Kinds Of Errors Research Articles

Quantum search degeneration under amplitude noise in queries to the oracle

We examine how amplitude noise in queries to the oracle degrades a performance of quantum search algorithm. The Grover search and similar techniques are widely used in various quantum algorithms, including cases where rival parties are fighting over confidential data. Hence, the oracle-box wires become the subject of competing activity with an alteration of their normal functioning. Of course, many kinds of errors could arise in this way. Possible influence of dephasing on quantum search was already addressed in the literature. Amplitude damping is another type of errors that should be analyzed first. To study the problem, we introduce a simple model of collective distortions with the use of amplitude damping channel. All the quantities of interest should be considered as functions of the number of Grover's iterations. In particular, we investigate the success probability with respect to variations the parameter that characterizes the level of amplitude errors. The success probability degrades significantly even if the error amount is not essential. Namely, this probability soon enough reduces to a value around one half. We also study trade-off relations between quantum coherence and the success probability in the presence of amplitude noise.

The The Implementation of Surface Strategy Taxonomy Through Report Text Translation

The aims of this research is to find out grammar mistakes and obstacles in learning English through writing. Through writing, students can improve their understanding by paying close attention to the grammar and content. The implementation of CRS is meant to enhance the understanding of students of STMIK STIKOM Indonesia. And the implementation of Surface Strategy Taxonomy (SST) is done through documentation of students’ work through translation. The data source is taken from a report text in Indonesian as source language and translated it into English as target language. In order to know the benefit of SST, students are given the questionnaire to gain the data. Data collection is the results of students’ translations sheets. The results of 68 students’ translation work show that there are 98 error found in 4 kinds (errors omission, addition, mis-ordering and mis-formation) and each kind of error has its own sub-kind errors (SKE). The biggest error is omission with 39 errors (39,8%) and lowest error is mis-ordering with 12 errors (12,2%). Omission has 2 SKE, those are grammatical morpheme with 23 errors (23,5%) and content morpheme with 16 error (16,3%). Addition has 29 errors (29,6%) with SKE 16 simple errors addition (16,3%), and 13 double error addition (13,3%). Mis-ordering has 12 errors (12,2%) with SKE 4 misplacement of verbs (4,0%) and 8 misplacements of words/items (8,1%). Mis-formation has 18 errors with SKE 5 error of alternating form/ prepositions (5,1%), 6 errors of content oriented lexical/quantifiers (6,1%) and tenses with 7 errors (7,1%). There were 9 errors found in the agreement between demonstrative pronoun and noun (9,1%) and the lowest number of errors are apostrophe s and misplacement of verb with 4 errors each (4,0%). The questionnaire shows that the implementation of SST does help the students where 30 students (46,2%) said that they know their mistake, 24 students (36,9%) stated that they know about the words’ choice/diction and 11 students (16,9%) said that SST helps them to improve their English grammar. Besides, students are encouraged to practice more English in their daily.

Reflecting geometrical shapes: approaches of primary students to reflection tasks and relations to typical error patterns

Students’ issues dealing with reflection tasks, especially with inclined mirror lines, are widely known. Previous research conducted with primary school students mainly focussed on task difficulty and students’ outcomes, especially their errors of reflection. Little is known about which not obviously sustainable understanding of reflection leads to what kind of errors. To address this issue, the purpose of this study is to explore which approaches students use in their solution finding processes when dealing with reflection tasks. We also investigated whether there was a relationship between the students’ approaches and correct or incorrect solution products. This article discusses a task-based interview study focussing on approaches used in the students’ solution process of four reflection tasks. To gather data, 42 interviews with students at the end of primary school in Germany were conducted. For the analysis, a category system with the categories “properties of reflection,” “holistic approaches,” “gestures and actions illustrating the process of reflection,” and “mental images illustrating the process of reflection,” was developed and validated. We observed that there is a relation between the approaches used by the students during their process of solving reflection tasks and their solution products. The observed relationship between the approaches applied during the solution processes and the solutions themselves leads to the assumption that the approaches the students used reveal varying degrees of mathematical understanding of reflection. As emphasized in the discussion of the results, it is important to take a differentiated look on the students’ solution products and the importance of adequate instruction methods.

