Reviewerly/exHarmony huggingface dataset:
Practical regular expression constrained sequence alignment

The regular expression constrained sequence alignment problem is that of producing an optimal score alignment of two sequences which, at the same time, has aligned portions that are both described by a regular expression. In this article we introduce a series of improvements over existing algorithms to solve the problem when the regular expression has no Kleene closures. This is the case of biological patterns, such as those described by PROSITE and other databases. Such improvements include the construction of more compact ϵ-free NFAs for patterns and a significant reduction on automata transitions tracking during the alignment. The time complexity of our solution is O(nmk2), improving on the best known solution O(nmk3|Σ|3/log⁡(k|Σ|)), where n and m are sequence lengths, k is the number of single elements in a PROSITE pattern and Σ is the alphabet. Our experiments have shown that practical running time is significantly smaller than the theoretical worst case mentioned above, and that the solution may be used efficiently in practice for solving the regular expression constrained alignment problem for biological sequences.
Adapting Standard External Clustering Metrics for Repetitive, Noisy Observations

Clustering for data analysis often makes use of external metrics to evaluate how closely clustering assignments match a gold standard. In order to use external clustering metrics, explicit noise points are usually removed or treated as a single cluster. This modification reduces the relevancy of external metrics as a predictor of performance on unlabeled data, where it is not possible to identify noise points. We propose a modification of standard external metrics to explicitly handle noise points in experimental data. We illustrate the effect of this explicit treatment of noise on clustering evaluation using several examples of common noisy clustering problems as well as a real data set from mass spectrometry. We demonstrate that (external) clustering metrics that explicitly treat noise are more robust than standard (external) clustering metrics in the presence of noise.
Automatic Annotation Method for Document Image Binarization in Real Systems

The accuracy of optical character recognition (OCR) has significantly improved recently through the use of deep learning. However, when OCR is used in real applications, the shortage of annotated images often makes training difficult. To solve this problem, there are automatic annotation methods. However, many of these methods are based on active learning, and operators need to confirm generated annotation candidates. I propose a practical automatic annotation method for binarization, which is one of the components of OCR. The purpose with the proposed method is to automatically confirm the quality of annotation candidates. This method consists of three simple processes to achieve this. First, cropping a text from a whole image. Second, applying binarization to the cropped image at all thresholds. Third, recognizing all binarized cropped images and matching the recognition results and correct character database. If the characters match, the cropped binary image is correctly binarized. The method selects that cropped binarized image as an annotation for binarization. Cropping coordinates and the correct character database (DB) can be obtained from a practical OCR system. Because users of such a system usually input corrections for misrecognition of OCR to the system, the system can obtain the correct characters and coordinates. The experimental results indicate that the annotations generated with the proposed method can improve the performance of deep-learning-based binarization. As a result, the normalized edit distance between the recognized text and grand truth text can be reduced by 38.56% on the Find it! receipt image dataset.
Vesicle paleobarometry in the Pongola Supergroup: A cautionary note and guidelines for future studies

Abstract Earth’s global barometric pressure, currently 1 bar at sea level, may have changed over its 4.5-billion-year history. Proxy measurements, including N2/36Ar ratios in ~3.5 to 3.0 Ga hydrothermal quartz, ~2.7 Ga raindrop imprints, and ~2.7 Ga vesicle sizes in subaerial basalt lava flows indicate Archean air pressure could have been between 0.1 and 1.2 bar. However, some models argue air pressure in the Archean should have been much higher than now and could allow pressure broadening of greenhouse gas absorption lines to counteract the “Faint Young Sun”. Thus, additional paleobarometric measurements would be useful to further constrain Earth’s atmospheric evolution. We attempted to use vesicle sizes in lavas erupted near sea-level from the ~2.9 Ga Pongola Supergroup from Mahlangatsha and Mooihoek, eSwatini (formerly Swaziland) and the White Mfolozi River gorge of KwaZulu-Natal, South Africa to provide further Archean paleobarometric data. However, reliable results were unobtainable due to small and scarce amygdales, irregular vesicle morphologies and metamorphic mineralogical homogenization preventing the use of X-ray Computed Tomography for accurate vesicle size determination. Researchers attempting paleobarometric analysis using lava vesicle sizes should henceforth avoid these areas of the Pongola Supergroup and instead look at other subaerially emplaced lava flows. With this being only the second time this method has been used on Precambrian rocks, we provide a list of guidelines informed by this study to aid future attempts at vesicular paleobarometry.
Stained Visible Watermarking: A Securely Tunable Way of Joint Image Copyright and Privacy Protection

