Reviewerly/exHarmony huggingface dataset:
Applying mutual information for discretization to support the discovery of rare-unusual association rule in cerebrovascular examination dataset

In knowledge discovery studies, association rules mining has been extensively studied to discover hidden knowledge and relationships among set of items in a transactional dataset. Most research on association rule mining focuses on discovering frequent patterns based on the most frequent items occurring in the dataset. However, the process of extracting rare rules has received less attention. In medical dataset studies, the discovery of rare association rules (RARs) is more challenging, because it could likely be used to obtain more potentially rare and unusual knowledge for physicians, beside frequent association rules. Hence, the aim of this paper is to discover non-frequent or rare-unusual association rules (RUARs) from a stroke medical dataset to provide potential meaningful knowledge to the user domain. A discretization method needs to be performed as the data preprocessing step before generating rules. To the best of our knowledge, fewer studies have focused on the role of discretization results to support the extraction of a better amount and quality of RUARs, particularly for medical datasets. In addition, the extracted RUARs is expected to provide potential new unusual insights on stroke risk patterns. This paper applies mutual information measure to discretize a stroke examination dataset collected from a medical center in Taiwan. The interval merging method was proposed to simplify the discrete form and enrich the quality of generated rules. Towards the end, rare association rules, with relatively low support, were generated by employing the Apriori-Rare method accordingly. In addition, a filtering process was applied to the content of the rule itemsets to discover the expected set of RUARs for physicians. Furthermore, the extracted RUARs was analyzed based on the relative risk values toward the occurrence of stroke. Results indicated that the mutual information discretization outperformed the traditional discretization methods in terms of how the discretization scheme can support the extraction of RUARs with a better quantity and quality measurements for further analysis purpose in medical point of view. Moreover, the proposed method had a relatively higher number of RUARs. The knowledge of unusual rule patterns from rare association rules might provide potential new and unusual insights for medical pratitioners and increase the awareness of stroke examination results.
A Multi-Agent Architecture for the Design of Hierarchical Interval Type-2 Beta Fuzzy System

This paper presents a new methodology for building and evolving hierarchical fuzzy systems. For the system design, a tree-based encoding method is adopted to hierarchically link low-dimensional fuzzy systems. Such tree structural representation has by nature a flexible design offering more adjustable and modifiable structures. The proposed hierarchical structure employs a type-2 beta fuzzy system to cope with the faced uncertainties, and the resulting system is called the hierarchical interval type-2 beta fuzzy system (HT2BFS). For the system optimization, two main tasks of structure learning and parameter tuning are applied. The structure learning phase aims to evolve and learn the structures of a population of the HT2BFS in a multi-objective context taking into account the optimization of both the accuracy and the interpretability metrics. The parameter tuning phase is applied to refine and adjust the parameters of the system. To accomplish these two tasks in the most optimal way, we further employ a multi-agent architecture to provide both a distributed and a cooperative management of the optimization tasks. Agents are divided into two different types based on their functions: a structure agent and a parameter agent. The main function of the structure agent is to perform a multi-objective evolutionary structure learning step by means of the multi-objective immune programming algorithm. The parameter agents have the function of managing different hierarchical structures simultaneously to refine their parameters by means of the hybrid harmony search algorithm. In this architecture, agents use cooperation and communication concepts to create high-performance HT2BFSs. The performance of the proposed system is evaluated by several comparisons with various state-of-the-art approaches on noise-free and noisy time series prediction datasets and regression problems. The results clearly demonstrate a great improvement in accuracy rate, convergence speed, and the number of used rules as compared to other existing approaches.
eMotion: An SGX extension for migrating enclaves

Software Guard Extensions (SGX) is a good candidate to address sensitive information disclosure in cloud computing because SGX creates enclaves for applications that protect security sensitive code and data from malicious access. However, existing SGX-enabled Virtual Machine Managers (VMMs) do not provide live migration of SGX-enabled Virtual Machines (VMs). This management operation is impossible because the VMM cannot directly access the Enclave Page Cache (EPC) pages where the VM's enclaves reside. SGX supports the EPC page swapping mechanism that evicts the EPC pages into the untrusted memory which the VMM can access. However, this mechanism has the limitations to be applied to enclave migration. In this paper, we propose an SGX extension for migrating enclaves called eMotion that adds additional instructions and migration support to the SGX architecture for enabling the secure managed migration of running enclaves. eMotion allows that the participating hosts establish a key used in enclave migration and the VMMs in the hosts migrate running enclaves using the established key. We implement a prototype on top of OpenSGX, an open source SGX emulator, to demonstrate the operations of eMotion and to estimate the impact on enclave migration.
Near-100% two-photon-like coincidence-visibility dip with classical light and the role of complementarity

