Reviewerly/exHarmony huggingface dataset:
Continual Learning for Anomaly based Network Intrusion Detection

To defend computing systems against ever-growing cyber attacks, Anomaly-based Network Intrusion Detection Systems (A-NIDS) have to evolve continuously. This requirement renders classical machine learning algorithms ineffective since they do not handle sequentially evolving tasks gracefully. Specifically, neural networks (NNs) are prone to catastrophic forgetting (CF) when trained on sequential data. Recent advances in addressing this drawback of NNs have resulted in a paradigm called Continual Learning (CL) which mitigates CF by introducing suitable constraints during the sequential training of these NNs. CL has been shown to be very effective in improving the performance of NNs on computer vision tasks. However, its application to the design of A-NIDS has not been explored. In this work, we evaluate the suitability of CL to address the challenges posed in A-NIDS design. Unlike computer vision datasets, network datasets suffer from the Class Imbalance (CI) problem, which makes the direct application of CL algorithms challenging. To evaluate the suitability of CL algorithms on network datasets, we study the impact of class imbalance on task ordering and its effect on the design of CL- based A-NIDS in the Class Incremental (CIL) and Domain Incremental (DIL) learning settings. Towards this end, we apply two popular CL algorithms viz. Elastic Weight Consolidation (EWC) and Gradient Episodic Memory (GEM) on two datasets viz., CICIDS and KDD Cup'99, and evaluate their performance. We found that CI affects task order sensitivity to a greater extent in the CIL setting when compared to the DIL setting. The performance of DIL setting can be further enhanced by incorporating experience forgetting aware memory population techniques, and we recommend this as a practical approach to building CL-based A-NIDS.
Controllable Fused Semantic Segmentation with Adaptive Edge Loss for Remote Sensing Parsing

High-resolution remote sensing images have been put into the application in remote sensing parsing. General remote sensing parsing methods based on semantic segmentation still have limitations, which include frequent neglect of tiny objects, high complexity in image understanding and sample imbalance. Therefore, a controllable fusion module (CFM) is proposed to alleviate the problem of implicit understanding of complicated categories. Moreover, an adaptive edge loss function (AEL) was proposed to alleviate the problem of the recognition of tiny objects and sample imbalance. Our proposed method combining CFM and AEL optimizes edge features and body features in a coupled mode. The verification on Potsdam and Vaihingen datasets shows that our method can significantly improve the parsing effect of satellite images in terms of mIoU and MPA.
Knowledge Enhanced Graph Neural Networks for Explainable Recommendation

Recently, explainable recommendation has attracted increasing attentions, which can make the recommender system more transparent and improve user satisfactions by recommending products with useful explanations. However, existing methods trend to trade-off between the recommendation accuracy and the interpretability of recommendation results. In this manuscript, we propose Knowledge Enhanced Graph Neural Networks (KEGNN) for explainable recommendation. Semantic knowledge from the external knowledge base is leveraged into representation learning of three sides, respectively user, items and user-item interactions, and the knowledge enhanced semantic embedding are exploited to initialize the user/item entities and user-item relations of one constructed user behavior graph. We design a graph neural networks based user behavior learning and reasoning model to perform both semantic and relational knowledge propagation and reasoning over the user behavior graph for comprehensive understanding of user behaviors. On the top of comprehensive representations of users/items and user-item interactions, hierarchical neural collaborative filtering layers are developed for precise rating prediction, and one generation-mode and copy-mode combined generator is devised for human-like semantic explanation generation by integrating the copy mechanism into gated recurrent neural networks. Quantitative and qualitative results demonstrate the superiority of KEGNN over the state-of-art methods, and the explainability and interpretability of our method.
DistPreserv: Maintaining User Distribution for Privacy-Preserving Location-Based Services

Location-Based Services (LBSs) are one of the most frequently used mobile applications in the modern society. Geo-Indistinguishability (Geo-Ind) is a promising privacy protection model for LBSs since it can provide formal security guarantees for location privacy. However, Geo-Ind undermines the statistical location distribution of users on the LBS server because of perturbed locations, thereby disabling the server to provide distribution-based services (e.g., traffic congestion maps). To overcome this issue, we give a privacy definition, called DistPreserv, to enable the LBS server to acquire valid location distributions while providing users with strict location protection. Then we propose a privacy-preserving LBS scheme to benefit both users and the server, in which a location perturbation mechanism is designed to achieve the given definition under the guide of the incentive compatibility, and a retrieval area determination method is presented to ensure query accuracy of users by using the dynamic programming on the two-dimensional map plane. Finally, we theoretically prove that the designed mechanism can achieve the definition of DistPreserv and the property of incentive compatibility. Experimental explorations using a real-world dataset indicate that our proposal prominently improves the availability of users’ location distributions by over 90%, while providing high precision and recall of queries.
A Model For risk-Based adaptive security controls

