Reviewerly/exHarmony huggingface dataset:
Highly Efficient Self-Checking Matrix Multiplication on Tiled AMX Accelerators

Authors:
raw_author_name: Victor C. Ferreira
raw_affiliation_string: Intel Corporation, India
raw_author_name: Sudarshan Srinivasan
raw_affiliation_string: Intel Corporation, India
raw_author_name: Sandip Kundu
raw_affiliation_string: University of Massachusetts, USA

Abstract: General Matrix Multiplication (GEMM) is a computationally expensive operation that is used in many applications such as machine learning. Hardware accelerators are increasingly popular for speeding up GEMM computation, with Tiled Matrix Multiplication (TMUL) in recent Intel processors being an example. Unfortunately, the TMUL hardware is susceptible to errors, necessitating online error detection. The Algorithm-based Error Detection (ABED) technique is a powerful technique to detect errors in matrix multiplications. In this article, we consider implementation of an ABED technique that integrates seamlessly with the TMUL hardware to minimize performance overhead. Unfortunately, rounding errors introduced by floating-point operations do not allow a straightforward implementation of ABED in TMUL. Previously an error bound was considered for addressing rounding errors in ABED. If the error detection threshold is set too low, it will a trigger false alarm, while a loose bound will allow errors to escape detection. In this article, we propose an adaptive error threshold that takes into account the TMUL input values to address the problem of false triggers and error escapes and provide a taxonomy of various error classes. This threshold is obtained from theoretical error analysis but is not easy to implement in hardware. Consequently, we relax the threshold such that it can be easily computed in hardware. While ABED ensures error-free computation, it does not guarantee full coverage of all hardware faults. To address this problem, we propose an algorithmic pattern generation technique to ensure full coverage for all hardware faults. To evaluate the benefits of our proposed solution, we conducted fault injection experiments and show that our approach does not produce any false alarms or detection escapes for observable errors. We conducted additional fault injection experiments on a Deep Neural Network (DNN) model and find that if a fault is not detected, it does not cause any misclassification.
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Authors:
raw_author_name: Naonori Kakimura
raw_affiliation_string: Keio University, Yokohama, 223-8522, Japan

Abstract: This note studies the problem of estimating frequencies of items over data streams. We propose a simple streaming algorithm for the problem in small space complexity. Our algorithm is a counter-based algorithm with the aid of probabilistic counting. We show that our algorithm with k counters computes, with probability at least 1−δ, the estimation with relative error at most (1+ε)N/k, taking O(klog⁡log⁡Nk+klog⁡(ε−1δ−1k)+klog⁡ℓ) space in expectation, where N is the total number of items and ℓ is the number of different items.
OneSpace: Detecting cross-language clones by learning a common embedding space

Authors:
raw_author_name: Francisco Servant
raw_affiliation_string: ITIS Software, Universidad de Málaga, Málaga, Spain

Abstract: Identifying clone code fragments across different languages can enhance the productivity of software developers in several ways. However, the clone detection task is often studied in the context of a single language and less explored for code snippets spanning different languages. In this paper, we present OneSpace, a new cross-language clone detection approach. OneSpace projects different programming languages to the same embedding space using both code and API data. OneSpace, hence, leverages a Siamese Network to infer the similarity of the embedded programs. We evaluate OneSpace by detecting clones across three language pairs; JAVA-Python, Java-C++ and Java-C. We compared OneSpace with the other state-of-art techniques, SupLearn and CLCDSA. In our evaluation, OneSpace provided higher effectiveness than the state of the art. Our ablation study validated some of our intuitions in designing OneSpace, particularly that using a single embedding space (as opposed to separate ones) provides higher effectiveness. Additionally, we designed a variant of OneSpace that uses Word-Mover-Distance Algorithm and provides lower effectiveness, but is much more efficient. We also found that OneSpace provides higher effectiveness than the state of the art, even for: complex implementations, single-method implementations, varying ratios of positive to negative clones in training, varying amounts of training data, and for additional programming languages.
Harmonizing DevOps taxonomies — A grounded theory study

Authors:
raw_author_name: Jessica Díaz
raw_affiliation_string: ETSI Sistemas Informáticos, Departamento de Sistemas Informáticos, Universidad Politécnica de Madrid, Spain
raw_author_name: Jorge Pérez
raw_affiliation_string: ETSI Sistemas Informáticos, Departamento de Sistemas Informáticos, Universidad Politécnica de Madrid, Spain
raw_author_name: Isaque Alves
raw_affiliation_string: Department of Computer Science, University of São Paulo, Brazil
raw_author_name: Fabio Kon
raw_affiliation_string: Department of Computer Science, University of São Paulo, Brazil
raw_author_name: Leonardo Leite
raw_affiliation_string: Department of Computer Science, University of São Paulo, Brazil
raw_author_name: Paulo Meirelles
raw_affiliation_string: Department of Computer Science, University of São Paulo, Brazil
raw_author_name: Carla Rocha
raw_affiliation_string: Faculty of Gama, University of Brasilia, Brazil