The Hong-Ou-Mandel effect is considered a signature of the quantumness of light, as the dip in coincidence probability using semi-classical theories has an upper bound of 50%. Here we show, theoretically and experimentally, that, with proper phase control of the signals, classical pulses can mimic a Hong-Ou-Mandel-like dip. We demonstrate a dip of 99.635 +/- 0.002% with classical microwave fields. Quantumness manifests in wave-particle complementarity of the two-photon state. We construct quantum and classical interferometers for the complementarity test and show that while the two-photon state shows wave-particle complementarity, the classical pulses do not.
Differential Features for Pedestrian Detection: A Taylor Series Perspective

Differential features are popularly used in computer vision tasks, such as object detection. In this paper, we revisit these features from a functional approximation perspective. In particular, we view an image as a 2-D functional and investigate its Taylor series approximation. Differential features are derived from the approximation coefficients and, therefore, are naturally collected for appearance representation. Thus motivated, we propose to use the zeroth-, first-, and second-order differential features for pedestrian detection and call such features Taylor feature transform (TAFT). In practice, the TAFT features are computed by discrete sampling to address scale issues and meanwhile achieve computational efficiency. In addition, orientation insensitivity is handled by using directional versions of differentials. When applied to pedestrian detection, the TAFT is sampled on grid pixels and calculated from multiple channels following previous solutions. In our extensive experiments on the INRIA, Caltech, TUD-Brussel, and KITTI data sets, the TAFT achieves state-of-the-art results. It outperforms all handcrafted features and performs on par with many deep-learning solutions. Moreover, when a low false-positive rate is requested, the TAFT generates results that are better than or comparable to the state-of-the-art deep learning-based methods. Meanwhile, our implementation runs at 33 fps for 640×480 images without GPU, making TAFT favorable in many practical scenarios.
Spark testing to measure carbon content in carbon steels based on fractal box counting

Spark testing is an effective and inexpensive way to classify the steel materials in industrial practice. Especially, it could determine carbon content in a moment. However, this method has reliance upon the skill and experience of operator. Therefore, computer recognition of spark has been developed to determine carbon content for increasing reliability and efficiency. The numerable high-resolution images of sparks emitting at high speed are taken pictures by camera or mobile phone, then input the images into computer, and calculated the fractal dimension of spark based on fractal box counting method. Result shows that the fractal dimension has a good correlation with carbon content, and the similar natural logarithm function is obtained by a least squares fit through plots of carbon content against fractal dimension. Carbon content measurement error is about 0.06%. It means a convenient and accurate way to identify an unknown carbon steel may be achieved.
High-temperature DIC based on aluminium dihydrogen phosphate speckle

Digital image correlation (DIC) method has been widely used for measurement in high temperature environment, where speckle pattern that can withstand high temperature and maintain good stability is required. In this work we develop a simple method to fabricate speckle pattern for high temperature application. The curing temperature of the speckle is controlled under 350 °C to avoid potential damage to the substrate material. The speckle can resist temperature up to 1050 °C for minimum 0.5 h. Three point bending experiment was carried out at 700 °C to test the speckle. Results show that this high temperature speckle exhibits excellent adhesion to the substrate. This non-invasive speckle shows great potential for improving the measurement reliability at high temperature in engineering application.
A Hierarchical Receding Horizon Algorithm for QoS-Driven Control of Multi-IaaS Applications

