Reviewerly/exHarmony huggingface dataset:
Antilock braking system fuzzy controller optimization with a genetic algorithm in a form of cellular automaton

This paper is devoted to the issue of efficient and intuitive visualization of optimization processes. The problem is considered on the example of ground wheeled vehicle antilock braking system fuzzy control element optimization using a genetic algorithm in the form of a cellular automaton.
Dynamic Entity-Based Named Entity Recognition Under Unconstrained Tagging Schemes

As increasingly more textual information becomes available, named entity recognition (NER) systems are thriving, benefiting from powerful models and expressive tagging schemes that promote the full use of diverse features at different levels. To improve performance, traditional approaches have focused mainly on changing the structures of NER models but have always ignored the hard constraints and left the NER tagging schemes unchanged. To solve this problem, this article proposes a dynamic entity-based NER approach under unconstrained tagging schemes. To eliminate the constraints, we reorganize widely used tagging schemes and propose two novel unconstrained schemes: one in which tags are assigned to words and entities separately, and one where words and entities are labeled indiscriminately by uniformly taking them as chunks. Associated with the unconstrained tagging schemes, two entity-based neural architectures are also presented that recognize entities at the same time that the sentence is dynamically segmented. Unlike other static NER models that process all the tags after labeling each word, our models address the inputs dynamically by the interactions between the input text and the output labels. The dynamic mechanism can ensure that the entity-level features are included in the NER system, which is helpful for correctly recognizing entities. Except for word embeddings pretrained from unlabeled corpora, no external language-specific knowledge or other resources such as gazetteers are used. The experiments with English, German, Dutch, and Spanish datasets show that our methods can perform very well with different languages. Particularly, the results of the recall rate against the entity’s length reveal that the proposed entity-based models are suitable for recognizing entities with long lengths.
Evaluation and comparison between smartphone and photomicrography based whole slide imaging

In recent years, a new concept has emerged at the forefront of slide-based diagnosis and telepathology. This is the concept of whole slide imaging. This has very recently also been tried with smartphones using complex software for photo analysis and stitching. This study is aimed to evaluate and compare the use of Jenoptik photomicrograph camera and smartphone camera-based whole slide imaging (WSI) for various microscopic preparations and slides. The study also uses a commonly available IOS smartphone device, a commonly available phone to microscope attachment, and the most commonly used Adobe Photoshop software for all stitching purposes.To evaluate and compare smartphone and photomicrography-based whole slide imaging.The study was conducted at the Department of Oral Pathology and Microbiology in Bapuji Dental College and Hospital, Davangere. The samples included in this study are 10 slides of routine hematoxylin and eosin stain slides and 10 ground section slides of teeth and bone samples. Continuous photographs of the entire slide were captured using an IOS device and a Jenoptik camera attached research microscope at 10× magnification. The photographs were stitched using Adobe Photoshop 2017 software to obtain a whole slide image. Then, two observers analyzed the whole slide image for a possible diagnosis and thus compared the efficiency of both the methods.The statistical Chi-square test and analysis of variance (ANOVA) H test were done using Statistical Package for the Social Sciences (SPSS) 2010 Software.Percentage of measure of agreement was 79.5%, 83.7% and 86.3%, 89.1% for the whole slide images taken in the smartphone and photomicrograph and analyzed by Observer 1 and Observer 2, respectively.In this study, it can be seen that the WSI by research microscopes is better than that with a smartphone and a compound microscope but still both the methods are equally good and can be followed with accurate results.
Soft Skills Simulation and Assessment: Qualimetric Approach for Smart University

The article represents the application of the qualimetric approach to the diagnostics of the soft skills level which is considered to be necessary for both the university graduates and the regional development according to the "society-smart university-enterprise" triad. Qualimetric approach is represented by Taguchi methods implementing signal-to-noise ratio for assessment noise factors that reduce the quality level of the research object. For the practical application of Taguchi's ideas, the methods of mathematical statistics and probability theory are used. The expert methods are also used to monitor and assess the level of the graduates' soft skills. The expertise was proposed and tested on the base of some departments in Togliatti State University. The key concepts of ABC-analysis were used and revealed. The assessment of the targeted soft skills for technical faculties was conducted. The additional models suggested by the authors determine such characteristics as "the coefficient of unrealized learning opportunities" and "the cost of achieving a given level of quality of education." The suggested simulations allow supervisors at smart university to clearly indicate the required didactic units for the educational purposes. The considered models and the achieved results allow authors to determine the focus of management and educational influence on the competences and soft skills training that level turned out to be insufficient. The educational process in a smart university can be adjusted and improved due to the above-mentioned methods.
Fuzzy Ontology-Based Querying User' Requests Under Uncertain Environment

