Reviewerly/exHarmony huggingface dataset:
Indirect Revocable KP-ABE With Revocation Undoing Resistance

Lately, many cloud-based applications proposed attribute-based encryption (ABE) as an all-in-one solution for achieving confidentiality and access control. Within this paradigm, data producers store the encrypted data on a semi-trusted cloud server, and users, holding decryption keys issued by a key authority, can decrypt data according to some access control policy. To be used in practical cases, any ABE scheme should implement a key revocation mechanism which assures that a compromised decryption key cannot be used anymore to decrypt data. Yu <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">et al.</i> (2010) introduced an ABE scheme with revocation capabilities that enjoys several unique advantages, such as reactivity and efficiency. In the scheme, the cloud server is entitled to update keys and ciphertexts in order to achieve revocation. Unfortunately, the cloud server retains the power to undo the revocation of a key (revocation undoing attack) so endangering confidentiality. In this article, we propose a revocable ABE scheme that still ensures the advantages of Yu <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">et al.</i> ’s scheme, but it also resists to the revocation undoing attack. We formally prove the security of our scheme and show through simulations that the user experiences a slightly higher computational cost with respect to Yu <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">et al.</i> ’s scheme.
A Spatial-Diversity MIMO Dataset for RF Signal Processing Research

The procedure of classifying a detected signal’s modulation scheme with no <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a priori</i> information is known as automatic modulation classification (AMC). AMC has presented numerous contributions toward civilian and military applications. However, in the scientific literature, designing automatic modulation classifiers has been limited to a single particular simulated environment in most cases of study. Hence, the performance of such classifiers lacks: 1) reliability in real-world environments and 2) multivariate environment analysis for AMC operation in dissimilar surroundings. These two reasons represent a significant obstacle to the real-world implementation of such classifiers. Therefore, in this research, we present our contribution to remove these obstacles by generating an emulated signal reference dataset named MIMOSigRef-SD. It includes a wide variety of signal streams modulated by different digital modulation schemes, such as M-QAM, MIL-STD-188-110 B/C standard-specific QAM, M-PSK, M-APSK, DVB-S2/S2X/SH standard-specific APSK, and M-PAM with different modulation orders, each with different multiple-input multiple-output (MIMO) system configurations in order to provide an extensive signal reference. Each signal is also exposed to the realistic impact of different channel models at 2450 MHz, specifically Vehicular-A/B, Pedestrian-A/B, and the Butler, through a channel emulator. This signal reference includes the randomly generated transmit signals, characteristics of the emulated environments, and the recorded received symbols. This enables the dataset to be highly applicable to applications beyond AMC as well. In general, this signal reference can help analyze, evaluate, and design any other radio frequency transceiver tasks under realistic environmental effects. We also validate the elements of the signal reference by comparing the emulation and simulation bit error rate of the designed communication system.
Towards a framework for trustworthy data security level agreement in cloud procurement

After the post-Snowden upheavals, there is a growing concern about preserving the confidentiality of sensitive data across government agencies when using global cloud service providers, such as Amazon Web Services and Microsoft Azure. The use of certification schemes is becoming more critical to assure the security of services offered. This situation is problematic because many certification schemes aim to demonstrate compliance with a security standard rather than achieve a specified security level. Despite the benefits of security certification schemes like Common Criteria (CC), an assurance-based certification process does not scale well to service provision. To this end, this paper aims to investigate the concept of system assurance and trustworthiness in service provisioning, especially when government agencies procure cloud-based services. By using work on the Indonesian Government's data confidentiality requirements, this work develops principles as foundations for a trustworthy data security level agreement (TDSLA) capability framework as a new assurance mechanism for service provisioning based on discrete levels of security assurance incorporated into the formulation of a service level agreement (SLA). The principles which have emerged from the empirical qualitative data collection were evaluated and validated using four approaches: 1) reflection against related work; 2) testimonial validity through participants' feedback; 3) use cases, and 4) application of transferability using cases from the UK Government Cloud (G-Cloud) and the US Federal Risk and Authorization Management Program (FedRAMP). The TDSLA capability framework can form the basis for constructing a legal language in contracts or SLAs.
Introspective False Negative Prediction for Black-Box Object Detectors in Autonomous Driving

