Reviewerly/exHarmony huggingface dataset:
Penerapan Konsep Non-Deterministic Finite Automata untuk Pembuatan Sereal Menggunakan Mesin Jual Otomatis dengan Dua Sistem Pembayaran

In this research, the concept of Non-Deterministic Finite Automata is used to make a vending machine with two payment options. In Indonesia, there has been an increase in vending machine business as of 2017. From the vending machines that have existed in Indonesia, there are only a few vending machines that can make cereal. From previous researches, there lacked a cashless payment system and there is no research that shows how to build a vending machine that sells cereal. This research uses an NDFA with Mealy Machine in order to illustrate the concepts. In this research, C language (with console application) is used to simulate the conceptual vending machine, and it is working very effectively, from ordering cereals to payment system. The simulation is tested multiple times with different scenarios and the conceptual vending machine works effectively for every single scenario. It is proven that the concept of Non-Deterministic Finite Automata is possible to be used as an alternative to build a vending machine that makes and sells cereal. With this research, vending machine business could be more profitable and more varied with cereal vending machines.
Aspects of Categorical Recursion Theory

We present a survey of some developments in the general area of category-theoretic approaches to the theory of computation, with a focus on topics and ideas particularly close to the interests of Jim Lambek.
The Correlation between Self-Esteem and Student's Reading Comprehension.

The aim of this study was to know the correlation between self-esteem to studentâ€™s reading comprehension. This study employed quantitative approach in the form of correlation design to identify the correlation between self-esteem and students reading comprehension. The population of the present study was English department students of ULM academic year 2018/2019. Data collection involved 35 students who took part in the reading course. There are two kinds of the instrument used: test and questionnaire. The data in this study were analyzed by using Pearson product-moment. The result of the study reveals that there is a correlation between self-esteem and studentâ€™s reading comprehension. The result showed that the r of the first calculation was .0457. It meant that H0 was rejected and self-esteem has a correlation to the reading comprehension with significance tailed 0.006< 0.05
On the null relationship between personality types and passwords

We performed a preliminary investigation of the relationship between the Big-five personality traits and the strength of selected and created online security passwords. Five-hundred and ten participants were recruited through MTurk and asked a) to complete a Big-five personality inventory, b) to select from a list of available passwords the one they felt was most secure, and c) to create unique, secure passwords for their own online protection. The security strength of participants' selected/created passwords was rated via Zxcvbn, and evaluated on a variety of additional security-based criteria (e.g., password length, inclusion of special characters). In all cases results failed to identify significant relationship between the strength of the selected/chosen password and any Big-five personality traits (when employing appropriately stringent control for multiple corrections). These null results suggest that other factors beyond an individual's personality may hold greater influence over the strength of selected/created online passwords. We present detailed observations and findings from our experiment, discuss potential considerations for contradictions, and suggest possibilities for future research into the password/personality relationship, including the potential use of enhanced password strength meters and tailored security nudges.
Recognizing and Testing Isomorphism of Cayley Graphs over an Abelian Group of Order 4p in Polynomial Time

We construct a polynomial-time algorithm that, for a graph X with 4p vertices (p is prime), finds (if any) a Cayley representation of X over the group $$C_2\times C_2\times C_p$$. This result, together with the known similar result for circulant graphs, shows that recognizing and testing isomorphism of Cayley graphs over an abelian group of order 4p can be done in polynomial time.
The2-adic and3-adic valuation of the Tripell sequence and an application

Let (Tn)n≥0 denote the Tripell sequence, defined by the linear recurrence Tn=2Tn−1+Tn−2+Tn−3 for n≥3 with T0=0, T1=1 and T2=2 as initial conditions. In this paper, we study the 2-adic and 3-adic valuation of the Tripell sequence and, as an application, we determine all Tripell numbers which are factorials.
A quantum view on convex optimization

In this dissertation we consider quantum algorithms for convex optimization. We start by considering a black-box setting of convex optimization. In this setting we show that quantum computers require exponentially fewer queries to a membership oracle for a convex set in order to implement a separation oracle for that set. We do so by proving that Jordan's quantum gradient algorithm can also be applied to find sub-gradients of convex Lipschitz functions, even though these functions might not even be differentiable. As a corollary we get a quadraticly faster algorithm for convex optimization using membership queries. As a second set of results we give sub-linear time quantum algorithms for semidefinite optimization by speeding up the iterations of the Arora-Kale algorithm. For the problem of finding approximate Nash equilibria for zero-sum games we then give specific algorithms that improve the error-dependence and only depend on the sparsity of the game, not it's size. These last results yield improved algorithms for linear programming as a corollary. We also show several lower bounds in these settings, matching the upper bounds in most or all parameters.
How to store a random walk

