Character Strings Research Articles

Предупреждение инцидентов информационной безопасности в автоматизированных информационных системах

The purpose of the article is to support the processes of preventing information security incidents in conditions of high uncertainty. Method: methods of mathematical (theoretical) computer science and fuzzy set theory. Result: an information security Incident, including a computer incident, is considered as a violation or termination of the functioning of an automated information system and (or) a violation of information stored and processed in this system, including those caused by a computer attack. Information descriptions are presented in the form of structured data about signs of computer attacks. Structured data is the final sequence of strings of symbols in a formal language. The Damerau-Levenstein editorial rule is proposed as a metric for measuring the distance between strings of characters from a particular alphabet. The possibility of presenting the semantics of information descriptions of attack features in the form of fuzzy sets is proved. Thresholds (degrees) of separation of fuzzy information descriptions are defined. The influence of semantic certainty of information descriptions of features (degrees of blurring of fuzzy information descriptions) on the decision-making about their identity (similarity) is evaluated. It is shown that the semantic component of information descriptions of signs of computer attacks presupposes the presence of some semantic metric (for its measurement and interpretation), which, as a rule, is formally poorly defined, ambiguously interpreted and characterized by uncertainty of the type of fuzziness, the presence of semantic information and the inability to directly apply a probabilistic measure to determine the degree of similarity of input and stored information descriptions of signs. An approach is proposed to identify fuzzy information descriptions of computer attacks and to apply methods for separating elements of reference sets on which these information descriptions are defined. It is shown that the results of the procedure for identifying fuzzy information descriptions of computer attacks depend on the degree of separation of the reference sets and on the indicators of semantic uncertainty of these descriptions

Segmentation and Recognition for Historical Tibetan Document Images

As a shining pearl in traditional Tibetan culture, historical Tibetan documents have received extensive attention from historians, linguists and Buddhist scholars. These documents are converted into digital form using Tibetan document segmentation and recognition methods. The document digitization is of great significance for the research, protection and inheritance of Tibetan history. This paper proposes an overall segmentation and recognition framework for historical Tibetan document images. Firstly, the historical Tibetan document image is preprocessed to correct imbalanced illumination, tilt and noises, and is further transformed into the binarized image. Secondly, we propose a layout segmentation method based on block projection to segment Tibetan document images into texts, lines and frames. Thirdly, in order to solve the problems of touching strokes between text-lines and curvilinear text-lines, we present a text-line segmentation method based on graph model for historical Tibetan text-line segmentation. Lastly, we present a touching segmentation method to segment touching Tibetan character string, and then recognize Tibetan characters. Experimental results show our proposed methods on layout segmentation, text-line segmentation and touching character string segmentation, achieve the satisfactory performance. The proposed methods can also be applied to other fonts in Tibetan font family.

A Superficial Analysis Approach for Identifying Malicious Domain Names Generated by DGA Malware

Some of the most serious security threats facing computer networks involve malware. To prevent malware-related damage, administrators must swiftly identify and remove the infected machines that may reside in their networks. However, many malware families have domain generation algorithms (DGAs) to avoid detection. A DGA is a technique in which the domain name is changed frequently to hide the callback communication from the infected machine to the command-and-control server. In this article, we propose an approach for estimating the randomness of domain names by superficially analyzing their character strings. This approach is based on the following observations: human-generated benign domain names tend to reflect the intent of their domain registrants, such as an organization, product, or content. In contrast, dynamically generated malicious domain names consist of meaningless character strings because conflicts with already registered domain names must be avoided; hence, there are discernible differences in the strings of dynamically generated and human-generated domain names. Notably, our approach does not require any prior knowledge about DGAs. Our evaluation indicates that the proposed approach is capable of achieving recall and precision as high as 0.9960 and 0.9029, respectively, when used with labeled datasets. Additionally, this approach has proven to be highly effective for datasets collected via a campus network. Thus, these results suggest that malware-infected machines can be swiftly identified and removed from networks using DNS queries for detected malicious domains as triggers.