Tunability of the visual degradation of the publicized image may impact the security of the protected content when selective encryption and visible watermarking are separately used to protect the image copyright and privacy. Either the consistency inside the visible watermark or the relevance of the public data to the encrypted one can be exploited for the unauthorized recovery without keys. In this paper, we propose a novel framework, referred to as stained visible watermarking, that integrates the perceptual cryptography and visible watermarking into a secure and tunable system to protect both the copyright and the privacy. Key-generated noises as tunable stains work together with the visible watermark to disguise the protected data with the required level of intelligibility and break the consistency of neighboring pixels inside the visible watermark to enhance the robustness of the watermark. The tunability of the visual degradation is fully controlled by the strength parameters of the stains and watermark. Data normalization is introduced for reversibility to avoid potential under/over-flow. Experimental results and security analysis have demonstrated that our proposed scheme can jointly protect the privacy and copyright of images that are posted online, and the publicized image can be securely fine-tuned to adapt to the requirement of the applications.
Navigation, Routing and Nature-Inspired Optimization

Navigation abilities are crucial for survival in nature, and there are a wide range of sophisticated abilities concerning animal navigation and migration. Many applications are related to navigation and routing problems, which are in turn related to optimization problems. This chapter provides an overview of navigation in nature, navigation and routing problems as well as their mathematical formulations. We will then introduce some nature-inspired algorithms for solving optimization problems with discussions about their main characteristics and the ways of solution representations.
An Investigation of Pre-trained Embeddings in Dependency Parsing

In recent years, research in Natural Language Processing has witnessed an explosion in the application of neural networks and word embeddings. The goal of this paper is to explore how different word embedding models affect the performance of a dependency parser based on neural networks, and demonstrate that it is possible to reduce the cost of parsing using pre-trained embeddings. In order to do that, a jPTDP model was proposed. The results indicate the efficiency of the jPTDP as a dependency parser and the advantages of using embeddings with pre-trained words. The best results were obtained through the use of GloVe.
HCI and SE: Integration Experiences Between Theory and Practice from a SE Program

The Software Engineering education faces the challenge of engaging and qualifying professionals with competence to work in an interdisciplinary way and in teams, with flexibility to perform different roles and capable of adapting to change. This study present an experience of integration between theory and practice in a Software Engineering Program, focusing on the application of methods studied in a course from the Human-Computer Interaction (HCI) area. We discuss the lessons learned, challenges, and perspectives of change in those involved courses, bringing the students’ opinions, who have highlighted, among other points, the importance of such integration to bring theory closer to their real universe of action.
An Encryption and Decryption Outsourcing CP-ABE scheme Supporting Efficient Ciphertext Evolution

Attribute-based encryption (ABE) provides fine-grained access control policy for encrypted data in semi-trusted cloud storage system, while encryption and decryption are quite expensive for users with resource constrained devices. Besides, key compromise also brings great security risks to the stored ciphertext for its long-term immutability. On one hand, ABE with decryption outsourcing is preferred to relieve user's computation cost, but the computational overhead of encryption for users also needs to be taken into account. On the other, the stored ciphertext needs to be updated periodically with a new method instead of data re-encryption with heavy computation and communication overhead. Therefore, a novel ciphertext-policy attribute-based encryption scheme supporting efficient periodic ciphertext evolution with encryption and decryption outsourcing (EDO-CE-CPABE) was proposed which can solve the aforementioned issues. It can ensure that the ciphertext and the key are transformed synchronously. As a result, legitimate users can always access data, while the key and the ciphertext are changing periodically from the view of an attacker. Security and performance analysis demonstrate the security, effectiveness and practicability of the proposed scheme, which also illustrates that periodic ciphertext evolution can reduce the probability for successful attacks and attribute revocation is supported to change user's access rights timely and effectively. Also, computation cost for users can be effectively reduced by encryption and decryption outsourcing.
Method of Automatic Creation of Curriculum with a Competency-Based Learning Model

The reality of digital economy changes rapidly and sets flexible, modular and asynchronous structure of the training process, to prepare specialists possessing the necessary professional competence. At the same time, the bachelor’s program must meet the requirements of regulatory documents. Therefore, an educational institution needs an automated development of individual curricula that ensure formation of relevant competence through student’s participation in various forms of educational activities, including electronic courses. This work aims at creation of an information model of the curriculum allowing to automate the process of curriculum development in the framework of the competence-based learning approach. The proposed model is based on competence maps as a part of educational standard describing the disciplines of the curriculum in terms of ensuring the development of professional competences of a future specialist. Using these maps Competences and Disciplines matrices are built. The model curriculum is implemented as a three-dimensional array and curriculum requirements are formalized using the apparatus of Boolean algebra. A method proposes an automated curriculum development by constructing corresponding arrays of Boolean vectors allowing to develop the curriculum that forms the competence of a specialist in line with the requirements of regulatory documents.
Native Language Identification on L2 Portuguese

