Reviewerly/exHarmony huggingface dataset:
From Low-Level Fault Modeling (of a Pipeline Attack) to a Proven Hardening Scheme

Authors:
raw_author_name: Laure Gonnord
raw_affiliation_string: Université Grenoble Alpes, Valence, France

Abstract: Fault attacks present unique safety and security challenges that require dedicated countermeasures, even for bug-free programs. Models of these complex attacks are made workable by approximating their effects to a suitable level of abstraction. The common practice of targeting the Instruction Set Architecture (ISA) level isn't ideal because it discards important micro-architectural information, leading to weaker security guarantees. Conversely, including micro-architectural details makes countermeasures harder to model and reason about, creating a new challenge in validating and trusting protections.
Microexpression Recognition Model based on Non Negative Matrix Decomposition in Intelligent Classroom

Authors:
raw_author_name: Jianyuan Li
raw_affiliation_string: School of Management Engineering, Tangshan Polytechnic College, Tangshan, 063299, China

Abstract: At present, intelligent systems with the function of automatic micro expression recognition are gradually applied in intelligent classrooms, but there is still a problem of low recognition rate. Therefore, based on the dual graph regularization, the research constructs a joint non negative matrix decomposition algorithm model for micro expression recognition, and verifies its effectiveness. The experimental results showed that the research algorithm had the highest performance in both micro and macro expression (micro) databases, at 75.4%. In algorithm comparison, the recognition rates of algorithm L and algorithm M in different databases were higher than those of group A and B algorithms. Among them, Algorithm 6 in Group A had the highest recognition rate in all three databases, with the highest being 57.4%; Algorithm 12 in Group B had the highest recognition rate in Zhongke Microexpression 2 and micro and macro expression databases, with 61.1% and 59.4%; Algorithm 11 had the highest recognition rate in self generated micro expression databases, with 54.0%. And algorithm L and algorithm M had a minimum of 58.5% and a maximum of 75.4%. In parameter sensitivity analysis, the recognition rates of parameters η, χ, and γ in all databases showed good recognition results within a certain range of values, but they would decrease when they exceeded a specific value. Overall, the algorithm model proposed in the study has high effectiveness in improving the recognition rate of micro expressions, which is significant for the micro expression recognition of students in practical intelligent classrooms.
Security analysis of agricultural energy internet considering electricity load control for dragon fruit cultivation

Authors:
raw_author_name: Xueqian Fu
raw_affiliation_string: National Innovation Center for Digital Fishery, China Agricultural University, Beijing, China
raw_author_name: Jiahui Zhang
raw_affiliation_string: Shanxi Energy Internet Research Institute, Taiyuan, China

Abstract: With the increasing emphasis on sustainable energy and the advancements in modern agriculture, flexible agricultural power loads present challenges to the reliable operation of the agricultural energy internet. However, research on the coupling of energy system security with agricultural security is insufficient and fails to consider the impacts of meteorological elements on agricultural power loads. To address these gaps, this paper establishes load models for irrigation and light supplementation based on the actual cultivation demands of winter dragon fruit in Guangxi province. A static security index system is developed to analyze the security, considering the unique features of agricultural power demands. The condition of the distribution network is assessed by comparing the indexes with predefined limits, using a China 41-bus distribution network. Finally, the optimal scheme for nocturnal supplemental lighting treatment and irrigation is determined based on the method for maintaining secure operation of the distribution network. This study serves as a guide for simulating current farming power loads and demonstrates how security analysis of the agricultural energy internet contributes to the large-scale and sophisticated development of modern agriculture.
Evaluating the Security of CAPTCHAs utilized on Bangladeshi Websites

Authors:
raw_author_name: Hasibur Rahman
raw_affiliation_string: Shahjalal University of Science and Technology, Sylhet, Bangladesh
raw_author_name: Md Sadek Ferdous
raw_affiliation_string: BRAC University, Dhaka, Bangladesh
raw_author_name: Farida Chowdhury
raw_affiliation_string: BRAC University, Dhaka, Bangladesh