Motivated by storage applications, we study the following data structure problem: An encoder wishes to store a collection of jointly-distributed files [MATH HERE] which are correlated [MATH HERE], using as little (expected) memory as possible, such that each individual file Xi can be recovered quickly with few (ideally constant) memory accesses. In the case of independent random files, a dramatic result by Pǎtrascu (FOCS'08) and subsequently by Dodis, Pǎtrascu and Thorup (STOC'10) shows that it is possible to store [MATH HERE] using just a constant number of extra bits beyond the information-theoretic minimum space, while at the same time decoding each Xi in constant time. However, in the (realistic) case where the files are correlated, much weaker results are known, requiring at least Ω(n/poly lg n) extra bits for constant decoding time, even for simple joint distributions μ. We focus on the natural case of compressing Markov chains, i.e., storing a length-n random walk on any (possibly directed) graph G. Denoting by K(G, n) the number of length-n walks on G, we show that there is a succinct data structure storing a random walk using lg2 K(G, n) + O(lg n) bits of space, such that any vertex along the walk can be decoded in O(1) time on a word-RAM. If the graph is strongly connected (e.g., undirected), the space can be improved to only lg2 K(G, n) + 5 extra bits. For the harder task of matching the point-wise optimal space of the walk, i.e., the empirical entropy [MATH HERE], we present a data structure with O(1) extra bits at the price of O(lg n) decoding time, and show that any improvement on this would lead to an improved solution on the long-standing Dictionary problem. All of our data structures support the online version of the problem with constant update and query time.
Classification of catenary equipment based on a coupled genetic algorithm-extreme learning machine method

In an online monitoring system for an electrified railway, it is important to classify the catenary equipment successfully. The extreme learning machine (ELM) is an effective image classification algorithm and the genetic algorithm (GA) is a typical optimisation method. In this paper, a coupled genetic algorithm-extreme learning machine (GA-ELM) technique is proposed for the classification of catenary equipment. Firstly, the GA is used to search for optimal features by reducing the initial multi-dimensional features to low-dimensional features. Next, the optimised features are used as the input to the ELM. The ELM algorithm is then used to classify the catenary equipment. In this process, the impacts of the activation function, the number of hidden layer neurons and different models on the performance of the ELM are discussed in turn. Finally, the proposed method is compared with traditional methods in terms of classification accuracy and efficiency. Experimental results show that the number of feature dimensions decreases to 58% of the original number and the computational complexity is greatly decreased. Moreover, the reduced features and the few steps of the ELM improve the classification accuracy and speed. Noticeably, when the performance of the GA-ELM method is compared with that of the ELM method, the classification accuracy rate is 93.33% compared with 85.83% and the time consumption is 2.25 s compared with 8.85 s, respectively. That is to say, the proposed method not only decreases the number of features but also increases the classification accuracy and efficiency. This meets the needs of a real-time online condition monitoring system.
Intrusion Detection Systems for Mitigating SQL Injection Attacks

Databases are widely used by organizations to store business-critical information, which makes them one of the most attractive targets for security attacks. SQL Injection is the most common attack to webpages with dynamic content. To mitigate it, organizations use Intrusion Detection Systems (IDS) as part of the security infrastructure, to detect this type of attack. However, the authors observe a gap between the comprehensive state-of-the-art in detecting SQL Injection attacks and the state-of-practice regarding existing tools capable of detecting such attacks. The majority of IDS implementations provide little or no protection against SQL Injection attacks, with exceptions like the tools Bro and ModSecurity. In this article, the authors compare these tools using the CSIC dataset in order to examine the state-of-practice in database protection from SQL Injection attacks, identifying the main characteristics and implementation details needed for IDSs to successfully detect such attacks. The experiments indicate that signature-based IDS provide the greatest coverage against SQL Injection.
Model Retrieval with 3D Radon Moments

