Miracle-dz/UniFAR-FTUs huggingface dataset:
The North Anatolian transform fault: its age, offset and tectonic significance

The North Anatolian transform fault is a morphologically distinct and seismically active strike-slip fault which extends for about 1200 km from Karliova to the Gulf of Saros along the Black Sea mountains of N. Anatolia. It takes up the relative motion between the Black Sea and the Anatolian plates, thereby connecting the E. Anatolian convergent zone with the Hellenic Trench through the complex plate boundary zone of the Aegean. For most of its length, the transform has a typical strike-slip fault zone morphology, characterized by narrow ‘rift zone,’ offset, captured and dammed streams, sag ponds and other deformed morphological features. The fault zone is a broad region of extensively crushed country rock cut by a number of parallel and/or anastomosing strike-slip faults. The transform has periods of seismic activity the last of which, from 1939 to the present, is characterized by frequent 6 ≤ M≤7earthquakes; these are separated by quiet periods of about 150 years. The crust along the fault zone is thinner than normal. The transform probably originated some time between the Burdigalian and the Pliocene and has an offset of about 85 km. Whether the offset of the fault changes systematically along its strike is not known. The North Anatolian transform fault seems to have originated as a consequence of the Arabia-Anatolia collision durmg the late (? middle) Miocene, when the Anatolian Plate originated and was wedged out into the oceanic tract of the E. Mediterranean from the converging jaws of Arabia and Eurasia to prevent excessive crustal thickening in E. Anatolia. The westerly motion of Anatolia with respect to Eurasia and Africa caused a great change in the tectonic evolution of the eastern Mediterranean, giving rise to the Aegean extensional regime and to internal deformation of Anatolia.
G-Quadruplexes in the Viral Genome: Unlocking Targets for Therapeutic Interventions and Antiviral Strategies

G-quadruplexes (G4s) are unique non-canonical four-stranded nucleic acid secondary structures formed by guanine-rich DNA or RNA sequences. Sequences with the potential to form quadruplex motifs (pG4s) are prevalent throughout the genomes of all organisms, spanning from prokaryotes to eukaryotes, and are enriched within regions of biological significance. In the past few years, the identification of pG4s within most of the Baltimore group viruses has attracted increasing attention due to their occurrence in regulatory regions of the genome and the subsequent implications for regulating critical stages of viral life cycles. In this context, the employment of specific G4 ligands has aided in comprehending the intricate G4-mediated regulatory mechanisms in the viral life cycle, showcasing the potential of targeting viral G4s as a novel antiviral strategy. This review offers a thorough update on the literature concerning G4s in viruses, including their identification and functional significance across most of the human-infecting viruses. Furthermore, it delves into potential therapeutic avenues targeting G4s, encompassing various G4-binding ligands, G4-interacting proteins, and oligonucleotide-based strategies. Finally, the article highlights both progress and challenges in the field, providing valuable insights into leveraging this unusual nucleic acid structure for therapeutic purposes.
A Fast and Elitist Multi-Objective Genetic Algorithm: NSGA-II