Abstract: DevOps responds to the growing need of companies to streamline the software development process and thus has experienced widespread adoption in the past few years. However, the successful adoption of DevOps requires companies to address important cultural and organizational changes. Nevertheless, it is crucial to recognize that various DevOps taxonomies exist, both from academic and practitioner perspectives, which may lead to misleading or failed adoption of DevOps. This paper presents empirical research on the structure of DevOps teams in software-producing organizations. The goal is to better understand the organizational structure and characteristics of teams adopting DevOps by harmonizing the existing knowledge. To achieve this, we employed a grounded theory approach with collaborative coding, involving two research groups. Inter-Coder Agreement (ICA) was utilized to guide the discussion rounds. We conducted a comprehensive analysis of existing studies on DevOps teams and taxonomies to gain a deeper understanding of the subject. From the analysis, we built a substantive and analytic theory of DevOps taxonomies. The theory is substantive in that the scope of validity refers to the ten secondary studies processed and analytic in that it analyzes “what is” rather than explaining causality or attempting predictive generalizations. A public repository with all the data related to the products resulting from the analysis and generation of the theory is available. We built a theory on DevOps taxonomies and tested whether it harmonizes the existing taxonomies, i.e., whether our theory can instantiate the others. This is the first step to define which taxonomies are best suited to approach DevOps culture and practices according to the companies’ objectives and capabilities. Editor’s note: Open Science material was validated by the Journal of Systems and Software Open Science Board.
Improved generative adversarial network with deep metric learning for missing data imputation

Authors:
raw_author_name: Yu Wang
raw_affiliation_string: College of Intelligence and Computing, Tianjin University, Tianjin 300350, China
raw_author_name: Pengfei Zhu
raw_affiliation_string: College of Intelligence and Computing, Tianjin University, Tianjin 300350, China
raw_author_name: Qinghua Hu
raw_affiliation_string: College of Intelligence and Computing, Tianjin University, Tianjin 300350, China
raw_author_name: Abdulrahman Al-badwi
raw_affiliation_string: Computer Science College, Central South University, Changsha, China

Abstract: Incomplete data are ubiquitous in real-world computer vision tasks. Imputing missing data is crucial for modeling machine learning algorithms. Although existing methods, such as MisGAN, have achieved considerable success, two problems remain: (1) generating a complete image from random noise weakens the model's imputation capability; (2) owing to the influence of random noise, the training of the generator is unstable. Therefore, this study proposes a novel approach that uses deep metric learning to enable MisGAN to perform multi-tasking missing data imputation. First, an image feature extraction network is applied to extract the semantic representation of the images. Then, deep metric learning is performed to learn good features embedding by maximizing inter-class variation and minimizing intra-class variation. Such an embedding replaces the random noise and is fed into MisGAN to obtain an improved generated complete image. Several experiments are conducted on datasets, demonstrating that the proposed method can significantly outperform baseline methods.
Features based adaptive augmentation for graph contrastive learning

Authors:
raw_author_name: Adnan Ali
raw_affiliation_string: School of Computer Science and Technology, University of Science and Technology of China, Hefei 230026, Anhui, China
raw_author_name: Jinlong Li
raw_affiliation_string: School of Computer Science and Technology, University of Science and Technology of China, Hefei 230026, Anhui, China

Abstract: Self-supervised learning aims to eliminate the need for expensive annotation in graph representation learning, where graph contrastive learning (GCL) is trained with the self-supervision signals containing data-data pairs. These data-data pairs in GCL are generated with augmentation employing stochastic functions on the original graph. We argue that some features can be more critical than others depending on the downstream task, and applying stochastic function uniformly vandalizes the influential features, leading to diminished accuracy. To fix this issue, we introduce a Feature Based Adaptive Augmentation (FebAA) approach, which identifies and preserves potentially influential features and corrupts the remaining ones. We implement FebAA as a plug-and-play layer and use it with state-of-the-art Deep Graph Contrastive Representation Learning (GRACE) and Large-Scale Representation Learning on Graphs via Bootstrapping (BGRL). We successfully improved the accuracy of GRACE and BGRL on eight graph representation learning benchmark datasets.
Multi-layered self-attention mechanism for weakly supervised semantic segmentation

Authors:
raw_author_name: Mingon Kang
raw_affiliation_string: Department of Computer Science, University of Nevada Las Vegas, 4505 S. Maryland Pkwy. Las Vegas, NV 89154-4022, United States of America