We present a 3D normalized radon moments based on 3D radon transform for 3D model search in this paper. Scaling and translation invariance are achieved by the new normal 3D Radon moments directly. Rotation invariance is performed by principal component analysis on 3D geometric models. The features extracted by the proposed method are invariant to translation, scaling and rotation based on theories and experiments. The retrieval experiments show that this method has higher retrieval efficiency than the comparative methods.
Dynamic of hashtag functions development in new media

The article is devoted to the study of the hashtag functioning as an operational tool for new media. The relevance of the study is determined by the insufficient knowledge of the hashtag as a linguistic phenomenon, while it is the most important unit that determines the development of interactive media (in particular, social networks) at the present stage. The purpose of this article is to study the features of the localization of the hashtag on the Instagram social network, as well as the spectrum of its functionality. The main attention is paid to the specific formal characteristics of the hashtag (graphic appearance, position in relation to the text, grammatical features) and the functions performed - both in the network space and beyond. Considering the functionality of the hashtag on Instagram, the authors of the article note that it acts, firstly, as a navigation tool that allows you to instantly search for publications on topics of interest. Secondly, the hashtag is a powerful tool for marketing promotion in the social network of a product or service. Thirdly, the hashtag can act as an evaluation marker that can set the interpretation model of the message marked by the addressee, which indicates its manipulative potential. Fourth, the hashtag discovers the ability to form a network of certain associations fixed in the minds of people to a particular event, person, subject, situation, thus finding itself closely related to the features of the conceptualization of reality. And finally, the hashtag is recognized as a unique phenomenon that went beyond the scope of online space and determined the specifics of the new era of communication in the digital world, as well as in real life. At the same time, the hashtag functions may vary depending on the communicative intentions of the author who uses it in his publication. The results provide an incentive for further research on the hashtag as a phenomenon that reflects the interaction of the language and consciousness of the modern "media" person.
Application design for privacy and security in healthcare

Electronic health records (EHRs) were seen as a panacea for managing all medical related data about the patient. The goal was to identify the siloed data fragmented across various healthcare providers and combine it in a uniform way in order to provide the best possible diagnostics and make it easy for the practitioners to gather and study patient&apos;s medical data. However, despite decades into development, such record systems are still long ways off in terms of practical implementation. They have been dogged by various shortcomings and above all concerns regarding potential loopholes in the privacy and security of patient data. Given the poor track record of the industry when it comes to preventing illicit access and sharing of data, there is an ever-growing impetus to stanch the consistent violation of the privacy of an individual&apos;s personal information by illegitimate parties. In this chapter, we aim to develop an understanding of an EHR system, identify associated shortcomings, and the requirements that it is supposed to fulfil. We also present various proposed implementations that show promise in the development of a safe, secure, and unified EHR system. In the last section, we discuss the potential of blockchains and how they have the goods to deliver a perfect EHR system.
An Expert System for Diagnosing Leptospirosis Disease Using Forward Chaining and Bayes Theorem

Leptospirosis is the most serious zoonotic disease caused by Leptospira spp. Leptospirosis has spread throughout the world and spread more in the tropics than in temperate countries. Organisms are contained in the urine of infected animals, including rodents and pets. Humans can get this disease after contact with infected urine, or through contact with contaminated water, soil or food. This disease has several symptoms similar to other diseases such as influenza, dengue fever, typhoid fever, rickettsiosis, aseptic meningitides, hepatitis, and malaria. Similar to these symptoms make this disease a little difficult to diagnose quickly. the similarity of the symptoms of this disease to other diseases can also cause errors in diagnosing the disease, this can result in the wrong medication to be given to the patient. Slow handling can cause death to patients. Expert systems can be the solution to dealing with these problems. Expert systems can be computer software systems that use science, facts, and thinking techniques in the process of making decisions to solve problems that are usually only resolved by experts. Expert systems must be equipped with tracking techniques and mathematical calculation methods to improve the quality of diagnosis. The results of this study are combining forward chaining with Bayes theorem so that the diagnosis of leptospirosis is better.
Single Image Dehazing Using Manifold Particle Filter

