Reviewerly/exHarmony huggingface dataset:
Editorial of the Special Issue on Latest Advancements in Linguistic Linked Data

Since the inception of the Open Linguistics Working Group in 2010, there have been numerous efforts in transforming language resources into Linked Data. The research field of Linguistic Linked Data (LLD) has gained in importance, visibility and impact, with the Linguistic Linked Open Data (LLOD) cloud gathering nowadays over 200 resources. With this increasing growth, new challenges have emerged concerning particular domain and task applications, quality dimensions, and linguistic features to take into account. This special issue aims to review and summarize the progress and status of LLD research in recent years, as well as to offer an understanding of the challenges ahead of the field for the years to come. The papers in this issue indicate that there are still aspects to address for a wider community adoption of LLD, as well as a lack of resources for specific tasks and (interdisciplinary) domains. Likewise, the integration of LLD resources into Natural Language Processing (NLP) architectures and the search for long-term infrastructure solutions to host LLD resources continue to be essential points to which to attend in the foreseeable future of the research line.
A characterization study of testing contributors and their contributions in open source projects.

Even though open source projects have some different characteristics from projects in the industry, the commitment of maintainers and contributors to achieve a high level of software quality is constant. Therefore, tests are among the main practices of the communities. Thus, motivating contributors to write new tests and maintain regression tests during testing activities is essential for the project’s health. The objective of our work is to characterize testers and their contributions to open source projects as part of a broad study about testers’ motivation. Thus, we conducted a study with 3,936 repositories and 7 different and important programming languages (C, C++, C#, Java, Javascript, Python, and Ruby), analyzing a total of 4,409,142 contributions to classify contributing members and their contributions. Our results show that test-only contributors exist, regardless of programming language or project. We conclude that, despite the unfavorable scenario, there are contributors who feel motivated and dedicate their time and effort to contribute to new tests or to the evolution of existing tests.
Smell Patterns as Indicators of Design Degradation

Design degradation problems occur as a result of sub-optimal decisions that negatively impact internal software quality. These problems can affect isolated elements (e.g., a class) or multiple code elements (e.g., components). When neglected, design degradation increases maintenance costs. In this scenario, refactoring is paramount to keep internal software quality. However, the identification and removal of design degradation problems are not trivial tasks. For example, architectural documentation is an artifact that facilitates the identification of architectural problems, but often the documentation is unavailable or outdated. Hence, developers rely on the source code, mainly by identifying code smells. A code smell is a sub-optimal decision in the system implementation that can often be considered a counterpart of design degradation problems. Code smells allow developers to spot a design degradation problem and use them to reason about which refactoring operations need to be applied. Certain groups of code smells, called smell patterns, seem to indicate some specific design degradation problems. Intending to investigate the usefulness of smell patterns to help developers in the identification and removal of design degradation problems, we have conducted a quasi-experiment with 13 professional developers. Developers agree that smell patterns are indicators of design degradation problems, but their perceptions are influenced by factors such as the development platform (e.g., Android), type of functionality, and effort for refactoring the problem. Furthermore, they see as degradation problems only the sub-optimal decisions not intentionally taken. Among the smell patterns investigated, the patterns for Fat Interface, Concern Overload, and Scattered Concern were the most useful indicators of design degradation. We also observed that refactorings associated with code smells contribute to the (partial) removal of degradation problems. Our results led us to develop an automated tool to support developers during the identification and removal of some design degradation problems.
An information asset priority evaluation method with analytic network process

IT infrastructures are indispensable parts of today’s organizations, and keeping them secure is very important for successfully running the business. Due to the complexity of information infrastructure and network topology, traditional security measures no longer meet the security needs of current enterprises. Therefore, the holistic information security management analysis method has received extensive attention. An important task of these holistic security analysis methods is to analyze the value of assets so that IT managers can effectively allocate resources to protect the information infrastructure. Most of the current asset valuation methods are based on analyzing the dependencies between assets. In some cases, the dependencies are not easy to find, which makes the evaluation results inaccurate. Therefore, we propose model with analytic network process network (ANP) to evaluate the value of assets. This method not only considers the security factors and considers the importance of the assets to the business as well. It can evaluate the value of the assets and prioritize them, which can help the system administrator in making the decision for security enhancement.
Trust based COVID-19 vaccine distribution using blockchain technology

