Reviewerly/exHarmony huggingface dataset:
Comparative Study of Fuzzy Controller Optimization with Dynamic Parameter Adjustment Based on Type 1 and Type 2 Fuzzy Logic

This paper presents a comparison of fuzzy controller optimization results using dynamic parameter adjustment Type 1 (T1) and Interval Type 2 (T2) fuzzy logic to the Firefly Algorithm (FA). The FA is used for optimizations parameters of the membership functions in the fuzzy controllers. The dynamic adjustment is applied to the randomness parameter of the search space, which represents the exploration of the method, avoiding stagnation or premature convergence. The FA generates the values that the parameters of the membership functions take for optimization use in the fuzzy systems for control. The control plants have one or more input variables that are processed and result in one or more output variables, it would be very difficult to model the human reasoning in equations to achieve a machine acquires the knowledge acquired by humans. For that reason the fuzzy logic that generates that insertion is used as if it were human reasoning.
Goal-directed Sequence Generation with Simulation Feedback Method

Reinforcement Learning (RL) is used in the language sequence generation. The reason for the occurrence of neglected loss is that simulation representing the environment calculates rewards of environment without direct control rules and uses self-feedback strategies typically. This process is that the agent provides feedback to itself as an environment. To overcome drawback, we propose a goal-directed language sequence generation method for Natural Language Generation (NLG) tasks and a reliable implementation strategy. It helps the agent learn generation rules without human help and improve the accuracy of goal. The work demonstrates the way of using a preliminary generator as the environment simulator during the initialization phase to provide feedback for the training generator on behalf of the environment. When the training level meets the decision condition, the replacement strategy is selected for correction because the environment simulator is not enough to provide positive feedback. The discriminator provides the specific target reward, and the guide content is generated in the existing method based on RL and Generative Adversarial Net (GAN) method. Experimental results on the synthetic data and real-world tasks prove the advantages of goal-directed sequence generation with simulation feedback method over sequence generative adversarial nets models.
Rapid determination of source parameters for the 2017 Mw 8.2 Mexico earthquake based on high-rate GPS data

Aiming to rapidly determine source parameters (i.e., static offset, seismic wave arrival, moment magnitude, and hypocentral location) for the Mexico 8.2 earthquake, the records of 7 high-rate GPS stations are utilized. Smoothness Priors Method (SPM) is introduced to rapidly extract the static offset from GPS displacement waveforms, and the result suggests this approach is feasible. The seismic wave arrival detection is carried out with the aid of S-transform, and the propagation velocity of seismic wave detected by most GPS stations are within 4–5 km/s, implying the seismic wave captured by GPS station may be S wave or surface wave. After that, an empirical regression model is adopted to characterize the moment magnitude for this earthquake, and this empirical formula can obtain reliable magnitude in comparison with the reference magnitude. The convergence time of average moment magnitude is 298 s, suggesting that a reliable and robust magnitude (Mw 8.37) can be estimated by the 7 GPS stations with about 298 s after earthquake occurrence. Considering the influence of the spatial distribution of GPS stations, 4 nearest GPS stations evenly distributed on both sides of the fault are selected to determine the warning-magnitude for the Earthquake Early Warning (EEW). A reliable and robust moment magnitude (Mw 8.2) can be estimated by the 4 stations with about 251 s, which is 57 s ahead of 7 stations. Finally, the coordinates of 6 GPS stations and corresponding seismic wave arrival time are utilized to determine the hypocentral location, and the latitude and longitude of estimated location is 14.925°N and 93.765°W, which is 17.9 km from the reference location. The results indicate that the source parameters required for EEW can be rapidly determined based on high-rate GPS displacement waveforms, and integrating real-time GPS into a joint EEW system will be crucial going forward.
Automated security testing for web applications on industrial automation and control systems

