key security challenges that need attention, some of which we address in this position paper. We present a cloud-based infrastructure that can deliver sensors and actuators as a service, providing secure communication between them and the control nodes on which IoT applications rely while implementing Big Data algorithms. For mapping our proposal, two scenarios related to Health Assistance are discussed, considering secure communications in a sensor network. In conclusion, we propose a scope for future research in this field considering digital twin concepts. Since domains exploiting IoT technologies can benefit from adopting Digital Twin, our goal is to evolve a virtual sensor and actuator system into this technology.
Diabetes Mellitus Prediction using Supervised Machine Learning Techniques

Diabetes is a long-term condition that occurs when either the body cannot use insulin properly or the pancreas does not produce sufficient amounts of hormone to control blood glucose levels. High blood sugar levels are a hallmark of diabetes, which belongs to a group of metabolic diseases. The two most prevalent varieties of diabetes are type 1 and type 2, but there are other types as well, such as gestational diabetes, which develops during pregnancy. The number of people with type 1 diabetes has significantly increased. The genetic condition known as type 1 diabetes has a long incubation period and frequently manifests early in life. Cells in people with type 2 diabetes do not properly respond to insulin. It changes over time and mostly depends on how people live their lives. According to a 2022 report by the International Diabetes Federation, currently around 382 million people worldwide have diabetes. By 2035, the Figure is expected to increase to 592 million. One of the most common causes of tissue and organ damage and dysfunction, including blindness, kidney failure, heart failure, and stroke, is diabetes. As a result, early detection of diabetes is critical. This work aims at implementing two machine learning methods like Logistic Regression and Random Forest for diabetes prediction. Each algorithm is calculated to determine the model’s accuracy. Furthermore, the highest accuracy of 99.03% is received by Random Forest.
Switching Convolution of Node Graph and Line Graph-driven Method for Fast Static Security Analysis

Increasing scale of power grids induces higher computational complexity of static contingency analysis, which is in contradiction with the real-time requirements. Towards fast credible contingency analysis, a power flow calculation method based on graph neural network was proposed. This method can realize fast fitting of nonlinear relationship between new energy output, load data and branch power flow, node voltage in multiple scenarios. To avoid the disappearance of edge graph nodes caused by branch disconnection and to ensure the robustness of the model, a matrix reflecting the topology changes is designed in response to the network structure changes caused by various anticipated accidents. Besides, the load-source data is separated and the input eigenvector considering the change of the load-source data is constructed. The test of IEEE 30-bus and 118-bus system shows that the proposed model can adapt to changes of network topology brought by N-1 faults and the fluctuations of new energy, realize fast fitting of power flow, and provide a new tool for online fast credible contingency analysis.
Pneumonia Detection using Chest X-ray Images using CNN Algorithm

Pneumonia is a serious health condition where the patient’s lungs get infected by bacteria, virus or sometimes fungi. This causes burning sensation in the alveoli present in the lungs. In response to this, body forms fluids in the lungs that can cause trouble in breathing, coughing and even chest pain. The increasing demand of medical care for elderly care and children below five years of age requires advanced healthcare technologies. Premature diagnosis is vital for fast recovery. This research study intends to propose a novel technique for detecting pneumonia in chest x-ray images by training a model to classify the patient’s condition as normal or pneumonic. The proposed model applies CNN, a deep learning algorithm to detect the health condition of the patient by analyzing their chest X-ray images. The objective of this research study is to attain the model accuracy as high as possible to provide accurate results to the user.
Few-Shot Semantic Segmentation Based on Dual-Branch Feature Extraction

