Reviewerly/exHarmony huggingface dataset:
Highway II, an extended version of highway networks and its application to densely connected Bi-LSTM

The increase of depth is essential for the success of Deep Neural Networks while also leads to the difficulty of training. In light of this, the authors propose a novel multi-layer LSTM model called Highway-DC via introducing Highway Networks (Highway) to Densely Connected Bi-LSTM (DC-Bi-LSTM) whic h representation of each layer concatenates the output of itself and all preceding layers. Highway is applied to control the volume of input or output of each layer in DC-Bi-LSTM to the next. However, results reveal that Highway-DC shows no improvement over DC-Bi-LSTM, thus an extended version of Highway named Highway II is proposed via eliminating the multiplicative connections between transform gate and the output in Highway thus preserve the learning of each layer. And the Highway II-based model is named Highway II-DC. Evaluated on 7 benchmark datasets of text classification with compare to DC-Bi-LSTM and other state-of-the-art approaches, results indicate that Highway II-DC shows promising performance for achieving state-of-the-art on 3 datasets and surpassing DC-Bi-LSTM on 6 datasets with faster speed to converge. Besides, it can still enjoy the gain of increased layers with depth up to 30, while DC-Bi-LSTM gets saturated early at a depth of 15.
Improving the Perceptual Quality of Document Images Using Deep Neural Network

Given a low-resolution binary document image, we aim to improve its perceptual quality for enhanced readability. We have proposed a simple, deep learning based model, that uses convolution with transposed convolution and sub-pixel layers in the best possible way to construct the high-resolution image. The proposed architecture scales across the three different scripts tested, namely Tamil, Kannada and Roman. To show that the reconstructed output has enhanced readability, we have used the objective criterion of optical character recognizer (OCR) character level accuracy. The reported results by our CTCS architecture shows significant improvement in terms of the subjective criterion of human readability and objective criterion of OCR character level accuracy.
Detection and Prediction of Geo-environmental Hazards in Urban Areas and Desert Lands Using an Integrated Structural and Geophysical Approach: Cases from Egypt

Nowadays, geo-environmental assessment becomes an immediate concern all over the world. In general, the flow paths of pollutants and radioactive source allocations are related to rock types, subsurface layer distributions and structural settings. Thus, this chapter aims to present a non-conventional integrative approach of remote sensing, structural data and geophysical methods for geo-environmental hazard assessment. Notably, such hazards commonly result from human activities where contaminants are accumulated and spread on or beneath the ground surface. Detection of organic contamination and hydrocarbon flow path is a difficult task for geophysical techniques at hazardous waste areas. Furthermore, radioactive-emission detection is of specific value in the vicinity of mineral resources for human health and environmental preservation. Accordingly, waste management as well as a subsurface contaminant and geological structure mapping are prerequisite for urbanization, mining and civil engineering. In this chapter, we presented cases for waste management to indicate how recent technologies can be used for detection and prediction of geo-environmental hazards in urban areas and desert lands. The following techniques are used to achieve the objective of the chapter: (1) remote sensing, (2) information on structural geology, (3) direct current (DC) resistivity method and (4) airborne geophysics. Regarding four case studies, the results indicate the effectiveness of the suggested approach to map surface and subsurface geological conditions concerning the pollutants and radioactive emissions in 2D/3D. Even when the contaminant itself produces no detectable direct geophysical signature, sometimes it may be found by indirect means through the structural mapping as a guide to predict the contaminant flow paths. Accordingly, the approach will be helpful to the decision-makers to achieve sustainable development in urban and desert areas regarding the waste management aspects. Moreover, the present nondestructive approach can be considered as an initial step in waste assessment projects.
Addition Circuit optimization Using Carry-Lookahead and SIMD for Homomorphic Encryption

Homomorphic encryption can be applied to cloud computing to solve the privacy problem. However, it is not practical because of lack of efficiency. So, methods to reduce the high computational cost of HE is needed. In this paper, we introduce a novel way to evaluate addition circuit under homomorphic encryption circumstances. We apply Ladner-Fischer Carry-Lookahead Addition (CLA) circuit that performs best among all kinds of CLA structure. Moreover, we use Single-Instruction-Multiple-Data (SIMD) technique by organizing several bits packed in one plaintext to further improve performance in the situation where the numbers are larger. This method leads to a reduction of depth and the number of multiplications, achieves 8% increase in speed for 16-bit circuit without SIMD and 63% for 64-bit circuit with SIMD.
Digital album with augmented reality: Francisco de Paula Santander Ocaña University botanic garden “Jorge Enrique Quintero Arenas”