Multi-obje tive evolutionary algorithms whi h use non-dominated sorting and sharing have been mainly riti ized for their (i) O(MN 3 ) omputational omplexity (where M is the number of obje tives and N is the population size), (ii) non-elitism approa h, and (iii) the need for spe ifying a sharing parameter. In this paper, we suggest a non-dominated sorting based multi-obje tive evolutionary algorithm (we alled it the Non-dominated Sorting GA-II or NSGA-II) whi h alleviates all the above three diÆ ulties. Spe i(cid:12) ally, a fast non-dominated sorting approa h with O(MN 2 ) omputational omplexity is presented. Se ond, a sele tion operator is presented whi h reates a mating pool by ombining the parent and hild populations and sele ting the best (with respe t to (cid:12)tness and spread) N solutions. Simulation results on a number of diÆ ult test problems show that the proposed NSGA-II, in most problems, is able to (cid:12)nd mu h better spread of solutions and better onvergen e near the true Pareto-optimal front ompared to PAES and SPEA|two other elitist multi-obje tive EAs whi h pay spe ial attention towards reating a diverse Pareto-optimal front. Moreover, we modify the de(cid:12)nition of dominan e in order to solve onstrained multi-obje tive problems eÆ iently. Simulation results of the onstrained NSGA-II on a number of test problems, in luding a (cid:12)ve-obje tive, seven-onstraint non-linear problem, are ompared with another onstrained multi-obje tive optimizer and mu h better performan e of NSGA-II is observed. Be ause of NSGA-II's low omputational requirements, elitist approa h, parameter-less ni hing approa h, and simple onstraint-handling strategy, NSGA-II should (cid:12)nd in reasing appli ations in the oming years.
Analysis of Student Pair Teamwork Using GitHub Activities

Few studies have analyzed students’ teamwork (pairwork) habits in programming projects due to the challenges and high cost of analyzing complex, long-term collaborative processes. In this work, we analyze student teamwork data collected from the GitHub platform with the goal of identifying speciﬁc pair teamwork styles. This analysis builds on an initial corpus of commit message data that was manually labeled by subject matter experts. We then extend this annotation through the use of self-supervised, semi-supervised learning to develop a large-scale annotated dataset that covers multiple course offerings from a second-semester CS2 course. Further, we develop a series of predictive models to automatically identify student teamwork styles. Finally, we compare trends in students’ performance and team selection for each teamwork style to see if any of them reﬂected better student outcomes or different trends of help-seeking among students. Our analysis showed that applying self-supervised semi-supervised meth-ods helps us to label larger subsets of data automatically and maintains and even sometimes improves the performance of the fully supervised models on a held-out validation set. Our analysis also showed that members of teams in which all members have signiﬁcant contributions tend to have better performance in class, but their help-seeking behaviors are not signiﬁcantly different.
COMPARATIVE ANALYSIS OF THE PORTRAYAL OF PAKISTANI AND INDIAN CRICKETERS ON ELECTRONIC MEDIA

Cricket is the second-most widely played game in the world afterfootball, and it is popular in the most populous nations of South Asia. The media hugely cover this event especially when Pakistan and India are playing against each other. The comparison of players and special broadcasts are made regarding this game. Similarly at large level, Media is theplatform which depicts the image of our country and people at large level. In cricket matches enthusiasm is created through media and our team is appreciated and considered among best. Positive self image is presented on television and news programs are based on it. A negative self image which can distort our image is less presented or is abolished. The Study was conducted to check the comparative portrayal of Indian and Pakistani cricketers on Indian media as well as on Pakistani media. Like the positiveand negative portrayal of Indian cricketers on Indian media, positive and negative portrayal of Pakistani cricketers on Pakistani media, positive and negative portrayal of Indian cricketers on Pakistani media and positive and negative portrayal of Pakistani cricketers on Indian media. After determine this, a comparative portrayal is presented. It has been seen that every media while comparing their own side with the rival country's side, always portray themselves more positively while attribute negative properties to the opponent.
Identification and Recognition of Rice Diseases and Pests Using Deep Convolutional Neural Networks

An accurate and timely detection of diseases and pests in rice plants can help to reduce economic losses substantially. It can help farmers in applying timely treatment. Recent developments in deep learning based convolutional neural networks (CNN) have allowed researchers to greatly improve the accuracy of image classiﬁcation. In this paper, we present a deep learning based approach to detect diseases and pests in rice plants using images captured in real life scenerio with heterogeneous background. We have experimented with various state-of-the-art convolutional neural networks on our large dataset of rice diseases and pests, which contain both inter-class and intra-class variations. The results show that we can eﬀectively detect and recognize nine classes of rice diseases and pests including healthy plant class using a deep convolutional neural network, with the best accuracy of 99.53% on test set.
Threads and or-parallelism unied

