Reviewerly/exHarmony huggingface dataset:
Lossless Contrast Enhancement of Color Images with Reversible Data Hiding

Recently, lossless contrast enhancement (CE) has been proposed so that a contrast-changed image can be converted to its original version by maintaining information entropy in it. As most of the lossless CE methods are proposed for grayscale images, artifacts are probably introduced after directly applying them to color images. For instance, color distortions may be caused after CE is separately conducted in each channel of the RGB (red, green, and blue) model. To cope with this issue, a new scheme is proposed based on the HSV (hue, saturation, and value) color model. Specifically, both hue and saturation components are kept unchanged while only the value component is modified. More precisely, the ratios between the RGB components are maintained while a reversible data hiding method is applied to the value component to achieve CE effects. The experimental results clearly show CE effects obtained with the proposed scheme, while the original color images can be perfectly recovered. Several metrics including image entropy were adopted to measure the changes made in CE procedure, while the performances were compared with those of one existing scheme. The evaluation results demonstrate that better image quality and increased information entropy can be simultaneously achieved with our proposed scheme.
Randomness as source for inspiring solution search methods: Music based approaches

As the world progresses towards industrialization, engineering problems become increasingly complex and it becomes even more difficult to optimize these problems. The reason for this is the increasing complexity of variables, dimensions, space complexity, and time complexity. In order to be able to cope with such a situation, randomized intelligent optimization and search algorithms are proposed to optimize numerical benchmarking problems, multi-objective problems, and solve difficult problems including a large number of variables, dimensions, constraints, and objectives. Metaheuristic random search and optimization methods are widely used to search and find the most appropriate solutions for large-scale optimization problems in an acceptable time. They are general-purposed methods that can be efficiently applied to optimization and search problems without too much modification to accommodate a specific probing. Metaheuristic optimization algorithms are generally categorized as physics, music, sociology, biology, swarm, mathematics, plant, chemistry, sports, water, and hybrid based. Although most of the intelligent metaheuristic methods are inspired by physics and biology; concepts, activities, rules, and processes in music can be an inspiration source of new intelligent optimization and search techniques. That is why; novel and efficient music inspired intelligent optimization and search methods having effective exploitation and exploration capabilities have been proposed. In this paper, music based metaheuristic optimization algorithms were gathered and analyzed for the first time. Harmony search and its versions, melody search algorithm, and method of musical composition have been examined in detail. Furthermore, their performances have been compared within both unconstrained numerical benchmark functions and constrained problems and the obtained results from music based algorithms have been comparatively studied.
The Human in Formal Methods

Formal methods are invaluable for reasoning about complex systems. As these techniques and tools have improved in expressiveness and scale, their adoption has grown rapidly. Sustaining this growth, however, requires attention to not only the technical but also the human side. In this paper (and accompanying talk), we discuss some of the challenges and opportunities for human factors in formal methods.
On Computing Entity Relatedness in Wikipedia, with Applications

Abstract Many text mining tasks, such as clustering, classification, retrieval, and named entity linking, benefit from a measure of relatedness between entities in a knowledge graph. We present a thorough study of all entity relatedness measures in recent literature based on Wikipedia as the knowledge graph. To facilitate this study, we introduce a new dataset with human judgments of entity relatedness. No clear dominance is seen between measures based on textual similarity and graph proximity. Some of the better measures involve expensive global graph computations. We propose a new, space-efficient, computationally lightweight, two-stage framework for relatedness computation. In the first stage, a small weighted subgraph is dynamically grown around the two query entities; in the second stage, relatedness is derived based on computations on this subgraph. Our system shows better agreement with human judgment than existing proposals both on the new dataset and on an established one. Our framework also shows improvements with respect to the state-of-the-art on three different extrinsic evaluations in the domains of ranking entity pairs, entity linking, and synonym extraction.
Performance Comparison of Deep Neural Networks on Image Datasets

With the introduction of convolutional neural networks, error rate of 2.5 percent has been successfully achieved on the image classification. In this paper, four popular convolutional neural network based models namely, VGG16, mobileNet, Resnet50 and InceptionV3 have been considered for the comparative study. The efficiency of the considered models is evaluated on various image classification datasets namely; cats and dogs for binary classification and plant seedling dataset for multiclass classification in terms of accuracy.
How to Evaluate BDD Scenarios' Quality?

