Reviewerly/exHarmony huggingface dataset:
A Study on the Interpretability of a Fuzzy System to Control an Inverted Pendulum

Fuzzy systems mimic human reasoning and provide solutions to problems under uncertainty via `computing with words'. This particular strength of fuzzy systems is often discarded in some real world applications where the fuzzy sets are designed for control problems or created through training using historical data. This study explores the interpretability of fuzzy systems by generating `meaningful' fuzzy sets using a dictionary constructed by humans and fuzzy transfer learning. The inverted pendulum control problem is used as a case study. The empirical results show that intepretability of a fuzzy system is achievable even for this problem at the expense of a `slightly' reduced performance.
Covert Channel Establishment Through the Dynamic Adaptation of the Sequential Probability Ratio Test to Sensor Data in IoT

Owing to their hidden natures, covert channels can be utilized such that trojan applications can communicate stealthily with each other or exchange stolen private information without being revealed. To prevent damage incurred by covert channels, researchers have preemptively scrutinized diverse covert channels that can be devised by an attacker. Although covert channels based on sensor data may interest an attacker because sensing operation is a key task in Internet of Things (IoT), we do not find any covert channel studies that adapted the Sequential Probability Ratio Test (SPRT) to sensor data except our prior study [27], where the SPRT is applied to sensor data in Android systems before an attacker's conception; however, our previous study showed limitations owing to the static nature of the SPRT parameter settings and the method of mapping sensor data to sample types for covert channel creation. To demonstrate that these limitations can be pacified, we propose a covert channel that dynamically applies the SPRT to sensor data in IoT. In our proposed covert channel, stealthy information bit 1 (resp. 0) is encoded to and decoded from a sequence of sensor data when the SPRT with dynamic parameter settings accepts an alternate (resp. null) hypothesis. We implement our proposed covert channel in Raspberry Pi 3 Model B devices and evaluate it in terms of various metrics. Evaluation results indicate that every encoded stealthy information byte is successfully decoded in our covert channel. Furthermore, 3.513 samples and 28.105 SPRT executions at the most are required for encoding/decoding a stealthy information byte in our devised covert channel on an average, thus resulting in fast encoding/decoding in our covert channel. Finally, our developed covert channel yields a throughput ranging from 4097.5 to 9061.67 bits/sec.
FPGA implementation of adaptive p‐norm filter for non‐stationary power signal parameter estimation

A p-norm extreme learning machine (ELM) based on sparsity constraint is presented in this study for tracking of fundamental frequency, harmonic and dc in current power signals which finds application in phasor measurement units for wide area power network in smart grid environment. Real-time power applications typically are furnished with on-board controller and hence have constraints to stock a complex architecture. Moreover, the data from online practices are polluted by noises of diverse statistical features obtained on a sample-by-sample basis. Hence, approaches with improved learning paradigm and close model dealing with noises of varied statistical characteristics are essential. The proposed approach formulates a cost function with recursive p-norm error criterion and sparsity penalty that updates the output weights in succession besides adjusting some coefficients of the output weights to zeros that promotes quicker convergence and higher accuracy results. Exhaustive computer simulations have been carried out with synthetic signals and real-time signals to track the dynamic changes in the power signal amplitude, phase and frequency that demonstrate the accuracy, efficiency and robustness of the proposed p-norm ELM. Additionally, the new ELM network also is validated on a field programmable gate array (FPGA) hardware to prove its practicability towards current developments on phasor measurement units.
Application of EEG Metrics in the Decision-Making Process

The decision-making process is a complex task uses the multi-criteria methods in the formalized decision support. Decisions are direct reflection of decision maker preferences. Multi-criteria methods use different methodological approaches (algorithms) to determine the final assessment of decision variants (e.g., ranking). Decision maker must do many actions (partial evaluations) in some of these methods. Issues of the decision maker’s engagement in the assessment process arise which can be identified using measurements by EEG. It is possible to identify various internal processes occurring with the decision maker during individual stages of the calculation procedure. Various types of EEG metrics are used for this, such as the index of frontal asymmetry, engagement, distraction, etc.
Research identifiers: <scp>ORCID</scp>,<scp> DOI</scp>, and the issue with Wang and Smith

The Photogrammetric RecordVolume 34, Issue 167 p. 236-243 Editorial Research identifiers: ORCID, DOI, and the issue with Wang and Smith Stuart I. Granshaw, orcid.org/0000-0002-5746-1751 Harlech, WalesSearch for more papers by this author Stuart I. Granshaw, orcid.org/0000-0002-5746-1751 Harlech, WalesSearch for more papers by this author First published: 10 October 2019 https://doi.org/10.1111/phor.12290Citations: 2Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onEmailFacebookTwitterLinked InRedditWechat Citing Literature Volume34, Issue167September 2019Pages 236-243 RelatedInformation
Semantic Error Detection and Correction in Bangla Sentence