Few-shot semantic segmentation (FSS) requires only few labeled samples to achieve good segmentation performance and thus has received extensive attention. However, existing FFS methods usually adopt a simple convolutional structure as the backbone, which suffers from poor feature extraction ability. In order to address this issue, a novel few-shot segmentation network based on dual-branch feature extraction (DFESN) is proposed. First, an attention-enhanced ResNet is used as the local feature extraction branch. Specifically, we in-corporate channel attention operations into each building block of ResNet to model the importance among channels, which enables DFESN to learn important class information for the segmentation task. Besides, we introduce a Vision Transformer as the global feature extraction branch. This branch leverages the multi-head self-attention mechanism in Vision Transformer to model the global dependencies of support and query image features, further enhancing the feature extraction capabilities of DFESN. We conduct experiments on the PASCAL-5i dataset and demonstrate the superiority of our DFESN.
S2NetM: A Semantic Social Network of Things Middleware for Developing Smart and Collaborative IoT-Based Solutions

The Social Internet of Things (SIoT) paradigm combines the benefits of social networks with IoT networks to create more collaborative and efficient systems, offering enhanced scalability, better navigability, flexibility, and dynamic decision making. However, SIoT also presents challenges related to dynamic friendship selection, privacy and security, interoperability, and standardization. To fully unlock the potential of SIoT, it is crucial to establish semantic interoperability between the various entities, applications, and networks that comprise the system. This paper introduces the Semantic Social Network of Things Middleware (S2NetM), which leverages social relationships to enhance semantic interoperability in SIoT systems. The S2NetM employs semantic reasoning and alignment techniques to facilitate the creation of dynamic, context-aware social networks of things that can collaboratively work together and enable new opportunities for IoT-based solutions. The main contributions of this paper are the specification of the S2NetM and the associated ontology, as well as the discussion of a case study demonstrating the effectiveness of the proposed solution.
Challenges for Achieving Supply Chain Resilience and Transparency within CoyPu (invited paper)

Supply chain resilience and transparency are important for the ability of companies to react flexibly to the changing conditions of the environment and the market, especially in crisis situations. Events such as pandemics or economic crises can lead to sharp changes in demand or a halt in production as well as bottlenecks along supply chains. In the project CoyPu, which tackles the complex economic challenges in crisis situations by integrating heterogeneous data and solutions into an intelligent platform, we put particular focus on the two use cases demand forecasting and resilient production. An accurate demand forecast is important for companies to accurately plan production. This need is amplified in supply chains containing semiconductors, since long production times limit flexibility. Especially in times of disruptions tactical demand forecasts and the Bullwhip Effect, which amplifies demand along the supply chains, further complicate demand forecasting. This requires alternative demand forecasting approaches to tackle the before-mentioned issues. To meet the forecasted demand, smooth production processes are necessary, which rely on parameters such as precise predictions of material availability to optimize the production start time. Due to intransparent supply chains, which lack information such as subsuppliers or specific source locations, it is challenging to obtain accurate predictions and find the best alternative solution in case of adverse events. To address these issues on both the demand and supply side, CoyPu aims at enabling high-quality and insights into economic trends and forecasts, based on cognitive modeling of data within a system of networked knowledge graphs and configurable artificial intelligence analysis tools. This paper highlights major challenges on the demand and supply side and possible solutions to achieve an optimized demand forecasting and a more resilient production.
Abstract of News Text Based on Theme and Emotional Relationship

In the current rapidly developing society, people can easily and quickly get all kinds of news information from the Internet, such as browsing micro-blogs. Which contains a large amount of news text with emotion, the positive or negative emotional news subtly changing people's attitude to current events, affect social development situation of the news public opinion, the existing text based methods tend to consider factors such as the theme and sentence features, unable to get the text with emotional opinions in this paper. Therefore, this paper proposes a sentiment summarization method of Chinese news text which integrates sentence sentiment and topic similarity. In the in-depth analysis of TextRank graph model algorithm, the relationship between nodes in the model and the calculation of edge weight are improved through the integration of emotion information, and the sentence emotion weight with emotion is obtained. Then, by synthesizing the sentence emotion and topic relevance and other factors, the weight parameter is used to balance, and the sentence weight which integrates the sentence emotion and topic similarity is obtained. Finally, the emotion summary of news text is obtained.
Numerical Investigation of Vortex-Induced Vibrations of a Rotating Cylinder near a Plane Wall