One of the main advantages of Logic Programming (LP) is that it provides an excellent framework for the parallel execution of programs. In this work we investigate novel techniques to eﬃciently exploit parallelism from real-world applications in low cost multi-core architectures. To achieve these goals, we revive and redesign the YapOr system to exploit or-parallelism based on a multi-threaded implementation. Our new approach takes full advantage of the state-of-the-art fast and optimized YAP Prolog engine and shares the underlying execution environment, scheduler and most of the data structures used to support YapOr’s model. Initial experiments with our new approach consistently achieve almost linear speedups for most of the applications, proving itself as a good alternative for exploiting implicit parallelism in the currently available low cost multi-core architectures.
SISTEMA DE INFORMACIÓN EVALUACIÓN Y ECOLOGÍA PESQUERA, SIEEP (VERSIÓN 1.1). ACTUALIZACIÓN PARA ESTUDIOS DE LA CAPTURA ACOMPAÑANTE EN PESQUERÍAS DE CAMARÓN FISHERIES ASSESSMENT AND ECOLOGY INFORMATION SYSTEM, SIEEP (VERSION 1.1). BYCATCH STUDIES OF SHRIMP FISHERIES UPDATE

The Fisheries Assessment and Ecology Information System (SIEEP) was constructed to validate, standardize and store biotic and abiotic information for ecological, oceanographic and fisheries studies. Since bycatch studies are required in the shrimp trawl fisheries, the SIEEP has been updated to store fisheries and biological data from bycatch samples analysed on board and in laboratory. In addition, the performance of the system
Extraction and Bundling of Related Scientific

Automatic classification of scientific articles based on common characteristics is an interesting problem with many applications in digital library and information retrieval systems. Properly organized articles can be useful for automatic generation of taxonomies in scientific writings, textual summarization, efficient information retrieval etc. Generating article bundles from a large number of input articles, based on the associated features of the articles is tedious and computationally expensive task. In this report we propose an automatic two-step approach for topic extraction and bundling of related articles from a set of scientific articles in real time. For topic extraction, we make use of Latent Dirichlet Allocation (LDA) topic modeling techniques and for bundling, we make use of hierarchical agglomerative clustering techniques. We run experiments to validate our bundling semantics and compare it with existing models in use. We make use of an online crowdsourcing marketplace provided by Amazon called Amazon Mechanical Turk to carry out experiments. We explain our experimental setup and empirical results in detail and show that our method is advantageous over existing ones.
Topic Extraction and Bundling of Related Scientific

Automatic classification of scientific articles based on common characteristics is an interesting problem with many applications in digital library and information retrieval systems. Properly organized articles can be useful for automatic generation of taxonomies in scientific writings, textual summarization, efficient information retrieval etc. Generating article bundles from a large number of input articles, based on the associated features of the articles is tedious and computationally expensive task. In this report we propose an automatic two-step approach for topic extraction and bundling of related articles from a set of scientific articles in real-time. For topic extraction, we make use of Latent Dirichlet Allocation (LDA) topic modeling techniques and for bundling, we make use of hierarchical agglomerative clustering techniques. We run experiments to validate our bundling semantics and compare it with existing models in use. We make use of an online crowdsourcing marketplace provided by Amazon called Amazon Mechanical Turk to carry out experiments. We explain our experimental setup and empirical results in detail and show that our method is advantageous over existing ones.
Structured Local Radiance Fields for Human Avatar Modeling

This supplementary document provides more discussions and experimental details. In Sec. B, we discuss in detail the differences between our method and state-of-theart approaches. Details about network architecture are presented in Sec. C. In Sec. D we present more details about how we collect the data and how we conduct the experiments. We conduct additional experiments in Sec. E to further evaluate our method design. Finally, we discuss the limitations and potential future work in Sec. F. Please refer to the supplementary video for more visualizations.
AttDLNet: Attention-based DL Network for 3D LiDAR Place Recognition