Cloud Computing is emerging as a major trend in ICT industry. However, as with any new technology, new major challenges lie ahead, one of them concerning the resource provisioning. Indeed, modern Cloud applications deal with a dynamic context that requires a continuous adaptation process in order to meet satisfactory Quality of Service (QoS) but even the most titled Cloud platform provide just simple rule-based tools; the rudimentary autoscaling mechanisms that can be carried out may be unsuitable in many situations as they do not prevent SLA violations, but only react to them. In addition, these approaches are inherently static and cannot catch the dynamic behavior of the application and are unsuitable to manage multi-Cloud/data center deployments required for mission critical services. This situation calls for advanced solutions designed to provide Cloud resources in a predictive and dynamic way. This work presents capacity allocation algorithms, whose goal is to minimize the total execution cost while satisfying some constraints on the average response time of multi-Cloud based applications. This paper proposes a joint load balancing and receding horizon capacity allocation techniques, which can be employed to handle multiple classes of requests. An extensive evaluation of the proposed solution against an Oracle with perfect knowledge of the future and well-known heuristics proposed in the literature is provided. The analysis shows that our solution outperforms the heuristics producing results very close to the optimal ones, and reducing the number of QoS violations (in the worst case QoS constraints violation rate is 4.26 percent versus up to 17.25 percent of other approaches and can easily reduced by roughly a factor of four by exploiting the receding horizon approach). Furthermore, a sensitivity analysis over two different time scales indicates that finer grained time scales are more appropriate for spiky workloads. Analytical results are validated through simulation, which also analyzes the impact of Cloud environment random perturbations. Finally, experiments on a prototype environment demonstrate the effectiveness of the proposed approach under real workloads.
A New Generalized Paraunitary Generator for Complementary Sets and Complete Complementary Codes of Size ${2^m}$

Complementary sequence sets (CSSs) and complete complementary codes (CCCs) have many applications in science and engineering, especially in wireless communications. A construction of CSS and CCC, having size M = 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m</sup> and length M <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">K</sup> , where m and K are positive integers, is presented. The proposed construction is a generalized paraunitary (PU) algorithm that greatly increases the number of permutations from K! to (mK)! compared to those of previous PU constructions. Moreover, this new construction can be generalized to the case M = p <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m</sup> , where p is a positive integer. The increase in the number of permutations means that a wide range of CCCs and CSSs can be obtained.
Optimal learning group formation: A multi-objective heuristic search strategy for enhancing inter-group homogeneity and intra-group heterogeneity

In modern education systems, plenty of research suggests that clustering the learners into optimal learning groups based on their multiple characteristics is a determining effort in enhancing the effectiveness of collaborative learning. Although there have been several evidences on developing and implementing appropriate computational tools to handle classification processes in expert and intelligent systems, the effectiveness and accuracy of optimal grouping algorithms are still worth improving. For instance, the majority of grouping processes in collaborative learning environments is orchestrated through single-objective optimization algorithms, which need to be revisited due to some intrinsic limitations. In this paper, we propose a novel algorithm capable of properly addressing a variety of optimization problems in optimal learning group formation processes. To this end, a multi-objective version of Genetic Algorithms, i.e. Non-dominated Sorting Genetic Algorithm, NSGA-II, was successfully implemented and applied to improve the performance and accuracy of optimally formed learning groups. In contrast to the previous related works applying single-objective algorithms, the main advantage of our work is simultaneous satisfaction of multiple targets predefined for the formation of optimal learning groups, especially the inter-homogeneity and intra-heterogeneity of each learning group, which significantly enhance both effectiveness and accuracy of optimal grouping processes in the underlying intelligent systems. Challenging the proposed optimization algorithms, both single- and multi-objective optimizers, with a similar grouping problem, clearly proved that the single-objective optimization technique has limited control and sensitivity to the quality of individual groups. Contrary to single-objective optimization techniques, which are mainly governed by adjusting the quality of the groups altogether in average, the proposed multi-objective algorithm not only takes the average desirability of all formed groups into account but also precisely monitors the fitness of each group in a potential solution distinctively. The generality of the proposed algorithm makes it a suitable candidate not only to handle optimal grouping in learning environments but also to be competent enough for grouping problems in other domains as well.
Encryption-Free Framework of Privacy-Preserving Image Recognition for Photo-Based Information Services

Nowadays, mobile devices, such as smartphones, have been widely used all over the world. In addition, the performance of image recognition has drastically increased with deep learning technologies. From these backgrounds, some photo-based information services provided in a client-server architecture are getting popular: client users take a photo of a certain spot and send it to a server, while the server identifies the spot with an image recognizer and returns its related information to the users. However, this kind of client-server image recognition can cause a privacy issue because image recognition results are sometimes privacy-sensitive. To tackle the privacy issue, in this paper, we propose a framework of privacy-preserving image recognition called EnfPire, in which the server cannot uniquely determine the recognition result but client users can do so. An overview of EnfPire is as follows. First, client users extract a visual feature from their taken photo and transform it so that the server cannot uniquely determine the recognition result. Then, the users send the transformed feature to the server that returns a set of candidates of the recognition result to the users. Finally, the users compare the candidates to the original visual feature for obtaining the final result. Our experimental results demonstrate that EnfPire successfully degrades the server's spot-recognition accuracy from 99.8% to 41.4% while keeping 86.9% of the spot-recognition accuracy on the user side.
A Key Recovery Reaction Attack on QC-MDPC

