Miracle-dz/UniFAR-FTUs huggingface dataset:
Hypernym Information and Sentiment Bias Probing in Distributed Data Representation

Neural word embedding vectors have been exhaustively investigated by probing tasks, whether they contain semantic and syn- tactic information. Perhaps the most popular task is a test on gender relation “king - man + woman ≈ queen”, other probings include tests on singular/plural relation (apple∼apples), analogy (good:better∼rough:X), purity of the clusters of word embeddings (categorization). Here, we propose two novel probing tasks to evaluate the compositionality of word embeddings. First, we propose probing tests to evaluate whether word embeddings contain structural information about their hypernyms; more specifically, whether it is possible to predict the hypernym (e.g. color) of a word (e.g. red) using word embedding. Our experimental results show that a simple logistic regression can predict well the hypernyms of words; therefore, making the word embeddings linearly separable with respect to their hypernyms. Second, we also provide a methodology to investigate whether word embeddings encode undesired sentiment. For instance, we show that the word embedding of American or Euro- pean are surrounded by more positive words than the embeddings around the word “Chinese”. Thus, we conclude that word embed- dings also can capture sentiment, leading to undesired machine bias in downstream applications.
TC-GAN: A Transformer-based Conditional Generative Adversarial Network for Low-Dose SPECT Image Reconstruction

Low-dose single photon emission computed tomography (SPECT) technology can reduce radiation damage to the subject but lead to reconstructed image quality degradation, such as severe noise and artifacts. Many existing works utilize convolutional neural networks (CNNs) to improve the quality of reconstructed images at low doses and demonstrate impressive performance. However, the intrinsic locality nature of convolutional operation limits the ability of CNNs to encode explicitly long-range dependencies. Combining the long-range dependencies effectively during reconstruction might improve the quality of reconstructed images. Therefore, we designed a novel transformer-based network, dubbed TC-GAN, which combines the conditional generative adversarial network (CGAN) and CSwin Transformer for high-quality low-dose SPECT reconstruction. In TC-GAN, the CSwin transformer was integrated into the generator of CGAN as a strong feature extractor to establish the intra-relationships in the whole image and provide plenty of global contextual information for image reconstruction. The CGAN was used as the backbone of our network to synthesize reconstructed images with accurate structure and pixel-level details via adversarial training fashion. In the experiment, we used the Monte Carlo simulation software SIMIND to produce low-dose SPECT image datasets. The results show that the proposed method has better reconstruction performance than several recent methods. In terms of visual analysis, the proposed method maintains more accurate image structure and details while noise suppression. In terms of quantitative analysis, the proposed method obtains the best results in global image quality metrics. Our results suggest that the proposed network has good potential to reduce radiation dose without compromising the reconstructed image quality.
The detrimental effect of high mobility on long-term mental health trajectories following Hurricane Katrina

This article uses longitudinal data from the Gulf Coast Child and Family Health Study to examine the impact of residential mobility on the long-term mental health trajectories of individuals displaced or severely impacted by Hurricane Katrina. Analyses begin with a descriptive understanding of residential mobility post-Katrina, including an explanation of who moved and how often in the 13 years after the storm, and determine which social groups were more likely to experience residential mobility than others. Secondly, it builds on these descriptive results by examining how residential mobility can influence the mental health of disaster survivors over time. Lastly, analyses determine how residential mobility rates differ depending on various social characteristics, and how such relationships influence mental health recovery. Results demonstrate that there is a statistically significant relationship between high rates of residential mobility and mental health recovery, and that particular social characteristics increase the risk for high residential mobility.
Enhanced Memory Corruption Detection in C/C++ Programs

Out-of-bound memory accesses, which often occur in programs written in unsafe languages such as C or C++, cause severe troubles. Though there are many useful tools aiming at this problem, we report a new tool, called mcds, for detecting spatial and temporal memory corruptions in x86-64 ELF binary. Mcds allocates each memory object to a separate virtual page. The rest is left blank. Due to a facility in the memory management library, we can set up memory protection so that accessing the “blank” part of a virtual page causes a hardware trap. Because it is a hardware trap, there is little run-time overhead. In order to save memory space, we may squeeze several virtual pages into a single physical page. Our first experimental result is that mcds can find all the bugs in the Firefox 78 package, the Chrome package and the PHP7.0 package that are recorded on the CVE Details website. Furthermore, mcds can detect three classes of memory corruptions that are beyond the capability of the current AddressSanitizer (Asan). Then we compare the time for compilation and fuzzing tests. The fuzzing test is done with AFL++ fuzzer on Ubuntu 22.04 LTS with Intel i5-9600K chip. According to our experimental results, mcds shows approximately 6x speedup in fuzzing tests against AddressSanitizer. There is not significant difference between compiling the source with AddressSanitizer or with mcds, though both of them result in 2x slowdown compared with compilation without a sanitizer.
Who really wrote Martin v. Hunter's Lessee?: Applying a machine learning ensemble to uncover a Chief Justice's unethical behavior

