Reviewerly/exHarmony huggingface dataset:
Academic community’s use of university digital library and their searching behaviour

Purpose This study aims to explore the evidence-based usage patterns of higher education commission (HEC) subscribed e-journal databases in the university digital library used by the scholarly community and the academics’ online searching behaviour at a higher education institution in Pakistan. Design/methodology/approach The study used an explanatory sequential mixed methods approach. Raw transaction log data were collected for quantitative analysis, and the interview technique was used for qualitative data collection and thematic analysis. Findings Log analysis revealed that HEC subscribed databases were used significantly, and among those, scholarly databases covering various subjects were more frequently used than subject-specific society-based databases. Furthermore, the users frequently accessed the needed e-journal articles through search engines like Google and Google Scholar, considering them sources of free material instead of the HEC subscribed databases. Practical implications It provides practical implications for examining the evidence-based use patterns of e-journal databases. It suggests the need for improving the access management of HEC databases, keeping in view the usage statistics and the demands of the scholars. The study may also help create market venues for the publishers of scholarly databases by offering attractive and economical packages for researchers of various disciplines in developing and underdeveloped countries. The study results also guide the information professionals to arrange orientation and information literacy programs to improve the searching behaviour of their less frequent users and enhance the utilization of these subscribed databases. Originality/value The study is part of a PhD project and, to the best of the authors’ knowledge, is the first such work in the context of a developing country like Pakistan.
Distributed maximum correntropy unscented Kalman filter under hybrid attacks in non‐Gaussian environment

Abstract This paper addresses a distributed nonlinear filtering issue based on maximum correntropy for dealing with randomly occurring hybrid cyber‐attacks in non‐Gaussian environment. The types of cyber‐attacks include denial of service attacks and deception attacks. First, a modified distributed unscented Kalman filter is proposed, using Cauchy kernel‐based maximum correntropy criterion instead of the traditional mean square error criterion, against cyber‐attacks and non‐Gaussian noise. Then based on fixed‐point iterative rules and information fusion strategy, the update equations of state estimates and covariance matrices of the proposed filter are obtained. Furthermore, the sufficient condition guaranteeing its convergence and computational complexity are also given. Finally, an illustrative example is presented to verify the effectiveness of the proposed filter under hybrid cyber‐attacks in non‐Gaussian environment.
A scoping review of gaze and eye tracking-based control methods for assistive robotic arms

Background: Assistive Robotic Arms are designed to assist physically disabled people with daily activities. Existing joysticks and head controls are not applicable for severely disabled people such as people with Locked-in Syndrome. Therefore, eye tracking control is part of ongoing research. The related literature spans many disciplines, creating a heterogeneous field that makes it difficult to gain an overview. Objectives: This work focuses on ARAs that are controlled by gaze and eye movements. By answering the research questions, this paper provides details on the design of the systems, a comparison of input modalities, methods for measuring the performance of these controls, and an outlook on research areas that gained interest in recent years. Methods: This review was conducted as outlined in the PRISMA 2020 Statement. After identifying a wide range of approaches in use the authors decided to use the PRISMA-ScR extension for a scoping review to present the results. The identification process was carried out by screening three databases. After the screening process, a snowball search was conducted. Results: 39 articles and 6 reviews were included in this article. Characteristics related to the system and study design were extracted and presented divided into three groups based on the use of eye tracking. Conclusion: This paper aims to provide an overview for researchers new to the field by offering insight into eye tracking based robot controllers. We have identified open questions that need to be answered in order to provide people with severe motor function loss with systems that are highly useable and accessible.
Multi-Identity Recognition of Darknet Vendors Based on Metric Learning

