T-Crowd: Effective Crowdsourcing for Tabular Data

Crowdsourcing employs human workers to solve computer-hard problems, such as data cleaning, entity resolution, and sentiment analysis. When crowdsourcing tabular data, e.g., the attribute values of an entity set, a worker's answers on the different attributes (e.g., the nationality and age of a celebrity star) are often treated independently. This assumption is not always true and can lead to suboptimal crowdsourcing performance. In this paper, we present the T-Crowd system, which takes into consideration the intricate relationships among tasks, in order to converge faster to their true values. Particularly, T-Crowd integrates each worker's answers on different attributes to effectively learn his/her trustworthiness and the true data values. The attribute relationship information is also used to guide task allocation to workers. Finally, T-Crowd seamlessly supports categorical and continuous attributes, which are the two main datatypes found in typical databases. Our extensive experiments on real and synthetic datasets show that T-Crowd outperforms state-of-the-art methods in terms of truth inference and reducing the cost of crowdsourcing

Local Buckling of Concrete Filled Rectangular Steel Tube with Longitudinal Stiffener under Axial Compression

Width-thickness ratio was an important parameter for designing Concrete Filled Rectangular Steel Tube (CFRST). Welding longitudinal stiffener on the internal wall of steel pipe could delay the local buckling, which increased the limit of width-thickness ratio. If there was not enough stiffener and its sectional dimension was too small, the local buckling of steel pipe would occur, inducing its bearing capacity seriously. If the stiffener sectional dimension was too large, concrete filled in steel tube would be broken up, which reduces its bearing capacity. To solve that problem, this paper studied local buckling of CFRST with longitudinal stiffener under axial compression and design of longitudinal stiffener. It established buckling analysis model, simplified local buckling analysis as calculating buckling load of thin plate clamped on loading side and unloading side under axial force. It deduced buckling load and buckling coefficient based on the principle of energy. The results showed that buckling mode depended on stiffening rigidity. Therefore, it put forward minimum stiffening rigidity ratio that controlled the stiffener design. This paper also came up with a formula to calculate minimum stiffening rigidity ratio. It provided guidance on designing number, sectional dimension and material performance

Hierarchical temperature imaging using pseudoinversed convolutional neural network aided TDLAS tomography

As an in situ combustion diagnostic tool, Tunable Diode Laser Absorption Spectroscopy (TDLAS) tomography has been widely used for imaging of two-dimensional temperature distributions in reactive flows. Compared with the computational tomographic algorithms, Convolutional Neural Networks (CNNs) have been proofed to be more robust and accurate for image reconstruction, particularly in case of limited access of laser beams in the Region of Interest (RoI). In practice, flame in the RoI that requires to be reconstructed with good spatial resolution is commonly surrounded by low-temperature background. Although the background is not of high interest, spectroscopic absorption still exists due to heat dissipation and gas convection. Therefore, we propose a Pseudo-Inversed CNN (PI-CNN) for hierarchical temperature imaging that (a) uses efficiently the training and learning resources for temperature imaging in the RoI with good spatial resolution, and (b) reconstructs the less spatially resolved background temperature by adequately addressing the integrity of the spectroscopic absorption model. In comparison with the traditional CNN, the newly introduced pseudo inversion of the RoI sensitivity matrix is more penetrating for revealing the inherent correlation between the projection data and the RoI to be reconstructed, thus prioritising the temperature imaging in the RoI with high accuracy and high computational efficiency. In this paper, the proposed algorithm was validated by both numerical simulation and lab-scale experiment, indicating good agreement between the phantoms and the high-fidelity reconstructions.Comment: Submitted to IEEE Transactions on Instrumentation and Measuremen

AnycostFL: Efficient On-Demand Federated Learning over Heterogeneous Edge Devices

In this work, we investigate the challenging problem of on-demand federated learning (FL) over heterogeneous edge devices with diverse resource constraints. We propose a cost-adjustable FL framework, named AnycostFL, that enables diverse edge devices to efficiently perform local updates under a wide range of efficiency constraints. To this end, we design the model shrinking to support local model training with elastic computation cost, and the gradient compression to allow parameter transmission with dynamic communication overhead. An enhanced parameter aggregation is conducted in an element-wise manner to improve the model performance. Focusing on AnycostFL, we further propose an optimization design to minimize the global training loss with personalized latency and energy constraints. By revealing the theoretical insights of the convergence analysis, personalized training strategies are deduced for different devices to match their locally available resources. Experiment results indicate that, when compared to the state-of-the-art efficient FL algorithms, our learning framework can reduce up to 1.9 times of the training latency and energy consumption for realizing a reasonable global testing accuracy. Moreover, the results also demonstrate that, our approach significantly improves the converged global accuracy.Comment: Accepted to IEEE INFOCOM 202

FAST: Fidelity-Adjustable Semantic Transmission over Heterogeneous Wireless Networks