Novel computational mathematical algorithms for structural optimization using graph-theoretical methods

PurposeThe purpose is to reduce round-off errors in numerical simulations. In the numerical simulation, different kinds of errors may be created during analysis. Round-off error is one of the sources of errors. In numerical analysis, sometimes handling numerical errors is challenging. However, by applying appropriate algorithms, these errors are manageable and can be reduced. In this study, five novel topological algorithms were proposed in setting up a structural flexibility matrix, and five different examples were used in applying the proposed algorithms. In doing so round-off errors were reduced remarkably.Design/methodology/approachFive new algorithms were proposed in order to optimize the conditioning of structural matrices. Along with decreasing the size and duration of analyses, minimizing analytical errors is a critical factor in the optimal computer analysis of skeletal structures. Appropriate matrices with a greater number of zeros (sparse), a well structure and a well condition are advantageous for this objective. As a result, a problem of optimization with various goals will be addressed. This study seeks to minimize analytical errors such as rounding errors in skeletal structural flexibility matrixes via the use of more consistent and appropriate mathematical methods. These errors become more pronounced in particular designs with ill-suited flexibility matrixes; structures with varying stiffness are a frequent example of this. Due to the usage of weak elements, the flexibility matrix has a large number of non-diagonal terms, resulting in analytical errors. In numerical analysis, the ill-condition of a matrix may be resolved by moving or substituting rows; this study examined the definition and execution of these modifications prior to creating the flexibility matrix. Simple topological and algebraic features have been mostly utilized in this study to find fundamental cycle bases with particular characteristics. In conclusion, appropriately conditioned flexibility matrices are obtained, and analytical errors are reduced accordingly.Findings(1) Five new algorithms were proposed in order to optimize the conditioning of structural flexibility matrices. (2) A JAVA programming language was written for all five algorithms and a friendly GUI software tool is developed to visualize sub-optimal cycle bases. (3) Topological and algebraic features of the structures were utilized in this study.Research limitations/implicationsThis is a multi-objective optimization problem which means that sparsity and well conditioning of a matrix cannot be optimized simultaneously. In conclusion, well-conditioned flexibility matrices are obtained, and analytical errors are reduced accordingly.Practical implicationsEngineers always finding mathematical modeling of real-world problems and make them as simple as possible. In doing so, lots of errors will be created and these errors could cause the mathematical models useless. Applying decent algorithms could make the mathematical model as precise as possible.Social implicationsErrors in numerical simulations should reduce due to the fact that they are toxic for real-world applications and problems.Originality/valueThis is an original research. This paper proposes five novel topological mathematical algorithms in order to optimize the structural flexibility matrix.

Trusted quantum remote sensing based on self-testing of entangled states

In a typical scenario of quantum remote sensing, a server at the remote site sends the sensing information to the local site, so that the client there can make a diagnosis about the sensed field. In practice, the reliability of the sensing information may be undermined by kinds of errors, e.g., the imperfections of measurement devices or the attacks from a malicious eavesdropper. An alternative way to circumvent this problem is to encode the sensing information into entangled states, of which the form can be inferred in a device-independent manner, namely, self-testing. In this paper, we propose a protocol to make secure sensing of a parameter at the remote side. Upon receiving the remote sensing data, the local client can figure out the parameter range by inspecting the joint probabilities. In stark contrast, a malicious eavesdropper who is allowed to access all the remote data cannot either acquire the information of the parameter or cheat the client by replacing returned data with fake ones. We apply this protocol to a magnetic sensing scenario, and we show that the client can reliably estimate the strength range of the magnetic field, which could be intimately related to various mineral resources. Consequently, the client should not overslip any mineral resources by identifying the upper bound of magnetic strength. We also show that our protocol is valid even if the entangled states are mildly noisy.

AN ERROR ANALYSIS IN WRITING DESCRIPTIVE TEXT OF THE EIGHTH GRADE STUDENT OF JUNIOR HIGH SCHOOL IN CIANJUR

Writing is one of the skills in English. The five components of writing that must be grasped are content, organization, vocabulary, mechanics, and grammar. English grammar is different from Indonesian grammar. Therefore, many students still have difficulty in writing texts by paying attention to aspects of writing. Especially in the grammatical aspect, there are still many errors made by students. The goals of this research is to find out kinds of errors in writing descriptive texts of the eighth grade students of junior high school in cianjur. The descriptive qualitative method was employed in this research. The research instrument was acquired from the students' tests. The result showed that the errors made by students based on Dulay (1982) was four type of the grammarical errors, they were omission (14 with percentage 18%), addition (27 with percentage 35%), misformation (33 with percentage 42%), dan misordering (4 with percentage 5%). It means that misformation was the most common grammar error type committed by students. Keywords: Writing, Error Analysis, Descriptive Text

Language Error Found on Social Media of Indonesian Celebrities


 
 