—Deep networks have been progressively adapted to new sensor modalities, namely to 3D LiDAR, which led to unprecedented achievements in autonomous vehicle-related applications such as place recognition. One of the main challenges of deep models in place recognition is to extract efﬁcient and descriptive feature representations that relate places based on their similarity. To address the problem of place recognition using LiDAR data, this paper proposes a novel 3D LiDAR-based deep learning network (named AttDLNet) that comprises an encoder network and exploits an attention mechanism to selectively focus on long-range context and inter-feature relationships. The proposed network is trained and validated on the KITTI dataset, using the cosine loss for training and a retrieval-based place recognition pipeline for validation. Additionally, an ablation study is presented to assess the best network conﬁguration. Results show that the encoder network features are already very descriptive, but adding attention to the network further improves performance. From the ablation study, results indicate that the middle encoder layers have the highest mean performance, while deeper layers are more robust to orientation change. The code is publicly available on the project website: https://github.com/Cybonic/ AttDLNet
qgraph : Network Visualizations of Relationships in Psychometric Data

We present the qgraph package for R , which provides an interface to visualize data through network modeling techniques. For instance, a correlation matrix can be represented as a network in which each variable is a node and each correlation an edge; by varying the width of the edges according to the magnitude of the correlation, the structure of the correlation matrix can be visualized. A wide variety of matrices that are used in statistics can be represented in this fashion, for example matrices that contain (implied) covariances, factor loadings, regression parameters and p values. qgraph can also be used as a psychometric tool, as it performs exploratory and conﬁrmatory factor analysis, using sem and lavaan ; the output of these packages is automatically visualized in qgraph , which may aid the interpretation of results. In this article, we introduce qgraph by applying the package functions to data from the NEO-PI-R, a widely used personality questionnaire.
Intrusion detection system in cloud computing environment

In recent years, with the growing popularity of cloud computing, security in cloud has become an important issue. As ‘prevention is better than cure’, detecting and blocking an attack is better than responding to an attack after a system has been compromised. This paper proposes architecture capable of detecting intrusions, in a distributed cloud computing environment, and safeguarding it from possible security breaches. It deploys a separate instance of IDS for each user and uses a separate controller to manage the instances. IDS in this architecture can be signature-based as well as learning-based method.
Postal Envelope Segmentation using Learning-Based Approach

This paper presents a learning-based approach to segment postal address blocks where the learning step uses only one pair of images (a sample image and its ideal segmented solution). First, this approach learns the available knowledge among pixels (each gray level) in an input image and its corresponding output in the ideal segmented solution. A classification array is generated which is re-utilized during the segmentation of new images. Features are extracted and updated by means of an adaptive square neighborhood. At the moment of new image segmentation, the submitted images are segmented by means of a k-Nearest Neighbor (k-NN) algorithm that seeks, for each pixel, the best solution in the classification array. Tests on a database of 200 complex envelope images were performed and a pixel to pixel accuracy measure validates the new approach. Results compared to other approaches for the same database show the efficiency and performance of the proposed learning-based approach. Success rates achieved for address block, stamps, rubber stamps and noise suggest that the features used in the proposed approach improves the segmentation results.
Neural Network-based Graph Embedding for Cross-Platform Binary Code Similarity Detection