In this work, we investigate the challenging problem of on-demand semantic communication over heterogeneous wireless networks. We propose a fidelity-adjustable semantic transmission framework (FAST) that empowers wireless devices to send data efficiently under different application scenarios and resource conditions. To this end, we first design a dynamic sub-model training scheme to learn the flexible semantic model, which enables edge devices to customize the transmission fidelity with different widths of the semantic model. After that, we focus on the FAST optimization problem to minimize the system energy consumption with latency and fidelity constraints. Following that, the optimal transmission strategies including the scaling factor of the semantic model, computing frequency, and transmitting power are derived for the devices. Experiment results indicate that, when compared to the baseline transmission schemes, the proposed framework can reduce up to one order of magnitude of the system energy consumption and data size for maintaining reasonable data fidelity.Comment: 6 pages, 4 figures. Accepted by ICC 202

Knowing What LLMs DO NOT Know: A Simple Yet Effective Self-Detection Method

Large Language Models (LLMs) have shown great potential in Natural Language Processing (NLP) tasks. However, recent literature reveals that LLMs generate nonfactual responses intermittently, which impedes the LLMs' reliability for further utilization. In this paper, we propose a novel self-detection method to detect which questions that a LLM does not know that are prone to generate nonfactual results. Specifically, we first diversify the textual expressions for a given question and collect the corresponding answers. Then we examine the divergencies between the generated answers to identify the questions that the model may generate falsehoods. All of the above steps can be accomplished by prompting the LLMs themselves without referring to any other external resources. We conduct comprehensive experiments and demonstrate the effectiveness of our method on recently released LLMs, e.g., Vicuna, ChatGPT, and GPT-4

Truth Inference in Crowdsourcing: Is the Problem Solved?

Crowdsourcing has emerged as a novel problem-solving paradigm, which facilitates addressing problems that are hard for computers, e.g., entity resolution and sentiment analysis. However, due to the openness of crowdsourcing, workers may yield low-quality answers, and a redundancy-based method is widely employed, which first assigns each task to multiple workers and then infers the correct answer (called truth) for the task based on the answers of the assigned workers. A fundamental problem in this method is Truth Inference, which decides how to effectively infer the truth. Recently, the database community and data mining community independently study this problem and propose various algorithms. However, these algorithms are not compared extensively under the same framework and it is hard for practitioners to select appropriate algorithms. To alleviate this problem, we provide a detailed survey on 17 existing algorithms and perform a comprehensive evaluation using 5 real datasets. We make all codes and datasets public for future research. Through experiments we find that existing algorithms are not stable across different datasets and there is no algorithm that outperforms others consistently. We believe that the truth inference problem is not fully solved, and identify the limitations of existing algorithms and point out promising research directions

Dynamic behaviour of prestressed concrete beams considering moving loads

U ovom se radu ispituje pravilo dinamičkog odziva prednapetih betonskih greda s metalnim i plastičnim valovitim cijevima različitih vrijednosti kompaktnosti injekcijske smjese kanala na pokretna konstantna koncentrirana opterećenja i pokretna jednostavna harmonijska opterećenja. Prednapete betonske grede s metalnim i plastičnim valovitim cijevima analizirane su metodom konačnih elemenata u slučajevima bez pukotina. Rezultati pokazuju da kompaktnost injekcijske smjese kanala i materijal za oblikovanje utječu na karakteristike prirodnih vibracija prednapetih betonskih greda. Pod istom kompaktnošću injekcijske smjese kanala, prednapete betonske grede s metalnim valovitim cijevima pokazuju veće frekvencije u usporedbi s onima koja imaju plastiče valovite cijevi. Dinamički progib prednapetih betonskih greda u sredini raspona smanjuje se s povećanjem kompaktnosti injekcijske smjese kanala, što je neovisno o materijalu za oblikovanje. Razlike u brzini i ubrzanju srednjeg raspona uzrokovane pokretnim opterećenjem između prednapetih betonskih greda metalnih i plastičnih valovitih cijevi su male. To pokazuje da materijal koji stvara pore ima mali učinak na brzinu čvora u sredini raspona i reakciju ubrzanja prednapetih betonskih greda.with different duct grouting compactness values under moving constant concentrated loads and moving simple harmonic loads is studied. Prestressed concrete beams with metal and plastic corrugated pipes are analysed using the finite element method for the case of non-cracking. The results indicate that duct grouting compactness and forming material affect the natural vibration characteristics of prestressed concrete beams. Under identical duct grouting compactness, prestressed concrete beams with metal corrugated pipes exhibit higher frequencies compared with those with plastic corrugated pipes. The mid-span dynamic deflection of the prestressed concrete beams decreases with an increase in duct grouting compactness, which is independent of the forming material. The differences in the midspan speed and acceleration caused by the moving loads between the corrugated duct prestressed concrete beams made of metal and those made of plastic are small; this indicates that the pore-forming material has little effect on the midspan node speed and acceleration response of prestressed concrete beams