Security controls and countermeasures have shifted from static desktop-based and corporate network environments to heterogeneous, distributed and dynamic environments (e.g., cloud and mobile computing or Internet of Things). Due to this paradigm shift, adaptive and risk-based approaches have gained significant importance. These approaches allow security managers to perform context-aware decision making, adapting controls’ deployment, configuration or use to every specific situation, depending on the current value of risk indicators or scores and on the level of risk tolerated by the organisation at any given time. This paper proposes a model to automatically adapt security controls to different risk scenarios in almost real-time (if required). This model is based on a three-layer architecture and a three-step flow (measurement-decision-adaptation), relying on a scalable policies&rules framework capable of integrating with different kinds of controls. Furthermore, the proposed model is validated and evaluated with an actual use case.
Chinese EmoBank: Building Valence-Arousal Resources for Dimensional Sentiment Analysis

An increasing amount of research has recently focused on dimensional sentiment analysis that represents affective states as continuous numerical values on multiple dimensions, such as valence-arousal (VA) space. Compared to the categorical approach that represents affective states as distinct classes (e.g., positive and negative), the dimensional approach can provide more fine-grained (real-valued) sentiment analysis. However, dimensional sentiment resources with valence-arousal ratings are very rare, especially for the Chinese language. Therefore, this study aims to: (1) Build a Chinese valence-arousal resource called Chinese EmoBank, the first Chinese dimensional sentiment resource featuring various levels of text granularity including 5,512 single words, 2,998 multi-word phrases, 2,582 single sentences, and 2,969 multi-sentence texts. The valence-arousal ratings are annotated by crowdsourcing based on the Self-Assessment Manikin (SAM) rating scale. A corpus cleanup procedure is then performed to improve annotation quality by removing outlier ratings and improper texts. (2) Evaluate the proposed resource using different categories of classifiers such as lexicon-based, regression-based, and neural-network-based methods, and comparing their performance to a similar evaluation of an English dimensional sentiment resource.
Design Method of High-Order Kalman Filter for Strong Nonlinear System Based on Kronecker Product Transform

In this paper, a novel design idea of high-order Kalman filter based on Kronecker product transform is proposed for a class of strong nonlinear stochastic dynamic systems. Firstly, those augmenting systems are modeled with help of the Kronecker product without system noise. Secondly, the augmented system errors are illustratively charactered by Gaussian white noise. Thirdly, at the expanded space a creative high-order Kalman filter is delicately designed, which consists of high-order Taylor expansion, introducing magical intermediate variables, representing linear systems converted from strongly nonlinear systems, designing Kalman filter, etc. The performance of the proposed filter will be much better than one of EKF, because it uses more information than EKF. Finally, its promise is verified through commonly used digital simulation examples.
An Efficient Triple-Layered and Double Secured Cryptography Technique in Wireless Sensor Networks

When implemented in a complex environment, wireless network security is the main factor, and it is the sensor networks’ primary concern. Cryptology is a vital component in wireless sensor networks to accomplish this. Many existing cryptographic techniques had not shown good and better results till now. An efficient, strong, triple phased, double secured, and integrated cryptographic approach has been introduced in this study that utilizes both secret-key and public-key methods. Rijndael Encryption Approach (REA), Horst Feistel’s Encryption Approach (HFEA), and enhanced Rivest-Shamir-Adleman (e- RSA) are employed in the propounded technique in various stages of the algorithm since secret-key based system offers a significant level of protection and enable key management through publickey based techniques. REA was used in stage 1 of the algorithm; REA+HFEA was used in stage 2, and REA+HFEA+e-RSA was used in the last stage, and all three stages were performed in parallel. Parameters like execution time and decryption time were taken into account for measuring the performance levels of the propounded approach. The propounded algorithm is differentiated from existing techniques using a single evaluation parameter i.e computation time. It is found that propounded approach gave a good performance in terms of computation time with an Average Encryption Time (AET) and Average Decryption Time (ADT) of 1.12 and 1.26 on text sizes of 6, 25, 35, 61, and 184MegaBytes (MB) respectively. The proposed hybrid model is 1.36 times faster than ECC+RSA+MD-5,3.25 times faster than AES+ECC, 2.7 times faster than AES+RSA, and 3.24 times faster than AES+ECC+RSA+MD5.
Video Pembelajaran Berbasis Model Discovery Learning pada Muatan IPS Kelas IV Sekolah Dasar

