Bilgisayar Mühendisliği Bölümü Koleksiyonu

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Türkçe hibrit metin özetleme için aday özet eleme yaklaşımı
(2024) Kayalı, Nihal Zuhal; Omurca, Sevinç İlhan
The importance of text summarization in the field of natural language processing has increased significantly in recent years. With increasing amounts of text data, it is crucial to develop effective methods for automatic text summarization to extract information quickly and accurately. Text summarization methods are generally divided into extractive and abstractive. In this study, both methods were used together in a hybrid manner. Luhn, LSA, TextRank, LexRank, SumBasic, PageRank and Hidden Markov Model were used as extractive methods, and the transformer-based BERT2BERT model was used as the abstractor method. The summaries were first summarized separately from the raw text using subtractive summarization methods. The obtained extractive summaries were summarized again by the abstractive model according to the two highest-scoring extractive method summaries, based on the score consisting of the combination of the ROUGE score, BERTScore and METEOR scores at certain weights, which we collectively call "BIG3". As a result of the study, it was observed that the summaries created with our hybrid model achieved higher scores in the evaluation criteria.
Web application firewall based on anomaly detection using deep learning
(2022) Toprak, Sezer; Yavuz, Ali Gökhan
Anomali tespiti, farklı sektörlerde ve uygulama alanlarında araştırılmaya devam etmektedir. Anomali tespitindeki temel zorluk, benzersiz özelliklere ve yeni değerlere sahip bir girdi ile karşılaşılması durumunda normallerden aykırı değerleri belirlemektir. Araştırmalar, bu görevi yerine getirmek için Makine Öğrenmesi ve Derin Öğrenme tekniklerini kullanmaya odaklanmaktadır. Internet dünyasında, bir web sitesi isteğinin kötü niyetli veya sadece normal bir istek olup olmadığını belirlemek istediğimizde yine benzer bir sınıflandırma problemiyle karşı karşıya kalmaktayız. Web Uygulama Güvenlik Duvarı (WAF) sistemleri kötü niyetli faaliyetlere ve isteklere karşı, kural tabanlı ve son yıllarda kullanılan anomali tabanlı çözüm kullanarak koruma sağlar. Bu tür çözümler bir noktaya kadar güvenlik sağlar ve kullanılan teknikler, arka uç sistemlerini savunmasız bırakan hatalı sonuçlar üretmektedirler. Bu çalışmanın odak noktası, karakter sıralaması tabanlı bir LSTM (tekli ve yığılmış olmak üzere) yapısı kullanılarak bir WAF sistemi oluşturmak ve derin öğrenme modelinin optimum sonuç üretmesi için hiper parametrelerin hangi değerleri alması gerektiğini ortaya koymaktır. Semi-supervised öğrenme yaklaşımı için PayloadAllTheThings verisetinde yer alan gerçek saldırı verilerinin yanı sıra HTTP CSIC 2010 verisetinde yer alan ve normal olarak etiketlenen veriler hem modelin öğrenmesi sırasında hem de test edilmesi adımında kullanılmıştır. Önerilen tekniğin başarı oranının analizini için F1 skor değeri baz alınmıştır. Yapılan analizler ve deneyler sonucunda elde edilen derin öğrenme modelinin F1 başarı oranının yüksek olduğu ve saldırıları tespit etme ve sınıflandırma noktasında da başarı elde edildiği gösterilmiştir.
A comprehensive database analysis of post-translational modifications with chemical and functional categories. A throughout PTM study of 2022 databases.
(2023) Gül, Nadir; Yıldız, Ahmet
One of the most important mechanisms that directly affect protein diversity and function is post-translational modification, which has grown exponentially and now encompasses a vast quantity of data. In order to better understand the nature of Post Translational modifications, in this study we elaborated those modifications and classified them into different perspectives In order to base our investigations on high-confidence PTM occurrences, we confined our study to post-translational modifications that have been experimentally verified on humans, leaving out any annotated PTMs based solely on computation prediction. Utilizing chemical and functional details from external databases and literature, 170 distinct alterations from the neXtProt database were examined and identified. These modifications had a total occurrence of 187.220. A careful examination of the data at hand led to the identification of 47 types that contained all 170 modifications. It was revealed that every amino acid underwent at least one type of modification, with cysteine serving as the target of the most diverse modifications—28 in all. Another finding was that most modifications function by converting the target amino acid, while small chemical groups are the most frequent functional groups. The purpose of comprehensively classifying post-translational modifications is to provide an approach that may help understand the effects of these modifications on protein function. It's known that the physicochemical nature of these modifications exerts significant effects on function. However, there is a need to further deepen the current classifications.
