Reviewerly/exHarmony huggingface dataset:
One step at a time: An efficient approach to query-based ontology debugging

When ontologies reach a certain size and complexity, faults such as inconsistencies, unsatisfiable classes or wrong entailments are hardly avoidable. Locating the incorrect axioms that cause these faults is a hard and time-consuming task. Addressing this issue, several techniques for a semi-automatic fault localization in ontologies have been proposed and extensively studied. One class of these approaches involve a human expert who provides answers to system-generated queries about the intended (correct) ontology in order to reduce the possible fault locations. To suggest as informative questions as possible, existing methods draw on various algorithmic optimizations as well as heuristics. However, these computations are often based on certain assumptions about the interacting expert. In this work, we demonstrate that these assumptions might not always be adequate and discuss consequences of their violations. In particular, we characterize a range of expert types with different query answering behavior and show that existing approaches are far from achieving optimal efficiency for all of them. In addition, we find that the cost metric adopted by state-of-the-art techniques might not always be realistic and that a change of metric has a decisive impact on the best choice of query answering strategy. As a remedy, we suggest a new – and simpler – type of expert question that leads to a stable fault localization performance for all analyzed expert types and effort metrics, and has numerous further advantages over existing techniques. Moreover, we present an algorithm which computes and optimizes this new query type in worst-case polynomial time and which is fully compatible with existing concepts (e.g., query selection heuristics) and infrastructure (e.g., debugging user interfaces) in the field. Comprehensive experiments on faulty real-world ontologies attest that the new querying method is substantially and statistically significantly superior to existing techniques both in terms of the number of necessary expert interactions and in terms of the query computation time. We find that relying on the new querying method can save an interacting expert more than 80% of their work, and can reduce the expert’s waiting time for the next query by more than three orders of magnitude. Beside these findings, we demonstrate that the efficiency of existing query-based tools can be significantly boosted by suggesting an appropriate query answering strategy to an expert; we also make recommendations in this regard. Further, we suggest optimal configurations of a debugger for situations where the new type of query is used. Remarkably, the proposed approach is not only applicable to ontologies, but to any monotonic knowledge representation language, and can even be adopted to solve general model-based diagnosis problems expressible using Reiter’s theory.
Robust distance metric optimization driven GEPSVM classifier for pattern classification

Proximal support vector machine via generalized eigenvalues (GEPSVM) is one of the most successful methods for classification problems. However, GEPSVM is vulnerable to outliers since it learns classifiers based on the squared L2-norm distance without a specific strategy to deal with the outliers. Motivated by existing studies that improve the robustness of GEPSVM via the L1-norm distance or not-squared L2-norm distance formulation, a novel GEPSVM formulation that minimizes the p-order of L2-norm distance is proposed, namely, L2,p-GEPSVM. This formulation weakens the negative effects of both light and heavy outliers in the data. An iterative algorithm is designed to solve the general L2,p-norm distance minimization problems and rigorously prove its convergence. In addition, we adjust the parameters of L2,p-GEPSVM to balance the accuracy and training time. This is especially useful for larger datasets. Extensive results indicate that the L2,p-GEPSVM improves the classification performance and robustness in various experimental settings.
No-reference image quality metrics for color domain modified images

Predicting the quality of natural images without using a reference image has always been a challenging task. Numerous approaches have been proposed in the past, but they mainly focused on spatial and frequency domain degradations like blur, noise, and compression. Image quality metrics (IQMs) in literature perform with quite a high accuracy for such types of degraded images. However, their performances are not good on the images modified in the color domain. In this study, psychophysical experiments were conducted to assess the quality of the color domain images. A new dataset was developed for this purpose. Additionally, a second dataset consisting of color domain modified images from the three previously published datasets were used in the psychophysical experiments. The newly developed dataset was then used to develop three IQMs based on absolute values, relative values, and statistical analysis of image color appearance attributes. Their performances were then evaluated together with five spatial domain IQMs from the literature using cross-database evaluation methodology. The results showed that the color-domain IQMs outperformed the other models. The absolute and relative attributes-based models, when combined, achieved the best performance. The present results suggest that more effort is needed to improve the performance of color domain IQMs for image quality estimation.
Towards a new semantic middleware for service description, discovery, selection, and composition in the Internet of Things