Degraded images taken in bad weather such as fog or haze puts negative impacts on automation without pre-processing. To eliminate halo artifacts and improve the image visibility, in this paper, based on the dehazing algorithm of the dark channel prior, a manifold particle filtering algorithm is proposed. Specifically, the proposed technique uses manifold particle filter to optimize the transmission and achieve accurate transmission estimation. The proposed algorithm was compared with existing defogging techniques on nature foggy images and synthetic images. Experimental results demonstrate that the proposed algorithm is capable of removing halo artifacts and restoring the visibility of foggy image, which also achieves higher restoration quality, both quantitatively and qualitatively.
DCSA: Using Density-Based Clustering and Sequential Association Analysis to Predict Alarms in Telecommunication Networks

Traditional alarm prediction in telecommunication networks mainly relies on expert knowledge. However, with the increasing complexity of telecommunication network, the traditional methods may not work well. It's necessary to study new automatic association rules extraction methods. In this paper, we proposed a method called DCSA (Density-based Clustering and Sequential Association Analysis) for alarm association rules mining. We use time density-based clustering and FP-Growth algorithm to mine the association rules in alarm data, which overcomes the drawbacks of sliding windows method. In addition, we design a sequential rules filtering module to eliminate the items that do not meet the sequential conditions in the original rules. Experiments on 7.5 million real alarm items from a telecommunication company of China show the sequential rules filtering module can greatly reduce the redundancy of association rules. We also demonstrate that the proposed DCSA method could effectively predict the occurrence of alarms.
On Optimal Thickness of the Curve at Calculating the Fractal Dimension Using the Box-Counting Method

Fractal curves are found in various fields of science and technology, and the calculation of the dimensions of the curves is an actual direction. The fractal dimension (FD) is a metric used to characterize the filling of a plane with a curve. The choice of the thickness of the curve line at calculating its FD using the box-counting method (BCM) is not obvious. This is the subject of the present paper. First, we consider a straight horizontal line with different thicknesses, and calculate FD for different thicknesses, using BCM. Using the exact dimensional value for a straight line, the initial conclusion is made about the thicknesses that give a correct FD. In the next step, the dimensions of straight lines rotated by a certain angle in the range from to 90 degrees are calculated. At any angle of inclination of the line, the FD values should also remain equal to unity. The range of FD values depending on the rotation angle is analyzed. The result is an “optimal” line thickness equal to 3 px. At this value, the FD values are the closest to one for any angle of inclination. As a result it is received that at dimensional calculation by means of BCM, it is enough to choose the initial image of 200 on 200 pixels, and boxes to choose the sizes from 2 to 50 pixels with step of 2 pixels. The stability of dimension calculations on the grid offset has been verified. The algorithm’s error is estimated on the example of the calculation of FD for the Koch fractal.
State Detection Method of Secondary Equipment in Smart Substation Based on Deep Belief Network and Trend Prediction

Information sharing and interoperability before equipment can provide abundant operation and status information of secondary equipment in smart substation, which brings great convenience to condition monitoring and condition maintenance of secondary equipment. However, the accuracy of detection methods for secondary equipment is not high, and there is a certain lag in state detection and evaluation. Therefore, this paper presents a state detection method for secondary equipment in Smart Substation based on Deep Belief Network and trend prediction. Firstly, the feature extraction ability of Deep Belief Network is used to extract the feature from the measured state data, which solves the problem of over-reliance on expert experience for feature extraction by manual method, and the multi-hidden layer structure network learning feature is used to realize equipment state identification. Then, using the penalty correction strategy of information trend prediction, the running state of the equipment is predicted according to the real-time self-checking state characteristics and key historical state characteristics, in order to prepare for the system and make early warning in time. Finally, the proposed method is compared with the existing typical methods. The results show that the proposed method is superior in feature extraction and identification, and can effectively improve the accuracy and timeliness of secondary equipment condition detection results, which is suitable for practical engineering.
Code-switching in Irish tweets: a preliminary analysis

As is the case with many languages, research into code-switching in Modern Irishhas, until recently, mainly been focusedon the spoken language. Online usergenerated content (UGC) is less restrictive than traditional written text, allowingfor code-switching, and as such, providesa new platform for text-based research inthis field of study. This paper reports onthe annotation of (English) code-switchingin a corpus of 1496 Irish tweets andprovides a computational analysis of thenature of code-switching amongst Irish speaking Twitter users, with a view toproviding a basis for future linguistic andsocio-linguistic studies.