 
 This thesis discusses the kinds of language error found on social media of Indonesian celebrities and the factors that affect the language of Indonesian celebrities. This study is designed to find out the kinds of language error is made Indonesian celebrity used in communicating on social media and to find out the factors that influence the Indonesian celebrity language. This research uses qualitative and descriptive research method. The object of this research is Indonesian celebrities. And there are 10 Indonesia celebrties who were sampled in this study, and the researcher took 3 captions from each celebrity. The research data was taken from screenshots of Indonesian celebrity captions and videos. The way of collecting data is by using documentation. The data are classified according to the kinds of language error and factors that affect language according to Ellis & Brown's theory. In this study, the researcher found that there were 28 data that writer got from social media of Indonesian celebrities, such as Instagram and YouTube. From the 3 kinds of language error according to Ellis' theory, the researcher got 7 data from omission, the researcher got 14 data from misinformation, 1 data from misordering, and 6 data from combination of 2 kinds of error. Then the language error factor, the researcher found 6 data from the interlingual factor, 14 data from the intralingual factor, 3 data from the context of learning factor, and 5 data from combination of 2 factors of error.
 
 
 
 

SoundexGR: An algorithm for phonetic matching for the Greek language

AbstractText usually suffers from typos which can negatively affect various Information Retrieval and Natural Language Processing tasks. Although there is a wide variety of choices for tackling this issue in the English language, this is not the case for other languages. For the Greek language, most of the existing phonetic algorithms provide rather insufficient support. For this reason, in this paper, we introduce an algorithm for phonetic matching designed for the Greek language: we start from the original Soundex and we redesign and extend it for accommodating the Greek language’s phonetic rules, ending up to a family of algorithms, that we call ${\tt Soundex}_{GR}$ . Then, we report various experimental results showcasing how the algorithm behaves in different scenarios, and we provide comparative results for various parameters of the algorithm for revealing the trade-off between precision and recall in datasets with different kinds of errors. We also provide comparative results with matching using stemming, full phonemic transcription, and edit distance, that demonstrate that ${\tt Soundex}_{GR}$ performs better (indicatively, it achieves F-Score over 95% in collections of similar-sounded words). The simplicity, efficiency, and effectiveness of the proposed algorithm make it applicable and adaptable to a wide range of tasks.

PGE

Although product graphs (PGs) have gained increasing attentions in recent years for their successful applications in product search and recommendations, the extensive power of PGs can be limited by the inevitable involvement of various kinds of errors. Thus, it is critical to validate the correctness of triples in PGs to improve their reliability. Knowledge graph (KG) embedding methods have strong error detection abilities. Yet, existing KG embedding methods may not be directly applicable to a PG due to its distinct characteristics: (1) PG contains rich textual signals, which necessitates a joint exploration of both text information and graph structure; (2) PG contains a large number of attribute triples, in which attribute values are represented by free texts. Since free texts are too flexible to define entities in KGs, traditional way to map entities to their embeddings using ids is no longer appropriate for attribute value representation; (3) Noisy triples in a PG mislead the embedding learning and significantly hurt the performance of error detection. To address the aforementioned challenges, we propose an end-to-end noise-tolerant embedding learning framework, PGE, to jointly leverage both text information and graph structure in PG to learn embeddings for error detection. Experimental results on real-world product graph demonstrate the effectiveness of the proposed framework comparing with the state-of-the-art approaches.

UNGRAMMATICAL SENTENCES: A GRAMMATICAL ERROR ANALYSIS ON ARCHITECTS STUDENTS’ CORPUSES OF PONTIANAK STATE POLYTECHNIC

Writing has become a challenging skill for EFL or ESL students to overcome. It is not only required the ability to compose a good writing but also a deep layer known as the grammatical aspect. Understanding grammar is very important for building good sentences. Hence, this study aims to find out the most grammatical error found in students’ corpuses and classify the students’ error. To generate this purpose, the researcher explored the grammatical mistakes made with the aid of using college students at the first semester of Pontianak State Polytechnic as the sample of research. This study was conducted using descriptive research using students’ corpus as the data collection. The results of this study discovered that the addition error is the most frequent type of error made based on the frequency on the percentage that was 39,02%. Furthermore, this kind of error is classified as substantial. As for the other types of errors made by students, namely mis formation with the total of errors as 35.36%, omission of error as 23.17% and error in disordering as 2.44%. It can be concluded that the students made errors due to their background of knowledge and their lack of understanding of English structure.