Abstract: Weakly Supervised Semantic Segmentation (WSSS) provides efficient solutions for semantic image segmentation using image-level annotations. WSSS requires no pixel-level labeling that Fully Supervised Semantic Segmentation (FSSS) does, which is time-consuming and label-intensive. Most WSSS approaches have leveraged Class Activation Maps (CAM) or Self-Attention (SA) to generate pseudo pixel-level annotations to perform semantic segmentation tasks coupled with fully supervised approaches (e.g., Fully Convolutional Network). However, those approaches often provides incomplete supervision that mainly includes discriminative regions from the last convolutional layer. They may fail to capture regions of low- or intermediate-level features that may not be present in the last convolutional layer. To address the issue, we proposed a novel Multi-layered Self-Attention (Multi-SA) method that applies a self-attention module to multiple convolutional layers, and then stack feature maps from the self-attention layers to generate pseudo pixel-level annotations. We demonstrated that integrated feature maps from multiple self-attention layers produce higher coverage in semantic segmentation than using only the last convolutional layer through intensive experiments using standard benchmark datasets.
Revealing code change propagation channels by evolution history mining

Authors:
raw_author_name: Daihong Zhou
raw_affiliation_string: School of Computer Science, Fudan University, 2005 Songhu Road, Shanghai, China; Shanghai Key Laboratory of Data Science, 2005 Songhu Road, Shanghai, China
raw_author_name: Xin Peng
raw_affiliation_string: School of Computer Science, Fudan University, 2005 Songhu Road, Shanghai, China; Shanghai Key Laboratory of Data Science, 2005 Songhu Road, Shanghai, China
raw_author_name: Ziliang Li
raw_affiliation_string: School of Computer Science, Fudan University, 2005 Songhu Road, Shanghai, China; Shanghai Key Laboratory of Data Science, 2005 Songhu Road, Shanghai, China

Abstract: Changes on source code may propagate to distant code entities through various kinds of relationships, which may form up change propagation channels. It is however difficult for developers to reveal code change propagate channels due to sophisticated interrelationships among code entities. In this work, we propose a novel graph representation for the changed code entities and related code entities changed within a range of space and time so that the types of relationships along which the changes are propagated can be explicitly presented. Then a subgraph mining technique is used to find the frequent change propagation channels. We finally reveal 40 types of frequent change propagation channels that cover over 98% cases of code change propagation in five well-known open-source Java projects. We find evidence that the code changes propagated through an unchanged intermediate code entity consume more time than those through a changed one, indicating the difficulties in maintaining code entities that related through indirect relationships. We find that a small proportion of code entities frequently appear in the FCPCs, and confirm the semantic relationships between code entities covered by 50 instances of FCPCs, indicating potential usefulness for developers to explain the range of change impact from given source code changes.
Powerful-IoU: More straightforward and faster bounding box regression loss with a nonmonotonic focusing mechanism

Authors:
raw_author_name: Kaige Wang
raw_affiliation_string: China Academy of Aerospace Science and Innovation, Beijing, 100048, China
raw_author_name: Qing Li
raw_affiliation_string: Institute of Microelectronics of the Chinese Academy of Sciences, Beijing, 100029, China
raw_author_name: Kun Zhao
raw_affiliation_string: Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China

Abstract: Bounding box regression (BBR) is one of the core tasks in object detection, and the BBR loss function significantly impacts its performance. However, we have observed that existing IoU-based loss functions suffer from unreasonable penalty factors, leading to anchor boxes expanding during regression and significantly slowing down convergence. To address this issue, we intensively analyzed the reasons for anchor box enlargement. In response, we propose a Powerful-IoU (PIoU) loss function, which combines a target size-adaptive penalty factor and a gradient-adjusting function based on anchor box quality. The PIoU loss guides anchor boxes to regress along efficient paths, resulting in faster convergence than existing IoU-based losses. Additionally, we investigate the focusing mechanism and introduce a non-monotonic attention layer that was combined with PIoU to obtain a new loss function PIoU v2. PIoU v2 loss enhances the capability to focus on anchor boxes of medium quality. By incorporating PIoU v2 into popular object detectors such as YOLOv8 and DINO, we achieved an increase in average precision (AP) and improved performance compared to their original loss functions on the MS COCO and PASCAL VOC datasets, thus validating the effectiveness of our proposed improvement strategies.
AMAD: Active learning-based multivariate time series anomaly detection for large-scale IT systems

Authors:
raw_author_name: Rongwei Yu
raw_affiliation_string: School of Cyber Science and Engineering, Wuhan University, Wuhan, China
raw_author_name: Yong Wang
raw_affiliation_string: School of Cyber Science and Engineering, Wuhan University, Wuhan, China
raw_author_name: Wang Wang
raw_affiliation_string: Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China; School of Cyber Security, University of Chinese Academy of Sciences, Beijing, China