Abstract: A number of attacks such as spamming, email-address harvesting, dictionary attacks, brute-force attacks, credential surfing attacks, DOS attacks and others can be executed relatively easily if the target system cannot differentiate between requests coming from a real human user and those generated artificially by bots. In order to distinguish between human users and bots, the Completely Automatic Public Turing Test (CAPTCHA) has been used for almost 20 years. Nevertheless, CAPTCHA can be easily broken if it lacks security features. Consequently, the motivation for utilizing CAPTCHA is undermined. This article proposes a framework that can evaluate the security aspects of text CAPTCHAs. The effectiveness of the framework is validated by using a generalized attack method consisting of pre-processing and utilizing an Optical Character Recognition (OCR) library such as Tesseract OCR. We have carried out a systematic study of existing CAPTCHAs utilized on Bangladeshi websites. We have studied 238 Bangladeshi websites and found that only 44 websites used a CAPTCHA mechanism. Then, we have assessed the security levels of the selected text CAPTCHAs by the proposed framework. As per our analysis, we have found that there are 29 different CAPTCHA schemes being adopted on Bangladeshi websites and among them 23 are vulnerable to automated attacks. In order to address these issues, we also present a number of recommendations.
MECOS: A Bilingual Manipuri-English Spontaneous Code-Switching Speech Corpus for Automatic Speech Recognition

Authors:
raw_author_name: Naorem Karline Singh
raw_affiliation_string: Department of Computer Science, NIT Manipur, Imphal, India
raw_author_name: Yambem Jina Chanu
raw_affiliation_string: Department of Computer Science, NIT Manipur, Imphal, India
raw_author_name: Hoomexsun Pangsatabam
raw_affiliation_string: Department of Computer Science, NIT Manipur, Imphal, India

Abstract: In this study, we introduce a new code-switched speech database with 57 hours of Manipuri-English annotated spontaneous speech. Manipuri is an official language of India and is primarily spoken in the north-eastern Indian state of Manipur. Most Manipur native speakers today are bilingual and frequently use code switching in everyday discussions. By carefully assessing the amount of code-switched speech in each video, recordings from YouTube are gathered. 21339 utterances and 291731 instances of code switching are present in the database. Given the code-switching nature of the data, a proper annotation procedure is used, and the data are manually annotated using the Meitei Mayek unicode font and the roman alphabets for Manipuri and English, respectively. The transcription includes the names of the speakers, non-speech information, and the corresponding annotation. The aim of this research is to construct an automatic speech recognition (ASR) system as well as offer a thorough analysis and details of the speech corpus. We believe that our research is the first to use an ASR system for Manipuri-English code-switched speech. To evaluate the performance, ASR systems based on hybrid deep neural network and hidden markov model (DNN-HMM), time delay neural network (TDNN), hybrid time delay neural network and long short-term memory (TDNN-LSTM) and three end-to-end (E2E) models i.e. hybrid connectionist temporal classification and attention model (CTC-Attention), Conformer, wav2vec XLSR are developed for Manipuri-English language. In comparison to other models, pure TDNN produces outcomes that are clearly superior.
Adversarial patch-based false positive creation attacks against aerial imagery object detectors

Authors:
raw_author_name: Guijian Tang
raw_affiliation_string: College of Aerospace Science and Engineering, National University of Defense Technology, No. 109, Deya Road, Changsha 410073, China; Defense Innovation Institute, Chinese Academy of Military Sciences, No. 53, Fengtai East Street, Beijing 100071, China
raw_author_name: Wen Yao
raw_affiliation_string: Defense Innovation Institute, Chinese Academy of Military Sciences, No. 53, Fengtai East Street, Beijing 100071, China
raw_author_name: Tingsong Jiang
raw_affiliation_string: Defense Innovation Institute, Chinese Academy of Military Sciences, No. 53, Fengtai East Street, Beijing 100071, China
raw_author_name: Yong Zhao
raw_affiliation_string: College of Aerospace Science and Engineering, National University of Defense Technology, No. 109, Deya Road, Changsha 410073, China
raw_author_name: Jialiang Sun
raw_affiliation_string: Defense Innovation Institute, Chinese Academy of Military Sciences, No. 53, Fengtai East Street, Beijing 100071, China