Detection and correction of errors in Bengali text is essential. In general, Bengali text error can be classified into non-word error and semantic error (also known as context sensitive error). Till date, auto-correction for semantic error in Bengali sentence is challenging since there is no significant research works on this very topic. In this paper, we bring out the concept of Semantic Error detection and correction. We have developed a method that can detect and correct this kind of errors. Semantic error includes typographical error, grammatical errors, homophone errors, homonym error etc. Our goal to this study is to develop an approach to handle multiple semantic errors in a sentence. We have used our own built confused word list by edit distance and apply Naïve Bayes Classifier to detect and correct typographical and homophone error. For a candidate word from a sentence, we pick out a set of words which is a collection of confused words. We use all other neighbor words as features for each word from confusion set. Then we apply naïve theorem to calculate the probability and decide whether a target word is error or not. We have used 28,057 sentences to evaluate our model and we have achieved more than 90% accuracy. All data corpora used to evaluate the model are built by us. We strongly believe that the problem we have solved may shed light on the advancement of Bengali language processing significantly.
A Novel Framework of Fuzzy Rule Interpolation for Takagi-Sugeno-Kang Inference Systems

Fuzzy rule interpolation (FRI) technique has been proposed to infer conclusions for unmatched instances when a fuzzy rule-based system is presented with a sparse rule base. Most existing FRI methodologies are not developed for Takagi-Sugeno-Kang (TSK) inference models. TSK inference extension (TSK+) is one of the methodologies proposed for TSK models with sparse rule bases. It works by replacing matching degrees with similarity measures across all the given rules, instead of just the matched ones, to generate the final conclusion. However, those rules with low similarities bring bias to the final result, which is mainly determined by the closest rules. To significantly strengthen the efficacy of this, a novel framework is presented here through the use of just a small number of closest rules to derive the final outcome. Compared with TSK+, the proposed method reduces the computational overheads of the inference process while avoiding the adverse impact caused by the rules of low similarities with the new observation. More importantly, to deal with large sized sparse rule bases, where neighbourhood rules may be similar with each other, a rule-clustering approach is proposed. That is, a clustering algorithm (say, fuzzy c-means) is first employed to cluster rules into different groups and then, the final interpolated conclusion is computed by the use of the closest rules selected from a small number of closest rule clusters. This approach helps further decrease the time complexity. The efficacy of these two modified methods is demonstrated via systematic experimental comparisons against the performance of the original TSK+.
Experimenting with computational methods for large-scale studies of tracking technologies in web archives

The use of tracking technologies to collect data about web users and their online behaviour has played an important role in the development of the web. Most studies of tracking examine the current extent of tracking on popular websites on the online web, while historical studies are rare. Large-scale historical studies of web tracking are important for a more comprehensive understanding of the development, spread and implications of tracking technologies across the web. Historical studies of tracking are challenged by the lack of access to the original data flows in the tracking and by the characteristics of the archived web. This article proposes an approach to a web historiography of tracking, which focusses on the potentials of analysing the source code and metadata, which can be processed on a large scale. Using the archived Danish web as a case, the article describes an approach to studying web beacons in historical web data using computational methods, and showcases how experimenting with new approaches can bring new knowledge about the historical development of web tracking but also about the significance of understanding the technical aspects of archiving and archived web when using sources like source code, crawl logs and indices as data.
A Novel Addressing and Routing Architecture for Cloud-Service Datacenter Networks

Datacenter networks (DCNs) play a key role in providing cloud services. The energy consumption and cost of a DCN are growing sharply with the extensions of network bandwidth and network size. The energy consumption, complexity and cost of a DCN depend on some design factors such as the topology structure, addressing scheme and routing mechanism. A novel addressing and routing architecture for cloud-service DCNs with regular topologies is proposed in this paper. First of all, we propose a port-based source-routing addressing (PSRA) scheme, which makes the table-lookup operation unnecessary and decreases the switch complexity. Next, leveraging the characteristics of PSRA and the regularity of DCN topologies, an extremely simple routing mechanism is designed, without switch involvement, control message interaction and topology information storage. Lastly, a high-efficiency fault-tolerance mechanism is proposed for the addressing and routing architecture. The analysis, implementation and simulation results indicate that the proposed architecture not only decreases the energy consumption and thus the cost of a DCN, but also enhances the routing performance and solves the fault-tolerance problem in a very efficient way.
Five Years Later: How Effective Is the MAC Randomization in Practice? The No-at-All Attack