A Discovery Method for New Words From Mobile Product Comments

A large number of new words in product reviews generated by mobile terminals are valuable indicators of the privacy preferences of customers. By clustering these privacy preferences, sufficient information can be collected to characterize users and provide a data basis for the research issues of privacy protection. The widespread use of mobile clients shortens the string length of the comment corpus generated by product reviews, resulting in a high repetition rate. Therefore, the effective and accurate recognition of new words is a problem that requires an urgent solution. Hence, in this paper, we propose a method for discovering new words from product comments based on Mutual Information and improved Branch Entropy. Firstly, by calculating the Co-occurrence Frequency and Mutual Information between words and adjacent words, the character strings of words after pre-processing and word segmentation are expanded left and right respectively to discover the potential word set. The candidate set of new words is obtained by means of an improved support filtering algorithm. Finally, a new word set is built by applying an improved Branch Entropy filtering algorithm and removing old words. The experimental results show that this method can accurately and effectively identify new words in product comments.

The brain as a network cable: Transmission of a modulated optical signal between two computers via the human brain

Abstract The SSVEP response is used to transmit information in a unidirectional through-brain non-cognitive (entirely passive) communication channel. This, in effect, is connecting two otherwise disconnected computers through the brain, allowing us to consider the bandwidth of SSVEP encoding. The system uses green LEDs driven by a square wave to stimulate SSVEPs with EEG measurements being recorded by an EMOTIV EPOC. The stimulation and detection of SSVEPs is at the base of the channel - upon which a communication stack is implemented, allowing for character strings to be transmitted between an independent sender and receiver. The system was tested on four subjects, the best BER performance being achieved when using a PSDA detector on the O1 electrode with a 7,4 Hamming code being used as the FEC. A practical ITR of 25.6 bits/min was achieved. A prefix based transmission protocol was developed which allows the receiver to determine when a message is being sent and also allows the receiver to synchronise with the sender without the need for a secondary connection. The developed protocol correctly determined that a message was being sent across the channel 90% of the time with an average absolute synchronisation error of 0.397 s between sender and receiver.

Calculating the average distance between horizontal projection peaks

During the analysis and construction of the noise filtering algorithm at the stage of segmentation of character strings, the need to describe a special database or dictionary for storing model and skeletal projections of alphabet characters became clear. However, when formatting entries for alphabetical projection in the dictionary, the following questions arose: how many projection values are needed to describe a single character, and also how this value may change depending on the number of strings analyzed and their characters. The objects of research in the article are the vertical projection of the characters, as well as their width of the projection segments. The subject of research is the change of the average width of the projection segment for a certain type of license plates (character string). The main goal is to calculate and justify the average size of the segment. Data about the average width of the projection segment that describes a character allows to determine the number of projection values (coordinates along the ox axis) to store the projection values of this character from an alphabet. Several language alphabets are considered, where each of them is associated with a unique type. In particular, license plates of the Republic of Belarus and the Slovak Republic are considered. Using an elementary statistical apparatus, calculations and analysis of samples were carried out, taking into account the case of their high variation. As a result, the average values of the segment width and the variance of the segment width were obtained using two types of alphabets as an example. In research an algorithm for using the obtained values in the formation of model projection records is presented. The algorithm takes into account «special» cases of going beyond the segment boundaries. The described steps and calculations can be applied to a larger number of alphabets, which indicates the possibility of describing model projection dictionaries for them, with subsequent widespread use of character strings in noise filtering.