Abstract: Although adversarial attacks have revealed weaknesses in Deep Neural Networks (DNNs)-based aerial detectors, they present a new paradigm for concealing vulnerable assets from autonomous detection systems onboard satellites. Among them, adversarial patches were widely applied due to their physical realizability. Nonetheless, most existing adversarial patch-based attack methods are developed to hide objects from detectors to achieve a vanishing attack. This paper proposes a novel Adversarial Patch False Positive Creation Attack (APFP-CA) framework that enables aerial detectors to recognize non-existent objects, thereby realizing a creation attack. Concretely, the APFP-CA models the creation attack as a two-stage optimization problem. The first stage uses a well-designed efficient loss function to increase the objectness score of patches placed anywhere in the scene to achieve untargeted attacks. The second stage involves elaborating two category-dependent loss functions for fulfilling targeted attacks. Experiments conducted on diverse datasets and detectors demonstrate the effectiveness and universality of our method. Transfer attacks across different datasets and models validate the generalizability of the optimized adversarial patches. Finally, we perform several proportionally scaled experiments physically to illustrate that the optimized adversarial patches can successfully deceive aerial detectors in the physical world. The proposed approach offers a novel contribution to adversarial attacks against aerial imagery objectors and holds potential for practical applications in high-security systems.
Open Knowledge Graph Link Prediction with Semantic-Aware Embedding

Authors:
raw_author_name: Jingbin Wang
raw_affiliation_string: College of Computer and Data Science, Fuzhou University, Fuzhou, 350108, China
raw_author_name: Hao Huang
raw_affiliation_string: College of Computer and Data Science, Fuzhou University, Fuzhou, 350108, China
raw_author_name: Yuwei Wu
raw_affiliation_string: College of Computer and Data Science, Fuzhou University, Fuzhou, 350108, China
raw_author_name: Fuyuan Zhang
raw_affiliation_string: College of Computer and Data Science, Fuzhou University, Fuzhou, 350108, China
raw_author_name: Sirui Zhang
raw_affiliation_string: College of Computer and Data Science, Fuzhou University, Fuzhou, 350108, China
raw_author_name: Kun Guo
raw_affiliation_string: College of Computer and Data Science, Fuzhou University, Fuzhou, 350108, China

Abstract: Link prediction in open knowledge graphs (OpenKGs) is crucial for applications like question answering and recommendation systems. Existing OpenKG models leverage the semantic information of noun phrases (NPs) to enhance the performance in the link prediction task. However, these models only extract superficial semantic information from NPs, ignoring the fact that an NP possesses diverse semantics. Furthermore, these models have not fully exploited the semantic information of the relation phrases (RPs). To address these issues, we propose a model for link prediction called Open Knowledge Graph Link Prediction with Semantic-Aware Embedding (SeAE). First, we develop an adaptive disentanglement embedding (ADE) mechanism to learn the intrinsically abundant semantics of NPs. The ADE mechanism can adaptively calculate the embedding segmentation number according to the dataset and has an ingenious method for updating embeddings. Second, we integrate the attention mechanism into the GRU encoder to obtain the distribution of importance inside RP, facilitating a more comprehensive capture of the RP’s semantic information and enhancing the model’s interpretability. Finally, we design a relation gate, which extracts the RP semantic features of tail NP from the shared edge. This gate realizes the relation constraints on entities while enhancing the interaction between entities and relations. Extensive experiments on four benchmarks demonstrate that SeAE outperforms the state-of-the-art models, resulting in improvements of approximately 5.4% and 7.4% in MRR on ReVerb45K and ReVerb45KF datasets respectively.
FCPFNet: Feature Complementation Network with Pyramid Fusion for Semantic Segmentation

Authors:
raw_author_name: Jingsheng Lei
raw_author_name: Chente Shu
raw_author_name: Qiang Xu
raw_author_name: Yunxiang Yu
raw_author_name: Shengying Yang

