OpinSummEval: Revisiting Automated Evaluation for Opinion Summarization

Opinion summarization sets itself apart from other types of summarization tasks due to its distinctive focus on aspects and sentiments. Although certain automated evaluation methods like ROUGE have gained popularity, we have found them to be unreliable measures for assessing the quality of opinion summaries. In this paper, we present OpinSummEval, a dataset comprising human judgments and outputs from 14 opinion summarization models. We further explore the correlation between 24 automatic metrics and human ratings across four dimensions. Our findings indicate that metrics based on neural networks generally outperform non-neural ones. However, even metrics built on powerful backbones, such as BART and GPT-3/3.5, do not consistently correlate well across all dimensions, highlighting the need for advancements in automated evaluation methods for opinion summarization. The code and data are publicly available at https://github.com/A-Chicharito-S/OpinSummEval/tree/main.Comment: preprint, included 2 more metrics compared with the previous submissio

Frost Porosity Measurement Using Capacitive Sensor

Frosting is a dynamic process because of the changes in the frost-air interface temperature as the frost layer grows. The frost properties, such as frost density and frost porosity, are highly dependent on the frosting conditions and vary with time even under a constant environmental and operational conditions. Precise detection of frost properties is important for understanding frosting mechanisms and predicting frost growth, and it is also important for defrost control in many applications. So far there have been very few research reports on dynamic frost porosity measurement, and most work reports an average measurement approach, which is undertaken by measuring the mass and volume within certain frost accumulating period to estimate the averaged frost properties. Those approaches ignore the temporal variation of frost properties with an assumption that the frost buildup at a constant porosity, at least within a certain time period. As the result, there is a distinct deviation between different frost models, because as a very important input of models, most empirical frost porosity correlations were based on different time intervals of measurement. Frost, as a mixture of ice crystal and air, could have its properties estimated based on the percentage of each component. In this work, a capacitive sensor is developed to detect the capacitance variation as frost growing, which together with the dielectric constant of ice and air, could be used to determine the temporal porosity according to the Maxwell-Garnett (MG) theory. An interdigital electrode designed in this work is fabricated using photolithography technique (shown in Figure 1), together with the PCB connector and a commercial digital converter (FDC 2214) can sense the capacitance reading with a 0.0001 pF resolution. 3-D printed Polyvinyl-chloride porous structure with controlled porosity filled with/without gelatin of different concentration (shown in Figure 2) has been used to valid the sensor’s responding function. Frost porosity was measured under different conditions with known sensor function and the empirical correlation of frost porosity is provided in this work and compared with existing work. This work presents a new method to dynamically detect the frost porosity as frost growing, and it is a big contribution to the mass-based defrost strategy development and frost growth modeling

Composite Quantum Phases in Non-Hermitian Systems

Non-Hermitian systems have attracted considerable interest in recent years owing to their unique topological properties that are absent in Hermitian systems. While such properties have been thoroughly characterized in free fermion models, they remain an open question for interacting bosonic systems. In this Letter, we present a precise definition of quantum phases for non-Hermitian systems and propose a new family of phases referred to as composite quantum phases. We demonstrate the existence of these phases in a one-dimensional spin-$1$ system and show their robustness against perturbations through numerical simulations. Furthermore, we investigate the phase diagram of our model, indicating the extensive presence of these new phases in non-Hermitian systems. Our work establishes a new framework for studying and constructing quantum phases in non-Hermitian interacting systems, revealing exciting possibilities beyond the single-particle picture.Comment: 9 pages, 5 figure