The limitation of teachers in finding the right learning media in learning situations in the network is a problem in the learning process. The purpose of this research is to produce a product in the form of a discovery learning-based learning video that is feasible to use. The research model used is the ADDIE model (Analyze, Design, Development, Implementation, Evaluation). Data collection methods used are interviews and questionnaires. The product design test was carried out by several experts and students including (1) content experts, (2) learning design experts, (3) learning media experts, and (4) individual trials consisting of three students. The data analysis method used is quantitative and qualitative descriptive data analysis. Based on the research data, the percentage level of feasibility of learning videos according to content experts is 93,75% with very good qualifications, the percentage level of feasibility of learning videos according to learning design experts is 90,38% with very good qualifications, and the feasibility level of learning videos according to media experts learning by 87,5% with good qualifications and according to individual trials the percentage level of eligibility for learning videos is 91,66% with very good qualifications. Based on the results of the study, it can be concluded that the development of learning videos based on the discovery learning model is feasible to use in the learning process on social studies content for fourth-grade students in elementary schools.
Reinforcement Learning Path Planning Method with Error Estimation

Path planning is often considered as an important task in autonomous driving applications. Current planning method only concerns the knowledge of robot kinematics, however, in GPS denied environments, the robot odometry sensor often causes accumulated error. To address this problem, an improved path planning algorithm is proposed based on reinforcement learning method, which also calculates the characteristics of the cumulated error during the planning procedure. The cumulative error path is calculated by the map with convex target processing, while modifying the algorithm reward and punishment parameters based on the error estimation strategy. To verify the proposed approach, simulation experiments exhibited that the algorithm effectively avoid the error drift in path planning.
A Reinforced Active Learning Algorithm for Semantic Segmentation in Complex Imaging

Semantic segmentation annotation helps train computer vision based Artificial Intelligence models where each image pixel is assigned to a specific object class. The model developers try to identify the features helpful for determining the objects of interest by using various supervised deep learning techniques. However, this is a difficult task due to the complexity of object structures. Two difficulties arise in the current approaches for semantic segmentation. The pixel-wise label approach is costly to obtain and is time consuming. Second, the datasets taken for the semantic segmentation task are not balanced since certain classes are present more than the others. This biases the model performance to the most represented ones. We propose a new reinforced active learning strategy based on a deep reinforcement learning algorithm. This work presents a modified Deep Q Learning formulation for active learning. An agent learns the strategy of selecting a subset of small image regions, which are more knowledgeable than the who leset of images from an unlabeled data pool. The decision on the area of selection is dependent on the assumptions and segmentation model uncertainties taken for training purposes. We use the CamVid and RGB indoor test scenes dataset to evaluate the proof of concept. Our results infer that our approach demands more labels from under-represented groups than the baselines, thus enhancing their efficiency and mitigating the class imbalance. Our method’s performance is superior to the conventional deep learning models in detecting 8 out of 11 classes on the Camvid road segmentation scene dataset. It achieves an accuracy of 90.56%, a mIoU score of 87.17%, and a BF score of 93.14%. On the SUNRGB indoor scenes dataset, it gives an accuracy of around 75.82% and a BF score of 77.25%, thus out performing the current state-of-the-art methods.
Local perspective based synthesis for vehicle re-identification: A transformation state adversarial method

Vehicle re-identification (V-ReID) aims at discovering an image of a specific vehicle from a set of images typically captured by different cameras. Vehicles are one of the most important objects in cross-camera target recognition systems, and recognizing them is one of the most difficult tasks due to the subtle differences in the visible characteristics of vehicle rigid objects. Compared to various methods that can improve re-identification accuracy, data augmentation is a more straightforward and effective technique. In this paper, we propose a novel data synthesis method for V-ReID based on local-region perspective transformation, transformation state adversarial learning and a candidate pool. Specifically, we first propose a parameter generator network, which is a lightweight convolutional neural network, to generate the transformation states. Secondly, an adversarial module is designed in our work, it ensures that noise information is added as much as possible while keeping the labeling and structure of the dataset intact. With this adversarial module, we are able to promote the performance of the network and generate more proper and harder training samples. Furthermore, we use a candidate pool to store harder samples for further selection to improve the performance of the model. Our system pays more balanced attention to the features of vehicles. Extensive experiments show that our method significantly boosts the performance of V-ReID on the VeRi-776, VehicleID and VERI-Wild datasets.
Passivity analysis of fractional-order neural networks with interval parameter uncertainties via an interval matrix polytope approach