This study advances on Native Language Identification (NLI) for L2 Portuguese. We use texts from the NLI-PT dataset corresponding to five native languages: Chinese, English, German, Italian, and Spanish. We include the same L1s as in previous works, and more texts per language. We investigate the impact of different lexical representations, the use of syntactic dependencies and the performance of diverse classification methods. Our best model achieves an accuracy of 0.66 including lexical features, and of 0.61 excluding them. Both results improve previous works on NLI for L2 Portuguese.
SecFlush: A Hardware/Software Collaborative Design for Real-Time Detection and Defense Against Flush-Based Cache Attacks

In recent years, cache attacks against micro architectures have posed a daunting threat to modern processors such as x86 and ARM. Of the attacks, flush-based cache attacks have attracted increasing attention from researchers due to their low noise, high resolution and high efficiency. However, existing defenses against flush-based cache attacks have some problems such as lack of platform versatility, high overhead, and low detection accuracy. In this study, we find that flush-based cache attacks have a fundamental feature of flushing a cache line multiple times at regular intervals. Based on this feature of flush-based cache attacks, we propose a hardware/software collaborative design of real-time safeguard on the ARM-FPGA embedded SoC, called SecFlush. SecFlush detects attacks using a hardware monitoring module, and defends against attacks by prohibiting malicious processes from performing flush operations in a kernel driver. It also provides a flush API for users to call the driver. The experimental results show that SecFlush can reduce the success rate of flush-based cache attacks to less than 1% within 6.01 ms. The evaluation results show that the time overhead is only about 5%–21%.
Parallel Steepest Ascent Hill-Climbing for High Nonlinear Boolean and Vectorial Boolean Functions (S-Boxes)

Boolean functions and their generalization Vectorial Boolean functions or Substitution Boxes (S-Boxes) have attracted much attention in the domain of modern block ciphers that use only these elements to provide the necessary confusion against the cryptanalysis attacks. Thus, a significant number of research has been done to construct cryptographically strong Boolean functions and S-Boxes. Among these researches, several heuristics were applied and therefore the hill climbing heuristic was largely investigated. In this paper, we propose a new variant of Hill Climbing heuristic called Parallel Steepest Ascent Hill Climbing to construct Boolean functions and $$n \times m$$ S-Boxes through the progressive construction and incorporation of their m coordinate Boolean functions. The obtained results demonstrate that this new variant provides solutions with high cryptographic properties.
Social Mechanisms of the Subject Area Formation. The Case of “Digital Economy”

The structure of natural language could be considered a semantic network. This implies the allocation of the speech markers, which describe the subject and semantic areas. In this article, a wide range of texts about the digital economy was analyzed, making it possible to show the thematic structure of this subject area. Central and peripheral concepts were identified to characterize theoretical core concepts and related topics clarifying the application of the digital economy. Identification of the thematic areas was performed in two ways—through the construction of a thematic tree (neural network modeling in the Text Analyst) and the analysis of semantic networks. The results, approaches, and methods of this study could be used during the investigation of the other large thematic fields related to new ideological currents, being developed as an element of social design and management.
The Hardware Design Prospective of Object Detection by Using Background Subtraction Techniques: An Analytical Review

Kumar, Kaushal Nandan, Durgesh Mishra, Ritesh KumarSpeed and precision are two important metrics which plays crucial role in real-time video processing systems. Detection of objects is a fundamental process of an intelligent video processing system. Due to increased price and complications of serial implementation of general purpose processors, it does not lead to a good choice for real-time surveillance system. The parallel processing capability and flexibility of field programmable gate array (FPGA) are a big advantage in this field. The aim of this paper is to present the hardware design prospective of object detection by using background subtraction techniques implemented on FPGA which can be used in a video processing system.
Hierarchical Decomposition based Cooperative Coevolution for Large-Scale Black-Box Optimization

Most state-of-the-art decomposition approaches for cooperative coevolution adopt a static graph (variable interaction matrix) viewpoint to recognize the possible separability of real-valued objective functions. Although generally they work well for partially additively separable scenarios, all of them cannot solve non-separable problems and non-additively separable problems effectively. To tackle these issues, based on our recent theoretical advance, this paper proposes a hierarchical-decomposition-based cooperative coevolutionary framework for large-scale black-box optimization (LSBBO). Specifically, the well-known cyclically random decomposition strategy is embedded in a hierarchical fashion, in order to facilitate the continuous evolution of the best-so-far solution. Such a hierarchy provides a flexible way to make a proper trade-off between computational complexity and search performance as well as exploration and exploitation. Numerical experiments on the IEEE CEC'2010 LSBBO test suite showed the complementary performance of the proposed framework (when integrated with the automatic decomposition technique).
Bandung smart technology readiness index