Hybrid Tokenization Strategy for Turkish Abstractive Text Summarization
(2024) Kayalı, Nihal Zuhal; Omurca, Sevinç İlhan
Text summarization is a significant topic in natural language processing. Tokenization approaches are important in this regard as they underpin text recognition and processing. The aim of this paper is to research the efficiency of different tokenization approaches when summarizing Turkish texts and their combinations impact on summarization performance. Whitespace, ULM, BPE and WordPiece tokenization methods are mixed in different ways with pre-trained BERTurk, mT5 and mBART models on MLSUM dataset. We evaluate every tokenization method’s performance as well as all possible combinations based on generated summaries and ROUGE scores. Our results show that if we combine some strategies of tokenization together and use it as a hybrid method, the accuracy and consistency of the summaries will be significantly enhanced. This study gives useful hints about how to optimize models for Turkish in terms of text summarization and emphasizes on selecting suitable tokenization strategies.
Konvolüsyonel sinir ağları (CNN) ile Çin sayı orüntülerinin sınıflandırması
(2021) Kayalı, Nihal Zuhal; Omurca, Sevinç İlhan
The MNIST dataset, which has become a standard for Machine Learning and Deep Learning applications, is quite popular. In thisstudy, a dataset containing images of handwritten Chinese numbers is introduced as a variation of the MNIST dataset. Convolutional Neural Networks (ConvNN or CNN) model, which is a type of multi-layer artificial neural networks, which is one of the current and challenging research topics of Machine Learning methods, has been applied and a classification study has been made. At the classification stage, a correct classification rate of 96.5% was achieved. As a result, it is thought that the proposed model can be used on Chinese number patterns with high performance.
Evaluation-focused multidimensional score for Turkish abstractive text summarization
(Sakarya University, 2024) Kayalı, Nihal Zuhal; Omurca, Sevinç İlhan
Despite the inherent complexity of Abstractive Text Summarization, which is widely acknowledged as one of the most challenging tasks in the field of natural language processing, transformer-based models have emerged as an effective solution capable of delivering highly accurate and coherent summaries. In this study, the effectiveness of transformer-based text summarization models for Turkish language is investigated. For this purpose, we utilize BERTurk, mT5 and mBART as transformer-based encoder-decoder models. Each of the models was trained separately with MLSUM, TR-News, WikiLingua and Fırat_DS datasets. While obtaining experimental results, various optimizations were made in the summary functions of the models. Our study makes an important contribution to the limited Turkish text summarization literature by comparing the performance of different language models on existing Turkish datasets. We first evaluate ROUGE, BERTScore, FastText-based Cosine Similarity and Novelty Rate metrics separately for each model and dataset, then normalize and combine the scores we obtain to obtain a multidimensional score. We validate our innovative approach by comparing the summaries produced with the human evaluation results.
Temporal transaction scraping assisted point of compromise detection with autoencoder based feature engineering
(2021) Ogme, Fuat; Yavuz, Ali Gökhan; Güvensan, M. Amaç; Karsligil, Mine Elif
Credit card fraudsters exploit various methods to capture card information. One of the common methods is to duplicate the credit cards by skimming. In this study, we introduce a new point of compromise detection method in order to trace and identify merchants where the skimming operation took place and card information has been captured by criminals. The proposed method first extracts discriminative features by using principle component analysis(PCA) and Autoencoder extractors and then it clusters similar fraudulent transactions with K-Means algorithm, afterwards it highlights possible merchants that are involved in this scheme by finding matching merchants in the produced clusters with a retrospective analysis of all transactions. Our experiments showed that the proposed method could achieve promising results with zeroknowledge on the existing skimming points. The application of our proposed method on real-life card transactions enabled us to pinpoint 7 out of 9 point of compromise previously identified by the reporting bank.
