Reviewerly/exHarmony huggingface dataset:
Unboxing Default Argument Breaking Changes in Scikit Learn

Machine Learning (ML) has revolutionized the field of computer software development, enabling data-based predictions and decision-making across several domains. Following modern software development practices, developers use third-party libraries—e.g., Scikit Learn, TensorFlow, and PyTorch—to integrate ML-based functionalities into their applications. Due to the complexity inherent in ML techniques, the models available in the APIs of these tools often require an extensive list of arguments to be set up. Library maintainers overcome this issue by defining default values for most of these arguments so developers can use ML models in their client applications effortlessly. By relying on these default arguments, the clients inadvertently depend on the value defined in these parameters to keep running as expected. We interpret this problem as a semantical breaking change variant, which we named Default Argument Breaking Change (DABC). In this work, we leverage 77 DABCs in Scikit Learn—a well-known ML library—and investigate how 194K client applications are vulnerable to them. Our results show that 72 DABCs (93%) are responsible for exposing 67,747 clients (35%). We also detected that most DABCs (61, 79%) involve APIs used in ML model training and model evaluation stages. Finally, we discuss the importance of managing DABCs in third-party ML libraries and provide insights for developers to mitigate the potential impact of these changes in their applications.
Leveraging User-Defined Identifiers for Counterfactual Data Generation in Source Code Vulnerability Detection

Software vulnerability detection is a critical aspect of ensuring the security and reliability of software systems. However, traditional vulnerability detection approaches often have limitations due to the scarcity and need for more diversity in labeled data. This research introduces a novel approach to overcome these challenges by utilizing user-defined identifiers in the source code to generate counterfactual training data. User-defined identffiers, such as variable and function names, contain essential information about the intentions and logic of the program. By perturbing these identifiers while maintaining the syntactic and semantic structure of the code, we create a diverse set of counterfactual examples that simulate potential vulnerabilities. When combined with existing labeled data, these counterfactual examples enrich the training process for vulnerability detection models. To evaluate the effectiveness of our approach, we conduct experiments on various datasets, achieving state-of-the-art performance on the VulDeePecker and Draper datasets. Our approach also outperforms models that utilize the same pre-trained language model in terms of accuracy.
MIME: Fast and Accurate Flow Information Compression for Multi-Spread Estimation

Spread estimation is an essential issue in high-speed networks with wide applications, such as network billing, quality of service, anomaly detection, etc. As a promising technique, sketch can efficiently estimate per-flow spread with only a small memory cost. Many studies focus on improving the performance of sketches. However, these works primarily focus on optimizing the counter level or sketch level without considering the scenario of multi-spread estimation, which is crucial for improving memory utilization and detecting anomalies. In this paper, we propose an efficient flow information compression algorithm based on the on-chip/off-chip hybrid framework to estimate multiple flow spreads. In the on-chip memory, we filter out non-duplicates and sample them to the off-chip space for recording. In the off-chip memory, we compress each sampled non-duplicate to a carefully designed bit-cube. When the measurement is finished, we separate corresponding KV-flows from a specific flow based on the user query. Then, we rebuild this flow to a subset group based on the duplicate number of each KV-flow. Finally, according to the Multi-set theory, we derive an accurate multi-spread estimate formula to solve this issue with a high throughput and small on-chip memory usage. Furthermore, we evaluate the performance of our proposed estimator using real Internet traffic traces downloaded from CAIDA. Experiments show that, compared to the state-of-the-art, our proposal achieves a 97.2% lower average relative error in per-destination source flow spread estimation with a tight on-chip memory, e.g., 320KB. And our proposed method achieves 31.86 higher processing throughput.
Rotation-Invariant Descriptors Learned with Circulant Convolution Neural Networks

Extracting local features for accurate correspondences between image pairs is an essential basis for various computer vision tasks. Recent works have shown that deep neural networks (DNNs) have demonstrated promising performance in challenging environments. However, these state-of-the-art DNN-based approaches are not well suited for the scenario with geometry rotations due to their intrinsic deficiencies of square kernel structure. That is, square kernel structures in standard DNNs cannot fully identify the essentials of the rotations in geometry. To address this problem, we present RICNN, a novel deep learning framework that encodes invariance against the rotations in geometry explicitly into convolutional neural networks. Rather than using the square-shaped kernel structure, RICNN adopts sectorshaped convolutional kernels to achieve encoding invariance in all rotations. With the explicitness of such rotation encoding, RICNN enables the transfer of perspective DNN models to obtain rotation-invariant descriptions. Furthermore, we propose a novel multi-level hinge triplet loss function to strengthen the matching constraints against geometry rotations. Comprehensive experiments demonstrate the strong generalization ability of the RICNN descriptor on the HPatches dataset. Toward the rotation invariance evaluation, our method shows state-of-the-art results.
double PT: Enhancing Meta-Learning Performance with Pre-Train and Meta-Pre-Train Processes