This article applies multiple machine learning methods to strongly suggest that Chief Justice John Marshall behaved unethically in an important 19th Century case of American constitutional law: Martin v. Hunter's Lessee. He wrote large swathes of the opinion even though he had a large financial stake in the outcome of the case and even though he had formally recused himself. We establish this through multiple machine learning methods, including a character-sequence-to-image method known as the frequency chaos game representation (FCGR), sentence embeddings using BERT, and the reknowned Delta method involving bags of words. All these methods of stylometry reach the same conclusions.
Pair-wise selective classification with dynamic sampling for shipment importer prediction

Whenever a shipped package crosses the border, logistic companies have to declare importer information for the clearance process. This information is not always provided by the customer, causing delays and additional expenses. Fortunately, importer information can often be inferred from historical shipments. The current technical standard, even in big companies, is to use a feature-weighted nearest neighbor approach based on domain knowledge. Nearest neighbors assume that each sample point is represented independently and is fixed in some high dimensional space. This makes it difficult to integrate higher-order pair-wise relationships such as transaction frequency from shipper to receiver transaction, because the features are now changing dependent on the pairs. This would require a complex ad-hoc feature engineering and metric learning to capture pairwise relationships properly with nearest neighbors. In this paper, we propose a framework for importer prediction based on a pair-wise classification approach that allows us to capture higher order pair-wise relationships. We also incorporate an auxiliary neural network that can reliably reject shipments that our model could not predict well such as shipments with new importers that are not in the historical data. This allows us to pass the difficult cases to a human agent instead of naively making an incorrect prediction. Our proposed pair-wise solution outperforms the industry standard by a significant margin of precision across a wide range of recall values.
Generative and contrastive based self-supervised learning model for histopathology image analysis

In recent years, the deep neural network has achieved astonishing results in the automatic Whole slide images(WSI) processing. The current mainstream approach is inseparable from a large number of manual annotations. However, labeling such giant images with billions of pixels is very labor-intensive. So the shortage of annotation has become a bottleneck in developing Whole slide image diagnostic models. Therefore, we propose a new self-supervised learning(SSL) network to solve the problem of insufficient annotation. In our work, massive semantic information can be extracted from a large number of WSI, which significantly gets rid of our dependence on the label. At the same time, the results are further refined by a silhouette-coefficient-based recursive Spectral Clustering Bipartition, which significantly improved the classification accuracy. Moreover, our framework is highly transferable and can take on many downstream tasks in pathology. Our final results are verified on the NCT-CRC-100K and MSHIR datasets. Our code is available at https://github.com/Hongbo-Chu/generative-contrastive
Soybean Leaf Segmentation and Area Estimation Based on Extreme Points

Leaf area is an essential growth trait for plants throughout their lifespan. However, how to calculate the area effectively, non-destructive, and cheaper is a complex task, which needs more study. In this paper, we explore the leaf area estimation problem using image processing methods. We develop software which integrates the proposed segmentation algorithm and area estimation model. First we collect and construct a dataset with 200 images, which contains leaf images with different shooting heights and environment. Then, we utilize a CNN network, Resnet, to segment the leaves, for images with a complex backgrounds, a bounding-box formed by extreme points is used to focus on the leaf of interest. After obtaining the leaf, we explored the relationship between pixels and area, and finally estimate the leaf area. Experimental results show that our proposed framework outperforms other state-of-the-art algorithms in terms of image segmentation. These promising outcomes will benefit plant physiological and agronomic studies to better understand plant growth status and provide insights that will enable them to elucidate competition among different plant species, predict crop yield, and assist decision-making.
Industrial Production Index Forecast based on EMD-TCN

The industrial production index can be used to reflect the speed of industrial development and economic development. The prediction of industrial production index is of great significance for understanding the law of industrial development, industrial macro-control and guiding production practice. In this paper, we propose a deep learning method based on time empirical mode decomposition (EMD) and time convolution network (TCN). The model can capture the non-stationary characteristics of the data, and obtain the time series degradation signal, namely the intrinsic mode function, through empirical mode decomposition. In addition, the TCN architecture uses causal convolution and dilation convolution. Compared with traditional statistical models such as ARIMA and prophet and deep learning models such as LSTM and TCN, it can better capture the past information of time series. Through experimental verification, the EMD-TCN architecture has smaller prediction error.
Develop an AIoT Badminton Serving Machine