A scenario from the Behavior-driven development (BDD) practice is a known format to represent acceptance tests in agile methodologies, communicating assumptions and expectations by expressing the details that result from the conversations between customers and developers. We believe that this formalization of behavior need to be of good quality to avoid known requirement problems that arise from bad documentation, such as incomplete, underspecified and inconsistent requirements. However, there are only informal guidelines to guide practitioners on their BDD scenarios' elaboration and quality evaluations. To address this lack of guidance, we define a set of quality attributes and propose a question-based checklist to assist BDD scenarios' quality evaluations. [Methods] The quality attributes were identified from an interview-based study with 18 practitioners. In this study, practitioners shared their interpretations on an initial set of literature-informed quality attributes and their own personal evaluation criteria. We consolidated both in a single list of newly redefined attributes, used in the definition of our proposed checklist. We believe that our newly re-defined quality attributes and question-based checklist can enhance the existing guidelines and practitioners' ability to evaluate BDD scenario's quality by providing them with an standard guideline for scenarios' refinement conversations.
TextRank enhanced Topic Model for Query focussed Text Summarization

Topic model based query focused text summarization methods can meet the user's needs as they are able to find subtopics and their correlations. Topic model based summarization method does not score the sentences directly as they generates topics distributions, which are used to score the sentences. Graph based methods rank sentences using syntactic and semantic information. In this work, a topic model based summarization method namely two-tiered topic model is combined with graph based TextRank method. The combined method, called TextRank enhanced Two-Tiered topic model, uses the important sentences obtained from TextRank in the generative process of two-tiered model to extract better summary sentences. The proposed method's summary results outperform other topic model based summary results using ROUGE metrics evaluated on DUC 2005 dataset.
A Systematic Mapping of Translation-Enabling Technologies for Sign Languages

Sign languages (SL) are the first language for most deaf people. Consequently, bidirectional communication among deaf and non-deaf people has always been a challenging issue. Sign language usage has increased due to inclusion policies and general public agreement, which must then become evident in information technologies, in the many facets that comprise sign language understanding and its computational treatment. In this study, we conduct a thorough systematic mapping of translation-enabling technologies for sign languages. This mapping has considered the most recommended guidelines for systematic reviews, i.e., those pertaining software engineering, since there is a need to account for interdisciplinary areas of accessibility, human computer interaction, natural language processing, and education, all of them part of ACM (Association for Computing Machinery) computing classification system directly related to software engineering. An ongoing development of a software tool called SYMPLE (SYstematic Mapping and Parallel Loading Engine) facilitated the querying and construction of a base set of candidate studies. A great diversity of topics has been studied over the last 25 years or so, but this systematic mapping allows for comfortable visualization of predominant areas, venues, top authors, and different measures of concentration and dispersion. The systematic review clearly shows a large number of classifications and subclassifications interspersed over time. This is an area of study in which there is much interest, with a basically steady level of scientific publications over the last decade, concentrated mainly in the European continent. The publications by country, nevertheless, usually favor their local sign language.
Class Association and Attribute Relevancy Based Imputation Algorithm to Reduce Twitter Data for Optimal Sentiment Analysis

Twitter sentiment analysis is a challenging task that involves various preprocessing steps including dimensionality reduction. Dimensionality reduction helps ensure low computational complexity and performance improvement during the classification process. In Twitter data, each tweet has feature values which may or may not reflect a person's response. Therefore, a large number of sparse data points are generated when tweets are represented as feature matrix, eventually increasing computational overheads and error rates in Twitter sentiment analysis. This study proposes a novel preprocessing technique called class association and attribute relevancy based imputation algorithm (CAARIA) to reduce the Twitter data size. CAARIA achieves the dimensionality reduction goal by imputing those tweets that belong to the same class and also share useful information. The performance of two classifiers (Naïve Bayes and support vector machines) is evaluated on three Twitter datasets in terms of classification accuracy, measured as area under curve, and time efficiency. CAARIA is also compared against two widely used feature selection (dimensionality reduction) techniques, information gain (IG) and Pearson's correlation (PC). The findings reveal that CAARIA outperforms IG and PC in terms of classification accuracy and time efficiency. These results suggest that CAARIA is a robust data preprocessing technique for the classification task.
Locally distinguishing quantum states with limited classical communication