The COVID-19 outbreak has impacted huge number of individuals all around the world and has caused a great economic loss all over the world. Vaccination is most effective solution to prevent this disease. It helps in protecting the whole community. It improves the human immune system and fights against corona virus reducing the death rate. This paper deals with the different types of COVID-19 vaccine and their related distribution, it includes measures to ensure safe and secured distribution of the vaccine through block chain technology with the help of supply chain. Any malfunction in the chain is identified by the trust value of the function point method and the value of the Markov Chain.
A Predicate Construct for Declarative Programming in Imperative Languages

Imperative and object-oriented programming languages are among the most common languages for general-purpose programming. These languages work well for handling many common tasks necessary for most applications. However, there are still many hard problems that remain difficult to implement directly in imperative languages. Declarative languages have worked well for solving many of these problems by providing a syntax that allows the user to focus on modeling the problem rather than on designing an algorithm. Logic programming languages, like Prolog, have seen success in constraint satisfaction problems, logical databases, and various NP-Hard problems. Unfortunately, these languages have not seen the same success in general-purpose programming, and most of the problems they solve do not exist in isolation. Furthermore, many imperative programmers are still unfamiliar with and unaware of logic programming.
CheckINN: Wide Range Neural Network Verification in Imandra

Neural networks are increasingly relied upon as components of complex safety-critical systems such as autonomous vehicles. There is high demand for tools and methods that embed neural network verification in a larger verification cycle. However, neural network verification is difficult due to a wide range of verification properties of interest, each typically only amenable to verification in specialised solvers. In this paper, we show how Imandra, a functional programming language and a theorem prover originally designed for verification, validation and simulation of financial infrastructure can offer a holistic infrastructure for neural network verification. We develop a novel library CheckINN that formalises neural networks in Imandra, and covers different important facets of neural network verification.
User Guided Abductive Proof Generation for Answer Set Programming Queries

We present a method for generating possible proofs of a query with respect to a given Answer Set Programming (ASP) rule set using an abductive process where the space of abducibles is automatically constructed just from the input rules alone. Given a (possibly empty) set of user provided facts, our method infers any additional facts that may be needed for the entailment of a query and then outputs these extra facts, without the user needing to explicitly specify the space of all abducibles. We also present a method to generate a set of directed edges corresponding to the justification graph for the query. Furthermore, through different forms of implicit term substitution, our method can take user provided facts into account and suitably modify the abductive solutions. Past work on abduction has been primarily based on goal directed methods. However these methods can result in solvers that are not truly declarative. Much less work has been done on realizing abduction in a bottom up solver like the Clingo ASP solver. We describe novel ASP programs which can be run directly in Clingo to yield the abductive solutions and directed edge sets without needing to modify the underlying solving engine.
A Monadic Implementation of Functional Logic Programs

Functional logic languages are a high-level approach to programming by combining the most important declarative features. They abstract from small-step operational details so that programmers can concentrate on the logical aspects of an application. This is supported by appropriate evaluation strategies. Demand-driven evaluation from functional programming is amalgamated with non-determinism from logic programming so that solutions or values are computed whenever they exist. This frees the programmer from considering the influence of an operational strategy to the success of a computation but it is a challenge to the language implementer. A non-deterministic demand-driven strategy might duplicate unevaluated choices of an expression which could duplicate the computational efforts. In recent implementations, this problem has been tackled by adding a kind of memoization of non-deterministic choices to the expression under evaluation. Since this has been implemented in imperative target languages, it was unclear whether this could also be supported in a functional programming environment, like Haskell. This paper presents a solution to this challenge by transforming functional logic programs into a monadic representation. Although this transformation is not new, we present an implementation of the monadic interface which supports memoization in non-deterministic branches. We demonstrate that our approach yields a promising performance that outperforms current compilers for Curry.
Under-resourced dialect identification in Ao using source information

This paper reports the findings of an automatic dialect identification (DID) task conducted on Ao speech data using source features. Considering that Ao is a tone language, in this study for DID, the gammatonegram of the linear prediction residual is proposed as a feature. As Ao is an under-resourced language, data augmentation was carried out to increase the size of the speech corpus. The results showed that data augmentation improved DID by 14%. A perception test conducted on Ao speakers showed better DID by the subjects when utterance duration was 3 s. Accordingly, automatic DID was conducted on utterances of various duration. A baseline DID system with the Slms feature attained an average F1-score of 53.84% in a 3 s long utterance. Inclusion of source features, Silpr and S, improved the F1-score to 60.69%. In a final system, with a combination of Silpr, S, Slms, and Mel frequency cepstral coefficient features, the F1-score increased to 61.46%.
Continuous Sign Language Recognition Based on Spatial-Temporal Graph Attention Network