Abstract The emergence of the Internet of Things (IoT) allows the integration of everyday devices such as tags, sensors, and actuators into the Internet. IoT also provides handy and automatic techniques to interoperate ad‐hoc, heterogeneous, and mobile networks and simplify data sharing among devices by abstracting their capabilities as services. Nevertheless, the large number and heterogeneity of encompassed networks/devices and the diversity of the offered resources/services make the IoT service description, discovery, selection, and composition a challenging task. To deal with these issues, we introduce, in this article, a semantic middleware for IoT applications based on service composition. Such a solution supports the semantic description of IoT resources, including services and user requests, and provides a modular, end‐to‐end, and loosely coupled request resolution process that comprises a context‐aware service discovery, a semantic service selection, and an automatic lightweight service composition. The proposal is illustrated and extensively evaluated on a restricted smart‐city scenario. Obtained results show that the proposed service discovery, selection, and composition sub‐processes improve the scalability of equivalent state‐of‐the‐art solutions by around 15 , 20 , and 40 , respectively.
A New Security Solution Enhancing the Dynamic Array PIN Protocol

In order to authenticate a user on an Automated Teller Machine (ATM) using Near Field Communication (NFC) technology embedded on smartphones, we recently proposed a new approach called Dynamic Array PIN Protocol (DAP) that allows a user to enter his PIN code in a secure manner. We proved that the DAP protocol is resistant to 13 different attacks. Furthermore, by comparing it to several existing solutions, we demonstrated that DAP is much better and more cost effective. However, after a thorough analysis, we discovered that the DAP protocol is vulnerable to multiple eavesdropping video or camera records attack. Consequently, in this paper, we aim to address this vulnerability by proposing a new security solution that improves the DAP protocol.
A Study on Missing Data Imputation Methods for Improving Hourly Solar Dataset

In the era of big data, large period of missing data is a common problem which affect the data quality and final forecasting results if not handled properly. Therefore, filling missing data in datasets is importance since the most of real-time datasets have a huge number of missing values. This paper first gives a comprehensive overview of various imputation methods for filling missing data. Then proposes a technique based on a popular Multivariate Imputation by Chained Equation (MICE) to fill numeric data in PV dataset. Finally analyses the impact of this technique and compares the performance with other imputation algorithms. For practice, this study uses historical measurement PV generation from the North PV site of Taiwan, and Numerical Weather Prediction (NWP) data consists of solar irradiance, temperature, sea level pressure, humidity, rainfall, wind speed. The NWP dataset is provided by Taiwan Central Weather Bureau (CWB) which is called Deterministic Weather Research and Forecasting (WRFD). Experimental results showed that the proposed imputation algorithm can improve short-term PV generation forecasting accuracy based on RMSE.
Integrating Global and Local Feature Selection for Multi-Label Learning

Multi-label learning deals with the problem where an instance is associated with multiple labels simultaneously. Multi-label data is often of high dimensionality and has many noisy, irrelevant, and redundant features. As an important machine learning task, multi-label feature selection has received considerable attention in recent years due to its promising performance in dealing with high-dimensional multi-label data. Existing multi-label feature selection methods typically select the global features which are shared by all instances in a dataset. However, these multi-label feature selection methods may be suboptimal since they do not consider the specific characteristics of instances. In this paper, we propose a novel algorithm that integrates Global and Local Feature Selection (GLFS) to exploit both the global features and a subset of discriminative features shared only locally by a subgroup of instances in a multi-label dataset. Specifically, GLFS employs linear regression and ℓ 2,1 -norm on the regression parameters to achieve simultaneous global and local feature selection. Moreover, the proposed algorithm has an effective mechanism for utilizing label correlations to improve the feature selection. Experiments on real-world multi-label datasets show the superiority of GLFS over the state-of-the-art multi-label feature selection methods.
A transductive learning method to leverage graph structure for few-shot learning

Few-shot learning has attracted extensive research attention given its capability to classify unseen data from limited samples, potentially addressing the key issue of data scarcity commonly existing in many machine learning tasks. This paper proposes a new transductive learning method that integrates information propagation and prototype rectification in few-shot learning, which achieves state-of-the-art classification performance on four popular datasets. We use first-order information propagation instead of infinite order method to avoid the over-smoothing caused by iterations of information aggregation and node updating in graph neural networks. We further reveal that current transductive few-shot learning models often assume the datasets have balanced classes, which cannot be guaranteed in practice. We thus propose to estimate the distribution of task samples to optimize the number of iterations so as to enhance the robustness of the model. Extensive experiments validate the proposed model and reveal the confirmation bias that could be effectively addressed by the optimization strategy.
Evolutionary Large-Scale Dynamic Optimization Using Bilevel Variable Grouping