We consider different settings of the task to distinguish pure orthogonal quantum states under local operations and a limited amount of classical communication. In the first setting, the spatially separated parties are allowed to perform only local projective measurements without any classical communication during the measurements. Under such a restricted class of operations, if the states are indistinguishable, then within a second group of settings, the parties are allowed to use an additional resource for the distinguishing. The additional resource is either a pure entangled state or multiple identical copies of the given states. Comparisons between these two types of resources are also done in certain cases. Both probabilistic discrimination and perfect discrimination of the states are considered for the second group of settings. Within a third setting, the parties perform local projective measurements with a restriction on the availability of classical communication during the measurements. But in this setting the parties are allowed to use a maximally entangled state as a resource.
Hardware-Oriented Algebraic Fault Attack Framework with Multiple Fault Injection Support

The evaluation of fault attacks on security-critical hardware implementations of cryptographic primitives is an important concern. In such regards, we have created a framework for automated construction of fault attacks on hardware realization of ciphers. The framework can be used to quickly evaluate any cipher implementations, including any optimisations. It takes the circuit description of the cipher and the fault model as input. The output of the framework is a set of algebraic equations, such as conjunctive normal form (CNF) clauses, which is then fed to a SAT solver. We consider both attacking an actual implementation of a cipher on an field-programmable gate array (FPGA) platform using a fault injector and the evaluation of an early design of the cipher using idealized fault models. We report the successful application of our hardware-oriented framework to a collection of ciphers, including the advanced encryption standard (AES), and the lightweight block ciphers LED and PRESENT. The corresponding results and a discussion of the impact to different fault models on our framework are shown. Moreover, we report significant improvements compared to similar frameworks, such as speedups or more advanced features. Our framework is the first algebraic fault attack (AFA) tool to evaluate the state-of-the art cipher LED-64, PRESENT and full-scale AES using only hardware-oriented structural cipher descriptions.
Modified smoothing data association for target tracking in clutter

Tracking an unknown target in noisy environment is difficult especially when the target is maneuvering and has unknown trajectory. Smoother uses measurements from future scans to estimate the target state in past scan. This requires the fusion of forward and backward prediction. However, due to uncertain target motions and low detection probabilities, backward prediction could not associate with forward prediction which results in inequitable fusion pair and thus, smoothing performance could not be improved. To cope with these difficulties, the proposed algorithm modifies the fixed-lag smoothing data association based on the integrated probabilistic data association (IPDA) algorithm and a new algorithm called modified smoothing IPDA (MSIPDA) is developed. MSIPDA utilizes two IPDA filters to obtain forward IPDA (fIPDA) track and backward (bIPDA) track estimation in each scan. Each fIPDA prediction generates multiple fusion pairs in association with bIPDA multi-track prediction. These fusion pairs are created in the form of components. As a result, multiple smoothing components are formed with their smoothing component data association probabilities for computing MSIPDA track components state estimate. In addition, the smoothing data association probabilities upgrade the forward track which makes the forward track more powerful for target tracking in clutter. The numerical assessment of MSIPDA is verified using simulations. The result shows significant false track discrimination performance in comparison to existing algorithms.
Formation of Radar Image the Earth's Surface in the Front Zone Review Two-Position Systems Airborne Radar

The paper proposes an algorithm for the formation of high-resolution radar image coordinates azimuth-range in the front viewing area of the two-position radar system. The results of simulation of the proposed algorithm, allowing to estimate the magnitude of the methodical error of the algorithm and the gain in increasing the resolution of the system in azimuthal coordinate are presented.
Shared Ledger Accounting — Implementing the Economic Exchange pattern

Distributed Ledger Technology (DLT) suggests a new way to implement Accounting Information Systems, but an ontologically sound consensus-based design is missing to date. Against this research gap, the paper introduces a DLT-based shared ledger solution in a formal way and compliant with Financial Reporting Standards. We build on the COFRIS accounting ontology (grounded on UFO) and the blockchain ontology developed by De Kruijff & Weigand that distinguishes between a Datalogical level, an Infological and an Essential (conceptual) level. It is shown how both consensual and enterprise-specific parts of the business exchange transaction can be represented in a concise way, and how this pattern can be implemented using Smart Contracts. It is argued that the proposed Shared Ledger Accounting system increases the quality of the contents from an accounting perspective as well as the quality of the system in terms of auditability and interoperability.
Logic-Based Learning of Answer Set Programs