Continuous sign language recognition (CSLR) is challenging due to the complexity of video background, hand gesture variability, and temporal modeling difficulties.This work proposes a CSLR method based on a spatialtemporal graph attention network to focus on essential features of video series.The method considers local details of sign language movements by taking the information on joints and bones as inputs and constructing a spatialtemporal graph to reflect inter-frame relevance and physical connections between nodes.The graph-based multihead attention mechanism is utilized with adjacent matrix calculation for better local-feature exploration, and short-term motion correlation modeling is completed via a temporal convolutional network.We adopted BLSTM to learn the long-term dependence and connectionist temporal classification to align the word-level sequences.The proposed method achieves competitive results regarding word error rates (1.59%) on the Chinese Sign Language dataset and the mean Jaccard Index (65.78%) on the ChaLearn LAP Continuous Gesture Dataset.
Analogy as a search procedure: a dimensional view

In this paper, we outline a comprehensive approach to composed analogies based on the theory of conceptual spaces. Our algorithmic model understands analogy as a search procedure and builds upon the idea that analogical similarity depends on a conceptual phenomena called ‘dimensional salience.’ We distinguish between category-based, property-based, event-based, and part-whole analogies, and propose computationally-oriented methods for explicating them in terms of conceptual spaces.
Video behavior recognition based on actional-structural graph convolution and temporal extension module

&lt;abstract&gt; &lt;p&gt;Human behavior recognition has always been a hot spot for research in computer vision. In this paper, we propose a novel video behavior recognition method based on Actional-Structural Graph Convolution and a Temporal Extension Module under the framework of a Spatio-Temporal Graph Convolution Neural Network, which can optimize the spatial and temporal features simultaneously. The basic network framework of our method consists of three parts: spatial graph convolution module, temporal extension module and attention mechanism module. In the spatial dimension, the action graph convolution is utilized to obtain abundant spatial features by capturing the correlations of distant joint features, and the structural graph convolution expands the existing skeleton graph to acquire the spatial features of adjacent joints. In the time dimension, the sampling range of the temporal graph is expanded for extracting the same and adjacent joints of adjacent frames. Furthermore, attention mechanisms are introduced to improve the performance of our method. In order to verify the effectiveness and accuracy of our method, a large number of experiments were carried out on two standard behavior recognition datasets: NTU-RGB+D and Kinetics. Comparative experiment results show that our proposed method can achieve better performance.&lt;/p&gt; &lt;/abstract&gt;
Influence of Late Fusion of High-Level Features on User Relevance Feedback for Videos

Content-based media retrieval relies on multimodal data representations. For videos, these representations mainly focus on the textual, visual, and audio modalities. While the modality representations can be used individually, combining their information can improve the overall retrieval experience. For video collections, retrieval focuses on either finding a full length video or specific segment(s) from one or more videos. For the former, the textual metadata along with broad descriptions of the contents are useful. For the latter, visual and audio modality representations are preferable as they represent the contents of specific segments in videos. Interactive learning approaches, such as user relevance feedback, have shown promising results when solving exploration and search tasks in larger collections. When combining modality representations in user relevance feedback, often a form of late modality fusion method is applied. While this generally tends to improve retrieval, its performance for video collections with multiple modality representations of high-level features, is not well known. In this study we analyse the effects of late fusion using high-level features, such as semantic concepts, actions, scenes, and audio. From our experiments on three video datasets, V3C1, Charades, and VGG-Sound, we show that fusion works well, but depending on the task or dataset, excluding one or more modalities can improve results. When it is clear that a modality is better for a task, setting a preference to enhance that modality's influence in the fusion process can also be greatly beneficial. Furthermore, we show that mixing fusion results and results from individual modalities can be better than only performing fusion.
Impact of Blind Image Quality Assessment on the Retrieval of Lifelog Images