Assistance with the use of technical devices is required as soon as the tasks to be performed become complex. This assistance is also needed as soon as the authors provide assistance to users to find solutions to incidents that occur during the application of unsuitable procedures. The goal of this work is then to provide a knowledge extraction approach that can interpret user requests into valid system requests to respond appropriately to novice user requests. This approach is based on a fuzzy semantic network for the modeling of imprecise and uncertain knowledge and the automatic construction of temporary fuzzy ontology for the identification and interpretation of user requests. The proposed approach has the advantage of being able to integrate the notion of uncertain and imprecise knowledge into the representation of system objects and procedures. The experimental results show the efficiency and the effectiveness of the approach.
Clustering algorithm for mixed datasets using density peaks and Self-Organizing Generative Adversarial Networks

This paper presents a new Density-Peaks and Self-Organizing Generative Adversarial Networks (DP-SO-GAN) for clustering mixed datasets. Many clustering methods depend on the assumption that datasets contain either categorical or numerical attributes. Nevertheless, in real-time, most of the applications include mixed categorical and numerical attributes. In medicine, the clustering of cardiovascular disease is an essential task. The clustering of such data attributes is a vital and challenging issue. First, we transform mixed data attributes such as categorical attributes using a one-hot encoding technique and numerical attributes using normalization techniques. The converted characteristics are input to a Self-Organizing Generative Adversarial Networks (SO-GAN) to learn the feature map. Second, we train two kernel networks, such as the generator and discriminator, and each one holds a trivial amount of convolution kernels. Last, we propose an enhanced density peaks clustering algorithm and computing similarity measure between the data objects in the feature representation. The clustering accuracy for the cardiovascular disease dataset results in 88.32% with a standard deviation of 0.1 and is relatively higher than that of other existing algorithms. The training time for hand-written digits datasets over 300 epochs is 3148.26 ​s. Experiment results obtained on a set of five datasets demonstrate the merits of the proposed method, especially in terms of the stability and efficiency of network training. The computational complexity of the proposed method in terms of floating-point operations is reduced by around 18% as compared with the classical generative adversarial networks.
Parkinson’s disease subtype classification: Application of decision tree, logistic regression and logit leaf model

Parkinson’s Disease has two subtypes which are Tremor Dominant (TD) and Postural Instability/Gait Difficulty (PIGD). Each subtype has the difference in clinical treatment, so it is necessary to classify Parkinson’s Disease subtypes. Three classification methods were implemented: decision tree, logistic regression, and logit leaf model (LLM). Data on 229 people with early Parkinson’s disease from the PPMI (Parkinson’s Progression Markers Initiative) database were used in the analysis. Imbalanced data problem were handled using oversampling, undersampling, SMOTE (Synthetic Minority Over-sampling Technique). Logistic regression with SMOTE using parameter set-up α=600, γ=200 produced the best result, according to the accuracy of 98.3 %, sensitivity of 98.41 %, and specificity of 99.07 %.
UPC: An Open Word-Sense Annotated Parallel Corpora for Machine Translation Study

Machine translation (MT) has recently attracted much research on various advanced techniques (i.e., statistical-based and deep learning-based) and achieved great results for popular languages. However, the research on it involving low-resource languages such as Korean often suffer from the lack of openly available bilingual language resources. In this research, we built the open extensive parallel corpora for training MT models, named Ulsan parallel corpora (UPC). Currently, UPC contains two parallel corpora consisting of Korean-English and Korean-Vietnamese datasets. The Korean-English dataset has over 969 thousand sentence pairs, and the Korean-Vietnamese parallel corpus consists of over 412 thousand sentence pairs. Furthermore, the high rate of homographs of Korean causes an ambiguous word issue in MT. To address this problem, we developed a powerful word-sense annotation system based on a combination of sub-word conditional probability and knowledge-based methods, named UTagger. We applied UTagger to UPC and used these corpora to train both statistical-based and deep learning-based neural MT systems. The experimental results demonstrated that using UPC, high-quality MT systems (in terms of the Bi-Lingual Evaluation Understudy (BLEU) and Translation Error Rate (TER) score) can be built. Both UPC and UTagger are available for free download and usage.
Verified sequential Malloc/Free

We verify the functional correctness of an array-of-bins (segregated free-lists) single-thread malloc/free system with respect to a correctness specification written in separation logic. The memory allocator is written in standard C code compatible with the standard API; the specification is in the Verifiable C program logic, and the proof is done in the Verified Software Toolchain within the Coq proof assistant. Our "resource-aware" specification can guarantee when malloc will successfully return a block, unlike the standard Posix specification that allows malloc to return NULL whenever it wants to. We also prove subsumption (refinement): the resource-aware specification implies a resource-oblivious spec.
Remodularization Analysis for Microservice Discovery Using Syntactic and Semantic Clustering