The user privacy, in particular, user tracking, has always been a considerable concern, and moreover nowadays, when we are completely surrounded by Wi-Fi enabled devices (smartphones, tablets, wearables, etc.). These devices transmit unique unencrypted signals containing information which includes a device’s MAC (Media Access Control) address. Such signals can be monitored with a passive attack by using cheap hardware. Since the MAC address is unique for each device, there is an unquestionable privacy threat to the devices’ owners. To this moment, the only countermeasure vendors have the MAC Address Randomization. In this paper, we show that the effectiveness of this solution, five years after it was introduced for the first time, is insufficient to prevent Wi-Fi users from tracking. Moreover, the solution itself is not even widely used. To validate such conclusions, we have conducted a week-long passive attack using Single Board Computer (Raspberry PI), and we were able to obtain real-world sample data (7.522 total wireless probe requests). Thus, we were being able to: analyze data and count users, notify their presence measure time they spent in an area, determine the working hours and the busiest day, distinguish vendors, etc. The paper also suggests mitigations, including some that may affect the MAC Randomization implementation itself as well as the user behavior.
Comparative Analysis of Encryption and Decryption Techniques Using Mersenne Prime Numbers and Phony Modulus to Avoid Factorization Attack of RSA

In this advanced era, it is important to keep up an abnormal state of security for online exchanges. Public Key cryptography assumes an indispensable job in the field of security. Rivest, Shamir and Adleman (RSA) algorithm is being utilized for quite a long time to give online security. RSA is considered as one of the famous Public Key cryptographic algorithm. Nevertheless, a few fruitful assaults are created to break this algorithm because of specific confinements accepted in its derivation. The algorithm's security is principally founded on the issue of factoring large number. If the process factorization is done then, at that point the entire algorithm can end up fragile. This paper presents a methodology which is more secure than RSA algorithm by doing some modifications in it. Public Key exponent n, which is termed as common modulus replaced by phony modulus to avoid the factorization attack and it is constructed by Mersenne prime numbers to provide more efficiency and security. Paper presents a comparative analysis of the proposed algorithm with the conventional RSA algorithm and Dual RSA.
Design and Construction of a Cost-Effective Didactic Robotic Arm for Playing Chess, Using an Artificial Vision System

This paper presents the design and construction of a robotic arm that plays chess against a human opponent, based on an artificial vision system. The mechanical design was an adaptation of the robotic arm proposed by the rapid prototyping laboratory FabLab RUC (Fabrication Laboratory of the University of Roskilde). Using the software Solidworks, a gripper with 4 joints was designed. An artificial vision system was developed for detecting the corners of the squares on a chessboard and performing image segmentation. Then, an image recognition model was trained using convolutional neural networks to detect the movements of pieces on the board. An image-based visual servoing system was designed using the Kanade–Lucas–Tomasi method, in order to locate the manipulator. Additionally, an Arduino development board was programmed to control and receive information from the robotic arm using Gcode commands. Results show that with the Stockfish chess game engine, the system is able to make game decisions and manipulate the pieces on the board. In this way, it was possible to implement a didactic robotic arm as a relevant application in data processing and decision-making for programmable automatons.
A Novel Fuzzy Neural Network for Unsupervised Domain Adaptation in Heterogeneous Scenarios

How to leverage knowledge from labelled domain (source) to help classify unlabeled domain (target) is a key problem in the machine learning field. Unsupervised domain adaptation (UDA) provides a solution to this problem and has been well developed for two homogeneous domains. However, when the target domain is unlabeled and heterogeneous with the source domain, current UDA models cannot accurately transfer knowledge from a source domain to a target domain. Benefiting from development of neural networks, this paper presents a new neural network, shared fuzzy equivalence relations neural network (SFER-NN), to address the heterogeneous UDA (HeUDA) problem. SFER-NN transfers knowledge across two domains according to shared fuzzy equivalence relations that can simultaneously cluster features of two domains into several categories. Based on the clustered categories, SFER-NN is constructed to minimize the discrepancy between two domains. Compared to previous works, SFER-NN is more capable of minimizing discrepancy between two domains. As a result of this advantage, SFER-NN delivers a better performance than previous studies using two public datasets.
Ambiguity in Requirements Engineering: Towards a Unifying Framework