The problem of cross-platform binary code similarity detection aims at detecting whether two binary functions coming from different platforms are similar or not. It has many security applications, including plagiarism detection, malware detection, vulnerability search, etc. Existing approaches rely on approximate graph-matching algorithms, which are inevitably slow and sometimes inaccurate, and hard to adapt to a new task. To address these issues, in this work, we propose a novel neural network-based approach to compute the embedding , i.e., a numeric vector, based on the control flow graph of each binary function, then the similarity detection can be done efficiently by measuring the distance between the embeddings for two functions. We implement a prototype called Gemini . Our extensive evaluation shows that Gemini outperforms the state-of-the-art approaches by large margins with respect to similarity detection accuracy. Further, Gemini can speed up prior art’s embedding generation time by 3 to 4 orders of magnitude and reduce the required training time from more than 1 week down to 30 minutes to 10 hours. Our real world case studies demonstrate that Gemini can identify significantly more vulnerable firmware images than the state-of-the-art, i.e., Genius . Our research showcases a successful application of deep learning on computer security problems.
Working time around the world

Based on a series of 14 national studies undertaken between 2001-2005 to track trends in hours of work and the organization of working time, this book examines the diverse, and often complex, circumstances that exist the following countries: Brazil, Chile, China, the Czech Republic, Hungary, Korea, Jamaica, Malaysia, Mauritius, Mexico, Peru, the Russian Federation, Senegal and Tunisia. The study also analyses data from an even broader range of countries, drawing from: the ILO's new database of working time laws; existing ILO data on average weekly working hours; and a new standardized questionnaire collecting data on the distribution of weekly working hours from national statistics. Thus, the study provides the richest array of data ever assembled for analysing working time in the developing world. What comes to light is that traditional working time concerns, such as long hours combined with inadequate rest periods, remain a concern in many countries. At the same time, new issues associated with deregulation and more flexible working time arrangements (such as part-time work and compressed workweeks) are becoming increasingly significant, not only in industrialized countries, but in parts of the developing world as well. The book identifies broad trends in working time at the national level, including cross-country and sectoral variations. It also considers the structure and dynamics underlying the national working time trends, including changes in economic structure, such as 'tertiarization' (i.e., the expanding service sector) and informalization, as well as the specific circumstances of particular groups of workers, such as those with family responsibilities and older workers. The book concludes with a set of policy recommendations that are designed to apply the 'decent working time' framework put forth in a previous volume, Decent Working Time, to the very different realities which exist in developing and transition countries.
Statistical Characteristics of Driver Accelerating Behavior and Its Probability Model

The naturalistic driving data are employed to study the accelerating behavior of the driver. Firstly, the question that whether the database is big enough to achieve a convergent accelerating behavior of the driver is studied. The kernel density estimation is applied to estimate the distributions of the accelerations. The Kullback-Liebler divergence is employed to evaluate the distinction between datasets composed of different quantity of data. The results show that a convergent accelerating behavior of the driver can be obtained by using the database in this study. Secondly, the bivariate accelerating behavior is proposed. It is shown that the bivariate distribution between longitudinal acceleration and lateral acceleration follows the dual triangle distribution pattern. Two bivariate distribution models are proposed to explain this phenomenon, i.e. the bivariate Normal distribution model (BNDM) and the bivariate Pareto distribution model (BPDM). The univariate acceleration behavior is presented to examine which model is better. It is identified that the marginal distribution and conditional distribution of the accelerations approximately follow the univariate Pareto distribution. Hence, the BPDM is a more appropriate one to describe the bivariate accelerating behavior of the driver. This reveals that the bivariate distribution pattern will never reach a circle-shaped region.
Image Analysis for MRI Based Brain Tumor Detection Using Neural Network Techniques

Medical imaging techniques have widely been in use in the diagnosis and detection of brain tumor. The drawback of applying these techniques is the large time consumption in the manual diagnosis of each image pattern by a professional radiologist. Automated classifiers could substantially upgrade the diagnosis process, in terms of both accuracy and time requirement by distinguishing benign and malignant patterns automatically. Neural network (NN) plays an important role in this respect, especially in the application of brain tumor detection. The purpose of this review is to analyze the contents of recently published literature with special attention to techniques and states of the art of NN in medical imaging. To discuss the usage of NN in four different medical imaging applications to show that NN is not restricted to few areas of medicine. The address of hybrid NN adaptation in brain tumor detection.