Numerical simulations are carried out to investigate the vortex-induced vibrations of a two-degree-of-freedom (2DOF) near-wall rotating cylinder. Considering the effects of gap ratio, reduced velocity and rotational rate, the amplitude response, wake variations and fluid forces are analyzed, with the Reynolds number of 200 and the mass ratio set to 1.6. The correlative mechanism in the wake–hydrodynamics–vibration is revealed. The results show that the influence of the wall dominates the vortex-induced vibration of the cylinder. The effect of the wall on the vibration weakens as the gap ratio increases, and the effect of the wall on the vibration is negligible when H/D > 1.1. The forward rotation of the cylinder enhances the wall effect, while the backward rotation presents the reverse effect. The vortex-induced vibration of the cylinder is suppressed when 0 < α < 1, and the amplitude range is concentrated at Vr ∈ (3, 5). The wake mode can be divided into five modes, and the wake modes are clarified under different rotation rates and reduced velocities.
Extractive Text Summarization Using Word Frequency Algorithm for English Text

Text summarization is the method of retaining the key information of article without losing its vital content. Summarization is essential in all the areas since a large volume of data is generated day by day. Because of the availability of huge data, it becomes difficult to extract the exact information. People lack patience to understand the content by reading the entire article. Summarization plays a major role in these times to provide available vital information fast and effectively. This can be done in two ways: Extractive Summary and Abstractive Summary. Extractive summary is simple compared to abstractive summary. While an abstractive summary creates new phrases, an extractive summary entails locating highly ranked sentences from the given text. Various techniques, including sentence ranking, Graph Based Modeling, RBF Models, and Sentence Similarity Measures, can be used for extractive summarization. This paper provides extractive text summarization for code mixed English text provided by ILSUM track of FIRE 2022. In this work, Word Frequency Algorithm is used for summarization and the ILSUM team measured the performance of the system by standard ROUGE metrics.
Monitoring the Impact of Teacher's Intervention in Inquiry-Based Mathematics Learning with the use of Dynamic Geometry

In this study, we analyze computer-aided inquiry-based mathematics learning. A Moodle plug-in associated with the dynamic geometry software CindyJS which can record finegrained log data of learners’ manipulations on the web was used. Our previous study indicates that teacher intervention can make student’s inquiry systematic and exhaustive by helping them build a semantic circuit across language, symbolism, and visual images which are relevant to the targeted concept. In this study, we try to validate the impact of this kind of teacher intervention by monitoring the log data of manipulations.
Confidential Computing within an AI Accelerator

We present IPU Trusted Extensions (ITX), a set of hardware extensions that enables trusted execution environments in Graphcore’s AI accelerators. ITX enables the execution of AI workloads with strong confidentiality and integrity guarantees at low performance overheads. ITX isolates workloads from untrusted hosts, and ensures their data and models remain encrypted at all times except within the accelerator’s chip. ITX includes a hardware root-of-trust that provides attestation capabilities and orchestrates trusted execution, and on-chip programmable cryptographic engines for authenticated encryption of code/data at PCIe bandwidth. We also present software for ITX in the form of compiler and runtime extensions that support multi-party training without requiring a CPU-based TEE. We included experimental support for ITX in Graphcore’s GC200 IPU taped out at TSMC’s 7nm node. Its evaluation on a development board using standard DNN training workloads suggests that ITX adds < 5% performance overhead and de-livers up to 17x better performance compared to CPU-based confidential computing systems based on AMD SEV-SNP.
Towards High-Fidelity Facial Texture Reconstruction from Multi-View Images Using Self-supervised Learning

Deep learning-based monocular 3D face reconstruction methods have been a hot topic in the computer vision community recently. While it is still a great challenge to reconstruct high-fidelity facial texture due to the limitations of low-dimensional subspace of statistical texture model and the occlusion in face region. In this paper, we present an approach to get plausible UV texture with our elaborate Facial Texture Inpainting Module (FTIM) that can inpaint the missing area in the incomplete UV texture map. To ensure the rationality of generated UV texture, we train FTIM with triplets of facial images from different viewpoints. A novel loss is applied to train our framework without ground-truth UV texture maps in a self-supervision way. We provide comprehensive experiments to demonstrate the effectiveness of FTIM and novel loss. Compared with state-of-the-art methods, our method shows better performance in qualitative and quantitative results.
The Journal Pattern for Streaming Data