UNGRAMMATICAL SENTENCES: A GRAMMATICAL ERROR ANALYSIS ON ARCHITECTS STUDENTS’ CORPUSES OF PONTIANAK STATE POLYTECHNIC

Writing has become a challenging skill for EFL or ESL students to
 overcome. It is not only required the ability to compose a good writing but also a deep layer known as the grammatical aspect. Understanding grammar is very important for building good sentences. Hence, this study aims to find out the most
 grammatical error found in students’ corpuses and classify the students’ error. To generate this purpose, the researcher explored the grammatical mistakes made with the aid of using college students at the first semester of Pontianak State Polytechnic as the sample of research. This study was conducted using descriptive research using
 students’ corpus as the data collection. The results of this study discovered that the addition error is the most frequent type of error made based on the frequency on the percentage that was 39,02%. Furthermore, this kind of error is classified as substantial. As for the other types of errors made by students, namely mis formation with the total of errors as 35.36%, omission of error as 23.17% and error in disordering as 2.44%. It can be concluded that the students made errors due to their background of knowledge and their lack of understanding of English structure.

PCAM: A Data-driven Probabilistic Cyber-alert Management Framework

We propose PCAM , a Probabilistic Cyber-Alert Management framework, that enables chief information security officers to better manage cyber-alerts. Workers in Cyber Security Operation Centers usually work in 8- or 12-hour shifts. Before a shift, PCAM analyzes data about all past alerts and true alerts during the shift time-frame to schedule a given set of analysts in accordance with workplace constraints so that the expected number of “uncovered” true alerts (i.e., true alerts not shown to an analyst) is minimized. PCAM achieves this by formulating the problem as a bi-level non-linear optimization problem and then shows how to linearize and solve this complex problem. We have tested PCAM extensively. Using statistics derived from 44 days of real-world alert data, we are able to minimize the expected number of true alerts that are not manually examined by a team consisting of junior, senior, and principal analysts. We are also able to identify the optimal mix of junior, senior, and principal analysts needed during both day and night shifts given a budget, outperforming some reasonable baselines. We tested PCAM ’s proposed schedule (from statistics on 44 days) on a further 6 days of data, using an off-the-shelf false alarm classifier to predict which alerts are real and which ones are false. Moreover, we show experimentally that PCAM is robust to various kinds of errors in the statistics used.

Design and Evaluate the Efficiency of Ethiopic Local Integrating System in Open-Source Database

The software includes models of internationalization over the entire world. However, the localization work which has been conducted on Amharic locale development has limitations such as managing Ethiopian calendar and numbers. This has a considerable impact in managing the Amharic locale data in a database as well as in other software systems. Even though there are some researches reporting Amharic locale data is managed by the existing Database Management Systems/DBMS/, the data management way is noticeably exposed to error. However, these kinds of errors are handled in this research by introducing new and appropriate exception handling mechanisms. In this regard, it was not found in the literature review to overcome these problems. In this work, an Amharic Locale Extension module named am_ET was developed to integrate into an open source database environment. The most suitable open-source DBMS PostgreSQL and the C programming language were used. User acceptance testing was conducted to evaluate Amharic locale extension functionalities based on the ISO 9241-11 usability testing attributes, such as user satisfaction, effectiveness and efficiency. To validate it, 35 voluntary potential users were randomly selected to participate in the usability test of the developed system. A descriptive statistical analysis and percentage method had been used to test the collected data. Accordingly, the usability test result showed that a positive opinion for the users and developers to manage Amharic local data as it easily manages data formatted to suit Ethiopian locales system such as currency, calendar, and data/time, reduced costs for developers, and better understanding of product functionalities. Consequently, the developed Amharic locale extension solved problems of data management with Ethiopian locales. The paper found that integrated extension module is helpful in localization and design application areas for all entities that are using Amharic locale in open source databases.

Flexible Least Squares Algorithm for Switching Models

The self‐organizing model and expectation‐maximization method are two traditional identification methods for switching models. They interactively update the parameters and model identities based on offline algorithms. In this paper, we propose a flexible recursive least squares algorithm which constructs the cost function based on two kinds of errors: the neighboring two‐parameter estimation errors and the output estimation errors. Such an algorithm has several advantages over the two traditional identification algorithms: it (1) can estimate the parameters of all the sub‐models without prior knowledge of the model identities; (2) has less computational efforts; and (3) can update the parameters with newly arrived data. The convergence properties and simulation examples are provided to illustrate the efficiency of the algorithm.

Error Analysis on the Students' Construction of English Irregular Plural Nouns

In English, the forms of singular nouns commonly different from those of the plural ones. Many students are confused with how to construct the irregular plural noun forms. The formation of irregular plural nouns has complex and unique patterns. This current study aims to get information about the types of errors commonly made by EFL learners in constructing irregular plural nouns and to know how the errors occurred. This study is descriptive qualitative applying error analysis theory. The data were collected by giving the respondents a test consisting of 28 questions on irregular plural construction. The result of analysis shows that the errors made by the respondents can be classified into three types: misformation, addition, and omission. Misformation was the kind of error most respondents made. The error specifically occurred with the nouns that had quite dissimilar plural forms from the singular ones (e.g., foot – feet, goose – geese).