This paper addresses the challenge of automated remodularization of large systems as microservices. It focuses on the analysis of enterprise systems, which are widely used in corporate sectors and are notoriously large, monolithic and challenging to manually decouple because they manage asynchronous, user-driven business processes and business objects (BOs) having complex structural relationships. The technique presented leverages semantic knowledge of enterprise systems, i.e., BO structure, together with syntactic knowledge of the code, i.e., classes and interactions as part of static profiling and clustering. On a semantic level, BOs derived from databases form the basis for prospective clustering of classes as modules, while on a syntactic level, structural and interaction details of classes provide further insights for module dependencies and clustering, based on K-Means clustering and optimization. Our integrated techniques are validated using two open source enterprise customer relationship management systems, SugarCRM and ChurchCRM. The results demonstrate improved feasibility of remodularizing enterprise systems (inclusive of coded BOs and classes) as microservices. Furthermore, the recommended microservices, integrated with ‘backend’ enterprise systems, demonstrate improvements in key non-functional characteristics, namely high execution efficiency, scalability and availability.
Target-Focused Feature Selection Using Uncertainty Measurements in Healthcare Data

Healthcare big data remains under-utilized due to various incompatibility issues between the domains of data analytics and healthcare. The lack of generalizable iterative feature acquisition methods under budget and machine learning models that allow reasoning with a model’s uncertainty are two examples. Meanwhile, a boost to the available data is currently under way with the rapid growth in the Internet of Things applications and personalized healthcare. For the healthcare domain to be able to adopt models that take advantage of this big data, machine learning models should be coupled with more informative, germane feature acquisition methods, consequently adding robustness to the model’s results. We introduce an approach to feature selection that is based on Bayesian learning, allowing us to report the level of uncertainty in the model, combined with false-positive and false-negative rates. In addition, measuring target-specific uncertainty lifts the restriction on feature selection being target agnostic, allowing for feature acquisition based on a target of focus. We show that acquiring features for a specific target is at least as good as deep learning feature selection methods and common linear feature selection approaches for small non-sparse datasets, and surpasses these when faced with real-world data that is larger in scale and sparseness.
SelMon

Higher privileged trust anchors such as thin hypervisors and Trust-Zone have been adopted to protect mobile OSs. For instance, the Samsung Knox security platform implements a kernel integrity monitor based on a hardware-assisted virtualization technique for 64-bit devices. Although it protects the OS kernel integrity, the monitoring platform itself can be a target of attackers if it encompasses exploitable bugs. In this paper, we propose SelMon, a portable way of self-protecting kernel integrity monitors without introducing another higher privileged trust anchor. To this end, we first logically separate the regions of the integrity monitor into two parts: privileged and non-privileged regions. Then, we ensure that only the privileged region code can access the critical data objects that can be exploited to compromise the monitor integrity (e.g., the hypervisor page table). The non-critical operations in terms of preserving the monitor integrity are conducted in the non-privileged region. In addition to the privilege separation, we also illustrate how to utilize the general hardware features, watchpoint and data execution prevention (DEP), to ensure the robustness of the separation. In the evaluation, it was found that our approach imposes a negligible overhead of 2% in the worst case with SPEC CPU2006.
Compiler and runtime support for continuation marks

Continuation marks enable dynamic binding and context inspection in a language with proper handling of tail calls and first-class, multi-prompt, delimited continuations. The simplest and most direct use of continuation marks is to implement dynamically scoped variables, such as the current output stream or the current exception handler. Other uses include stack inspection for debugging or security checks, serialization of an in-progress computation, and run-time elision of redundant checks. By exposing continuation marks to users of a programming language, more kinds of language extensions can be implemented as libraries without further changes to the compiler. At the same time, the compiler and runtime system must provide an efficient implementation of continuation marks to ensure that library-implemented language extensions are as effective as changing the compiler. Our implementation of continuation marks for Chez Scheme (in support of Racket) makes dynamic binding and lookup constant-time and fast, preserves the performance of Chez Scheme's first-class continuations, and imposes negligible overhead on program fragments that do not use first-class continuations or marks.
Optimum Design of an Adaptive Fuzzy Controller as Active Suspension for a Quarter-Car Model