A De-Compositional Approach to Regular Expression Matching for Network Security

Regular Expression (RegEx) matching is the industry standard for Deep Packet Inspection (DPI) because RegExes are significantly more expressive than strings. To achieve high matching speed, we need to convert the RegExes to Deterministic Finite State Automata (DFA). However, DFA has the state explosion problem, that is, the number of DFA states and transitions can be exponential with the number of RegExes. Much work has addressed the DFA state explosion problem; however, none has met all the requirements of fast and automated construction, small memory image, and high matching speed. In this paper, we propose a decompositional approach, with fast and automated construction, small memory image, and high matching speed, to DFA state explosion. The first key idea is to decompose a complex RegEx that cause exponential state increases into a set of simpler RegExes that do not cause exponential state increases, where any character string that matches the complex RegEx also matches all the RegExes in the set of simpler RegExes; that is, the set of strings that match the complex RegEx is a subset of strings that match the set of simpler RegExes. The second key idea is to use a stateful post-processing engine to filter the matches that are actually the matches of the complex RegEx. Given an input string for matching, instead of using the large DFA constructed from the original complex RegEx to perform the matching, we first use the small DFA constructed from the set of simpler RegExes to perform the matching, and then, if the small DFA reports a match, we use the post-processing engine to determine whether it is a true match to the original complex RegEx. Because the pre-processing is simple, automaton construction can be automated and fast, and because most on-line processing is done by a DFA, its matching speed is close to that of a DFA alone. Our experimental results show that our decompositional approach achieves orders of magnitude faster DFA construction (in terms of seconds instead of minutes), 30 times smaller memory image, and 43% faster matching speeds, than state-of-the-art software based RegEx matching algorithms.

Graphical Authentication Passcode Memorability: Context, Length, and Number

Authentication verifies users’ identities to protect against costly attacks. Graphical authentication schemes utilize pictures as passcodes rather than strings of characters, numbers, and symbols. Pictures are more memorable than alphanumeric passwords. Two studies investigated the memorability of graphical passcodes when users need to remember multiple passcodes or longer passcodes. Longer passcodes provide advantages to security by protecting against brute force attacks, and multiple passcodes need to be investigated as users need to authenticate for numerous accounts. It was found that participants have strong item retention for passcodes of up to eight images and for up to eight accounts. Also, these studies examined whether context can facilitate memorability. The impact of context was not found as assumed in the literature. Overall, memorability was still higher for all the number and length passcode conditions compared with alphanumeric passwords. These studies take steps toward understanding the readiness of graphical schemes as an authentication option.

Universal character, phase model and topological strings on pmb {mathbb {C}^3}

In this paper, we consider two different subjects: the algebra of universal characters S_{[lambda ,mu ]}(mathbf{x},mathbf{y}) (a generalization of Schur functions) and the phase model of strongly correlated bosons. We find that the two-site generalized phase model can be realized in the algebra of universal characters, and the entries in the monodromy matrix of the phase model can be represented by the vertex operators Gamma _i^pm (z) (i=1,2) which generate universal characters. Meanwhile, we find that these vertex operators can also be used to obtain the A-model topological string partition function on mathbb {C}^3.

Comparison study of Fibonacci code algorithm and Even-Rodeh algorithm for data compression

The need for greater storage capacity is the cause of various compression techniques. By compressing, large data will be reduced in size to save storage capacity. This research will use Even-Rodeh code algorithm and Fibonacci code algorithm to compress the data. The performance of algorithms will be measured by Bitrate, Compression Ratio, Space Saving, and Compression Time (millisecond) and Time Decompression (millisecond). Compression is done by reading String in the text file, then Even-Rodeh Code algorithm and Fibonacci code algorithm create String code and perform compression process. The result of compression is the file with extension *. C and *. Fib which contains character information and String Bit. The results of compression can be decompressed to the original text file. The system test used a sample of Strings consisting of one type of character (Homogen String) and String comprising more than one kind of character (Heterogenous String) is stored in the text file by extension * .doc, * .docx, * .pdf. From the compression process on the homogenous String, the Fibonacci code algorithm is more efficient about the compression ratio of 0.25 and decompression time is 0.011 milliseconds compared with the Even-Rodeh code algorithm. While in the compression process on heterogeneous String Fibonacci Code algorithm is better than the Even-Rodeh code algorithm with an average Compression Ratio of 0.65 and decompression time faster with an average of 0.293 milliseconds.