Object detection plays a critical role in autonomous driving, but current state-of-the-art object detectors will inevitably fail in many driving scenes, which is unacceptable for safety-critical automated vehicles. Given the complexity of the real traffic scenarios, it is impractical to guarantee zero detection failure; thus, online failure prediction is of crucial importance to mitigate the risk of traffic accidents. Of all the failure cases, False Negative (FN) objects are most likely to cause catastrophic consequences, but little attention has been paid to the online FN prediction. In this paper, we propose a general introspection framework that can make online prediction of FN objects for black-box object detectors. In contrast to existing methods which rely on empirical assumptions or handcrafted features, we facilitate the FN feature extraction by an introspective FN predictor we designed in this framework. For this purpose, we extend the original concept of introspection to object-wise FN predictions, and propose a multi-branch cooperation mechanism to address the distinct foreground-background imbalance problem of FN objects. The effectiveness of the proposed framework is verified through extensive experiments and analysis, and the results show that our method successfully predicts the FN objects with 81.95% precision for 88.10% recall on the challenging KITTI Benchmark, and effectively improves object detection performance by taking FN predictions into consideration.
Optimized Genetic Algorithm for Efficient Allocation of Virtualized Resource in Cloud (OGA_EAVRC)

Efficient allocation of achievable virtual resources to the diverse users is a key challenging issue in a controlled and collaborative cloud environment. As well, balancing the load among the resources and mapping these virtual resources to the physical machines is even bigger challenge of present distributed computing arena. Numerous approaches were introduced by different researchers including Genetic Algorithm for dealing with these challenges, but their scope was limited to certain specific performance elements. Hence, there is a need of optimizing the existing research implementations for efficient allocation of virtualized resources in cloud computing environment. Usually, in a typical distributed computing environment like cloud computing, allocation of virtual resource and balancing of workload among them is realized by means of virtual machines live migration. This article introduces an optimization of existing Genetic algorithm (GA) that mainly intended for VM resource provisioning and load balancing. The proposed OGA_EAVRC considers Population size, Fitness function, Mutation probability, and success rate of resource for optimizing the performance through efficient resource allocation. Key objective of this work is to utilize each physical resource effectively and allocated them to end users efficiently. For studying the operational performance of OGA_EAVRC, an event based CloudSim was chosen. Simulation results states that the proposed OGA_EAVRC can efficiently allocates the workload among virtualized resource by reducing VM's migration among the physical machines.
Gender Classification and Writer Identification System based on Handwriting in Gurumukhi Script

Gender Classification and Writer Identification system are the challenging applications of artificial intelligence and machine learning and widely helpful in forensic, criminal, and suspected investigations. The proposed system is based on behavioral biometric science. Physiological and behavioral biometric traits are the two traits of biometric modality. The paper proposed a novel move in direction of the Gurumukhi (Punjabi) script using multiple feature extraction techniques and hybridization of classification algorithms. The dataset for the experimental evaluation consists of 200 writers with 100 males and 100 females. Two feature extraction methods namely, Intersection and Open Endpoint based feature extraction method and Curve fitting-based feature extraction are considered in this work. For classification, various classifiers namely, Support Vector Machine (SVM), Multi-Layered Perceptron (MLP), K-Neural Network (NN), Random forest, and hybridization of these classifiers are used for both the identification of writer and classification of gender based on the handwriting sample. It has been reported that the maximum gender classification accuracy of 90.57% is reported with curve fitting-based features and hybridization of classifiers. And for writer identification, an accuracy of 87.76% is reported with curve fitting-based features and hybridization of classifiers. The authors also revealed performance evaluation by calculating metrics such as True Positive Rate (TPR) and False Positive Rate (FPR). Regarding future perspective, authors also directed the researchers of handwriting-based communities, to explore gender classification for other Indic scripts and also to utilize handwriting modality for the development of many utilitarian applications such as age, nationality, autopsy, mood, left or right-handedness or nationality from the handwriting modality.
Extending Regularized Least Squares Support Vector Machines for Order Selection of Dynamical Takagi-Sugeno Models

In this paper, the problem of order selection for nonlinear dynamical Takagi-Sugeno (TS) fuzzy models is adressed. It is solved by reformulating the TS model in its Linear Parameter Varying (LPV) form and applying an extension of a recently proposed Regularized Least Squares Support Vector Machine (R-LSSVM) technique for LPV models. For that, a nonparametric formulation of the TS identification problem is proposed which uses data-dependent basis functions. By doing so, the partition of unity of the TS model is preserved and the scheduling dependencies of the model are obtained in a nonparametric manner. For the local order selection, a regularization approach is used which forces the coefficient functions of insignificant values of the lagged input and output towards zero.
An Unsupervised Machine Learning Approach for Medical Image Analysis