A better way of extracting dominant colors using salient objects with semantic segmentation
(2021) Gündüz, Ayşe Bilge; Taşkın, Berk; Yavuz, Ali Gökhan; Karsligil, Mine Elif
One of the most prominent parts of professional design consists of combining the right colors. This combination can affect emotions, psychology, and user experience since each color in the combination has a unique effect on each other. It is a very challenging to determine the combination of colors since there are no universally accepted rules for it. Yet finding the right color combination is crucial when it comes to designing a new product or decorating the interiors of a room. The main motivation of this study is to extract the dominant colors of a salient object from an image even if the objects overlap each other. In this way, it is possible to find frequent and popular color combinations of a specific object. So, first of all, a modified Inception-ResNet architecture was designed semantically segmentate objects in the image. Then, SALGAN was applied to find the salient object in the image since the aim here is to find the dominant colors of the salient object in a given image. After that, the outputs consisted of the SALGAN applied image and segmented image were combined to obtain the corresponding segment for the purpose of finding the salient object on the image. Finally, since we aimed to quantize the pixels of the corresponding segment in the image, we applied k-means clustering which partitions samples into K clusters. The algorithm works iteratively to assign each data point to one of the K groups based on their features. Data points were clustered according to feature similarity. As a result the clustering, the most relevant dominant colors were extracted. Our comprehensive experimental survey has demonstrated the effectiveness of the proposed method.
An attack detection framework based on BERT and deep learning
(IEEE, 2022) Seyyar, Yunus Emre; Yavuz, Ali Gökhan; Ünver, Halil Murat
Deep Learning (DL) and Natural Language Processing (NLP) techniques are improving and enriching with a rapid pace. Furthermore, we witness that the use of web applications is increasing in almost every direction in parallel with the related technologies. Web applications encompass a wide array of use cases utilizing personal, financial, defense, and political information (e.g., wikileaks incident). Indeed, to access and to manipulate such information are among the primary goals of attackers. Thus, vulnerability of the information targeted by adversaries is a vital problem and if such information is captured then the consequences can be devastating, which can, potentially, become national security risks in the extreme cases. In this study, as a remedy to this problem, we propose a novel model that is capable of distinguishing normal HTTP requests and anomalous HTTP requests. Our model employs NLP techniques, Bidirectional Encoder Representations from Transformers (BERT) model, and DL techniques. Our experimental results reveal that the proposed approach achieves a success rate over 99.98% and an F1 score over 98.70% in the classification of anomalous and normal requests. Furthermore, web attack detection time of our model is significantly lower (i.e., 0.4 ms) than the other approaches presented in the literature.
Faculae cancel out on the surfaces of active suns
(IOP Publishing, 2022) Nemec, N. E.; Shapiro, A. I.; Işık, Emre; Sowmya, K.; Solanki, S. K.; Krivova, N. A.; Cameron, R. H.; Gizon, L.
Surfaces of the Sun and other cool stars are filled with magnetic fields, which are either seen as dark compact spots or more diffuse bright structures like faculae. Both hamper detection and characterization of exoplanets, affecting stellar brightness and spectra, as well as transmission spectra. However, the expected facular and spot signals in stellar data are quite different, for instance, they have distinct temporal and spectral profiles. Consequently, corrections of stellar data for magnetic activity can greatly benefit from the insight on whether the stellar signal is dominated by spots or faculae. Here, we utilize a surface flux transport model to show that more effective cancellation of diffuse magnetic flux associated with faculae leads to spot area coverages increasing faster with stellar magnetic activity than that by faculae. Our calculations explain the observed dependence between solar spot and facular area coverages and allow its extension to stars that are more active than the Sun. This extension enables anticipating the properties of stellar signal and its more reliable mitigation, leading to a more accurate characterization of exoplanets and their atmospheres.
Predictions of astrometric jitter for sunlike stars. III. Fast Rotators
(IOP Publishing, 2022) Sowmya, K.; Nemec, N. E.; Shapiro, A., I.; Işık, Emre; Krivova, N. A.; Solanki, S. K.