In the distributed streaming data processing scenario, most of the frameworks implement minimal variations of the Publish-Subscribe pattern, where message passing happens directly between each Publishers and the group of its Subscribers. This work introduces a novel pattern, named Journal , that exploits a so called Editor for filtering or modifying the data stream in a principled manner. The Editor can be integrated into the Publish-Subscribe pattern with two different schemata, and has been used to implement multiple subsampling strategies, so to reduce the volume of the forwarded data, create new communication channels and match the ingestion capacity of the consumers. An actual test using Apache Kafka with a stream of simulated data has confirmed the viability of the Editor integration into Pub-Sub. We evidence that with the Journal pattern the risk of saturation of a channel can be significantly lowered and the latency of processing from clients can be notably reduced. We stress that the Journal pattern is very general and can be extended to multiple other purposes.
Minimizing Security Risks and Improving System Reliability in Blockchain Applications: а Testing Method Analysis

This article presents an analysis of testing methods used in the domain of blockchain applications, with a focus on their impact on system security and reliability. The main objective of the research is to enhance the testing process of blockchain systems and minimize security risks. The study demonstrates that implementing an appropriate approach to testing blockchain applications can considerably reduce the resources required to resolve bugs and increase the level of system reliability. In conclusion, the paper finds that the selection of testing methods during the planning phase has a substantial effect on the security and reliability of the
Beyond Audio: Towards a Design Space of Headphones as a Site for Interaction and Sensing

Via Research through Design (RtD), we explore the potential of headphones as a general-purpose input device for both foreground motion-gestures as well as background sensing of user activity. As a familiar wearable device, headphones offer a compelling site for head-situated interaction and sensing. Using emerging sensing modalities such as inertial motion, capacitive touch sensing, and depth cameras, our implemented prototypes explore sensing and interaction techniques that offer a range of compelling capabilities. User scenarios include context-aware privacy, gestural audio-visual control, and co-opting natural body language as context to drive animated avatars for "camera-off" scenarios in remote work–or to co-opt (oft-subconscious) head movements such as dodging attacks in video games to enhance the gameplay experience. Drawing from literature and other frameworks, we situate our prototypes and related techniques in a design space across the dual dimensions of (1) type of input (touch, mid-air, or head orientation); and (2) the context of user action (application, body, or environment). In particular, interactions that combine multiple inputs and contexts at the same time offer a rich design space of headphone-situated wearable interactions and sensing techniques.
UAV-Based Low Altitude Remote Sensing for Concrete Bridge Multi-Category Damage Automatic Detection System

Detecting damage in bridges can be an arduous task, fraught with challenges stemming from the limitations of the inspection environment and the considerable time and resources required for manual acquisition. Moreover, prevalent damage detection methods rely heavily on pixel-level segmentation, rendering it infeasible to classify and locate different damage types accurately. To address these issues, the present study proposes a novel fully automated concrete bridge damage detection system that harnesses the power of unmanned aerial vehicle (UAV) remote sensing technology. The proposed system employs a Swin Transformer-based backbone network, coupled with a multi-scale attention pyramid network featuring a lightweight residual global attention network (LRGA-Net), culminating in unprecedented breakthroughs in terms of speed and accuracy. Comparative analyses reveal that the proposed system outperforms commonly used target detection models, including the YOLOv5-L and YOLOX-L models. The proposed system’s robustness in visual inspection results in the real world reinforces its efficacy, ushering in a new paradigm for bridge inspection and maintenance. The study findings underscore the potential of UAV-based inspection as a means of bolstering the efficiency and accuracy of bridge damage detection, highlighting its pivotal role in ensuring the safety and longevity of vital infrastructure.