The research activity presented in this work focuses the results on data analysis of three-dimensional biomedical PET images by providing clusters that synthesize the extracted components for an objective analysis of the data with respect to the evolution of the diagnosis. The methodological approach is based on the creation of an unsupervised automatic method, through the definition of a feature selection algorithm and extraction of significant numerical attributes relating to the properties of the study elements belonging to the selected clusters. After the normalization of the data contained in the matrix, multivariate analysis was carried out using the concept that similar objects produce similar responses without knowing the entity, type, class descriptions, which are deduced by making observations on the clusters. The factorial analysis with the main components method, applied to the clusters extracted from the data and described by their properties, will allow the identification of homogeneous classes of clusters characterized mainly by only the variables that have a high correlation value between variable and factor, obtaining the automatic identification of classes or “phenomena” that have greater or lesser variance.
Outfit compatibility prediction with multi-layered feature fusion network

Clothing plays a critical role in people's daily lives, a perfect styling clothing is able to help people to avoid weaknesses and show their personal temperament, however, not everyone is good at styling. Compatibility is the core of styling, however, determining whether a pair of garments are compatible with each other is a challenging styling issue. Years of research have been devoted to fashion compatibility learning, whereas there are still several drawbacks in visual feature detection and compatibility calculation. In this paper, we propose an end-to-end framework to learn the compatibility among tops and bottoms. In order to improve the effects of visual feature extraction, a Multi-layer Non-local Feature Fusion framework (MNLFF) is developed. Feature fusion model is used to combine both high and low-level features, while non-local block is for global feature detection. We compare our technique with the prior state-of-the-art methods in the outfit compatibility prediction task and extensive experiments on existing datasets demonstrate its effectiveness.
Security Vulnerabilities and Intelligent Solutions for IoMT Systems

The Internet of Medical Things (IoMT) is one the most practical and useful applications of the Internet of Things (IoT), achieved by connecting medical devices and applications to the online healthcare system. IoMT, if fully and properly implemented, in our healthcare systems can save precious lives and can be a boon to healthcare professionals, especially when addressing challenges caused by pandemics such as COVID-19 and other communicable diseases. In an IoMT, machine-to machine connectivity is possible through short-range communication devices such as Bluetooth or long-range communications (using 5G communication). However security and privacy remain a major challenge faced by IoMT developers and stakeholders. Transmission of critical medical information to the cloud computing setup wirelessly without addressing security issues introduces the risk of hacking and data corruption. It is paramount that the potential security issues are identified and appropriate security measures are implemented. This chapter discussed the various security vulnerabilities that have been identified by researchers and also highlights innovative solutions that have been proposed by researchers. Various solutions such as malware detection, usage of multimodal feature-based biometric identification schemes, ontology-based recommendation tool for addressing various IoMT threat scenarios, and blockchain-enabled authentication techniques are some of the approaches that are discussed and summarized in this chapter.
Smooth compositions are candidates for robust fuzzy systems

This paper proposes the p-factor index for the comparison of the robustness of the fuzzy systems constructed by the different fuzzy t-norms and contrasts the robust performance of the so-called smooth t-norms with the standard product t-norm in the fuzzy systems. It is the author's conjecture that the improved robustness of the fuzzy systems with the smooth compositions rather than the standard product t-norm is something inherently different about the way that the smooth compositions handle the uncertainties. The comparison of the value of p-factor for the different compositions explains and justifies the previously published results and evidence on the robust performance of the smooth compositions in the theoretical and industrial applications. Hence, upon the p-factor analysis, we conclude that the smooth fuzzy systems are structurally robust to the uncertainties. We have clarified the proposed theories through the different simulations on two academic examples. More evidence and justifications on the robust performance of the smooth compositions are available in the literature.
ARBITRAR: User-Guided API Misuse Detection

Software APIs exhibit rich diversity and complexity which not only renders them a common source of programming errors but also hinders program analysis tools for checking them. Such tools either expect a precise API specification, which requires program analysis expertise, or presume that correct API usages follow simple idioms that can be automatically mined from code, which suffers from poor accuracy. We propose a new approach that allows regular programmers to find API misuses. Our approach interacts with the user to classify valid and invalid usages of each target API method. It minimizes user burden by employing an active learning algorithm that ranks API usages by their likelihood of being invalid. We implemented our approach in a tool called ARBITRAR for C/C++ programs, and applied it to check the uses of 18 API methods in 21 large real-world programs, including OpenSSL and Linux Kernel. Within just 3 rounds of user interaction on average per API method, ARBITRAR found 40 new bugs, with patches accepted for 18 of them. Moreover, ARBITRAR finds all known bugs reported by a state-of-the-art tool APISAN in a benchmark suite comprising 92 bugs with a false positive rate of only 51.5% compared to APISAN’s 87.9%.
Eye Know You: Metric Learning for End-to-End Biometric Authentication Using Eye Movements From a Longitudinal Dataset