12 lead electrocardiography signals compression by a new genetic programming based mathematical modeling algorithm

Telemedicine refers to a group of modern medical services that are provided on the platform of advanced telecommunication technologies. One of these services is the screening for heart diseases, which are the leading cause of mortality across the world. But the development of telemedicine systems for cardiac screening faces multiple challenges. One of these challenges is the large volume of ECG signals, which makes them difficult to store and transfer. Of the many algorithms proposed for the compression of ECG signals, most rely on the processing of data as discrete numerical values. The alternative approach followed in this study is to model the signal compression problem into a regression problem and then convert it into a text compression problem. Using this approach, the paper presents a new genetic programming based method for the compression of ECG signals. The proposed method starts with denoising and smoothing the ECG signal with discrete wavelet transform and then constructing its mathematical model with a genetic programming based algorithm. This model is a piecewise mathematical function where each sub-function models one part of the signal. Next, the model is converted to a character string and regular expressions are used to extract the function coefficients and encode the symbols contained in the string. Finally, the strings and coefficients are compressed using the LZW and arithmetic encoding methods, respectively. The efficiency of the algorithm is evaluated through compression ratio (CR), percent root-mean-square difference (PRD), root-mean-square-error (RMSE) and quality score (QS) on MIT-BIH Arrhythmia Database records. The evaluation results demonstrate the good performance of the proposed method in comparison with other state-of-the-art techniques.

Reconstruction of PC Display from Common-Mode Noise Emitting on Electrical Power Line

This paper presents a method for reconstruction of a personal computer (PC) display image from common-mode noises coupling with monitor signals on a PC power cable. While the signal cable, which connects the PC and the monitor, is usually near the user, the power cable is connected to the outside electrical network of the office or the building. Thus, the power cables may become dominant gateways and/or antennas for emission and conduction of the common-mode noise, which may lead to a serious security issue. The measured common-mode was found to include both the monitor signal and undesired beats, which were caused by step responses of the signal and conceal the meaningful information. The original monitor signal was then calculated by excluding the beats, which could be measured by using standard up-step and down-step responses, from the measured common-mode noise and using an inverse function of the noise current level. The experimental results show that the beats were removed almost completely from the noise waveform for a monochromatic image. Alphabetic character strings, each of which were composed of, at most, 9 × 9 dots, were confirmed to be reconstructed clearly both in the monitor resolutions of 800 × 600 pixels and 1280 × 1024 pixels from the common-mode noise.

A welding seam identification method based on cross-modal perception

PurposeAs an automatic welding process may experience some disturbances caused by, for example, splashes and/or welding fumes, misalignments/poor positioning, thermally induced deformations, strong arc lights and diversified welding joints/grooves, precisely identifying the welding seam has a great influence on the welding quality. This paper aims to propose a robust method for identifying this seam based on cross-modal perception.Design/methodology/approachFirst, after a welding image obtained from a structured-light vision sensor (here laser and vision are integrated into a cross-modal perception sensor) is filtered, in a sufficiently small area, the extended Kalman filter is used to prevent possible disturbances to search for its laser stripe. Second, to realize the extraction of the profile of welding seam, the least square method is used to fit a sequence of centroids determined by the scanning result of columns displayed on the tracking window. Third, this profile is then qualitatively described and matched using a proposed character string method.FindingsIt is demonstrated that it maintains real time and is clearly superior in terms of accuracy and robustness, though its real-time performance is not the best.Originality/valueThis paper proposes a robust method for automatically identifying and tracking a welding seam.