With meta-learning, models are trained on multiple tasks, and resulting trained models are expected to be capable of “ learning” new tasks effectively. MAML (Model Agnostic Meta-Learning) was one such early design which allowed models to reuse learned features, but with limited performance. Pre-training is another known method to improve the performance of a final trained model. Pre-train starts by assisting models to seek better initialization points, thus offering better feature representations. In this paper, we propose doublePT (double-pretrain) which is a two-stage pre-training method with goals to 1) reduce the number of parameters, 2) acquire better feature representations, and 3) achieve competitive overall performances in different benchmark measurements. For the first stage operation, we use a universal pre-training model to capture general features from a large dataset. In the second stage, we propose to use MAML to fine-tune the pre-training model to enhance the feature adaptability. Since the first-stage pre-training model has already learned general feature representations, it reduces the training activities for the second-stage fine-tuning operations, and enables better feature extractions in new tasks. Validated through our experiments, we find that our proposed doublePT approach fine-tunes across different tasks, and performs better than that of one-stage pre-training approach. Upon combining doublePT and DINOv2, and comparing to the latest PMF meta-learning design, the number of parameters required by the PMF pipelining model needs 304.8% more parameters than in our proposed DINOv2+doublePT model design. Performance-wise, the DINOv2+doublePT also has the best accuracies across different benchmark measurements.
RC R-CNN for Bone Marrow Cell Recognition

The morphological examination of bone marrow (BM) cells is an essential basis for the diagnosis and assessment of hematologic diseases. However, manual recognition of cell class and quantification in microscopic images of BM cells is time-consuming, labor-intensive, and prone to errors. Therefore, it is crucial to study an efficient algorithm for the automatic recognition of BM cells. Clinically, to determine the BM cell classes, experienced examiners usually rely on surrounding cells as references to provide auxiliary confirmation, but there is no model specially designed for contextual relationships of BM cells. To extract cell features comprehensively and explore the contextual relationships among BM cells, we propose RC RCNN based on Mask R-CNN, which includes positional encoding (PE), ROI self-attention module (RSAM), and ROI channel attention module (RCAM) as an extension to Mask RCNN. The PE and RCAM enable better capturing of contextual information between BM cells. By using the RSAM, the model directs its attention toward internal cell features while effectively suppressing irrelevant background information. Experimental results demonstrate the outstanding performance of the proposed method, with the AP and F1-Score reaching 73.21% and 87.71%, respectively, an improvement of about 2.3% and 2.2% over the baseline.
Super-resolution of SAR images using residual four-channel large kernel attention

The advantages of Synthetic Aperture Radar (SAR) include its ability to produce high-resolution images in all-weather conditions and its wide range of applications in both military and civilian fields. However, SAR images are frequently subject to degradation in resolution due to the presence of speckle noise. This paper presents a new super-resolution network, the residual four-channel large kernel attention (RFCLKA) network, which uses four different-scale large kernel attentions (LKA) as primary components and integrates residual learning. The RFCLKA network can learn feature information at multiple scales, allowing the network to focus on informative features and improve their representation. Moreover, residual learning in RFCLKA supports deeper network construction and efficient network optimization, enabling effective learning of global and local features. The proposed SAR super-resolution network further incorporates the bilinear interpolation results of low-resolution SAR images as auxiliary information to provide base features, accelerating training speed and improving network performance. Through ablation experiments and comparative experiments, the proposed network’s effectiveness is demonstrated based on objective metrics and visual quality of the super-resolved SAR images.
HYBRID BINARY WHALE OPTIMIZATION ALGORITHM BASED ON TAPER SHAPED TRANSFER FUNCTION FOR SOFTWARE DEFECT PREDICTION