A breakthrough in exoplanet detections is foreseen with the unprecedented astrometric measurement capabilities offered by instrumentation aboard the Gaia space observatory. Besides, astrometric discoveries of exoplanets are expected from the planned space mission, Small-JASMINE. In this setting, the present series of papers focuses on estimating the effect of the magnetic activity of G2V-type host stars on the astrometric signal. This effect interferes with the astrometric detections of Earth-mass planets. While the first two papers considered stars rotating at the solar rotation rate, this paper focuses on stars having solar effective temperature and metallicity but rotating faster than the Sun, and consequently more active. By simulating the distribution of active regions on such stars using the Flux Emergence And Transport model, we show that the contribution of magnetic activity to the astrometric measurements becomes increasingly significant with increasing rotation rates. We further show that the jitter for the most variable periodic Kepler stars is high enough to be detected by Gaia. Furthermore, due to a decrease in the facula-to-spot area ratio for more active stars, the magnetic jitter is found to be spot dominated for rapid rotators. Our simulations of the astrometric jitter have the potential to aid the interpretation of data from Gaia and upcoming space astrometry missions.
Predictions of astrometric jitter for sun-like stars. II. dependence on ınclination, metallicity, and active-region nesting
(IOP Publishing, 2021) Krishnamurthy, Sowmya; Nemec, Nina Elisabeth; Shapiro, Alexander; Işık, Emre; Witzke, Veronika; Mints, Alexey; Krivova, Natalie; Solanki, Sami
Ultra-precise astrometry from the Gaia mission is expected to lead to astrometric detections of more than 20,000 exoplanets in our Galaxy. One of the factors that could hamper such detections is the astrometric jitter caused by the magnetic activity of the planet host stars. In our previous study, we modeled astrometric jitter for the Sun observed equator-on. In this work, we generalize our model and calculate the photocenter jitter as it would be measured by the Gaia and Small-JASMINE missions for stars with solar rotation rate and effective temperature, but with various values of the inclination angle of the stellar rotation axis. In addition, we consider the effect of metallicity and of nesting of active regions (i.e., the tendency of active regions to emerge in the vicinity of each other). We find that, while the jitter of stars observed equator-on does not have any long-term trends and can be easily filtered out, the photocenters of stars observed out of their equatorial planes experience systematic shifts over the course of the activity cycle. Such trends allow the jitter to be detected with continuous measurements, in which case it can interfere with planet detectability. An increase in the metallicity is found to increase the jitter caused by stellar activity. Active-region nesting can further enhance the peak-to-peak amplitude of the photocenter jitter to a level that could be detected by Gaia.
Where have all the solar-like stars gone? Rotation period detectability at various inclinations and metallicities
(IOP Publishing, 2021) Reinhold, Timo; Shapiro, Alexander I.; Witzke, Veronika; Nèmec, Nina E.; Işık, Emre; Solanki, Sami K.
The plethora of photometric data collected by the Kepler space telescope has pro moted the detection of tens of thousands of stellar rotation periods. However, these periods are not found to an equal extent among different spectral types. Interest ingly, early G-type stars with near-solar rotation periods are strongly underrepre sented among those stars with known rotation periods. In this study we investigate whether the small number of such stars can be explained by difficulties in the period determination from photometric time series. For that purpose, we generate model light curves of early G-type stars with solar rotation periods for different inclination angles, metallicities and (magnitude-dependent) noise levels. We find that the de tectability is determined by the predominant type of activity (i.e. spot or faculae domination) on the surface, which defines the degree of irregularity of the light curve, and further depends on the level of photometric noise. These two effects significantly complicate the period detection and explain the lack of solar-like stars with known near-solar rotation periods. We conclude that the rotation periods of the majority of solar-like stars with near-solar rotation periods remain undetected to date. Finally, we promote the use of new techniques to recover more periods of near-solar rotators.
Spatio-temporal pedestrian accident analysis to improve urban pedestrian safety: the case of the Eskişehir motorway
(Gazi University Eti Mahallesi, 2015) Kaygısız, Ömür; Yıldız, Ahmet; Düzgün, Şebnem
Determining pedestrian accident hotspots on road segments is a crucial part of the pedestrian safety assessment as it is used to prioritize problematic parts of a road network for in particularly planning and implementation strategies. Moreover, the spatial pattern of the pedestrian accidents may change over time due to several factors. In order to better understand pedestrian safety conditions, pedestrian accident patterns have to be analysed with regard to both space and time. This paper adapts such a spatio-temporal hotspot detection method for the analysis of pedestrian accidents. In this study, 189 traffic accidents involving pedestrians that resulted in injury or fatality on the Eskişehir Motorway (Turkey) between the years of 2005 and 2010 are mapped with their spatial and temporal information. Network-based Kernel Density Estimation is used to examine the hotspots of pedestrian accidents and their changes over the years. Then, the significances of the results are evaluated by using Network-based the Nearest Neighbor Distance and the K-function methods. The impact of land use change and taken measures are evaluated based on spatio-temporal hotspot analysis. © 2015, Gazi University Eti Mahallesi. All rights reserved.