An AIoT badminton serving machine refers to a shuttlecock serving machine with an IoT wireless interface that can support the integration with computer vision and intelligent computing to develop innovative applications to improve teaching and learning efficiency and increase entertainment experience. The serving machine should be equipped with IoT capacity to support wireless communication and real-time control, provide APIs for programmable capability, and provide precise control on shuttlecock serving. Especially, APIs offer the interface to interact with intelligent technologies such as computer vision. In this work, we developed a MCU to control serving machine hardware, a RPi-based system to provide wireless interface and control API. MQTT protocol is utilized to sending/receiving messages. As an example, serving machine positioning and calibration functions were implemented by integrating computing vision.
A Self-attention Feature Metric Learning Method for Remote Sensing Image Retrieval

Remote sensing image retrieval (RSIR) is a challenging task due to the complex backgrounds of the image and the inclusion of various semantic objects. This paper presents a novel self-attention mechanism for RSIR using deep learning networks. We elaborate on the long-distance and the multi-layer dependence of image regions within a convolutional neural network to extract the salient features of remote sensing image against a complex background. We propose a new learning network structure of a multiple similarity (MS) loss function to further enhance the discriminability of features. The experiments on the typical datasets show that our method can significantly enhance the accuracy of RSIR. Moreover, when self-attention mechanism and MS loss function are simultaneously considered in the network, the retrieval precision is better than the results obtained by the previous methods.
Parallel Simulation of Lid-Driven Flow with Internal Square Obstacle

In order to study the fluid flow in a two-dimensional cavity containing an obstacle,Lattice Boltzmann method based on multi-relaxation time model is used in this paper to simulate the flow in lid-driven cavity contains an obstacle for a range of Reynolds numbers, and the implemented method is optimised for parallel simulation using CUDA.For convergence, we select suitable boundary treatment conditions, such as slip-free boundary conditions with fixed avoidance at the sides and bottom and a uniform horizontal velocity at the top of the cavity. Our work focuses on observing the fluid flow in the presence of an obstacle in the cavity below a certain range of Reynolds numbers and on the CUDA parallel optimisation of the lattice Boltzmann method with multi-relaxation time, with an average maximum speedup ratio of 13.72X based on the available experimental conditions.
Ensemble Two Stage Machine Learning for Network Abnormal Detection

With the endless emergence of network security problems, it also brings the corresponding security threats to the society. IDS is an effective means to deal with network threats, but in the modern large-scale network environment, the traditional IDS has a high false positive rate. Researchers apply machine learning method to intrusion detection and get good results. In this work, based on the network traffic features, we proposed an ensemble two stage machine learning method CNN-CatBoost to detect network attacking behavior. To solve imbalance problem between normal and anomaly traffic, we construct a CNN model with multi-scale convolutional adopted to extract relations among traffic features for anomaly traffic recognition. Next for a multi-class classification problem, we use classic CatBoost model to identify different attacking types. In the experiments, datasets is from CICIDS2017. We deploy comparison experiments among proposed method and other methods. The experimental results show that CNN-CatBoost outperforms others in performance. Lastly, we give feature analysis by SHAP, understand how features influence on the model.
A Dataset of German Legal Reference Annotations

The field of legal Natural Language Processing faces a lot of challenges due to the unavailability of properly structured datasets. One such instance is the need for a dataset that not only separates different parts of legal references, such as an article or paragraph number but also provides information about what a particular legal reference dictates. Having access to such a dataset can provide easy access to researchers working on experiments such as context similarity between law texts and legal cases that refer to a particular law. In this paper, we present a dataset of 2944 legal references in German law that are manually annotated by law experts. This dataset has 21 properties for each law reference in the dataset, such as Buch, Teil, Titel, Untertitel, etc. It also provides the complete text of each law reference in the dataset, along with specific paragraph text mentioned in the law reference. Furthermore, using this dataset together with Open Legal Data, we perform a law reference prediction task to compare the performance between predicting full law reference and only the base law reference.
Airport Target Detection Based on Yolov5 for Bird-Repellent System