The permanence of eye movements as a biometric modality remains largely unexplored in the literature. The present study addresses this limitation by evaluating a novel exponentially-dilated convolutional neural network for eye movement authentication using a recently proposed longitudinal dataset known as GazeBase. The network is trained using multi-similarity loss, which directly enables the enrollment and authentication of out-of-sample users. In addition, this study includes an exhaustive analysis of the effects of evaluating on various tasks and downsampling from 1000 Hz to several lower sampling rates. Our results reveal that reasonable authentication accuracy may be achieved even during both a low-cognitive-load task and at low sampling rates. Moreover, we find that eye movements are quite resilient against template aging after as long as 3 years.
Interactive Exploration-Exploitation Balancing for Generative Melody Composition

Recent content creation systems allow users to generate various high-quality content (e.g., images, 3D models, and melodies) by just specifying a parameter set (e.g., a latent vector of a deep generative model). The task here is to search for an appropriate parameter set that produces the desired content. To facilitate this task execution, researchers have investigated user-in-the-loop optimization, where the system samples candidate solutions, asks the user to provide preferential feedback on them, and iterates this procedure until finding the desired solution. In this work, we investigate a novel approach to enhance this interactive process: allowing users to control the sampling behavior. More specifically, we allow users to adjust the balance between exploration (i.e., favoring diverse samples) and exploitation (i.e., favoring focused samples) in each iteration. To evaluate how this approach affects the user experience and optimization behavior, we implement it into a melody composition system that combines a deep generative model with Bayesian optimization. Our experiments suggest that this approach could improve the user's engagement and optimization performance.
Bag of shape descriptor using unsupervised deep learning for non-rigid shape recognition

Highly discriminative feature expression for non-rigid shape recognition is an important and challenging task, which requires both abstract and robust shape descriptors. However, the majority of existing low-level descriptors are designed via hand-crafted, which are sensitive to local changes and larger deformation. To address this issue, this paper proposes a bag of shape descriptor based on unsupervised deep learning and Bag of Words (BoW) for shape recognition. Different from existing pipelines, our method is specially designed to learn high-level and hierarchical shape features from multi-scale context structures. It effectively overcomes obstacles, such as irregular topology, orientation ambiguity, and rigid or non-rigid transformation in the hierarchical learning of contour fragments. Specifically, by adopting an improved decomposing strategy, the shape can be decomposed to a series of valuable contour fragments, results in local to global feature learning. An unsupervised learning framework is also applied to the contour fragment for its feature expression based on the context structure and SSAE (Stack Sparse Auto Encode). In the process of shape representation, a high-level shape dictionary is learned by K-clustering to achieve discriminative feature coding. In addition, to achieve a compact and simplified shape representation, SPM (Spatial Pyramid Matching) is adopted by max-pooling, which effectively incorporates spatial layout information of the given shape. The experiments demonstrate that the proposed method achieves state-of-the-art performance on several public shape datasets comparing with the latest approaches. Our method also obtains high performance under the noisy and occlusion condition.
EM Side-Channel Countermeasure for Switched-Capacitor DC–DC Converters Based on Amplitude Modulation

The security of IoT devices is becoming a major concern in industry. Noninvasive side-channel attacks pose a serious threat as they are capable of extracting secret information from distance while using low-cost equipment. In this article, we propose an efficient countermeasure technique against the electromagnetic (EM) side-channel attacks. The technique is applied to switched-capacitor (SC) dc-dc converters and is based on amplitude modulation of the load signal by the converter capacitance that acts as a carrier. We verify the proposed technique by using a reliable evaluation metric that predicts the correlation between the encryption secret key and the attacker's measured signal. The results show that the proposed technique can achieve cross correlation coefficients as low as 0.2, disabling the attacker from extracting the sensitive information. In addition, test vector leakage assessment (TVLA) ρ-test indicates that the number of leaky points for advanced encryption standard (AES) execution drops from 62 for unprotected circuit to zero for the circuit secured by the proposed technique. TVLA t-statistic is decreased by three orders of magnitude in the protected AES execution.