Spatio-temporal accident analysis for accident prevention in relation to behavioral factors in driving: the case of South Anatolian Motorway
(Elsevier Sci Ltd, 2015) Kaygısız, Ömür; Düzgün, Şebnem; Yıldız, Ahmet; Şenbil, Metin
Analyzing the pattern of traffic accidents on road segments can highlight the hazardous locations where the accidents occur frequently and help to determine problematic parts of the roads. The objective of this paper is to utilize accident hotspots to analyze the effect adifferent measures on the behavioral factors in driving. Every change in the road and its environment affects the choices of the driver and therefore the safety of the road itself. A spatio-temporal analysis of hotspots therefore can highlight the road segments where measures had positive or negative effects on the behavioral factors in driving. In this paper 2175 accidents resulted in injury or death on the South Anatolian Motorway in Turkey for the years between 2006 and 2009 are considered. The network-based kernel density estimation is used as the hotspot detection method and the K-function and the nearest neighbor distance methods are taken into account to check the significance of the hotspots. A chi-square test is performed to find out whether temporal changes on hotspots are significant or not. A comparison of characteristics related driver attributes like age, experience, etc. for accidents in hotspots vs. accidents outside of hotspots is performed to see if the temporal change of hotspots is caused by structural changes on the road. For a better understanding of the effects on the driver characteristics, the accidents are analyzed in five groups based on three different grouping schemes. In the first grouping approach, all accident data are considered. Then the accident data is grouped according to direction of the traffic flow. Lastly, the accident data is classified in terms of the vehicle type. The resultant spatial and temporal changes in the accident patterns are evaluated and changes on the road structure related to behavioral factors in driving are suggested. (C) 2015 Elsevier Ltd. All rights reserved.
Gathering road safety critical information from users
(Igi Global, 2017) Yıldız, Ahmet
Traffic roads are a core element of GIS and many volunteered systems like openstreetmaps have the goal to make road data publicly available. Road users collecting geographical information and sharing them according some rules are a great opportunity to make our roads a safer place. Traffic accidents are a major cause of death and with increase in urbanization and motorization the risk is expected to rise higher. Research regarding road safety is mostly reactive; sections of the road where a lot of accidents has already happened are investigated and possibly causes are identified and then improved. This means, that people have to die in order to make those road sections safe. The system described in this chapter is a proactive method and can be operated by the community or some responsible authority. The gathered data is also very useful for different research areas like social sciences or civil engineering.
Influence of urban built environment on traffic accidents: the case of Eskisehir (Turkey)
(Elsevier, 2017) Kaygısız, Ömür; Şenbil, Metin; Yıldız, Ahmet
This paper explores effects of urban the built environment on traffic accidents in the context of a developing country. The basic objective of this study is to use simple and practical models to analyze traffic accidents, employing similar factors used by studies conducted in developed countries. The data collected at 107 road segments between the years 2008 and 2010 in Eskisehir (Turkey), include accidents with fatalities and injuries. Two sets of models have been developed: the first set uses binary logit models of traffic safety and the second set uses count data regression models to estimate occurrences of different accident types. Both sets consider three accident types: pedestrian-vehicle, vehicle-vehicle, and all accidents combined. Explanatory variables used in those models are derived from ambient land use characteristics, road segment properties, and traffic flow characteristics. Based on the results and our accompanying discussion, we outlined three types of direct policy implications. First, public transit should be regulated by relocating public transportation stations and by taking safety measures around stations. Second, traffic conditions and street networks should be improved by coordinating urban planning, street design, and transportation planning activities. Third, land use decisions should be reviewed focusing on taking precautions in areas associated with accident-increasing variables, rather than changing mixed land uses to monotonous land uses. (C) 2017 World Conference on Transport Research Society. Published by Elsevier Ltd. All rights reserved.
