Reviewerly/exHarmony huggingface dataset:
Face Detection under Non-uniform Low Light Based on Improved MTCNN

In order to improve the accuracy of face detection under non-uniform low-light conditions, an image preprocessing method based on illumination compensation and adaptive weight settings are proposed to apply to MTCNN&#x0027;s face detection algorithm. The algorithm uses light compensation theory to preprocess the face image, and then adds an adaptive module to the MTCNN model, which can significantly improve the accuracy and detection rate of MTCNN&#x0027;s face detection under non-uniform low-light conditions, making the accuracy rate reach 93.37 &#x0025;. Experiments show that under non-uniform low light conditions, this method has better accuracy and robustness than the original MTCNN method, which is beneficial to the later face recognition task.
A Unified Temporal Erasable Itemset Mining Approach

Erasable-itemset mining is often utilized by factories in production planning to find combinations of materials which could cause an acceptable loss if all items in the combination are not available. However, product databases can change over time: new materials or products may be introduced and out-of-date materials or products eliminated. Traditional erasable-itemset mining algorithms do not account for this. Thus, when mining erasable itemsets, we take such additional time information into account. Various temporal constraints (itemset lifespan definitions) are also discussed in this paper. We propose a general temporal erasable itemset mining approach, which, can successfully mine the desired results under different constraints. The experimental performance about the execution time and memory consumption of the proposed method is also shown.
Adversarial Learning Based on Global and Local Features for Cross-Modal Person Re-identification

In recent years, a great improvement has been achieved in cross-modal person re-identification (Re-ID) methods based on feature partition. However, many works do not use global and local features jointly to improve the accuracy of person identification. It is an important research topic to fully extract and use global features as well as local features, and effectively reduce modality differences. In this paper, we propose an adversarial learning based on global and local features (ALGL) method. We adopt a two-stream network with partially shared parameters as a feature extraction network to extract visible and infrared feature maps. Local features are obtained through Part-based Convolutional Baseline (PCB) operations on feature maps with the local feature learning module. In the global feature learning module, the average pooling is used to obtain the global features. In order to fully explore the discriminative abilities of local features and global features, hetero-center based triplet loss is designed, which brings features of the same category closer, and features of different categories farther away. At the same time, the adversarial learning module minimizes the modality difference between visible and infrared modalities. Experimental results on the SYSU-MM01 and RegDB datasets show that ALGL outperforms the state-of-the-art solutions.
Autoencoder Combined with CBAM Improves Denoising of MR Images

Magnetic resonance imaging (MRI) is a useful technique for clinical diagnostics. However, MRI requires relatively long data acquisition, and the image quality can be deteriorated by physiological and thermal noises. Denoising is an important post-processing step for MRI, particularly when quantitative measurements are involved. Traditional denoising algorithms are mostly based on various spatial filtering. In recent years, CNN based algorithms have been explored for denoising of MRI magnitude images. In this study, we take one step forward by combining RDN with CBAM to construct a more robust framework for denoising of MR images. We trained the model with simulated MRI data with known “ground truth”. To illustrate the effectiveness of the model, we compared the performance of the model with other two state-of-the-art baseline frameworks.
TransDE: A Transformer and Double Encoder Network for Medical Image Segmentation

Over the past decade, medical image segmentation has become a necessary prerequisite for disease diagnosis and treatment planning. The deep convolutional neural networks (CNN) have been widely adopted in medical image segmentation which achieves promising performance. However, due to the intrinsic locality of convolution operations, CNN demonstrates limitations in explicitly modeling long-range dependency. Recently proposed hybrid CNN-Transformer architectures that combine the global perception capability of local feature and the local details of global reppresentations. However, the serial structure of CNN and transformer will increase the computational complexity, and then the redundant information generated by convolution operation may leads to the failure of long-range modeling. To this end, this paper proposes a double encoder framework including global encoder and local encoder, TransDE for short, to medical image segmentation. The global encoder takes transformer that designed for sequence-to-sequence prediction, while the local encoder adopts VGG-19 combined with the atrous spatial pyramid pooling (ASPP) to bring about local feature extraction. The experimental results of enteroscopy dataset and dermoscopy dataset show the superiority of our TransDE achieving around 1.97% improvement on CVC-ClinicDB in terms of DSC and 1.6% improvement on Lesion Boundary Segmentation challenge.
IEEE ICCI*CC Series in Year 20: Latest Advances in Cognitive Informatics and Cognitive Computing towards General AI (Plenary Panel Report-I)