Dark web vendor identification can be seen as an authorship aliasing problem, aiming to determine whether different accounts on different markets belong to the same real-world vendor, in order to locate cybercriminals involved in dark web market transactions. Existing open-source datasets for dark web marketplaces are outdated and cannot simulate real-world situations, while data labeling methods are difficult and suffer from issues such as inaccurate labeling and limited cross-market research. The problem of identifying vendors’ multiple identities on the dark web involves a large number of categories and a limited number of samples, making it difficult to use traditional multiclass classification models. To address these issues, this paper proposes a metric learning-based method for dark web vendor identification, collecting product data from 21 currently active English dark web marketplaces and using a multi-dimensional feature extraction method based on product titles, descriptions, and images. Using pseudo-labeling technology combined with manual labeling improves data labeling accuracy compared to previous labeling methods. The proposed method uses a Siamese neural network with metric learning to learn the similarity between vendors and achieve the recognition of vendors’ multiple identities. This method achieved better performance with an average F1-score of 0.889 and an accuracy rate of 97.535% on the constructed dataset. The contributions of this paper lie in the proposed method for collecting and labeling data for dark web marketplaces and overcoming the limitations of traditional multiclass classifiers to achieve effective recognition of vendors’ multiple identities.
A customized ConvNeXt‐XL network with fusion of deep and handcrafted features for colposcopy image classification

Abstract Cervical cancer is the second most frequent cancer among women of all age groups worldwide. It occurs due to human papillomavirus. In the premature stages, the symptoms will not be predominant until they reach the final stage of cancer. Detection and classification of cervical cancer always demand gynecologists with the necessary skills and experience. The goal of the proposed work is to develop a deep learning framework to facilitate the automated classification of cervical cancer using colposcopy images. The following Deep Convolutional Neural Network (DCNN) models are proposed to detect cervical cancer and classify cervix‐type images. (i) the pre‐trained DCNNs, namely VGG16, ResNet50, InceptionV3, InceptionResNetV2, and ConvNeXtXLarge (ConvNeXt‐XL) using Softmax classifier based on deep features; (ii) the ConvNeXt‐XL model with classification using Support Vector Machine (SVM), K Nearest Neighbor (KNN) and Decision Tree (DT) based on deep features; (iii) a customized ConvNeXt‐XL network to enhance the classification accuracy using serially concatenated handcrafted and deep features. The research experiment was carried out separately using two datasets: the Cervix‐Type dataset (Type 1, Type 2, and Type 3) and the Real Time Cervical dataset (Normal and Abnormal). The simulation outcome confirms that the customized ConvNeXt‐XL helped to improve the classification accuracy with the Cervix‐Type dataset (&gt;97%) and Real Time Cervical dataset (&gt;98%).
Paguroidea: Fused Parser Generator with Transparent Semantic Actions

Parser generators have long been a savior for programmers, liberating them from the daunting task of crafting correct and maintainable parsers. Yet, this much-needed simplicity often comes at the expense of efficiency.
From Low-Level Fault Modeling (of a Pipeline Attack) to a Proven Hardening Scheme

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

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.
Realities Otherwise

Chapter 2 suggests that the experience of being in LA unlocks some of VR’s more ineffable qualities. The architectural façades of the movie set and theme park extend throughout the city, offering a glimpse of other places and other times while moving through the urban landscape. Building on theorists who analyze LA as a postmodern city and ground zero for hyperreality, this chapter proposes the unreal as a way for thinking about contemporary shifts in reality that the city makes legible and VR exemplifies. That one seems to be able to feel reality becoming something else in LA hones an intuition for the kinds of worlds and realities that VR similarly invites into being.
Security analysis of agricultural energy internet considering electricity load control for dragon fruit cultivation

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

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

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

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

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

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

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.
INFINEL: An efficient GPU-based processing method for unpredictable large output graph queries

With the introduction of GPUs, which are specialized for iterative parallel computations, the execution of computation-intensive graph queries using a GPU has seen significant performance improvements. However, due to the memory constraints of GPUs, there has been limited research on handling large-scale output graph queries with unpredictable output sizes on a GPU. Traditionally, two-phase methods have been used, where the query is re-executed after splitting it into sub-tasks while only considering the size of the output in a static manner. However, two-phase methods become highly inefficient when used with graph data with extreme skew, failing to maximize the GPU performance. This paper proposes INFINEL, which handles unpredictable large output graph queries in a one-phase method through chunk allocation per thread and kernel stop/restart methods. We also propose applicable optimization techniques due to the corresponding unique characteristics of operating with low time/space overhead and not heavily relying on the GPU output buffer size. Through extensive experiments, we demonstrate that our one-phase method of INFINEL improves the performance by up to 31.5 times over the conventional two-phase methods for triangle listing ULO query.
POSTER: FineCo: Fine-grained Heterogeneous Resource Management for Concurrent DNN Inferences

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.