The United Nations (2014) has projected that by 2050, 66% of the world’s population will become city residents. This situation may escalate into problems for cities caught unaware. Smart technology, which is one of the dimensions of the smart city concept, is a proposed solution. The research discussed in this article was based on previous research conducted by Indrawati, Setiawan, and Amani (2017). The aim was to learn the index value of smart technology readiness in Bandung. This research utilized a mixed method approach, and data collection was done through literature studies, interviews, observations, and surveys. The source of this research was based on the quadruple helix method. Based on the results of data processing, the index value of the application of smart technology in Bandung is 72.89. This shows that the level of readiness of smart technology in Bandung is quite good and satisfying, but in some cases, there are still shortcomings.
A Deep Learning Model of Common Sense Knowledge for Augmenting Natural Language Processing Tasks in Portuguese Language

Despite the richness and vastness of the common-sense knowledge bases, we argue that common-sense knowledge has to be integrated into the target applications (Text Classification, Dialogue systems, Information Extraction systems, etc.) more effectively. In order to consider this common-sense knowledge in target applications, we propose a deep learning model of common-sense knowledge in Portuguese language, which can be easily coupled in Natural Language Understanding (NLU) systems in order to leverage their performance. More specifically, the model is composed by a neural network LSTM (Long Short Term Memory) that receives a text from the target application, for example, an user message in a dialog, a response to a user tweet, a news text; and selects and learns what is the best set of common-sense relations to return to the target application, which should be considered in the target learning model or system. We implemented the common-sense learning module in two target applications - a Stance Classification system and an End-to-End Dialogue system. In both cases, incorporating the deep learning model improved the results.
Dimensionality Reduction for Low-Latency High-Throughput Fraud Detection on Datastreams

Given the exponential data growth and the recent focus on understanding high-dimensional "in-motion" data, fundamental machine learning tools, such as Principal Component Analysis (PCA), require computation-efficient streaming algorithms that operate near-real-time. Despite the different streaming PCA flavors, there is no algorithm that provably recovers the principal components in the same precision regime as the batch PCA algorithm does, while maintaining low-latency and high-throughput processing. This work, introduces a novel temporal accumulate / retract learning framework for streaming PCA. We consider the accumulate / retract framework implementation of several competitive PCA algorithms with proven theoretical advantages. We benchmark the improved PCA algorithms on real-world streams (i.e. bank transactions fraud detection) and prove their low-latency (millisecond level) and high-throughput (thousands events/second) processing guarantees.
Fast Image Dehazing Based on Guided Filter and Look-up-table

Video surveillance has been applied to all aspects of daily life such as security surveillance and driving records. However, it is inevitable that bad weather such as fog and haze will greatly reduce the visibility of the image, and make it impossible to record valuable data. In order to solve this problem, we propose in this paper a fast image dehazing algorithm based on dark channel prior and guiding filter. In addition, we further improve the processing speed by using look-up-table. Based on the prior theory of dark channel, this algorithm first extracts coarser transmission in blocks, then joint bilateral filtering is applied to refine the details. Finally, clear images are recovered with the refined dark channel map and the transmission map. In addition, in order to reduce the computation load in joint bilateral filtering and accelerate the algorithm, we use a two-dimensional look-up-table to avoid the cumbersome computation in weight calculation. Experimental results show that the proposed algorithm can improve the image quality, especially it removes halos on the edges at object boundaries. In addition, the method has low computation and can be applied in practice.
Towards Homograph-Confusable Domain Name Detection Using Dual-Channel CNN

Homograph attack is a common way of phishing attacks, which aims to generate visual spoofing domain names by replacing a single character or combinations of characters. To analyze and detect homograph domain names, former works mainly consider about distance based methods, analyzing edit distance or Euclidean distance between two domain names, or utilize OCR (Optical Character Recognition) technique. However, these methods may not only have a large number of false positive cases, but they also increase processing overhead. In this paper, we proposed a dual-channel CNN classifier with retrieving algorithm of minimum hash (MinHash) and locality sensitive hash (LSH) to detect homograph domain names. The dual-channel CNN classifier was trained to analyze dual-channel domain images. The MinHash and LSH were designed to search domain name with similar characters, which can reduce the large data efficiently. By comparing with other detection methods, our method can distinguish homograph domain names from normal ones effectively, which can achieve 98.5% detection rates. Experiments on DNS real log datasets indicate that MinHash and LSH scheme can perform well in reducing the large data.