Variable grouping provides an efficient approach to large-scale optimization, and multipopulation strategies are effective for both large-scale optimization and dynamic optimization. However, variable grouping is not well studied in large-scale dynamic optimization when cooperating with multipopulation strategies. Specifically, when the numbers/sizes of the variable subcomponents are large, the performance of the algorithms will be substantially degraded. To address this issue, we propose a bilevel variable grouping (BLVG)-based framework. First, the primary grouping applies a state-of-the-art variable grouping method based on variable interaction analysis to group the variables into subcomponents. Second, the secondary grouping further groups the subcomponents into variable cells, that is, combination variable cells and decomposition variable cells. We then tailor a multipopulation strategy to process the two types of variable cells efficiently in a cooperative coevolutionary (CC) way. As indicated by the empirical study on large-scale dynamic optimization problems (DOPs) of up to 300 dimensions, the proposed framework outperforms several state-of-the-art frameworks for large-scale dynamic optimization.
A Cross-Platform Cache Timing Attack Framework via Deep Learning

While deep learning methods have been adopted in power side-channel analysis, they have not been applied to cache timing attacks due to the limited dimension of cache timing data. This paper proposes a persistent cache monitor based on cache line flushing instructions, which runs concurrently to a victim execution and captures detailed memory access patterns in high-dimensional timing traces. We discover a new cache timing side-channel across both inclusive and non-inclusive caches, different from the traditional “Flush+Flush” timing leakage. We then propose a non-profiling differential deep learning analysis strategy to exploit the cache timing traces for key recovery. We further propose a framework for cross-platform cache timing attack via deep learning. Knowledge learned from profiling a common reference device can be transferred to build models to attack many other victim devices, even in different processor families. We take the OpenSSL AES-128 encryption algorithm as an example victim and deploy an asynchronous cache attack. We target three different devices from Intel, AMD, and ARM processors. We examine various scenarios for assigning the teacher role to one device and the student role to other devices, and evaluate the cross-platform deep-learning attack framework. Experimental results show that this new attack is easily extendable to victim devices and is more effective than attacks without any prior knowledge.
FlowAcc: Real-Time High-Accuracy DNN-based Optical Flow Accelerator in FPGA

Recently, accelerator architectures have been designed to use deep neural networks (DNNs) to accelerate computer vision tasks, possessing the advantages of both accuracy and speed. Optical flow accelerator is however not among these architectures that DNNs have been successfully deployed. Existing hardware accelerators for optical flow estimation are all designed for classic methods and generally perform poorly in estimated accuracy. In this paper, we present FlowAcc, a dedicated hardware accelerator for DNN-based optical flow estimation, adopting a pipelined hardware design for real-time processing of image streams. We design an efficient multiplexing binary neural network (BNN) architecture for pyramidal feature extraction to significantly reduce the hardware cost and make it independent of the pyramid level number. Furthermore, efficient hamming distance calculation and competent flow regularization are utilized for hierarchical optical flow estimation to greatly improve the system efficiency. Comprehensive experimental results demonstrate that FlowAcc achieves state-of-the-art estimation accuracy and real-time performance on the Middlebury dataset when compared with the existing optical flow accelerators.
Intelligent Decision Making in Autonomous Vehicles using Cognition Aided Reinforcement Learning

As recent advances in sensing, computing, and communications expedite proliferation of autonomous vehicles (AV), their sharing the road with human driven vehicles presents a challenge that demands urgent investigation. AVs can excel at deterministic programmed behavior, still human drivers have the edge because of the faculty of cognition, which evolved over millennia. This paper presents Cognition Aided Reinforcement Learning (CARL) algorithm that harnesses inputs from five principles of cognition &#x2014; memory, attention, language, perception, and intelligence. Sensors build perception, data facilitate memory, and safety messages enable language support. Intelligence fuses information with attention focused on specific actions for reward maximization. Simulation results show CARL to be 10 times faster as compared to the state of the art model-free reinforcement learning algorithms. Additionally, by using the principle of metacognition (art of learning how to learn), CARL achieves optimal rewards in a heterogeneous environment composed of vehicles with varying degrees of autonomy.
A Method for Degenerate Primer Design Based on Artificial Bee Colony Algorithm