Abstract Industrial automation and control systems (IACS) play a key role in modern production facilities. On the one hand, they provide real-time functionality to the connected field devices. On the other hand, they get more and more connected to local networks and the internet in order to facilitate use cases promoted by “Industrie 4.0”. A lot of IACS are equipped with web servers that provide web applications for configuration and management purposes. If an attacker gains access to such a web application operated on an IACS, he can exploit vulnerabilities and possibly interrupt the critical automation process. Cyber security research for web applications is well-known in the office IT. There exist a lot of best practices and tools for testing web applications for different kinds of vulnerabilities. Security testing targets at discovering those vulnerabilities before they can get exploited. In order to enable IACS manufacturers and integrators to perform security tests for their devices, ISuTest was developed, a modular security testing framework for IACS. This paper provides a classification of known types of web application vulnerabilities. Therefore, it makes use of the worst direct impact of a vulnerability. Based on this analysis, a subset of open-source vulnerability scanners to detect such vulnerabilities is selected to be integrated into ISuTest. Subsequently, the integration is evaluated. This evaluation is twofold: At first, willful vulnerable web applications are used. In a second step, seven real IACS, like a programmable logic controller, industrial switches and cloud gateways, are used. Both evaluation steps start with the manual examination of the web applications for vulnerabilities. They conclude with an automated test of the web applications using the vulnerability scanners automated by ISuTest. The results show that the vulnerability scanners detected 53 % of the existing vulnerabilities. In a former study using commercial vulnerability scanners, 54 % of the security flaws could be found. While performing the analysis, 45 new vulnerabilities were detected. Some of them did not only break the web server but crashed the whole IACS, stopping the critical automation process. This shows that security testing is crucial in the industrial domain and needs to cover all services provided by the devices.
Expressiveness of component-based frameworks: a study of the expressiveness of BIP

We extend our previous algebraic formalisation of the notion of component-based framework in order to formally define two forms—strong and weak—of the notion of full expressiveness and study their properties. Our earlier result shows that the BIP (Behaviour–Interaction–Priority) framework does not possess the strong full expressiveness with respect to the sub-class of GSOS rules used for the definition of its semantics. In this paper, we refine this comparison detailing the expressiveness of classical BIP, Offer BIP and a number of variations obtained either by relaxing the constraints in the definition of priority models or by introducing positive premises into the rule formats used to define the operational semantics of composition operators. The obtained results are organised into an expressiveness hierarchy.
Computer aided detection system for early cancerous pulmonary nodules by optimizing deep learning features

In this paper, a deep learning technique for the early detection of pulmonary nodules from low dose CT (LDCT) images is proposed. The proposed technique is composed from four stages. Firstly, a preprocessing stage is applied to enhance image contrast of low dose images. Secondly, a transfer learning is utilized to extract deep learning features that describe the LDCT images. Thirdly, a genetic algorithm (GA) is learned on the extracted deep learning features using a training subset of the data to optimize the feature-set and select the most relevant features for cancerous nodules detection. Finally, a classification step of the selected features is performed using supported vector machines (SVM) to detect cancerous pulmonary nodules. Preliminary results on a number of 320 LDCT images acquired from 50 different subjects from the International Early Lung Cancer Action Project, I-ELCAP, online public lung image database has achieved a detection accuracy of 92.5%, sensitivity of 90%, and specificity of 95% Comparison results has shown the outstanding results of the proposed method. These preliminary results confirm the promising of our proposed method.
VieLens,

With the appearance of many wearable devices like smartwatches, recording glasses (such as Google glass), smart phones, digital personal profiles have become more readily available nowadays. However, searching and navigating these multi-source, multi-modal, and often unstructured data to extract useful information is still a relatively challenging task. Therefore, the LSC2019 competition has been organized so that researchers can demonstrate novel search engines, as well as exchange ideas and collaborate on these types of problems. We present in this paper our approach for supporting interactive searches of lifelog data by employing a new retrieval system calledVieLens, which is an interactive retrieval system enhanced by natural language processing techniques to extend and improve search results mainly in the context of a user's activities in their daily life.
An Enhanced SHA3-based Hashing Method

When a software implementation of a security system is considered, side channel attacks are on the spot threat. Side-channel attacks, considered in this work, are a class of physical attacks in which an adversary tries to exploit physical information leakages such as timing information, power consumption, or electromagnetic radiation to estimate security system parameters during performing security algorithms. New system architecture features, such as larger cache sizes and multicore processors, have increased the prevalence of side channels, in addition to, the availability of measurement apparatuses to an attacker. Thus, software developers must be aware of the potential for side-channel attacks and plan appropriately. In this paper, we propose a software implementation of a hashing method based on SHA3-512 hashing algorithm that can counteract the side-channel attacks. To achieve our goal, we use three techniques, first, we shuffle the dataset, salt, and process selector arrays using "Fisher Yates" algorithm. Second, we use volatile memory objects to hold critical data. Finally, we apply the thread locking technique where at most one thread can access the critical objects at a time. Experimental results show that our proposed hashing method is more secure than other related methods. Although there is a trade-off between hashing security and hashing processing time, the processing time of the proposed hashing method is still acceptable.
A Dynamic Path Planning Model Based on K-means Algorithm and Simulated Annealing Algorithm