This research introduces an optimal adaptive fuzzy controller consists of two fuzzy systems which each of them includes 1 output, 2 inputs, and 25 fuzzy rules. The designed fuzzy sytems, in turn, comprises Gaussian membership functions and applies the singleton fuzzifier, center average defuzzifier, and product inference engine. Moreover, the Gravitational Search Algorithm (GSA) is utilized to ascertain the optimum parameters of the controller. The body acceleration and the relative displacement between the tire and the sprung mass are utilized to form a proper objective function in the optimization procedure. Results show the dominance of the proposed approach over the conventional controllers.
A light-weight on-line action detection with hand trajectories for industrial surveillance

Most of the intelligent surveillances in the industry only care about the safety of the workers. It is meaningful if the camera can know what, where and how the worker has performed the action in real time. In this paper, we propose a light-weight and robust algorithm to meet these requirements. By only two hands’ trajectories, our algorithm requires no Graphic Processing Unit (GPU) acceleration, which can be used in low-cost devices. In the training stage, in order to find potential topological structures of the training trajectories, spectral clustering with eigengap heuristic is applied to cluster trajectory points. A gradient descent based algorithm is proposed to find the topological structures, which reflects main representations for each cluster. In the fine-tuning stage, a topological optimization algorithm is proposed to fine-tune the parameters of topological structures in all training data. Finally, our method not only performs more robustly compared to some popular offline action detection methods, but also obtains better detection accuracy in an extended action sequence.
Tensorial graph learning for link prediction in generalized heterogeneous networks

Tensorial graph learning frameworks are proposed for link predictions in heterogeneous, homogeneous and generalized heterogeneous networks. In these frameworks, tensorial graphs are used to represent different networks by incorporating node and edge tensors into the graphs. A tensorial graph kernel method is developed for link predictions in these networks using four types of, i.e., structural, behavioral, content and node/edge characteristics, data. In this method, a n-strand iterated algorithm and a tensorial graph based random walk algorithm are proposed to measure node similarities in different networks within the generalized heterogeneous networks, and a tensorial graph multi-kernel learning method is developed to integrate the results. Experimental results on two real-world social media databases show that the tensorial graph kernel method has better performance using all types of data than using one type of data alone or combinations of some types of data. The tensorial graph kernel method also performs considerably better than existing competitive methods.
Nonlinear feature selection on attributed networks

The acceleratinnsional nodal attributes in various data mining tasks highlights the significance of feature selection on the networked data. Due to the lack of class labels of nodes, many feature selection methods are proposed in semi-supervised or unsupervised manners in various scenarios instead of supervised ones. More often than not, features and (pseudo) labels are correlated in a nonlinear way that is more intricate than linearity. In these circumstances, the vast majority of existing linear algorithms could not work well since they select features according to how well the feature can linearly explain the variance of labels. Moreover, although some methods focus on nonlinear feature selection, with the neglect of the link relations between data, they are difficult to be applied to attributed networks directly. In this paper, we investigate how to achieve nonlinear feature selection on attributed networks with the help of both labeled and unlabeled data. Methodologically, we first propose a novel semi-supervised nonlinear framework FS-GCN based on graph convolutional networks (GCNs) to select high-quality features, which can elaborately catch the nonlinear dependency between nodal attributes and class labels. To verify the importance of nonlinearity precisely, we further explore the possibility of totally removing the label information so that a variant of FS-GCN is proposed in the unsupervised form, referred to as UFS-GCN. Besides, experimental results on several real-world datasets validate the superiority of FS-GCN as well as UFS-GCN in terms of the quality of selected features, suggesting their robustness in the condition of extremely low even zero label ratio.
High-speed multi-person pose estimation with deep feature transfer

Recent advancements in deep learning have significantly improved the accuracy of multi-person pose estimation from RGB images. However, these deep learning methods typically rely on a large number of deep refinement modules to refine the features of body joints and limbs, which hugely reduce the run-time speed and therefore limit the application domain. In this paper, we propose a feature transfer framework to capture the concurrent correlations between body joint and limb features. The concurrent correlations of these features form a complementary structural relationship, which mutually strengthens the network’s inferences and reduces the needs of refinement modules. The transfer sub-network is implemented with multiple convolutional layers, and is merged with the body part detection network to form an end-to-end system. The transfer relationship is automatically learned from ground-truth data instead of being manually encoded, resulting in a more general and efficient design. The proposed framework is validated on the multiple popular multi-person pose estimation benchmarks - MPII, COCO 2018 and PoseTrack 2017 and 2018. Experimental results show that our method not only significantly increases the inference speed to 73.8 frame per second (FPS), but also attains comparable state-of-the-art performance.
SmokeBomb

Cache side-channel attacks abuse microarchitectural designs meant to optimize memory access to infer information about victim processes, threatening data privacy and security. Recently, the ARM architecture has come into the spotlight of cache side-channel attacks with its unprecedented growth in the market.