Algorithms for secure encryption in a post-quantum world are currently receiving a lot of attention in the research community. One of the most promising such algorithms is the code-based scheme called QC-MDPC, which has excellent performance and a small public key size. In this paper, we present a very efficient key recovery attack on the QC-MDPC scheme using the fact that decryption uses an iterative decoding step, and this can fail with some small probability. We identify a dependence between the secret key and the failure in decoding. This can be used to build what we refer to as a distance spectrum for the secret key, which is the set of all distances between any two ones in the secret key. In a reconstruction step, we then determine the secret key from the distance spectrum. The attack has been implemented and tested on a proposed instance of QC-MDPC for 80-bit security. It successfully recovers the secret key in minutes. A slightly modified version of the attack can be applied on proposed versions of the QC-MDPC scheme that provides IND-CCA security. The attack is a bit more complex in this case, but still very much below the security level. The reason why we can break schemes with proved CCA security is that the model for these proofs typically does not include the decoding error possibility. At last, we present several algorithms for key reconstruction from an empirical distance spectrum. We first improve the naïve algorithm for key reconstruction by a factor of about 3 0000, when the parameters for 80-bit security are implemented. We further develop the algorithm to deal with errors in the distance spectrum. This ultimately reduces the requirement on the number of ciphertexts that need to be collected for a successful key recovery.
The human behaviour indicator: A measure of behavioural evolution

Activities of daily living (ADL) or activities of daily working (ADW) may be affected by changes in a person’s health or well-being. Measuring progressive changes in one activity or multiple activities is representative of behavioural variations. By inspecting the trends in multiple activities, it is possible to identify and predict human behavioural changes. We refer to the trends in people’s behaviour as behavioural evolution. In this paper, we propose a novel indicator to measure the progressive changes representing a participant’s behavioural evolution. The proposed indicator presents activities as a holistic measure, which first combine multi-activities and then measure the progressive changes in the combined activities for each single day. Real data sets were collected from a wireless sensor network and used to examine our proposed technique. As part of this process, we were able to quantify progressive changes for individual and aggregated activities. Our experimental results demonstrated that: (1) the proposed approach can identify and distinguish normal and abnormal behaviours; (2) large data sets gathered from sensors in an intelligent environment represented in various time series can be visualised in a simple and more understandable format; (3) identifying trends in ADLs or ADWs is a relevant means of sharing information with carers or supervisors.
Semi-Supervised Learning Based on GAN With Mean and Variance Feature Matching

The improved generative adversarial network (improved GAN) is a successful method using a generative adversarial model to solve the problem of semi-supervised learning (SSL). The improved GAN learns a generator with the technique of mean feature matching which penalizes the discrepancy of the first-order moment of the latent features. To better describe common attributes of a distribution, this paper proposes a novel SSL method which incorporates the first-order and the secondorder moments of the features in an intermediate layer of the discriminator, called mean and variance feature matching GAN (MVFM-GAN). To capture more precisely the data manifold, not only the mean but also the variance is used in the latent feature learning. Compared with improved GAN and other traditional methods, MVFM-GAN achieves superior performance in semi-supervised classification tasks and a better stability of GAN training, particularly in the cases when the number of labeled samples is low. It shows a comparable performance with the state-of-the-art methods on several benchmark data sets. As a byproduct of the novel approach, MVFM-GAN generates realistic images of good visual quality.
Parallel K-Tree: A multicore, multinode solution to extreme clustering

Clustering is a popular technique that can help make large datasets more manageable and usable by grouping together similar objects. Most clustering approaches are too computationally expensive for datasets that are very large or complex. This work presents Parallel K-Tree, a hierarchical data structure and clustering algorithm that takes advantage of modern computing environments to cluster extremely large datasets. Parallel K-Tree produces high-quality clusters and scales more efficiently than traditional, parallelized and state-of-the-art approaches. Parallel K-Tree was applied to a large (8 terabyte) collection of Landsat 5 satellite images. This required clustering of 540 billion objects into eight billion clusters — a two orders of magnitude size increase over any reported alternative approach. Furthermore, Parallel K-Tree was executed on just two commodity servers — rather than a high-performance supercomputer.