Abstract: Abstract Traditional pyramid pooling modules have shown effective improvements in semantic segmentation tasks by capturing multi-scale feature information. However, their limitations arise from the shallow structure, which fails to fully extract contextual information, and the fused multi-scale feature information lacks distinctiveness, resulting in issues with the final segmentation discriminability. To address these issues, we proposes an effective solution called FCPFNet, which is based on global contextual prior for deep feature extraction of detailed information. Specifically, we introduce a novel deep feature aggregation module to extract semantic information from the output feature map of each layer through a deep aggregation of context information module, and expands the effective perception range. Additionally, we propose an Efficient Pyramid Pooling Module (EPPM) to capture distinctive features through communicating information between different sub-features and performs multi-scale fusion, which is integrated as a branch within the network to complement the information loss resulting from downsampling operations. Furthermore, in order to ensure the richness of image detail feature information and maintain a large receptive field to obtain more contextual information, EPPM concatenates the input feature map and the output feature map of the pyramid pooling module to acquire more comprehensive global contextual information. It has been demonstrated by experiment that the method described in this article achieves competitive performance on the challenging scene segmentation datasets Pascal VOC 2012, Cityscapes and Coco-Stuff, with MIOU of 81.0%, 78.8% and 40.1%, respectively.
POSTER: ParGNN: Efficient Training for Large-Scale Graph Neural Network on GPU Clusters

Authors:
raw_author_name: Shunde Li
raw_affiliation_string: Computer Network Information Center, Chinese Academy of Sciences, Beijing, China University of Chinese Academy of Sciences, Beijing, China
raw_author_name: Junyu Gu
raw_affiliation_string: Computer Network Information Center, Chinese Academy of Sciences, Beijing, China University of Chinese Academy of Sciences, Beijing, China
raw_author_name: Jue Wang
raw_affiliation_string: Computer Network Information Center, Chinese Academy of Sciences, Beijing, China University of Chinese Academy of Sciences, Beijing, China
raw_author_name: Tiechui Yao
raw_affiliation_string: State Grid Smart Grid Research Institute Co., LTD, Beijing, China Computer Network Information Center, Chinese Academy of Sciences, Beijing, China
raw_author_name: Zhiqiang Liang
raw_affiliation_string: Computer Network Information Center, Chinese Academy of Sciences, Beijing, China University of Chinese Academy of Sciences, Beijing, China
raw_author_name: Yumeng Shi
raw_affiliation_string: Computer Network Information Center, Chinese Academy of Sciences, Beijing, China University of Chinese Academy of Sciences, Beijing, China
raw_author_name: Shigang Li
raw_affiliation_string: School of Computer Science, Beijing University of Posts and Telecommunications, Beijing, China
raw_author_name: Shushen Li
raw_affiliation_string: North China Electric Power University, Beijing, China
raw_author_name: Yangang Wang
raw_affiliation_string: Computer Network Information Center, Chinese Academy of Sciences, Beijing, China University of Chinese Academy of Sciences, Beijing, China

Abstract: Full-batch graph neural network (GNN) training is essential for interdisciplinary applications. Large-scale graph data is usually divided into subgraphs and distributed across multiple compute units to train GNN. The state-of-the-art load balancing method based on direct graph partition is too rough to effectively achieve true load balancing on GPU clusters. We propose ParGNN, which employs a profiler-guided load balance workflow in conjunction with graph repartition to alleviate load imbalance and minimize communication traffic. Experiments have verified that ParGNN has the capability to scale to larger clusters.
POSTER: FineCo: Fine-grained Heterogeneous Resource Management for Concurrent DNN Inferences

Authors:
raw_author_name: En Shao
raw_affiliation_string: Nanjing Institute of InforSuperBahn, Nanjing, China; State Key Lab of Processors, Institute of Computing Technology, CAS, Beijing, China University of Chinese Academy of Sciences, Beijing, China Nanjing Institute of InforSuperBahn, Nanjing, China; University of Chinese Academy of Sciences, Beijing, China
raw_author_name: Quan Chen
raw_affiliation_string: Shanghai Jiao Tong University, Shanghai, China
raw_author_name: Guangming Tan
raw_affiliation_string: State Key Lab of Processors, Institute of Computing Technology, CAS, Beijing, China University of Chinese Academy of Sciences, Beijing, China

Abstract: Co-locating multiple DNN servings to share GPU resource is widely used to improve resource utilization while guaranteeing user QoS. Existing GPU sharing mechanism is restricted to model level, and fluctuations in kernel-level resource demands highlight a suboptimal utilization of the current sharing mechanism. We design a multi-DNN serving system, FineCo, that leverages a novel fine-grained resource sharing mechanism to optimize concurrent inference without modifications to the hardware or operating system. Our prototype implementation demonstrates that FineCo achieves up to 40% throughput improvement over the state-of-the-art work.
Low-Dose CT Image Super-resolution Network with Noise Inhibition Based on Feedback Feature Distillation Mechanism