The use of personal lifelogs can be beneficial to improve the quality of our life, as they can serve as tools for memory augmentation or for providing support to people with memory issues. In visual lifelogs, data are captured by cameras in the form of images or videos. However, a considerable amount of these images or videos are affected by different types of distortions or noise due to the non-controlled acquisition process. This article addresses the use of Blind Image Quality Assessment algorithms as a pre-processing approach in the retrieval of lifelogging images. As the amount of lifelog images has increased over the last few years, it is fundamental to find solutions to filter images in a lifelog data collection. We evaluate the impact of a Blind Image Quality Assessment algorithm by performing different retrieval experiments through a lifelogging system named MEMORIA. The results are promising and show that our approach can reduce the amount of images to process and retrieve in a lifelog data collection without losing valuable information, and provide to the user the most valuable images. By excluding a considerable amount of images in the pre-processing stage of a lifelogging system, its performance can be increased by saving time and resources.
Efficient Interaction with Automotive Heads-Up Displays using Appearance-based Gaze Tracking

Automotive Head-Up Displays (HUD) offer a promising alternative to the existing Head Down Displays (HDD) inside the car. Since HUDs lie closer to the driver’s line of sight, they reduce eyes-off-the-road time of the drivers while interacting with them. Yet existing HUDs do not provide interactivity restricting their potential to mere information visualization. In this work, we proposed a novel webcam-based gaze tracking system to interact with the icons on HUD. We conducted a user study on a driving simulator and compared the proposed system with a Gesture-based system. We collected quantitative and qualitative metrics from 8 participants while they were performing a dual-task trial. We observed that using the proposed eye gaze system, users were able to select icons on the HUD as fast as gesture modality. Further, users perceived significantly lower cognitive load and expressed significantly higher preference towards the proposed eye gaze control system than the gesture-based system.
Semantic segmentation of UAV image using combined U-net and heterogeneous UAV imagery datasets

Semantic segmentation of urban areas can provide useful information for analyzing and detecting changes in urban development. Recently, numerous remote sensing image datasets from various platforms have been acquired, and various semantic segmentation studies using them have been conducted. However, they do not contain many images because of their large data capacity and difficulty in constructing label data. Furthermore, it is difficult to use them simultaneously because each dataset has a different spatial resolution, shooting angle, and meaningful objects. In this study, two different UAV image datasets, such as UAVid semantic segmentation and semantic drone datasets, were used to train a combined U-net model to use heterogeneous remote sensing datasets for semantic segmentation tasks simultaneously. The UAVid dataset has a flight height of 50 m and 300 images with eight classes. However, the semantic drone dataset was acquired at an altitude of 5–30 m above the ground and contains 598 images with 20 classes. The combined U-net model is based on the U-net architecture, but it receives input from two different data sources. The experimental results showed that learning two datasets with a combined U-net improved semantic segmentation accuracy more than learning each data with a U-net. This study confirms the ability to train two different datasets acquired from different places and platforms simultaneously; thus, evaluating the applicability of semantic segmentation studies using heterogeneous remote sensing datasets.
Reconstruction of 3-D models of infrastructure objects from satellite images based on typed elements

The paper describes an approach to restoring a three-dimensional model of rigid objects from a single satellite image based on informative classes identified from the results of machine learning, which include railway rails and poles, roofs and walls of buildings, shadows of poles and buildings, and others. The proposed algorithms take into account various conditions for the presence of certain classes in the image, identified by the results of machine learning, as well as the conditions for the absence of metadata on the spatial resolution and spatial orientation of the shooting and the Sun (shooting angle, scanning azimuth, etc.).
Adaptive Differential Privacy Budget Allocation Algorithm Based on Random Forest

In the context of big data, privacy protection in the process of data mining has become a hot issue in the field of security. The commonly used privacy protection method is to add differential privacy in the process of data mining, but the unreasonable privacy budget allocation leads to unsatisfactory classification results. To solve this problem, an adaptive differential privacy budget allocation algorithm RFDPP-weight based on random forest is proposed. Construct multiple decision trees with balanced features, calculate the Balance Error Rate (BER) of the data outside the bag in order to calculate the feature weight and then reconstruct the feature set. And the random forest with higher classification performance was constructed based on the new feature set. The decision tree weight was calculated according to the feature weight, and the privacy protection budget was adaptively allocated according to the decision tree weight. In the final prediction stage, the classification result with the largest weight of the corresponding decision tree is taken as the result of the random forest algorithm, so as to further improve the classification accuracy of the random forest algorithm. The experimental results show that the F1 score of our algorithm reaches 0.977 and 0.893 with a small number of decision trees in the Mushroom and Adult data sets, which proves that the algorithm can reasonably allocate the privacy budget and further improve the availability of data while protecting data privacy.