A long stream of research in RE has been devoted to analyzing the occurrences and consequences of ambiguity in requirements documents. Ambiguity often occurs in documents, most often in natural language (NL) ones, but occasionally also in formal specifications, be it because of abstraction, or of imprecise designation of which real-world entities are denotated by certain expressions. In many of those studies, ambiguity has been considered a defect to be avoided. In this paper, we investigate the nature of ambiguity, and advocate that the simplistic view of ambiguity as merely a defect in the document does not do justice to the complexity of this phenomenon. We offer a more extensive analysis, based on the multiple linguistic sources of ambiguity, and present a list of real-world cases, both in written matter and in oral interviews, that we analyze based on our framework. We hope that a better understanding of the phenomenon can help in the analysis of practical experiences and in the design of more effective methods to detect, mark and handle ambiguity.
Towards efficient, multi-language dynamic taint analysis

Dynamic taint analysis is a program analysis technique in which data is marked and its propagation is tracked while the program is executing. It is applied to solve problems in many fields, especially in software security. Current taint analysis platforms are limited to a single programming language, and therefore cannot support programs which, as is common today, are implemented in multiple programming languages. Current implementations of dynamic taint analysis also incur a significant performance overhead.
Waste Sorting Neural Network Architecture Optimization

The results of experiments on the search of neural network architecture to classify plastic bottles, aluminum cans and other objects are presented in the article. This neural network is a part of a smart container intended for sorting and collecting waste. RaspberryPi microcomputer is used as a computing device in that container, so when designing a neural network it is necessary to take into account the device's computer power limitation. A smart container is aimed to sort correctly both flat and crushed bottles and cans. The results of previously conducted object classification using neural networks AlexNet, SqueezeNet and MobileNet experiments are also presented. However, the disadvantage of these networks is the need to create a large training set for their training. Moreover, it is necessary to create a set with all the possible crushes of bottles and cans, which will require a huge amount of time to recognize the crushed bottles and cans. Therefore, a small set should be required to train the network being searched for. The search for the architecture was carried out for the original neural network (approach to the development of which was proposed in the article) using the hyperparametric optimization algorithm and manual search of architectures. The developed neural network must meet the following requirements: high speed of image processing, a small amount of required memory, high accuracy rate of object recognition under various lighting, backgrounds, angles and geometric deformations.
Wireless Voting Machine

Traditional Voting systems used ballot-paper based voting systems. This system was accompanied by some problems. To curtail some of these problems, two new techniques were used which are biometric finger print recognization and retina scanning. In this paper, the techniques are illustrated in detail and these techniques when implemented in the wireless voting machine as safety measures has proven to be a boon. Thus, wireless voting system has proved to be a advantage in the new age of voting systems.
The Methods and Tools for Clustering Analysis

Cluster analysis is a widely employed branch in many domains of scientific research. To meet its growing applications in biomedical studies, this tutorial aims to provide a conceptual overview of the most important and representative clustering algorithms, pointing out their respective application prerequisites, technical issues, strengths and restrictions, as well as software implementations. These seven clustering methods are covered in each section: hierarchical clustering, k-means and related methods, mixture models, non-negative matrix factorization, spectral clustering, density-based clustering, and self-organizing maps. Hierarchical clustering and k-means are classic methods that are still in wide use and often with good performance. Some extensions of k-means have been discussed for remediation of certain practical limitations. Mixture models have a strong statistical foundation and can be estimated computationally. Non-negative matrix factorization views data as linear summations of meaningful parts, which has a natural clustering interpretation. Spectral clustering is closely related to graph cut problems and can be approximately solved mathematically. Density-based clustering emphasizes the distinct density distributions of clusters. Self-organizing map uses a neural-network to approximate the underlying data manifold with unique visualization capabilities. Each algorithm has certain parameter(s) that need to be tuned for better clustering, which will be discussed in each section. A comprehensive resource list of software packages in popular programming languages such as R, Python and Matlab has been compiled.
Ten Years of Self-adaptive Systems: From Dynamic Ensembles to Collective Adaptive Systems

Self-adaptive systems have been introduced to manage situations where software systems operate under continuous perturbations due to the unpredicted behaviors of their clients and the occurrence of exogenous changes in the environment in which they operate. Adaptation is triggered by the run-time occurrence of an extraordinary circumstance, and it is handled by an adaptation process that involves components affected by the issue, and is able to handle the run-time modification of the structure and behavior of a running system. In this paper we report our experience gained in the last 10 years on models, techniques and applications in the field of self-adaptation. We present the various steps taken by means of a formal framework introduced to characterize the different aspects of an ensemble-based software engineering approach. We present (i) how to model dynamic ensembles using typed graph grammars, (ii) how to specialize and re-configure ensembles and, (ii) how to manage collective adaptations in an ensemble. All these aspects have been part of our research on self-adaptation and have been used to specify and deploy concrete solutions in different application domains.