Aiming to address the complex degenerate primer design problem in the biological field, in this paper, we design a degenerate primer optimization model considering primer coverage and degeneracy that allows a small number of base mismatches, and propose a global optimization method based on the artificial bee colony algorithm. The proposed algorithm combines the idea of the ant colony algorithm with the optimization process of the artificial bee colony algorithm, overcomes the disadvantage of the uncertain candidate solution length of the artificial bee colony algorithm in solving discrete optimization problems, designs the search space model according to the construction process of candidate solution in ant colony optimization algorithm, and redesigns various bee foraging strategies according to the optimization process information. In the comparative experiments on DNA template sequences of different scales, the degenerate primer designed by the proposed algorithm is superior to the existing methods in terms of stability, specificity, coverage and degeneracy.
Family-Based and Product-Based Development of Correct-by-Construction Software Product Lines

Software product lines provide systematic software reuse to implement whole product families efficiently. Therefore, they are increasingly used to handle the growing demand of custom-tailored software variants including safety-critical systems as a domain of application. The requirements for safety-critical systems are generally higher and often demand for behavioral correctness to reduce the risk of dangerous situations in-field. However, the high variability of software product lines is still a big challenge for formal methods in general. We argue that the stepwise development approach correctness-by-construction is beneficial compared to post-hoc verification approaches in dealing with the growing variability. Correctness-by-construction uses a set of refinement rules to incrementally create a correct program from a formal specification. In previous work, we already introduced variational correctness-by-construction to develop correct-by-construction software product lines in a product-based way. In this article, we extend our approach in three directions. First, we propose a feature-based specification technique matching the fine-grained specifications of correctness-by-construction. Second, we propose a family-based development approach to show the correctness of the product line as an alternative to the previous product-based approach. Third, we present our usability-improved tool VarCorC implementing our approach. In the evaluation, we compare the product-based development approach to the family-based approach using two case studies implemented with VarCorC.
Secure Electronic Transaction Using AADHAAR Based QR Code and Biometric Authentication

Bank services have become more convenient in recent years thanks to electronic banking, but their security is also improving. Hence security in banking services is enhanced using QR code embedded in the aadhaar card and biometric authentication. Using aadhaar number enables the users to access their accounts in multiple banks. Of all the biometrics, fingerprint trait is chosen because of its characteristics like availability, reliability and high accuracy. Finger print recognition system is used for both verification and identification to make the system more secure. In this system, ATM machine can be operated using the aadhaar card instead of ATM card. The in-built camera in the ATM machine will scan the QR code embedded in the aadhaar card and then match it with fingerprint of the user obtained using the biometric sensor. Only after successful authentication, the user will be allowed to perform cash transactions. Hence the need of ATM card for each bank account is completely eliminated by this system. The work of the people as well as the banking sector is simplified. Not only cash withdrawal but also an android application is developed which uses aadhaar number to perform online cash transactions. Hence the system is designed with large number of security features so that a costumer can perform financial transactions without any security issues.
The Design Model of English Graded Teaching Assistant Expert System Based on Improved B/S Three-Tier Structure System

With the continuous development of Internet technology, the modern education system is becoming increasingly intelligent. The integration of information technology with the traditional curriculum can unlock the potential of modern education, which is essentially a change from the traditional teacher-centered classroom teaching structure. The disadvantage of the traditional education system is that it is difficult to teach students according to their abilities because of the differences in English language standards caused by differences in their knowledge and learning potential. Although tiered teaching is one way of addressing this difference, it can have a negative impact in some ways, and implicit tiered teaching is a great solution to this problem. In addition, artificial intelligence is a science that studies the laws of human intelligence. The application of artificial intelligence technology to develop an expert system for teaching English can effectively solve the issues of insufficient teachers and a single teaching mode in the current education system. This will enable the sharing of teaching resources to effectively improve teaching quality, reduce the burden on teachers, and strengthen student’ ability to learn on their own. This paper introduces the principles of artificial intelligence and the B/S three-tier structure system, analyses the current problems faced by English teaching, and proposes a solution for the implementation of a university English teaching aid system based on the B/S three-tier structure system. This system is based on the teacher English language teaching assistant expert system module, which uses a combination of generative and framework-based knowledge representations of the domain knowledge of English language experts to better enable teachers to understand their students’ mastery of knowledge points. The results of this research show that the system can be used to help teachers understand students’ learning through reasoning and analysis, and to improve students’ interest, learning strategies and learning abilities in English.