Cognitive Informatics (CI) and Cognitive Computing (CC) are fundamental intelligence theories and general AI technologies triggered by the transdisciplinary advances in intelligence, computer, brain, knowledge, cognitive, robotic, and cybernetic sciences for engineering implementations. This paper presents a summary of the plenary panel (Part I) on the theoretical foundations of CI/CC as well recent breakthroughs in AI engineering reported in the 20th IEEE International ICCI*CC Conference (ICCI*CC'21). The latest advances in CI and CC towards general AI are presented by twenty-two distinguished panelists. Strategic AI engineering applications in CI, CC, and cognitive systems are elaborated for abstract intelligence, general AI, cognitive robots, autonomous systems, intelligent vehicles, and safety-and-mission-critical systems.
Defending Against Adversarial Attacks in Speaker Verification Systems

In smart home, voice control becomes a main interface between users and smart devices. To make voice control more secure, speaker verification systems have been researched to apply human voice as biometrics to accurately identify a legitimate user and avoid illegal access. In recent studies, however, it has been shown that speaker verification systems are particularly vulnerable to adversarial attacks. In this work, we attempt to design and implement a defense system that is simple, light-weight, and effective against adversarial attacks for speaker verification. Specifically, we study two opposite operations to preprocess input audios in speaker verification systems against adversarial attacks: denoising that attempts to remove or reduce perturbations and noise-adding that adds small Gaussian noises to an input audio. We show through experiments that both methods can significantly degrade the performance of a state-of-the-art adversarial attack. Specifically, it is shown that denoising and noise-adding can reduce the targeted attack success rate of the attack from 100% to only 56% and 5.2%, respectively. Moreover, noise-adding can slow down the attack 25 times in speed and only has a minor effect on the normal operations of a speaker verification system.
Deep Learning-Based Cell Detection and Extraction in Thin Blood Smears for Malaria Diagnosis

Malaria is a major health threat caused by Plasmodium parasites that infect the red blood cells. Two predominant types of Plasmodium parasites are Plasmodium vivax (P. vivax) and Plasmodium falciparum (P. falciparum). Diagnosis of malaria typically involves visual microscopy examination of blood smears for malaria parasites. This is a tedious, error-prone visual inspection task requiring microscopy expertise which is often lacking in resource-poor settings. To address these problems, attempts have been made in recent years to automate malaria diagnosis using machine learning approaches. Several challenges need to be met for a machine learning approach to be successful in malaria diagnosis. Microscopy images acquired at different sites often vary in color, contrast, and consistency caused by different smear preparation and staining methods. Moreover, touching and overlapping cells complicate the red blood cell detection process, which can lead to inaccurate blood cell counts and thus incorrect parasitemia calculations. In this work, we propose a red blood cell detection and extraction framework to enable processing and analysis of single cells for follow-up processes like counting infected cells or identifying parasite species in thin blood smears. This framework consists of two modules: a cell detection module and a cell extraction module. The cell detection module trains a modified Channel-wise Feature Pyramid Network for Medicine (CFPNet-M) deep learning network that takes the green channel of the image and the color-deconvolution processed image as inputs, and learns a truncated distance transform image of cell annotations. CFPNet-M is chosen due to its low resource requirements, while the distance transform allows achieving more accurate cell counts for dense cells. Once the cells are detected by the network, the cell extraction module is used to extract single cells from the original image and count the number of cells. Our preliminary results based on 193 patients (including 148 P. Falciparum infected patients, and 45 uninfected patients) show that our framework achieves cell count accuracy of 92.2%.
Extending U-Net Network for Improved Nuclei Instance Segmentation Accuracy in Histopathology Images

Analysis of morphometric features of nuclei plays an important role in understanding disease progression and predict efficacy of treatment. First step towards this goal requires segmentation of individual nuclei within the imaged tissue. Accurate nuclei instance segmentation is one of the most challenging tasks in computational pathology due to broad morphological variances of individual nuclei and dense clustering of nuclei with indistinct boundaries. It is extremely laborious and costly to annotate nuclei instances, requiring experienced pathologists to manually draw the contours, which often results in the lack of annotated data. Inevitably subjective annotation and mislabeling prevent supervised learning approaches to learn from accurate samples and consequently decrease the generalization capacity to robustly segment unseen organ nuclei, leading to over- or under-segmentations as a result. To address these issues, we use a variation of U-Net that uses squeeze and excitation blocks (USE-Net) for robust nuclei segmentation. The squeeze and excitation blocks allow the network to perform feature recalibration by emphasizing informative features and suppressing less useful ones. Furthermore, we extend the proposed network USE-Net not to generate only a segmentation mask, but also to output shape markers to allow better separation of nuclei from each other particularly within dense clusters. The proposed network was trained, tested, and evaluated on 2018 MICCAI Multi-Organ-Nuclei-Segmentation (MoNuSeg) challenge dataset. Promising results were obtained on unseen data despite that the data used for training USE-Net was significantly small. The source code of the USE-Net is available at https://github.com/CIVA-Lab/USE-Net.
A Survey on Translating Embedding based Entity Alignment in Knowledge Graphs