Describing the patient experience from Yelp reviews of community pharmacies

ObjectivesTo examine the characteristics of patient experience in community pharmacies through pattern exploration techniques of the unstructured free-text data from an online review website. DesignRetrospective observational study design using structural topic model (STM) and term frequency–inverse document frequency (tf-idf) to categorize free-text data. Tf-idf scores words in terms of importance, and STM extracts latent themes from free-text data based on the co-occurrence of words in a review. Human labels were assigned to STM output, with each topic’s prevalence mapped to each level of the 1- to 5-star review ratings. Setting and participantsData were obtained from the Yelp Academic data set from April 2006 through December 2017. These data were available for analysis from certain cities in the United States, Canada, and Europe. Included reviews were filtered based on the presence of pharmacy-specific character strings (e.g., “prescri”). Main outcome measuresDescriptive statistics of Yelp review characteristics, tf-idf scores, and topics produced from STM were used to characterize the content of Yelp reviews at each star-rating level. ResultsThe filtered data set contained 4463 reviews from 964 pharmacies in 8 U.S. states. The mean (±SD) review rating was 2.97 ± 0.91. The mean number of words in a review was 135 ± 116. STM revealed 9 topics that influenced patient experiences at community pharmacies, including waiting time, service attitude, and physical store characteristics. Friendly and helpful staff accounted for 28.3% of content in 5-star ratings, whereas waiting time accounted for 19.4% of 1-star ratings. ConclusionYelp reviews provide a public look into patient experience at community pharmacies, and the reviews likely influence other patients’ decisions to use the pharmacy. Pharmacies should focus their efforts on enabling pharmacy staff to provide high-quality care and minimizing unnecessary waiting times for patients.

Expressiveness and machine processability of Knowledge Organization Systems (KOS): an analysis of concepts and relations

This study considers the expressiveness (that is the expressive power or expressivity) of different types of Knowledge Organization Systems (KOS) and discusses its potential to be machine-processable in the context of the Semantic Web. For this purpose, the theoretical foundations of KOS are reviewed based on conceptualizations introduced by the Functional Requirements for Subject Authority Data (FRSAD) and the Simple Knowledge Organization System (SKOS); natural language processing techniques are also implemented. Applying a comparative analysis, the dataset comprises a thesaurus (Eurovoc), a subject headings system (LCSH) and a classification scheme (DDC). These are compared with an ontology (CIDOC-CRM) by focusing on how they define and handle concepts and relations. It was observed that LCSH and DDC focus on the formalism of character strings (nomens) rather than on the modelling of semantics; their definition of what constitutes a concept is quite fuzzy, and they comprise a large number of complex concepts. By contrast, thesauri have a coherent definition of what constitutes a concept, and apply a systematic approach to the modelling of relations. Ontologies explicitly define diverse types of relations, and are by their nature machine-processable. The paper concludes that the potential of both the expressiveness and machine processability of each KOS is extensively regulated by its structural rules. It is harder to represent subject headings and classification schemes as semantic networks with nodes and arcs, while thesauri are more suitable for such a representation. In addition, a paradigm shift is revealed which focuses on the modelling of relations between concepts, rather than the concepts themselves.

Motivated to Gain: Awareness of an Impending Ending and the Ending Effect.

The ending effect describes the phenomenon that individuals are more risk-taking during the final round of a series of risky decision tasks. Previous research suggests that the ending effect might be caused by a motivational shift induced by changes in time perception. However, none of the existing research directly tested the motivational state immediately before the last round of a series of risky decision tasks. To fill in this gap of knowledge, the present study tested whether this motivational shift indeed occurs immediately before the last round. All participants worked on 11 rounds of risky decision tasks, half of them knew that the decision tasks included 11 rounds, whereas the other half did not know. Before the last round of the risky decision tasks, all participants completed a visual reaction time task. It was found that, compared with participants who were not aware of the impending ending, those who knew they were approaching the last round responded to peripherally located character strings appearing immediately after gain-related words slower than those appearing after loss-related words, suggesting that perceived endings lead participants to be more motivated toward gaining rewards. This work provides critical evidence which supports the motivational account of the ending effect proposed in previous research. Such a finding would represent a next step in unpacking the psychological consequences of perceived endings in everyday life.