Aiming at the problem that it is difficult to reduce logistics distribution cost through quantitative analysis, the paper takes the lowest logistics and transportation cost as objective function, comprehensively considers the location of logistics distribution centers, and constructs dynamic path planning model based on K-means algorithm and simulated annealing algorithm. Through simulation analysis, it can be found that by wisely selecting logistics distribution centers as transit service stations, the logistics distribution cost can be effectively reduced.
Data Discretization based on Maximum Information Coefficient

Most supervised discretization methods rely on category labels. However, in practical applications, supervisory information is not always category labels, it may also be continuous variables. Existing discretization methods cannot directly deal with the discretization, they need to convert it into category label, and then discretize other variables according to the category label. It is likely to amplify in the error in the second step because of the lack of feedback of the first step. In this paper, discretization method based on maximum information coefficient (MIC) is proposed. In the process of calculating MIC, the association between two continuous variables is measured by dividing data grids. Then the dividing intervals are given based on the division blocks. Because of the high time complexity of calculating MIC, this paper proposed a simplified method with equal-density intervals. Finally, the effectiveness of the method proposed in this paper is validated through case study.
Introducing Research into the Undergraduate Curriculum in Cybersecurity

In this paper, we describe a new approach for introducing a research experience based on real-world research problems into the two semester Senior Design course sequence of the B.S. in Cybersecurity program at Stevens Institute of Technology as part of the INSuRE consortium. We discuss the necessity of such a research experience in the context of the ever-changing nature of Cybersecurity in that attacks seen and countermeasures developed today may already be different and outdated tomorrow. We also detail the insights gained, challenges encountered, and lessons learned in order to provide the community with the necessary means to use this as a model to implement a research experience as part of other undergraduate Cybersecurity degree programs.
Note on Globally Sound Analytic Calculi for Quantifier Macros

This paper focuses on a globally sound but possibly locally unsound analytic sequent calculus for the quantifier macro Q defined by $$Q_{x,y} A(x,y) = \forall x \exists y A(x,y)$$. It is demonstrated that no locally sound analytic representation exists.
Research on Stacking-Based Integrated Algorithm of Anomaly Detection in Production Process

Monitoring the production process and detecting anomalies in time is very important to ensure product quality. Given the current methods are inadequate to solve problems in the production process anomaly detection, this paper proposes a stacking-based integrated anomaly detection method SBIOD, stacking five different component learners: HBOS (Histogram-based Outlier Detection), LOF (Local Outlier Factor), iForest (Isolation Forest), DT (Decision Tree) and LR (Logistic regression), normalize the output of these learners, and then input MLP Classifier as feature values. Experiments show that the AUC of SBIOD method has been significantly improved while providing good performance and practical value.
Semantic Representation Extraction from Unstructured Arabic Text

One of the crucial tasks in the Semantic Web research is extracting information from unstructured text and converting it into semantic form to be machine understandable. This semantic representation is useful for many purposes such as question answering, summarization and information retrieval. This paper provides a system for converting Arabic text into Resource Description Framework (RDF) semantic format. The proposed system includes syntactical parser that used to extract triples (subject-predicate-object) from preprocessed Arabic text. Moreover, name entity recognition is used to extract entities which mapped with DBpedia to get URIs. Finally, the corresponding RDF representation which captures semantics of Arabic text is generated.
Task Allocation and Route Planning for Robotic Service Networks with Multiple Depots in Indoor Environments

Studies suggest that the recent technological advancements in robotics can foster the automation capabilities of the built infrastructure by introducing service robots. Task-allocation and path-planning are two of the fundamental challenges faced by such task-oriented robots. Existing algorithms in this domain have been adapted from outdoor logistics based applications with context-specific assumptions. This study particularly addresses this issue, extends the authors’ previous efforts, and proposes a new methodology to optimize the task allocation and route planning in case of multiple starts and destination depots where each robot begin and end at the same depot. Scenario analysis is conducted to compare the performance (e.g., total distance) of the proposed multi-depot algorithm with the single-depot one. The developed methodology is generic and can be used for a wide range of indoor building environment applications. Finally, limitations of the current approach are identified, and future work directions that require further investigation are proposed.
Threshold Kleptographic Attacks on Discrete Logarithm Based Signatures

In an $$\ell $$ out of n threshold scheme, $$\ell $$ out of n members must cooperate to recover a secret. A kleptographic attack is a backdoor which can be implemented in an algorithm and further used to retrieve a user’s secret key. We combine the notions of threshold scheme and kleptographic attack to construct the first $$\ell $$ out of n threshold kleptographic attack on discrete logarithm based digital signatures and prove its security in the standard and random oracle models.
Intuitive Reasoning in Formalized Mathematics with Elfe