Reliability is one of the key factors used to gauge software quality. Software defect prediction (SDP) is one of the most important factors which affects measuring software's reliability. Additionally, the high dimensionality of the features has a direct effect on the accuracy of SDP models. The objective of this paper is to propose a hybrid binary whale optimization algorithm (BWOA) based on taper-shape transfer functions for solving feature selection problems and dimension reduction with a KNN classifier as a new software defect prediction method. In this paper, the values of a real vector that represents the individual encoding have been converted to binary vector by using the four types of Taper-shaped transfer functions to enhance the performance of BWOA to reduce the dimension of the search space. The performance of the suggested method (T-BWOA-KNN) was evaluated using eleven standard software defect prediction datasets from the PROMISE and NASA repositories depending on the K-Nearest Neighbor (KNN) classifier. Seven evaluation metrics have been used to assess the effectiveness of the suggested method. The experimental results have shown that the performance of T-BWOA-KNN produced promising results compared to other methods including ten methods from the literature, four types of T-BWOA with the KNN classifier. In addition, the obtained results are compared and analyzed with other methods from the literature in terms of the average number of selected features (SF) and accuracy rate (ACC) using the Kendall W test. In this paper, a new hybrid software defect prediction method called T-BWOA-KNN has been proposed which is concerned with the feature selection problem. The experimental results have proved that T-BWOA-KNN produced promising performance compared with other methods for most datasets.
The Art of Complaints: Analysing Complaints Management from a Social Media Banking Account

Banking institutions’ handling of complaints in today’s context has become one of the most highlighted areas in customer service. Customer complaints are the basis of any organisation, and an organisation’s ability to handle its customers reflects the competitive spirit in the business area. The resolution quality is what makes an organisation different from the others. Every business entity has now used an advanced tool to create customer satisfaction, and social media is a tool that has become familiar and popular among the public. Considering these phenomena, the banking industry has adopted social media to communicate with its customers. Subsequently, there are thousands of complaints posted through the social media platform and viral issues in social media could damage the organisation, affecting the profit made. While a happy customer could spread the information to 10 people, an unhappy customer could spread that issue to thousands of people on social media in a matter of seconds. Thus, the banking institution must strategize its standard of procedure in handling complaints so it would be beneficial instead of damaging. The present paper will analyse the types of complaints made by customers through the banking social media platform and the type of resolution the institution gave to rectify and solve the complaints. Findings showed that the highest type of customer complaint would be related to a banking service issue, followed by delayed service, general information, and issues on a social media campaign. In contrast, the most used type of resolution would focus more on explaining the predicaments, followed by resolution quality, being courteous, apology and accessibility.
SAE3D: Set Abstraction Enhancement Network for 3D Object Detection Based Distance Features

With the increasing demand from unmanned driving and robotics, more attention has been paid to point-cloud-based 3D object accurate detection technology. However, due to the sparseness and irregularity of the point cloud, the most critical problem is how to utilize the relevant features more efficiently. In this paper, we proposed a point-based object detection enhancement network to improve the detection accuracy in the 3D scenes understanding based on the distance features. Firstly, the distance features are extracted from the raw point sets and fused with the raw features regarding reflectivity of the point cloud to maximize the use of information in the point cloud. Secondly, we enhanced the distance features and raw features, which we collectively refer to as self-features of the key points, in set abstraction (SA) layers with the self-attention mechanism, so that the foreground points can be better distinguished from the background points. Finally, we revised the group aggregation module in SA layers to enhance the feature aggregation effect of key points. We conducted experiments on the KITTI dataset and nuScenes dataset and the results show that the enhancement method proposed in this paper has excellent performance.
VuFind: an open solution for integrating library collections

The article discusses the VuFind system as an open solution for effective integration of library collections. VuFind is a powerful search interface designed to improve access to a variety of resources, including books, articles, journals, scientific reports, and other materials. The authors discuss the key features of VuFind, such as flexible customization, search capabilities, metadata support, and integration with various data sources. They emphasize the role of VuFind in simplifying search for users and optimizing the management of collections from different libraries. VuFind provides an open and available solution for building modern library systems, facilitating effective integration and increasing user satisfaction.
Unified Spatio-Temporal Dynamic Routing for Efficient Video Object Segmentation

Existing methods for video object segmentation (VOS) have achieved significant success by performing semantic guidance, spatial constraint, or temporal consistency. However, VOS still remains highly challenging because it is difficult to collaboratively leverage spatial constraint, temporal consistency, and semantic guidance while reducing redundant information. In this paper, we propose an efficient unified spatio-temporal dynamic routing (STDR) framework to address VOS by achieving a better spatio-temporal balance while avoiding redundancy. Specifically, our unified spatio-temporal modeling contains three paths: 1) short-term spatial path is employed to mine the spatial constraints from the previous frame; 2) long-term semantic path is used to capture semantic cues from the first reference frame with ground-truth labels; 3) memory queue path is designed to efficiently exploit the temporal consistency of middle frames with a compact memory bank of constant size. To enhance the input of each path, we introduce a progressive contextual memory enhancement module to exploit the contextualized memory with growing receptive fields by progressively aggregating spatial contextual information from adjacent frames for each memory frame. Furthermore, we design a dynamic memory-routed module to globally refine the outputs of our three paths for unified modeling. Enhanced by the proposed modules, our STDR achieves state-of-the-art performance with fast speed on the DAVIS 2016, DAVIS 2017 Val/Test, YouTube-VOS 2018/2019, and real-world long-video benchmarks.
Gradient-Based Markov Chain Monte Carlo for MIMO Detection