Stable assessment of the quality of similarity algorithms of character strings and their normalizations

The choice of search tools for hidden commonality in the data of a new nature requires stable and reproducible comparative assessments of the quality of abstract algorithms for the proximity of symbol strings. Conventional estimates based on artificially generated or manually labeled tests vary significantly, rather evaluating the method of this artificial generation with respect to similarity algorithms, and estimates based on user data cannot be accurately reproduced. A simple, transparent, objective and reproducible numerical quality assessment of a string metric. Parallel texts of book translations in different languages are used. The quality of a measure is estimated by the percentage of errors in possible different tries of determining the translation of a given paragraph among two paragraphs of a book in another language, one of which is actually a translation. The stability of assessments is verified by independence from the choice of a book and a pair of languages. The numerical experiment steadily ranked by quality algorithms for abstract character string comparisons and showed a strong dependence on the choice of normalization.

Scalable Hardware Mechanism for Partitioned Circuits Operation

For designing hardware with a high-level synthesis tool using a programming language such as C or Java, its large size of logic circuit makes it difficult to implement the design in a single FPGA. In such a case, partitioning the logic circuit and implementing in multiple FPGAs is a commonly used approach.
 We propose the Scalable Hardware Mechanism, which enables the operation of a partitioned circuit to prevent the degradation of clock frequency by minimizing its dependence on the usage and the type of FPGA. Our mechanism provides a reduced delay by the collective signal transmission with the partitioned AES code generation circuit and the character string edit distance calculation circuit as partitioned circuits. The collective signal transmission has attained 1.27 times improvement in the speed for the AES code generation circuit and 3.16 times improvement for the character string edit distance calculation circuit compared with the circuit by the conventional method.

A new sequence logo plot to highlight enrichment and depletion

BackgroundSequence logo plots have become a standard graphical tool for visualizing sequence motifs in DNA, RNA or protein sequences. However standard logo plots primarily highlight enrichment of symbols, and may fail to highlight interesting depletions. Current alternatives that try to highlight depletion often produce visually cluttered logos.ResultsWe introduce a new sequence logo plot, the EDLogo plot, that highlights both enrichment and depletion, while minimizing visual clutter. We provide an easy-to-use and highly customizable R package Logolas to produce a range of logo plots, including EDLogo plots. This software also allows elements in the logo plot to be strings of characters, rather than a single character, extending the range of applications beyond the usual DNA, RNA or protein sequences. And the software includes new Empirical Bayes methods to stabilize estimates of enrichment and depletion, and thus better highlight the most significant patterns in data. We illustrate our methods and software on applications to transcription factor binding site motifs, protein sequence alignments and cancer mutation signature profiles.ConclusionsOur new EDLogo plots and flexible software implementation can help data analysts visualize both enrichment and depletion of characters (DNA sequence bases, amino acids, etc.) across a wide range of applications.

Orthographic-Phonological Mapping and the Emergence of Visual Expertise for Print: A Developmental Event-Related Potential Study.

The N1 effect is an electrophysiological marker of visual specialization for print. The phonological mapping hypothesis (Maurer & McCandliss, 2007) posits that the left-lateralized effect reflects grapheme-phoneme integration. In this event-related potential study, first (age=7.06years, N=32) and third-grade readers (age=9.29years, N=28) were presented with pairs of pseudowords and Armenian character strings in a novel implicit same-different paradigm. To test the phonological mapping hypothesis, stimuli were presented in visual-only and audiovisual conditions. The results demonstrated that tuning for print already emerges in first grade. Moreover, the parallel presentation of auditory stimuli enhanced the N1 effect suggesting a role of orthographic-phonological mapping in the development of specialization for print.