Learning interpretable models from data is stated as one of the main challenges of AI. The goal of logic-based learning is to compute interpretable (logic) programs that explain labelled examples in the context of given background knowledge. This tutorial introduces recent advances of logic-based learning, specifically learning non-monotonic logic programs under the answer set semantics. We introduce several learning frameworks and algorithms, which allow for learning highly expressive programs, containing rules representing non-determinism, choice, exceptions, constraints and preferences. Throughout the tutorial, we put a strong emphasis on the expressive power of the learning systems and frameworks, explaining why some systems are incapable of learning particular classes of programs.
On-Board Real-Time Trajectory Planning for Fixed Wing Unmanned Aerial Vehicles in Extreme Environments

A team from the University of Bristol have developed a method of operating fixed wing Unmanned Aerial Vehicles (UAVs) at long-range and high-altitude over Volcán de Fuego in Guatemala for the purposes of volcanic monitoring and ash-sampling. Conventionally, the mission plans must be carefully designed prior to flight, to cope with altitude gains in excess of 3000 m, reaching 9 km from the ground control station and 4500 m above mean sea level. This means the climb route cannot be modified mid-flight. At these scales, atmospheric conditions change over the course of a flight and so a real-time trajectory planner (RTTP) is desirable, calculating a route on-board the aircraft. This paper presents an RTTP based around a genetic algorithm optimisation running on a Raspberry Pi 3 B+, the first of its kind to be flown on-board a UAV. Four flights are presented, each having calculated a new and valid trajectory on-board, from the ground control station to the summit region of Volcań de Fuego. The RTTP flights are shown to have approximately equivalent efficiency characteristics to conventionally planned missions. This technology is promising for the future of long-range UAV operations and further development is likely to see significant energy and efficiency savings.
Joint Learning for Aspect Category Detection and Sentiment Analysis in Chinese Reviews

Aspect based sentiment analysis (ABSA) is a valuable task, aiming to predict the sentiment polarities of the given aspects (terms or categories) in review texts. Aspect-category sentiment analysis is a sub-task of ABSA, which mainly focus on aspect category detection and aspect category polarity identifying. Most of the previous methods employ a pipeline strategy, regarding aspect category detection and category sentiment analysis as two separate tasks, which could not meet the needs of practical application. To address this limitation, we propose an end-to-end neural network model based on joint learning, which can detect aspect category and identify aspect category polarity simultaneously. We conduct several comparable experiments on a Chinese review dataset and the experimental results show that our proposed model is simpler and more effective than the baseline models.
Tampering Detection of Distributed Databases using Blockchain Technology

Data stored in different kinds of Database management systems is always prone to tampering through internal or external sources. To detect such malicious changes in the database we use various Database Tampering Detection methods. A major find in this area was to use one-way cryptographic hash functions as well as use of digital watermarking to detect any tamper in the data. But these techniques won't work on Distributed Database. In this paper, we have put forth the idea of using a Distributed Database to store data and we can easily detect any malicious transaction using Blockchain technology in it. It is simply a digital ledger bound using cryptographic principles.
Realtime Multi-Person 2D Pose Estimation using ShuffleNet

This paper analyzes the principles of the realtime multi-person 2D pose estimation, and then describes the design of the new method called Pose-ShuffleNet. The Pose-ShuffleNet is based on the building blocks of ShuffleNet unit and has the architecture of multi-stages and two branches jointly learning parts detection and parts association. The paper presents the comparison results among the method and other state-of-the-art methods, showing Pose-ShuffleNet more suitable for resource-constrained scenarios.
The 2018 Hanabi competition

This paper outlines the Hanabi competition, first run at CIG 2018, and returning for COG 2019. Hanabi presents a useful domain for game agents which must function in a cooperative environment. The paper presents the results of the two tracks which formed the 2018 competition and introduces the learning track, a new track for 2019 which allows the agents to collect statistics across multiple games.
Procedural Generation using Spatial GANs for Region-Specific Learning of Elevation Data

Heightmap generation is currently a tedious topic with the majority of generation using Perlin noise which forms a reliable, but sometimes repetitive output. In this paper, a method of generating height maps from real-world digital elevation data taken from specific regions of the planet is proposed. Raw elevation data sourced from NASA's SRTM (30m) data set is transformed into a height map format, this data is then passed into a two type unsupervised model. The method uses a type of generative adversarial network to learn the spatially-invariant features within the input regions. Producing a network model that can output an extensive amount of varying, but visually and structurally similar height maps to that of the input regions. The visual validity of outputs from the network was tested using data from 262 human participants, with over 90.15% of generated samples being correctly assigned to the original input data with a significance of P <;0.001.