Authors:
raw_author_name: Jianning Chi
raw_affiliation_string: Faculty of Robot Science and Engineering, Northeastern University, Zhihui Street, Shenyang, 110169, Liaoning, China
raw_author_name: Xiaolin Wei
raw_affiliation_string: Department of Rehabilitation, the Second Hospital of Beijing, No. 36 Youfang Hutong, 100031, Beijing, China
raw_author_name: Zhiyi Sun
raw_affiliation_string: Faculty of Robot Science and Engineering, Northeastern University, Zhihui Street, Shenyang, 110169, Liaoning, China
raw_author_name: Yongming Yang
raw_affiliation_string: Faculty of Robot Science and Engineering, Northeastern University, Zhihui Street, Shenyang, 110169, Liaoning, China
raw_author_name: Bin Yang
raw_affiliation_string: Department of Radiology, the Second Hospital of Beijing, No. 36 Youfang Hutong, 100031, Beijing, China

Abstract: Low-dose computed tomography (LDCT) has been widely used in medical diagnosis. In practice, doctors often zoom in on LDCT slices for clearer lesions and issues, while, a simple zooming operation fails to suppress low-dose artifacts, leading to distorted details. Therefore, numerous LDCT super-resolution (SR) methods have been proposed to promote the quality of zooming without the increase of the dose in CT scanning. However, there are still some drawbacks that need to be addressed in existing methods. First, the region of interest (ROI) is not emphasized due to the lack of guidance in the reconstruction process. Second, the convolutional blocks extracting fix-resolution features fail to concentrate on the essential multi-scale features. Third, a single SR head cannot suppress the residual artifacts. To address these issues, we propose an LDCT CT joint SR and denoising reconstruction network. Our proposed network consists of global dual-guidance attention fusion modules (GDAFMs) and multi-scale anastomosis blocks (MABs). The GDAFM directs the network to focus on ROI by fusing the extra mask guidance and average CT image guidance, while the MAB introduces hierarchical features through anastomosis connections to leverage multi-scale features and promote the feature representation ability. To suppress radial residual artifacts, we optimize our network using the feedback feature distillation mechanism (FFDM) which shares the backbone to learn features corresponding to the denoising task. We apply the proposed method to the 3D-IRCADB and PANCREAS datasets to evaluate its ability on LDCT image SR reconstruction. The experimental results compared with state-of-the-art methods illustrate the superiority of our approach with respect to peak signal-to-noise (PSNR), structural similarity (SSIM), and qualitative observations. Our proposed LDCT joint SR and denoising reconstruction network has been extensively evaluated through ablation, quantitative, and qualitative experiments. The results demonstrate that our method can recover noise-free and detail-sharp images, resulting in better reconstruction results. Code is available at https://github.com/neu-szy/ldct_sr_dn_w_ffdm .
Stronger Heterogeneous Feature Learning for Visible-Infrared Person Re-Identification

Authors:
raw_author_name: Hao Wang
raw_author_name: Xiaojun Bi
raw_author_name: Changdong Yu

Abstract: Abstract Visible-Infrared person re-identification (VI-ReID) is of great importance in the field of intelligent surveillance. It enables re-identification of pedestrians between daytime and dark scenarios, which can help police find escaped criminals at night. Currently, existing methods suffer from inadequate utilisation of cross-modality information, missing modality-specific discriminative information and weaknesses in perceiving differences between different modalities. To solve the above problems, we innovatively propose a stronger heterogeneous feature learning (SHFL) method for VI-ReID. First, we innovatively propose a Cross-Modality Group-wise constraint to solve the problem of inadequate utilization of cross-modality information. Secondly, we innovatively propose a Second-Order Homogeneous Invariant Regularizer to address the problem that missing modality-specific discriminative information. Finally, we innovatively propose a Modality-Aware Batch Normalization to address the problem of weaknesses in perceiving differences between different modalities. Extensive experimental results on two generic VI-ReID datasets demonstrate that the proposed final method outperforms the state-of-the-art algorithms.