Abstract Francisco de Paula Santander Ocaña University has a botanic garden. The University Botanic Garden Jorge Enrique Quintero Arenas was created with the mission of preserving the dry forest ecosystem and the types of vegetation and flora present in the northeast of Colombia, providing adequate spaces for research and environmental education. From new technologies that can be integrated without affecting the environment and generate various changes in the way of learning, solutions are proposed for continuous improvement, which in the end is done in favor of sustainability, quality of life and a smart botanic garden. This paper presents the results of the software prototype, whose purpose is to make known all the biodiversity that is available. It was done through a digital album applying augmented reality, where those interested will find the different species with their relevant information.
Towards Protection Against a USB Device Whose Firmware Has Been Compromised or Turned as ‘BadUSB’

A BadUSB is a Universal Serial Bus (USB) device (usually a mass storage device) whose firmware has been modified so as to spoof itself as another device (such as a keyboard) in order to avoid being scanned by an anti-virus. This way, a pre-written script runs, after the infected USB device is plugged-in, and keystrokes from a keyboard are simulated. This can cause an attacker to install backdoors, keyloggers, password sniffers etc. This paper attempts to solving this problem by presenting hardware—software coupled design which allows the user to have an additional layer of security so that such devices can be identified and stopped.
Exploring Writing Pattern with Pop Culture Ingredients for Social User Modeling

Social networks have significantly altered the behavior patterns of netizens all around the world. Therefore, accurate and expressive model of social users is increasingly demanded as it pose great value in a variety of scenarios, such as e-commerce, cyber security, and entertainment to name a few. In this paper, we propose the Pop Culture Attention Writing Model (PAWM) to explore the writing patterns of social users by explicitly capturing the influence of Internet pop culture ingredients with an attention mechanism. The writing pattern representations are learned by a memory network through storing and updating historical latent patterns. We then develop the Deep Social User Model via jointly modeling basic properties of social users, temporal contents, and the learned writing patterns based on PAWM. This paper is the first trial, to the best of our knowledge, which captures Internet pop culture information and applies deep neural network to model user writing pattern. A series of experiments conducted on social bot detection and social user identification demonstrate and validate the effectiveness of the proposed models.
Research on Location of Emergency Sign Based on Virtual Reality and Eye Tracking Technology

In order to analyze the attention to exit sign during the emergency state, virtual reality technology (VR) was used to simulate escape scene and 3D Max was used to design the scene. To obtain the number of gaze points and the gaze duration of subjects, eye tracker was utilized to get data and spss 21.0 was taken advantage of to analyze the data. Results show that the position of emergency exit sign has significant influence on the recognition. And the exit sign of the height of 1.0 m on the front of the observer’s line of sight is the most beneficial to discovery and identification. Moreover, the height and the position of exit sign have no significant influence on reading time.
Industry Trends in Software Engineering Education: A Systematic Mapping Study

Contemporary approaches and trends, in software engineering courses have been continuously updated over the last four decades. Adaptation to industry needs is crucial for future educational purposes. The goal of this poster paper is to present some preliminary results related to what degree contemporary industry trends are being adopted in updating software engineering teaching approaches. This study is a systematic mapping. A total of 138 papers were selected based on education goals, research, and contribution type. Of the primary education topics, around 78% are related to teaching approaches, 9% to globalisation and training methods, and less than 5% to tech startup and industry innovation. Less investigated areas that have recently become common industry trends, such as tech startup models adopting lean methodology, require further attention and might create opportunities for updating the curricula. Future work will investigate possibilities for exploiting tech startups as a means for renewing future capstone courses.
Ensemble Classification Method for Imbalanced Data Using Deep Learning

Nowadays various types of devices provide abundant data, and many businesses want to pinpoint what they are interested in from the data such as target marketing, fraud transaction detection. However, current classification algorithms in data mining show a poor performance when classifying imbalanced data. To enhance the classification performance of minority class in imbalanced datasets, we present an ensemble learning method using the combination of an UnderBagging, a majority vote, and a meta classifier giving higher decision priority to the classifier that predicts a class better, basing on Deep Neural Network (DNN) as a classifier. We tested our method with two imbalanced datasets from UCI Data Repository and compared the performance of our method with four other techniques. The result showed that our method provided an improved performance on classifying minority class instances compared to the other four techniques.
Detection of Cutaneous Tumors in Dogs Using Deep Learning Techniques