Frost Growth Detection Using Capacitive Sensor

Frost buildup on surfaces could be an undesired situation in many applications. In refrigeration and heat pump system, typically, frost grows on the fin surface of the heat exchanger due to different environmental/operational conditions. On one hand, it can block the air flow and increase air-side pressure drop; on the other hand, can increase the thermal resistance and deteriorate heat transfer performance. As a result, frost buildup can significantly reduce the system’s COP. Therefore, most systems encountered frost buildup run the defrost cycle. The frost growth process is affected by many factors, such as environmental conditions (air humidity, temperature, flow rate), operational conditions (working fluids, saturated temperature), heat exchangers (structures, fin type and fin surface wettability) et. al.. All those factors are coupled together, which makes frost growth a very complex dynamic process with variable spatial distribution of its characteristic parameters. It is very important to dynamically detect frost growth for both effective defrost control and precise frost modelling. In this work, a capacitive sensor for frost detection has been developed, which consists of three parts as shown in Figure 1(a): 1) commercial capacitive to digital converter (FDC2214 from Texas Instruments and the resolution of the reading is 0.0001pF), 2) PCB connector and 3) fabricated electrodes. The fabricated copper electrode is attached to the PCB connector, which is mounted to the capacitive to digital converter and connected to the computer by a USB interface. Capacitance variation can be measured when the target properties changes. The interdigital electrodes has a high sensitivity and were fabricated by lithophotography, using copper laminates/ deposited copper thin layer as shown in Figure 1(b) The sensitivity can be affected by metallization ratios, width and thickness of the insulation layer, which are also explored in this work. The frost grows on a cold plate which is placed in the wind tunnel with a controlled air temperature, humidity and flow rate. The electrode of the capacitive sensor is located beside the side wall of the cold plate, as shown in Figure 1(c). The frost growth process can be detected and reflected by the capacitance variation of the sensor, as shown in Figure 2, the capacitance variation can reflect different stage of the frost growth period, starting from condensation to mature growth. Images are also captured by a CCD camera to calibrate the signal. This work demonstrates the dynamic frost growth detection at the first time and could play a significant role to understanding frost growth mechanism and defrost control strategy

Experimental Study of Condensation Heat Transfer of R134a on Oil-infusion Surfaces

Dropwise condensation, since first recognized in 1930, has stimulated interest because its heat transfer coefficient (HTC) is much higher than film condensation. For some applications, not only a higher heat transfer performance is desired, but also the retention of the fluids on the surface can be a big issue. For example, the refrigerant retention in some enhanced tube can block the contact of the vapor-solid interface and increase the thermal resistance; it also can increase the charge of refrigerant because certain amount of refrigerant could not go through the system cycle. Many efforts were dedicated to modifying the surface and promote dropwise condensation, and most research focus on the condensation of water vapor. It is very challenging to promote dropwise condensation for working fluids with a lower surface tension than water, such as refrigerant. Research have been conducted on dropwise condensation for low surface tension fluids using oil-infusion surface, which is promoted by the contact of drop to the liquid-vapor interface instead of solid-vapor interface. However, the effectiveness and efficiency of the oil-infusion surface is still a critical challenge, and the heat transfer mechanism of dropwise condensation with such liquid-liquid interface stays unclear. In this work, condensation of R134a on oil-immerged surfaces is investigated. Heat transfer coefficient is measured, and formation of the condensate is observed using a high speed camera. Two cavity surfaces of different porous scale are examined, of which, one is nanoscale pores and another is microscale pores Mineral oil of low miscibility to R134a is soaked to be saturated in the cavity prior to the experiment. All experiments were conducted under saturated condition of ambient temperature (around 22 °C) in a pressure chamber. The subcool level of the condensation is 10 °C. Images of the local condensation formation is analyzed and heat transfer coefficient is also compared for different surfaces. The duration of the oil-infusion surface is also tested for both surfaces

Label-Driven Denoising Framework for Multi-Label Few-Shot Aspect Category Detection

Multi-Label Few-Shot Aspect Category Detection (FS-ACD) is a new sub-task of aspect-based sentiment analysis, which aims to detect aspect categories accurately with limited training instances. Recently, dominant works use the prototypical network to accomplish this task, and employ the attention mechanism to extract keywords of aspect category from the sentences to produce the prototype for each aspect. However, they still suffer from serious noise problems: (1) due to lack of sufficient supervised data, the previous methods easily catch noisy words irrelevant to the current aspect category, which largely affects the quality of the generated prototype; (2) the semantically-close aspect categories usually generate similar prototypes, which are mutually noisy and confuse the classifier seriously. In this paper, we resort to the label information of each aspect to tackle the above problems, along with proposing a novel Label-Driven Denoising Framework (LDF). Extensive experimental results show that our framework achieves better performance than other state-of-the-art methods.Comment: Finding of EMNLP 2022 camera-read

System design for online foreign language education based on blockchain technology