Knowledge Graph (KG) as an ideal knowledge base can effectively support the mining, analysis and reasoning of complex relational data. It has been widely used by academia and industry. Entity alignment (EA) is one of the basic tasks of KG fusion. Its main goal is to align heterogeneous entities that refer to the same but from different sources. In recent years, a lot of researches have focused on this task. This paper presents a systematic survey of the KG EA based translating embeddings. The purpose is to provide a complete and systematic overview of these methods and challenges. Furthermore, we discuss the future research trends and the correlation with MDATA. Our detailed review can offer technical assistance for researchers or engineers who want to quickly have a comprehensive understanding about the KG EA and the trend lines.
MindFI: A Fault Injection Tool for Reliability Assessment of MindSpore Applicacions

With the emergence of big data and remarkable improvement of computational power, deep neural network (DNN) based intelligent systems, with the superb performance on computer vision, nature language processing, and optimization processing, etc, has been acceleratingly replacing traditional software in various aspects. However, due to the uncertainty of DNN modules learned from data, the intelligent systems are more likely to exhibit incorrect behaviors. Faults in software and hardware are also inevitably in practice, where the hidden defects can easily cause model failure. These will lead to severe accidents and losses in safety- and reliability-critical scenarios, such as autonomous driving. Techniques to test the differences between actual and desired behaviors and evaluate the reliability of DNN applications at faulty conditions is therefore significant for building a trustworthy DNN system. A popular method is fault injection and various fault injection tools have been developed for ML frameworks, such as Tensorflow, PyTorch. In this paper, we present a tool, MindFI, which targets to cover a variety of faults in ML programs written in Mindspore. Data, software and hardware faults can be easily injected in general Mindspore programs. We also use MindFI to evaluate the resilience of several commonly used ML programs against a assessment metrics.
A next-generation platform for Cyber Range-as-a-Service

In the last years, Cyber Ranges have become a widespread solution to train professionals for responding to cyber-threats and attacks. Cloud computing plays a key role in this context since it enables the creation of virtual infrastructures on which Cyber Ranges are based. However, the setup and management of Cyber Ranges are expensive and time-consuming activities. In this paper, we highlight the novel features for the next-generation Cyber Range platforms. In particular, these features include the creation of a virtual clone for an actual corporate infrastructure, relieving the security managers from the setup of the training scenarios and sessions, the automatic monitoring of the activities of the participants, and the emulation of their behavior.
Survey on Temporal Knowledge Graph

Knowledge Graph built by people is usually represented as a network with nodes representing entities and edges representing relations between entities. People need to use this form of network architecture to fill in the missing facts in the knowledge graph. Knowledge graph plays an important role in natural language processing. Link prediction task has been studied for a long time as one of the most important tasks in Knowledge Graph Reasoning. Most existing methods focus on static knowledge graph, which cannot get the temporal information in knowledge graph. Now some methods study on temporal knowledge graph, where each fact is associated with a timestamp. The task also becomes more challenging on temporal knowledge graph. In this paper, we introduce some models on static knowledge graph and temporal knowledge graph, and analyze the advantages and disadvantages of each model.
Mining Open-Source Cyber Threat Intelligence with Distant Supervision from the Web

Mining open-source cyber threat intelligence plays an important role for cybersecurity professionals in quickly understanding cyber threats and make preventive measures timely. Much work has been proposed, however, they utilize fixed-point monitoring strategy, and have the limitations in coverage, cost and accuracy. To overcome these limitations, we propose a novel approach of mining open-source cyber threat intelligence with distant supervision from the Web. The approach can cover as many open-source data feeds as possible with the help of search engines. We analyze the retrieval results from search engines to determine whether the search targets belong to cyber threat intelligence or not. Specifically, we reduce it to a multiple instance binary classification problem, and use multiple instance neural networks to achieve more accurate classification. Experimental results on a real-world dataset show the significant effectiveness of our work.
A Novel Malware Encrypted Traffic Detection Framework Based On Ensemble Learning