Cytological diagnosis is useful in the practical context compared to the histopathology, since it can classify pathologies among the cutaneous masses, the samples can be collected easily without anesthetizing the patient, at very low cost. However, an experimented veterinarian performs the cytological diagnosis in approximately 25 min. Artificial intelligence is being used for the diagnosis of many pathologies in human medicine, the experience gained by years of work in the area of work allow to issue correct diagnoses, this experience can be trained in an intelligent system. In this work, we collected a total of 1500 original cytologic images, performed some preliminary tests and also propose a deep learning based approach for image analysis and classification using convolutional neural networks (CNN). To adjust the parameters of the classification model, we recommend to perform a random and grid search will be applied, modifying the batch size of images for training, the number of layers, the learning speed and the selection of three optimizers: Adadelta, RMSProp and SGD. The performance of the classifiers will be evaluated by measuring the accuracy and two loss functions: cross-categorical entropy and mean square error. These metrics will be evaluated in a set of images different from those with which the model was trained (test set). By applying this model, an image classifier can be generated that efficiently identifies a cytology diagnostic in a short time and with an optimal detection rate. This is the first approach for the development of a more complex model of skin mass detection in all its types.
API copyrights revisited

Deliberating on the main arguments in recent sets of briefs filed in support of Google's U.S. Supreme Court petition.
Scalable Software Merging Studies with MERGANSER

Software merging researchers constantly need empirical data of real-world merge scenarios to analyze. Such data is currently extracted through individual and isolated efforts, often with non-systematically designed scripts that may not easily scale to large studies. This hinders replication and proper comparison of results. In this paper, we introduce MERGANSER, a scalable and easy-to-use tool for extracting and analyzing merge scenarios in Git repositories. In addition to extracting basic information about merge scenarios from Git history, our tool also replays each merge to detect conflicts and stores the corresponding information of conflicting files and regions. We design a normalized and extensible SQL data schema to store the information of the analyzed repositories, merge scenarios and involved commits, and merge replays and conflicts. By running only one command, our proposed tool clones the target repositories, detects their merge scenarios, and stores their information in a SQL database. MERGANSER is written in Python and released under the MIT license. In this tool paper, we describe MERGANSER's architecture and provide guidance for its usage in practice.
Use of Blockchain Smart Contracts in Software Engineering: A Systematic Mapping

A smart contract is one of the safest mechanisms in the form of computerized and authorized legitimate commitment between two parties. This form of securing commitments has been gaining popularity on the last years in a variety of fields. However, and maybe because of its relative novelty, there is not a clear understanding of its possibilities. This study is aimed to cover the usage, benefits and challenges on the use of Blockchain Smart Contracts in Software Engineering in the form of a Systematic mapping. The most common use of the smart contracts is to work as a digital code that plays the role of a mediator, which removes human intervention. The availability of professionals in this area is a reported challenge along with the client’s trust on usability with respect to security. Besides, this paper identifies various difficulties that have not been yet addressed by existing methodologies. As a consequence of our findings, more practical use of this system can open doors for further research.
On the Essentially Dynamic Nature of Concepts: Constant if Incremental Motion in Conceptual Spaces

Concepts are the means by which we structure our understanding of the world and consequently the primary means by which we encounter it. It is commonly assumed that one of the essential characteristics of concepts – regardless of referent – is their stability, tending toward stasis; and, indeed, it can be hard to see how concepts can otherwise be systematic and productive, in the way they are conventionally taken to be. Even the question has been raised whether concepts can change; on some prominent accounts, they cannot. The Unified Conceptual Space Theory (UCST) – an extension of Conceptual Spaces Theory – makes the controversial claim that concepts not only are subject to change over an iterative lifecycle but that, at an underlying level, they are in a state of continuous motion; indeed, they must be to function as they do. Mere openness to change is not enough. Even the most seemingly fixed of concepts – mathematical concepts are the paradigm example – can be seen to evolve and continually be evolving as our understanding of mathematics evolves. UCST suggests that concepts possess an intrinsic tension that appears to present a contradiction: to be able to apply in more or less the same way across unboundedly many contexts (systematicity) and to be able to combine coherently with other concepts (productivity), they must be relatively stable; and yet, since each new application context is, in some nontrivial way, different from every previous context in ways that do not fit within neat conceptual boundaries, they must adapt each time to fit. In a physical world we have reason to view as ultimately one of fluidity, of processes and motion rather than stable entities, concepts should probably have a similar nature.
Unbiased Low-Variance Estimators for Precision and Related Information Retrieval Effectiveness Measures