This paper investigates the passivity analysis problem of a class of fractional-order neural networks with interval parameter uncertainties (FONNs-IPUs). The previous IPU processing methods are mainly divided into three categories, i.e., directly using the maximum upper bound matrix or the minimum lower bound matrix, using the combination of these two matrices, and using the maximum norm bound matrix. These methods have some shortcomings, for example, the coupling of information between different intervals and the effect of the negative sign of connection weight are not considered. In order to better analyze the passivity of FONNs-IPUs, firstly, a novel treatment approach of IPUs called interval matrix polytope is proposed, which considers all possible boundary information matrices and the sign of IPUs. Secondly, based on the interval matrix polytope approach, the passivity criteria of FONNs-IPUs are established in linear matrix inequality forms. Finally, the effectiveness of the theoretical results is illustrated by simulation example.
Machine Learning Verification and Safety for Unmanned Aircraft - A Literature Study

Machine learning (ML) has proven to be the tool of choice for achieving human-like or even super-human performance with automation on specific tasks. As a result, this data-driven approach is currently experiencing massive interest in all industry domains. This increased use also applies for the safety critical aviation domain. With no human pilot on board, the potential use cases of ML for unmanned aircraft are particularly promising. Even upcoming Urban Air Mobility (UAM) concepts are planning to remove the onboard pilot and instead use ML to support a remote pilot, possibly supervising a fleet of vehicles. However, the verification of ML algorithms is a challenging problem, since established safety standards and assurance methods are not applicable. Thus, this work comprises a literature study on the topic of ML verification and safety. This research paper uses a systematic approach to map and categorize the research and focus on specific subtopics that are of particular interest in the context of existing guidance documents.
A Comprehensive Artificial Intelligence Based User Intention Assessment Model from Online Reviews and Social Media

Predictive analytics is being increasingly used to predict various aspects of applications and users. It offers vast opportunities in the growth of the modern era’s business transformation by enabling automated decision-making processes. Being able to determine the intention of users in an automated way is one of the important factors in enabling automated decision-making for applications and businesses using such applications. In this paper, we utilize and build upon the existing works, and propose a comprehensive intention assessment model that detects different possible intents of users by analyzing their text-based reviews on online forums, retail market websites, or on social media. If the information about a product or service experience is present somewhere in a review or post, our technique can accurately segregate different possible purchase intention labels (i.e., positive, negative, and unknown). Our proposed comprehensive model for intention assessment includes extensive data pre-processing, extended feature selection model, utilization of artificial intelligence (machine learning and deep learning) techniques, and customized cost and loss functions. We built a comprehensive testbed and carried out evaluations and comparisons. Our solution demonstrates high accuracy, precision, and F1 score. The proposed solution helps in mining and gaining deeper insights into behavior of consumers and market tendencies and can help in making informed decisions.
Improve generated adversarial imitation learning with reward variance regularization

Imitation learning aims at recovering expert policies from limited demonstration data. Generative Adversarial Imitation Learning (GAIL) employs the generative adversarial learning framework for imitation learning and has shown great potentials. GAIL and its variants, however, are found highly sensitive to hyperparameters and hard to converge well in practice. One key issue is that the supervised learning discriminator has a much faster learning speed than the reinforcement learning generator, making the generator gradient vanishing. Although GAIL is formulated as a zero-sum adversarial game, the ultimate goal of GAIL is to learn the generator, thus the discriminator should play the role more like a teacher rather than a real opponent. Therefore, the learning of the discriminator should consider how the generator could learn. In this paper, we disclose that enhancing the gradient of the generator training is equivalent to increase the variance of the fake reward provided by the discriminator output. We thus propose an improved version of GAIL, GAIL-VR, in which the discriminator also learns to avoid generator gradient vanishing through regularization of the fake rewards variance. Experiments in various tasks, including locomotion tasks and Atari games, indicate that GAIL-VR can improve the training stability and imitation scores.
Credit Card Fraud Detection using Machine Learning

Credit card fraud is a common and fast-growing issue. Such problems may be tackled with Data Science, which, together with Machine Learning, should not be overlooked. This paper compares the performance of Decision Tree, Random Forest, K-nearest neighbors, and Logistic regression on highly imbalanced data. This is done by merging all important features of cardholder transactions, such as the date, user zone, product category, amount, supplier, and client’s behavioral habits, among others. The information is then put into different models that look for patterns and rules to evaluate whether or not a transaction is fraudulent based on accuracy and sensitivity.
Readiness for mammography and artificial intelligence

One area that has attracted great attention for the use of deep learning artificial intelligence (AI) in health care is medical imaging, especially mammography. Many initial AI studies proclaimed remarkable improvement in accuracy over the performance of radiologists, but a recent systematic review highlighted there is insufficient scientific evidence to support such findings. The UK National Screening Committee commissioned Freeman and colleagues to review the quality and results of studies that assessed the accuracy of AI algorithms, alone or in combination with radiologists, to detect cancer in digital mammograms; 34 of 36 AI systems evaluated were less accurate than a single radiologist, and all were less accurate than the consensus of two or more radiologists.