The number of malware attacks that use encrypted HTTP traffic to self-propagate or communicate has increased dramatically in recent years. Traditional network traffic detection methods for non-encrypted traffic are difficult to be applied to encrypted traffic detection. While encryption is good for protecting users&#x0027; privacy, it also comes with a security risk: malicious traffic may hide in encrypted traffic, leading to a series of security problems. Since the encrypted payload cannot be directly observed and it is large, we need to combine domain knowledge and machine learning method to excavate the features hidden in malicious traffic, so as to realize automatic detection of malicious traffic. In this paper, we propose a malicious traffic detection framework based on ensemble learning using the encrypted traffic from real malware, including normal traffic generated by 3000 normal hosts and malicious traffic generated by 3000 malware-infected hosts, provided by DATACON2020 competition. Considering the complexity to decrypt traffic, we use statistical features and sequence features in both flow-level and in host-level perspectives to describe encrypted traffic. Then we build multiple classifiers according to heterogeneous features. Finally we do majority voting to get final result. We achieve 95.0&#x0025; TPR and 8.4&#x0025; FPR.
On the synthesis of visual illusions using deep generative models

Visual illusions expand our understanding of the visual system by imposing constraints in the models in two different ways: i) visual illusions for humans should induce equivalent illusions in the model, and ii) illusions synthesized from the model should be compelling for human viewers too. These constraints are alternative strategies to find good vision models. Following the first research strategy, recent studies have shown that artificial neural network architectures also have human-like illusory percepts when stimulated with classical hand-crafted stimuli designed to fool humans. In this work we focus on the second (less explored) strategy: we propose a framework to synthesize new visual illusions using the optimization abilities of current automatic differentiation techniques. The proposed framework can be used with classical vision models as well as with more recent artificial neural network architectures. This framework, validated by psychophysical experiments, can be used to study the difference between a vision model and the actual human perception and to optimize the vision model to decrease this difference.
A multi-objective particle swarm optimization with density and distribution-based competitive mechanism for sensor ontology meta-matching

Abstract Sensor ontology is a standard conceptual model that describes information of sensor device, which includes the concepts of various sensor modules and the relationships between them. The problem of heterogeneity between sensor ontologies is introduced because different sensor ontology engineers have different ways of describing sensor devices and different structures for the construction of sensor ontologies. Addressing the heterogeneity of sensor ontologies contributes to facilitate the semantic fusion of two sensor ontologies, enabling the sharing and reuse of sensor information. To solve the above problem, an ontology meta-matching method is proposed by this paper to find out the correspondence between entities in distinct sensor ontologies. How to measure the degree of similarity between entities with a set of suitable similarity measures and how to better integrate multiple measures to determine the equivalent entities are the challenges of the ontology meta-matching problem. In this paper, two approximate measurement methods of the quality for ontology matching results are designed, and a multi-objective optimization model for the ontology meta-matching problem is constructed based on these methods. Eventually, a multi-objective particle swarm optimization (MOPSO) algorithm is propounded to dispose of the problem and optimize the quality of ontology meta-matching results, which is named density and distribution-based competitive mechanism multi-objective particle swarm algorithm (D $$^{2}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mrow /> <mml:mn>2</mml:mn> </mml:msup> </mml:math> CMOPSO). The sophistication of the D $$^{2}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mrow /> <mml:mn>2</mml:mn> </mml:msup> </mml:math> CMOPSO based sensor ontology meta-matching method is verified through experiments. Comparing with other matching systems and advanced systems of Ontology Alignment Evaluation Initiative (OAEI), the proposed method can improve the quality of matching results more effectively.
Unsupervised deep learning supports reclassification of Bronze age cypriot writing system

Ancient undeciphered scripts present problems of different nature, not just tied to linguistic identification. The undeciphered Cypro-Minoan script from second millennium BCE Cyprus, for instance, currently does not have a standardized, definitive inventory of signs, and, in addition, stands divided into three separate subgroups (CM1, CM2, CM3), which have also been alleged to record different languages. However, this state of the art is not consensually accepted by the experts. In this article, we aim to apply a method that can aid to shed light on the tripartite division, to assess if it holds up against a multi-pronged, multi-disciplinary approach. This involves considerations linked to paleography (shapes of individual signs) and epigraphy (writing style tied to the support used), and crucially, deep learning-based strategies. These automatic methods, which are widely adopted in many fields such as computer vision and computational linguistics, allow us to look from an innovative perspective at the specific issues presented by ancient, poorly understood scripts in general, and Cypro-Minoan in particular. The usage of a state-of-the-art convolutional neural model that is unsupervised, and therefore does not use any prior knowledge of the script, is still underrepresented in the study of undeciphered writing systems, and helps to investigate the tripartite division from a fresh standpoint. The conclusions we reached show that: 1. the use of different media skews to a large extent the uniformity of the sign shapes; 2. the application of several neural techniques confirm this, since they highlight graphic proximity among signs inscribed on similar supports; 3. multi-stranded approaches prove to be a successful tool to investigate ancient scripts whose language is still unidentified. More crucially, these aspects, together, point in the same direction, namely the validation of a unitary, single Cypro-Minoan script, rather than the current division into three subgroups.