Bird collision is one of the important factors threatening aviation safety, which seriously affects the safety of human life and property. Therefore, effective measures must be taken to prevent such incidents. According to the problems in the existing ways and methods of bird-repelling, this paper proposes a scheme of building an airport bird-repelling linkage system by using the Internet of Things technology. As the core of the system, object detection is particularly important. The targets of foreign objects such as birds and UAVs are small and random, how to recognize these objects quickly and accurately determines the overall performance of the airport bird drive linkage system to a certain extent. The experimental results of the project team on different network models show that yolov5 is excellent in speed and accuracy, can be well embedded into the system, and enhance the bird driving effect. The object detection embedded with yolov5 can provide more powerful technical support for the existing supervision means of the airport and ensure the safety of the airport.
Enhancing LLVM Optimizations for Linear Recurrence Programs on RVV

The RISC-V Vector Extension (RVV) has emerged as a promising vector architecture for high-performance computing. It enables parallel computing capability for RISC-V CPUs by introducing additional vector instructions and vector registers. To fully utilize the potential of RVV, it is crucial to optimize the auto-vectorization capabilities of compilers. Auto-vectorization is a widely used method for parallelizing scalar programs on vector machines. It examines the input program to identify the parallelizable section and then maps it to the underlying vector architecture. This paper focuses on enhancing LLVM’s auto-vectorization for linear recurrence programs on RVV, with a specific emphasis on a well-studied computation pattern called scan (all-prefix-sums). Linear recurrence programs are prevalent in diverse computational domains, and their efficient execution is vital for achieving optimal performance. However, the auto-vectorization on current LLVM’s vectorizer is hindered by the loop-carried data dependence of recurrence programs. In this work, we propose novel techniques to address the challenges of auto-vectorizing linear recurrence programs on RVV, leveraging the unique features of RVV and extending LLVM’s vectorization capabilities. The experiment shows that our auto-vectorized plus-scan can achieved 17.64x speedup on RVV compared to LLVM 16.0. 0.
Feature Selections for Phishing URLs Detection Using Combination of Multiple Feature Selection Methods

In this internet era, we are very prone to fall under phishing attacks where attackers apply social engineering to persuade and manipulate the user. The core attack target is to steal users’ sensitive information or install malicious software to get control over users’ devices. Attackers use different approaches to persuade the user. However, one of the common approaches is sending a phishing URL to the user that looks legitimate and difficult to distinguish. Machine learning is a prominent approach used for phishing URLs detection. There are already some established machine learning models available for this purpose. However, the model's performance depends on the appropriate selection of features during model building. In this paper, we combine multiple filter methods for feature selections in a procedural way that allows us to reduce a large number of feature list into a reduced number of the feature list. Then we finally apply the wrapper method to select the features for building our phishing detection model. The result shows that combining multiple feature selection methods improves the model's detection accuracy. Moreover, since we apply the backward feature selection method as our wrapper method on the data set with a reduced number of features, the computational time for backward feature selection gets faster.
On the Semantic Difference of Judicial and Standard Language

Legal language is considered to be a key obstacle to the comprehensibility of court decisions for laypeople. While differences between written 'standard' and legal language have already been analysed with regard to syntactic peculiarities, there is still a lack of findings on the influence of divergent word meanings on comprehensibility. We present the course and the preliminary results of a study elaborating such ambiguities on the basis of over half a million German court decisions. As these differences are highly language-dependent, our study consequentially relates (only) to German.
Fusing Multi-Granularity Data for Stock Trend Prediction with Contrastive Pre-training

The advances in financial theory and power of computer technology have driven the development of quantitative investing. Stock trend forecasting as an important topic in quantitative investment has attracted more and more researchers. The development of deep learning has also led to the emergence of many interesting models in the field. However, most of the existing models only use daily frequency data to predict the stock price fluctuations, lacking the consideration of high-frequency data and relational data. At the same time, it remains a challenge to incorporate multi-granularity data into model training. Based on this, we propose a contrastive pre-training model fusing high frequency data and daily frequency data(HRC), which construct pre-training tasks with a positive and negative sample design in terms of data granularity and stock relationships. In addition to this, we also constructed a label-based training task using the performance of returns to classify stocks. The experiments on real-world stock dataset show significant improvements of our approach over the baselines.
A Review of Face Attribute Editing Based on Deep Learning

Face attribute editing is an important research area in computer vision, which refers to the modification of single or multiple attributes of human faces, such as age, expression, makeup, hairstyle, etc. The application of face attribute editing is popular in social networks because of its diversity, and it has important application value in a huge market. This paper briefly expounds the face attribute editing technology, and reviews the existing face attribute editing methods. We try to collect some methods of face attribute editing based on deep learning and classify them into DFI-based, 3DMM-based, GAN-based, and hybrid-based editing methods, summarize the previously proposed datasets and loss functions commonly used in the network. Finally, we summarize the current situation and future development trend of face attribute editing.