This work describes an estimator from which unbiased measurements of precision, rank-biased precision, and cumulative gain may be derived from a uniform or non-uniform sample of relevance assessments. Adversarial testing supports the theory that our estimator yields unbiased low-variance measurements from sparse samples, even when used to measure results that are qualitatively different from those returned by known information retrieval methods. Our results suggest that test collections using sampling to select documents for relevance assessment yield more accurate measurements than test collections using pooling, especially for the results of retrieval methods not contributing to the pool.
Simultaneous verification of optical triple-image encryption using sparse strategy

A simultaneous optical triple-image encryption and verification method based on sparse information strategy was proposed. The information of the encoded images could be integrated into a sparse representation. Only sparse (partial) data from the encrypted information were adopted for verification process. In sparse strategy, the partial data was extracted from the encrypted distribution. For complex-valued distribution, the sparse strategy was applied to the amplitude; thus, only the sparse amplitude pixels kept the phase data for verification process. Therefore, the proposed method can effectively facilitate optical information transmission and storage, as only part of the encrypted information was adopted. In addition, the simultaneous encryption and authentication verification of three images was achieved. The results proved that the proposed method was feasible and effective, and can provide an additional security layer for optical security systems. Moreover, the method that demonstrated secure optical triple-image can lead to a high flexibility and variety compared to that of photon counting technique which required a specific device.
A Comprehensive Review on Document Image (DIBCO) Database

Abstract One of the most significant current discussions in image processing is a document analysis. Now, many types of document database were established in order to address the issue of binarization effectiveness. In this paper, a comprehensive review of the document database was presented. Review based on an image from Document Image Binarization Contest (DIBCO) from 2013 to 2017 which consists of handwritten and printed image. The best algorithm for each year is discussed and analyzed. Based on the results, the technique using background estimation and stroke edges is better performance for the overall database. Besides, the method using the combination of Laplacian operator and canny edge detection also shows the successful result, especially in the printed image. Implications of the review give the direction for future binarization approach developments.
Implementing Smart Contracts in the Syndicated Loan Market: An Issue of Adoption

Distributed ledger technology allows for data to be recorded, shared, and synchronized across multiple distributed data stores. This brought forth the idea of using this technology to build consensus. Implementing this technology within the Syndicated Loan Market through Smart Contracts allows for reduced manual labor and back-office workloads as well as the removal of reconciliation and corporate actions. As a result, counterparty risk and settlement times will be minimized, and performance and transparency for regular reporting will increase. However, an individual's embrace of this new technology will determine its successful implementation. To address this issue, this article examines the trust model, trust in technology drivers, and the revised unified theory of acceptance and use of the technology model to construct the trust and adoption of the technology model. This article culminates in guidelines for the implementation of Smart Contracts. Avenues for future work include the investigation of a multimotive information systems acceptance model.
Continuous Design Decision Support

The main challenges of rational decision-making, documentation, and exploitation are intrusiveness of the activities and consistency between decisions and between decisions and artefacts. This chapter presents three approaches that address these challenges and that contribute to a continuous design decision support: They assist the design decision-making by using a catalogue of design patterns. Once the design decisions are documented, they are made visible in the program code and are accessible from other artefacts such as requirements to increase the developers’ awareness of the decisions and to enable an easy exploitation. In addition, short-cycled practices in continuous software engineering are used to support the documentation and exploitation of design decisions.
General Program Synthesis Using Guided Corpus Generation and Automatic Refactoring

Program synthesis aims to produce source code based on a user specification, raising the abstraction level of building systems and opening the potential for non-programmers to synthesise their own bespoke services. Both genetic programming (GP) and neural code synthesis have proposed a wide range of approaches to solving this problem, but both have limitations in generality and scope. We propose a hybrid search-based approach which combines (i) a genetic algorithm to autonomously generate a training corpus of programs centred around a set of highly abstracted hints describing interesting features; and (ii) a neural network which trains on this data and automatically refactors it towards a form which makes a more ideal use of the neural network’s representational capacity. When given an unseen program represented as a small set of input and output examples, our neural network is used to generate a rank-ordered search space of what it sees as the most promising programs; we then iterate through this list up to a given maximum search depth. Our results show that this approach is able to find up to 60% of a human-useful target set of programs that it has never seen before, including applying a clip function to the values in an array to restrict them to a given maximum, and offsetting all values in an array.