This study aims to solve the problem that the traditional online foreign language teaching system focuses on function development, ignoring system security, and has certain risks. An online foreign language education system is designed and developed based on the blockchain technology. First, the blockchain technology and key technologies of system design are described in detail. Second, the overall technical architecture of the system, functional modules, and business logic of each module are designed. Finally, the basic performance of the system is tested. The results show that the system can realize the user's unrestricted office work and zero maintenance of the system. The separation of presentation logic and business logic facilitates the development and maintenance of the system. The system mainly includes six functional modules: user management, course management, course order, course study, course certificate, and credit authentication. These modules are guaranteed for daily teaching use. The event processing success rate of the six functional modules of the system is greater than 99%, and the processing success rate is relatively high. The central processing unit (CPU) usage and memory usage are both below 30%. The host throughput of the six major modules is greater than 100 times/s when processing services. The average response time on the terminal side is maintained below 0.5 s. The average response time of business-side processing is maintained below 0.4 s, which is in line with the standard. The event processing success rate of the constructed system is 10.75% higher than that of other systems, and the average response time, CPU usage, and memory usage are 53.38%, 51.49%, and 50% lower than other systems, respectively. Therefore, the proposed system has better performance. To sum up, the designed system has excellent throughput, event processing capability, response speed, and low CPU and memory occupancy when processing business and is suitable for promotion and use in foreign language online education in colleges and universities. The use of the proposed system can improve its overall teaching efficiency and quality. The purpose is to provide important technical support for the improvement of the security of the online foreign language teaching system

Learning to Reduce Information Bottleneck for Object Detection in Aerial Images

Object detection in aerial images is a fundamental research topic in the domain of geoscience and remote sensing. However, advanced progresses on this topic are mainly focused on the designment of backbone networks or header networks, but surprisingly ignored the neck ones. In this letter, we first analyse the importance of the neck network in object detection frameworks from the theory of information bottleneck. Then, to alleviate the information loss problem in the current neck network, we propose a global semantic network, which acts as a bridge from the backbone to the head network in a bidirectional global convolution manner. Compared to the existing neck networks, our method has advantages of capturing rich detailed information and less computational costs. Moreover, we further propose a fusion refinement module, which is used for feature fusion with rich details from different scales. To demonstrate the effectiveness and efficiency of our method, experiments are carried out on two challenging datasets (i.e., DOTA and HRSC2016). Results in terms of accuracy and computational complexity both can verify the superiority of our method.Comment: 5 pages, 3 figure

Training-Free Instance Segmentation from Semantic Image Segmentation Masks

In recent years, the development of instance segmentation has garnered significant attention in a wide range of applications. However, the training of a fully-supervised instance segmentation model requires costly both instance-level and pixel-level annotations. In contrast, weakly-supervised instance segmentation methods (i.e., with image-level class labels or point labels) struggle to satisfy the accuracy and recall requirements of practical scenarios. In this paper, we propose a novel paradigm for instance segmentation called training-free instance segmentation (TFISeg), which achieves instance segmentation results from image masks predicted using off-the-shelf semantic segmentation models. TFISeg does not require training a semantic or/and instance segmentation model and avoids the need for instance-level image annotations. Therefore, it is highly efficient. Specifically, we first obtain a semantic segmentation mask of the input image via a trained semantic segmentation model. Then, we calculate a displacement field vector for each pixel based on the segmentation mask, which can indicate representations belonging to the same class but different instances, i.e., obtaining the instance-level object information. Finally, instance segmentation results are obtained after being refined by a learnable category-agnostic object boundary branch. Extensive experimental results on two challenging datasets and representative semantic segmentation baselines (including CNNs and Transformers) demonstrate that TFISeg can achieve competitive results compared to the state-of-the-art fully-supervised instance segmentation methods without the need for additional human resources or increased computational costs. The code is available at: TFISegComment: 14 pages,5 figure

Modeling Orders of User Behaviors via Differentiable Sorting: A Multi-task Framework to Predicting User Post-click Conversion

User post-click conversion prediction is of high interest to researchers and developers. Recent studies employ multi-task learning to tackle the selection bias and data sparsity problem, two severe challenges in post-click behavior prediction, by incorporating click data. However, prior works mainly focused on pointwise learning and the orders of labels (i.e., click and post-click) are not well explored, which naturally poses a listwise learning problem. Inspired by recent advances on differentiable sorting, in this paper, we propose a novel multi-task framework that leverages orders of user behaviors to predict user post-click conversion in an end-to-end approach. Specifically, we define an aggregation operator to combine predicted outputs of different tasks to a unified score, then we use the computed scores to model the label relations via differentiable sorting. Extensive experiments on public and industrial datasets show the superiority of our proposed model against competitive baselines.Comment: The paper is accepted as a short research paper by SIGIR 202