Abstract: Multivariate time series anomaly detection on key performance indicators helps mitigate the impact of large-scale IT system anomalies. Due to the large volume and the abstract nature of multivariate time series, previous works have tended to make overly strict or optimistic hypotheses on labeling costs and resulted in unsatisfactory results. Thus, it remains a challenge to make an appropriate trade-off between labeling costs and model performance. This research proposes AMAD, an active learning-based approach that works to address this problem. Its core idea is to provide the learner model high-value label queries via an ensemble query strategy, which dynamically adapts to the estimated anomaly ratio and the ever-changing model performance. Moreover, it is the first to discuss in detail the margin effect of query strategies on model performance, to our knowledge, this has not been investigated in previous works on time series anomaly detection. Extensive experiments on five public datasets demonstrate that AMAD works well and robustly on various real-world scenarios, which outperforms state-of-the-art baseline methods by 16% and 11% in terms of recall and F1, with only 3% of data being labeled.
Masked autoencoders with handcrafted feature predictions: Transformer for weakly supervised esophageal cancer classification

Authors:
raw_author_name: Yunhao Bai
raw_affiliation_string: the School of Electrical and Information Engineering, Tianjin University, Tianjin, China
raw_author_name: Wenqi Li
raw_affiliation_string: Department of Pathology, Key Laboratory of Cancer Prevention and Therapy, National Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China
raw_author_name: Jianpeng An
raw_affiliation_string: the School of Electrical and Information Engineering, Tianjin University, Tianjin, China
raw_author_name: Lili Xia
raw_affiliation_string: the School of Electrical and Information Engineering, Tianjin University, Tianjin, China
raw_author_name: Huazhen Chen
raw_affiliation_string: the School of Electrical and Information Engineering, Tianjin University, Tianjin, China
raw_author_name: Zhongke Gao
raw_affiliation_string: the School of Electrical and Information Engineering, Tianjin University, Tianjin, China

Abstract: Esophageal cancer is a serious disease with a high prevalence in Eastern Asia. Histopathology tissue analysis stands as the gold standard in diagnosing esophageal cancer. In recent years, there has been a shift towards digitizing histopathological images into whole slide images (WSIs), progressively integrating them into cancer diagnostics. However, the gigapixel sizes of WSIs present significant storage and processing challenges, and they often lack localized annotations. To address this issue, multi-instance learning (MIL) has been introduced for WSI classification, utilizing weakly supervised learning for diagnosis analysis. By applying the principles of MIL to WSI analysis, it is possible to reduce the workload of pathologists by facilitating the generation of localized annotations. Nevertheless, the approach's effectiveness is hindered by the traditional simple aggregation operation and the domain shift resulting from the prevalent use of convolutional feature extractors pretrained on ImageNet.We propose a MIL-based framework for WSI analysis and cancer classification. Concurrently, we introduce employing self-supervised learning, which obviates the need for manual annotation and demonstrates versatility in various tasks, to pretrain feature extractors. This method enhances the extraction of representative features from esophageal WSI for MIL, ensuring more robust and accurate performance.We build a comprehensive dataset of whole esophageal slide images and conduct extensive experiments utilizing this dataset. The performance on our dataset demonstrates the efficiency of our proposed MIL framework and the pretraining process, with our framework outperforming existing methods, achieving an accuracy of 93.07% and AUC (area under the curve) of 95.31%.This work proposes an effective MIL method to classify WSI of esophageal cancer. The promising results indicate that our cancer classification framework holds great potential in promoting the automatic whole esophageal slide image analysis.
Hedging inflation expectations in the cryptocurrency futures market

Authors:
raw_author_name: Jinan Liu
raw_affiliation_string: College of Business Administration, University of Nebraska Omaha., 300 Mammel Hall, 6708 Pine Street Omaha, NE 68182, United States of America

Abstract: This paper finds the first evidence of time variation in the relationship between inflation expectations and the price of cryptocurrency futures. Daily data on the futures markets of Bitcoin—starting in December 2017—and Ethereum—available since February 2021—reveal responses to inflation expectations that are consistently positive across both measures in a full sample encompassing 2022. These results hold for a sample that precedes the Luna crash in May 13, 2022. However, the response turns negative in the period between the failures of the Luna and FTX crypto exchanges. We find cryptocurrency futures provide an effective hedge against inflation expectations and may provide a hedge against idiosyncratic market risk if the ensuing uncertainty is embraced by traders leading them to search-for-yield behavior. Risk that is more systemic—and not properly digested by financial markets—may lead futures contract holders to exit their positions ahead of expiration, leading to a bid down of futures prices and an erosion of their hedging ability. This may have contributed to the turbulence in cryptocurrencies experienced during the latter part of 2022.