Accurately detecting symbols transmitted over multiple-input multiple-output (MIMO) wireless channels is crucial in realizing the benefits of MIMO techniques. However, optimal MIMO detection is associated with a complexity that grows exponentially with the MIMO dimensions and quickly becomes impractical. Recently, stochastic sampling-based Bayesian inference techniques, such as Markov chain Monte Carlo (MCMC), have been combined with the gradient descent (GD) method to provide a promising framework for MIMO detection. In this work, we propose to efficiently approach optimal detection by exploring the discrete search space via MCMC random walk accelerated by Nesterov’s gradient method. Nesterov’s GD guides MCMC to make efficient searches without the computationally expensive matrix inversion and line search. Our proposed method operates using multiple GDs per random walk, achieving sufficient descent towards important regions of the search space before adding random perturbations, guaranteeing high sampling efficiency. To provide augmented exploration, extra samples are derived through the trajectory of Nesterov’s GD by simple operations, effectively supplementing the sample list for statistical inference and boosting the overall MIMO detection performance. Furthermore, we design an early stopping tactic to terminate unnecessary further searches, remarkably reducing the complexity. Simulation results and complexity analysis reveal that the proposed method achieves exceptional performance in both uncoded and coded MIMO systems, adapts to realistic channel models, and scales well to large MIMO dimensions.
ONLINE LEARNING VARIETIES AS EFFORTS TO INCREASE STUDENTS’ READING MOTIVATION

The Coronavirus disease 2019 (Covid-19) became a pandemic in March 2020. As the impact, the learning activities experienced a shift from traditional learning methods to technology-based or online learning. The shifting forces the students and instructors to adapt to new things. This study aims to determine how various learning activities in English reading class motivate students and to determine students’ motivation in English reading class. This study involved 22 students from Universitas Kristen Indonesia in Jakarta utilizing experimental research using three strategies of teaching reading. This research recommended three activities that motivated the students to participate actively in class. The three activities include implementing online reading tests, assigning reflective reading, and presenting the summary of journal articles. The three different teaching reading strategies increase students’ motivation to trigger their enthusiasm to participate consistently in every activity. The research suggests that various ways of teaching academic reading are necessary since students feel contented. This study implies that it provides alternative strategies for teaching reading applicable in all reading classes, mainly to university students.
Algorithm for Creating 3d Scenes of Recognized Objects from Depth Maps

Purpose of research. Development of an algorithm for constructing 3d scenes of recognized objects from synthesized depth maps in order to improve the speed of real-time image processing. Methods. The 3d scene construction algorithm is based on the method of stereo image construction using a threelevel fuzzy depth map construction model. At the first level of this model the boundaries of objects are determined using a modified Canny algorithm, at the second level the values of disparity are calculated on the basis of the sum of absolute differences algorithm modified by fuzzy logic methods, and at the final level the gradients of distances from the boundaries of images to the edges of recognized objects are calculated first and then according to the obtained values of disparity at the second and third levels of the fuzzy hierarchical model, the refined values of disparity are calculated, which are used to carry out the analysis of the depth map. Results. An algorithm for constructing 3d scenes of recognized objects using synthesized depth maps has been developed. It was determined that the proposed algorithm has better performance compared to existing depth map algorithms such as conjugate point algorithm and pyramidal algorithm. Conclusion. The experimental results showed that the proposed algorithm has a lower complexity compared to the analyzed algorithms (conjugate points and pyramidal). The minimum average execution time of the 3d scene construction operation was about 1-2 minutes, which is almost 120 times better compared to the conjugate point algorithms.
Sketch-to-image Synthesis with Image-level Encoding Aggregation and Patch-level Semantic Refinement

Sketch-to-image synthesis is a hotspot in computer vision, which can be applied to criminal investigation and recreation industry. To better satisfy the requirements of users, many researchers concentrate on the reference-based sketch-to-image task which aims to convert a sketch-like image into a photo-like image with a given appearance reference image. However, existing methods tend to produce broken geometry and incomplete appearance. In this paper, we develop a novel network with image-level encoding aggregation and patch-level semantic refinement to generate high-quality images from sketches with reference. The image-level part is developed with an aggregated encoder and serves as a feature amplifier to extract detailed features and enhance geometry reservation. And the patch-level semantic part works with the CIELAB space and scan line seed-filling algorithm to focus on local appearance transfer. Extensive results demonstrate significant enhancements in both geometry and appearance.