Applications of non-monotonic reasoning to automotive product configuration using answer set programming
(Springer, 2019) Gençay, Eray; Schüller, Peter; Erdem, Esra
In automotive industry, validation and maintenance of product configuration data is a complex task. Both orders from the customers and new product line designs from the R&D department are subject to a set of configuration rules to be satisfied. In this work, non-monotonic computational logic, answer set programming in particular, is applied to industrial-scale automotive product configuration problems. This methodology provides basic validation of the product configuration documentation and validation of single product orders, where Reiter style diagnosis provides minimal changes needed to correct an invalid order or a product configuration rule set. In addition, a method for discovering groups of product configuration variables that are strongly related can be obtained by small modification of the basic logic program, and by the usage of cautious and brave reasoning methods. As a result, options that are used in every, or respectively in no configuration, can easily be identified, as well as groups of options that are always used together or not at all. Finally it is possible to single out mandatory and obsolete options, relative to a preselected set of included or excluded options. Experimental results on an industrial dataset show applicability, example results, and computational feasibility with computation times on the order of seconds using a state-of-the-art answer set solver on standard PC hardware.
Connecting measurements of solar and stellar brightness variations
(Edp Sciences S A, 2020) Nemec, Nina; Işık, Emre; Shapiro, A. I.; Solanki, Sami K.; Krivova, N. A.; Unruh, Y.
Context. A comparison of solar and stellar brightness variations is hampered by the difference in spectral passbands that are used in observations, and also by the possible difference in the inclination of the solar and stellar rotation axes from the line of sight.Aims. We calculate the rotational variability of the Sun as it would be measured in passbands used for stellar observations. In particular, we consider the filter systems used by the CoRoT, Kepler, TESS, and Gaia space missions. We also quantify the effect of the inclination of the rotation axis on the solar rotational variability.Methods. We employed the spectral and total irradiance reconstruction (SATIRE) model to calculate solar brightness variations in different filter systems as observed from the ecliptic plane. We then combined the simulations of the surface distribution of the magnetic features at different inclinations using a surface flux transport model with the SATIRE calculations to compute the dependence of the variability on the inclination.Results. For an ecliptic-bound observer, the amplitude of the solar rotational variability, as observed in the total solar irradiance (TSI), is 0.68 mmag (averaged over solar cycles 21-24). We obtained corresponding amplitudes in the Kepler (0.74 mmag), CoRoT (0.73 mmag), TESS (0.62 mmag), Gaia G (0.74 mmag), Gaia G(RP) (0.62 mmag), and Gaia G(BP) (0.86 mmag) passbands. Decreasing the inclination of the rotation axis decreases the rotational variability. For a sample of randomly inclined stars, the variability is on average 15% lower in all filter systems we considered. This almost compensates for the difference in amplitudes of the variability in TSI and Kepler passbands, making the amplitudes derived from the TSI records an ideal representation of the solar rotational variability for comparison to Kepler stars with unknown inclinations.Conclusions. The TSI appears to be a relatively good measure of solar variability for comparisons with stellar measurements in the CoRoT, Kepler, TESS Gaia G, and Gaia G(RP) filters. Whereas the correction factors can be used to convert the variability amplitude from solar measurements into the values expected for stellar missions, the inclination affects the shapes of the light curves so that a much more sophisticated correction than simple scaling is needed to obtain light curves out of the ecliptic for the Sun.
Amplification of brightness variability by active-region nesting in Solar-like stars
(Iop Publishing Ltd, 2020) Işık, Emre; Shapiro, Alexander I.; Solanki, Sami K.; Krivova, Natalie A.
Kepler observations revealed that hundreds of stars with near-solar fundamental parameters and rotation periods have much stronger and more regular brightness variations than the Sun. Here we identify one possible reason for the peculiar behavior of these stars. Inspired by solar nests of activity, we assume that the degree of inhomogeneity of active-region (AR) emergence on such stars is higher than on the Sun. To test our hypothesis, we model stellar light curves by injecting ARs consisting of spots and faculae on stellar surfaces at various rates and nesting patterns, using solar AR properties and differential rotation. We show that a moderate increase of the emergence frequency from the solar value combined with the increase of the degree of nesting can explain the full range of observed amplitudes of variability of Sun-like stars with nearly the solar rotation period. Furthermore, nesting in the form of active longitudes, in which ARs tend to emerge in the vicinity of two longitudes separated by 180 degrees, leads to highly regular, almost sine-like variability patterns, rather similar to those observed in a number of solar-like stars.

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