In teaching mathematics, theorem provers have been used only seldomly due to their technical nature. Theorem provers like Elfe can bridge the gap between informal and formal reasoning by using automated theorem provers to verify intermediate steps in a proof that are passed over when reasoning intuitively. In this paper we present the inner workings of Elfe and how it can be used to prove lemmas in synthetic geometry. We compare the system to other approaches to formalized mathematics and give an outlook where the development may lead.
RETRACTED ARTICLE: Cloud Computing in Libraries and Higher Education: An Innovative User-Centric Quality of Service Model

Click to increase image sizeClick to decrease image sizeView retraction statement:Retraction: Cloud Computing in Libraries and Higher Education: An Innovative User-Centric Quality of Service Model
Domain Knowledge Enhanced Error Correction Service for Intelligent Speech Interaction

Intelligent speech interaction systems have gained great popularity in recent years. For these systems, the accuracy of automatic speech recognition (ASR) has become a key factor of determining user experience. Due to the influence of environmental noise and the diversity and complexity of natural language, the performance of ASR still cannot meet the requirements of real-world application scenarios. To improve the accuracy of ASR, in this paper, we propose a domain knowledge enhanced error correction method which first the improved phonetic editing distance to select the candidates which have the same or similar phonetics with the error segment, and then adopts language model the find the most appropriate one from the domain knowledge set as the final result. We also encapsulate the method as a service with the Flask + Gunicorn + Nginx framework to improve the high concurrency performance. Experimental results demonstrate that our proposed method outperforms the comparison methods over 48.4% in terms of accuracy and almost 20–40 times concurrency performance.
Sensitivity of an image-plane digital holography interferometer for the measurement of pile-up

The accurate determination of the contact area between an instrumented indenter and the material under testing is important for material property measurements. Usually, it is masked by the pile-up or sink-in phenomenon. Due to the wide range of materials available in the industry, different amounts of pile-up would be generated depending on the grade of fragility of the material. This paper presents the sensitivity evaluation of dual-wavelength image-plane digital holography in order to identify these different degrees of pile-up. It was observed that the interferometer was able to identify pile-up with a height of 3&mu;m. Additionally, this result showed a good concordance with the measurement of the same part by using a Focus-Variation Microscope (FVM). Considering the application for in-situ measurements, corrections to deal with the misalignment between the interferometer and the evaluated surface was proposed and tested. Results showed that the setup is able to work with misalignments, which will produce up to 13 fringes in the measured phase.
Improving and Extending the Testing of Distributions for Shape-Restricted Properties

Distribution testing deals with what information can be deduced about an unknown distribution over $$\{1,\ldots ,n\}$$ , where the algorithm is only allowed to obtain a relatively small number of independent samples from the distribution. In the extended conditional sampling model, the algorithm is also allowed to obtain samples from the restriction of the original distribution on subsets of $$\{1,\ldots ,n\}$$ . In 2015, Canonne, Diakonikolas, Gouleakis and Rubinfeld unified several previous results, and showed that for any property of distributions satisfying a “decomposability” criterion, there exists an algorithm (in the basic model) that can distinguish with high probability distributions satisfying the property from distributions that are far from it in the variation distance. We present here a more efficient yet simpler algorithm for the basic model, as well as very efficient algorithms for the conditional model, which until now was not investigated under the umbrella of decomposable properties. Additionally, we provide an algorithm for the conditional model that handles a much larger class of properties. Our core mechanism is an algorithm for efficiently producing an interval-partition of $$\{1,\ldots ,n\}$$ that satisfies a “fine-grain” quality. We show that with such a partition at hand we can avoid the search for the “correct” partition of $$\{1,\ldots ,n\}$$ .
Location Recommendation with Privacy Protection

With the development of Internet technology, users pay more and more attention to the privacy of personal location data. In order to cover up the user's original check-in data information and prevent attackers from using the user's friend relationship to infer the privacy information of a single user, our paper proposed a hybrid privacy protection method based on differential privacy and random perturbation, and combined the user's friend relationship to realize the location recommendation with privacy protection. Data analysis shows that the privacy level can be set by adding different degrees of random noise to achieve the purpose of personalized privacy protection. Furthermore, differential privacy is used to protect the user's friend relationship, which makes the privacy protection effect of the location recommendation method better. Experiments on real datasets, show that this method can protect users' privacy information and at the same time have a certain accuracy of location recommendation.