ERROR ANALYSIS ON THE STUDENTS’ CONSTRUCTION OF ENGLISH IRREGULAR PLURAL NOUNS

In English, the forms of singular nouns commonly different from those of the plural ones. Many students are confused with how to construct the irregular plural noun forms. The formation of irregular plural nouns has complex and unique patterns. This current study aims to get information about the types of errors commonly made by EFL learners in constructing irregular plural nouns and to know how the errors occurred. This study is descriptive qualitative applying error analysis theory. The data were collected by giving the respondents a test consisting of 28 questions on irregular plural construction. The result of analysis shows that the errors made by the respondents can be classified into three types: misformation, addition, and omission. Misformation was the kind of error most respondents made. The error specifically occurred with the nouns that had quite dissimilar plural forms from the singular ones (e.g., foot – feet, goose – geese).

Tales from the lab

Tales from the lab

English Approximant Consonants: Pronunciation Difficulties Encountered by EFL Students

This research applied quantitative research. Quantitative is basically framed in term of using numbers, closed-end question. Quantitative research is characterized as an efficient examination of wonders by gathering quantifiable information and performing factual, scientific, or computational procedures. In collecting data, the researcher used pronunciation test as instrument in order to get the data. The items of pronunciation test consisted of a list of 120 words. Each sound consisted of 5 words. Pronunciation test was given to the respondents through asking them to pronounce the words clearly and correctly while the researcher was recorded. The result of the research data showed that the students made 1085 or 93.53% errors of substitution, 38 or 3.28% errors of omission, and 37 or 3.19% errors of addition from the total errors found. It indicated that most of the students made errors in substitution with high percentage than omission and errors of substitution had higher percentage than omission and addition. The result of the data analysis showed that there were 179 or 97.28% errors of substitution, 5 or 2.72% errors of omission, and there were no errors of addition found in pronouncing English approximant consonants. So, from all kinds of errors, most of the students made errors in substitution both fricative and approximant consonants.

Gloss perception: Searching for a deep neural network that behaves like humans.

The visual computations underlying human gloss perception remain poorly understood, and to date there is no image-computable model that reproduces human gloss judgments independent of shape and viewing conditions. Such a model could provide a powerful platform for testing hypotheses about the detailed workings of surface perception. Here, we made use of recent developments in artificial neural networks to test how well we could recreate human responses in a high-gloss versus low-gloss discrimination task. We rendered >70,000 scenes depicting familiar objects made of either mirror-like or near-matte textured materials. We trained numerous classifiers to distinguish the two materials in our images—ranging from linear classifiers using simple pixel statistics to convolutional neural networks (CNNs) with up to 12 layers—and compared their classifications with human judgments. To determine which classifiers made the same kinds of errors as humans, we painstakingly identified a set of 60 images in which human judgments are consistently decoupled from ground truth. We then conducted a Bayesian hyperparameter search to identify which out of several thousand CNNs most resembled humans. We found that, although architecture has only a relatively weak effect, high correlations with humans are somewhat more typical in networks of shallower to intermediate depths (three to five layers). We also trained deep convolutional generative adversarial networks (DCGANs) of different depths to recreate images based on our high- and low-gloss database. Responses from human observers show that two layers in a DCGAN can recreate gloss recognizably for human observers. Together, our results indicate that human gloss classification can best be explained by computations resembling early to mid-level vision.

AN ANALYSIS OF STUDENT'S ERROR IN WRITING ABILITY THROUGH DESCRIPTIVE TEXT OF VII GRADE OF SMP N 4 TAKENGON

The research is interested in analyzing and focusing on students' errors in writing descriptive text such as developing ideas, organizing ideas, grammar, mechanics, and using vocabulary better. This research aims to analyze the errors in writing students of descriptive text in the VII grade of SMP N 4 Takengon. This research used a qualitative method, technique of collecting data by observation, interview, and documentation. Based on the result, the writer concluded: After researching and analyzing the data, the writer concludes as the kinds of errors in writing students of descriptive text at VII grade of SMP N 4 Takengon are from three aspects they are adjective, conjunction, and present tense. The finding shows the total error of students in writing adjectives, which is 23 students. In the total error of students in writing conjunctions, there were 24 students. The total error of students in writing the present tense, which is 21 students. If it is related to the student's error statement in writing, the value of about 94% and 166.6% total wrong word in error because students' understanding is still low about writing.