Reviewerly/exHarmony huggingface dataset:
Towards Mapping Images to Text Using Deep-Learning Architectures

Images and text represent types of content that are used together for conveying a message. The process of mapping images to text can provide very useful information and can be included in many applications from the medical domain, applications for blind people, social networking, etc. In this paper, we investigate an approach for mapping images to text using a Kernel Ridge Regression model. We considered two types of features: simple RGB pixel-value features and image features extracted with deep-learning approaches. We investigated several neural network architectures for image feature extraction: VGG16, Inception V3, ResNet50, Xception. The experimental evaluation was performed on three data sets from different domains. The texts associated with images represent objective descriptions for two of the three data sets and subjective descriptions for the other data set. The experimental results show that the more complex deep-learning approaches that were used for feature extraction perform better than simple RGB pixel-value approaches. Moreover, the ResNet50 network architecture performs best in comparison to the other three deep network architectures considered for extracting image features. The model error obtained using the ResNet50 network is less by approx. 0.30 than other neural network architectures. We extracted natural language descriptors of images and we made a comparison between original and generated descriptive words. Furthermore, we investigated if there is a difference in performance between the type of text associated with the images: subjective or objective. The proposed model generated more similar descriptions to the original ones for the data set containing objective descriptions whose vocabulary is simpler, bigger and clearer.
Pandemic-Driven Technology Adoption

During the first six months of the COVID-19 pandemic, around the world, evidence is mounting as to the unenveness of impacts across communities. There are disproportionately more impacts on people who are elderly, economically marginalized, immunologically compromised, and members of racialized and equity-seeking communities. As part of the COVID-19 response, virus transmission mitigation efforts including the use of new technology tools like contract tracing apps are being explored. There are significant implications to the use of these tools, including how they impact different community members and exacerbate digital divide, exclusion, and surveillance issues. This article brings forward a citizen participation framework that is instructive for decision-makers charged with pandemic-driven technology adoption.
Large-Scale Dataset of Local Java Software Build Results

When a person decides to inspect or modify a third-party software project, the first necessary step is its successful compilation from source code using a build system. However, such attempts often end in failure. In this data descriptor paper, we provide a dataset of build results of open source Java software systems. We tried to automatically build a large number of Java projects from GitHub using their Maven, Gradle, and Ant build scripts in a Docker container simulating a standard programmer’s environment. The dataset consists of the output of two executions: 7264 build logs from a study executed in 2016 and 7233 logs from the 2020 execution. In addition to the logs, we collected exit codes, file counts, and various project metadata. The proportion of failed builds in our dataset is 38% in the 2016 execution and 59% in the 2020 execution. The published data can be helpful for multiple purposes, such as correlation analysis of factors affecting build success, build failure prediction, and research in the area of build breakage repair.
COVID-19 Contact Tracing

This article surveys the rise of contact tracing technologies during the COVID-19 pandemic and some of the privacy, ethical, and human rights issues they raise. It examines the relationship of these technologies to local public health initiatives, and how the privacy debate over these apps made the technology in some cases less responsive to public health agency needs. The article suggests that as countries enter the return to normal phase, the more important and more invasive contact tracing and disease surveillance technologies will be deployed at the local level in the context of employment, transit, retail services, and other activities. The smart city may be co-opted for COVID-19 surveillance, and individuals will experience tracking and monitoring as they go to work, shop, dine, and commute. The author questions whether the attention given to national contact tracing apps has overshadowed more local contexts where privacy, ethical, and human rights issues remain deeply important but relatively unexamined. This raises issues for city local governance and urban e-planning.
Lexical Competition and Change: A Corpus-Assisted Investigation of Gambling and Gaming in the Past Centuries

This article investigates the interplay of lexical competition and socio-historical events through a close examination of the use of gambling and gaming based on large-scale synchronic and diachronic corpora. We first set the background for comparison through a synchronic study of the collocational patterns and grammatical relations of the two words using Sketch Engine. We show that gambling tends to be associated with negatively perceived activities and strong disapproval, whereas gaming tends to collocate with recreational activities, business, and technology. Using Google Books Ngram Viewer, we focus on the drastic diachronic changes in use of the two words, from competition to co-development. Based on corpora trends, we correlate the rise and fall of the two words and the change in their competition relation to particular socio-historical events: gold rushes, sports betting, the popularity of video games, and the gaming industry boom. The classical competition model of near synonyms remained valid until recent socio-economic events introduced additional and unique meanings for both words. The article thus shows that linguistic variations as collective human behavior changes can be leveraged to evidence other collective human behavior changes.
An Anisotropic Gaussian Filtering Model for Image De-Hazing

This paper proposes a de-hazing algorithm on the basis of the anisotropic Gaussian filtering method to overcome some essential limitations of the DCP-based (the dark channel prior, DCP) methods, such as halo artifacts and over-saturation problems.In this method, the approximate range of the global atmospheric light A is obtained by using the spatial LOG edge detection method, and the accurate A is acquired by combing binary algorithm.And an anisotropic Gaussian filtering method is adopted to optimize the transmission, which can smooth the rough transmission map, improve the clarity of image details and inhibit halo artifact and over-saturation effect.With the accurate A and optimized transmission, the de-hazing results can be acquired on the basis of the atmospheric scattering model.The processing effect is verified by our own fog image data set constructed in real life or downloaded from the Internet.The visual effect of our method is more natural in brightness, contrast and detail recovery.Besides, the indicators SSIM, PSNR, Lum, Con, Inf, e and r are relatively high, and indicators MSE and ε are relatively low.Therefore, subjective and objective experimental results demonstrate that our method outperforms four state-of-the-art methods in terms of visual sense definition, robustness and time efficiency.
Visual tracking based on transfer learning of deep salience information

In this paper, we propose a new visual tracking method in light of salience information and deep learning.Salience detection is used to exploit features with salient information of the image.Complicated representations of image features can be gained by the function of every layer in convolution neural network (CNN).The characteristic of biology vision in attention-based salience is similar to the neuroscience features of convolution neural network.This motivates us to improve the representation ability of CNN with functions of salience detection.We adopt the fully-convolution networks (FCNs) to perform salience detection.We take parts of the network structure to perform salience extraction, which promotes the classification ability of the model.The network we propose shows great performance in tracking with the salient information.Compared with other excellent algorithms, our algorithm can track the target better in the open tracking datasets.We realize the 0.5592 accuracy on visual object tracking 2015 (VOT15) dataset.For unmanned aerial vehicle 123 (UAV123) dataset, the precision and success rate of our tracker is 0.710 and 0.429.
Integrated Technologies of Blockchain and Biometrics Based on Wireless Sensor Network for Library Management

The Internet of Things (IoT) is built on a strong internet infrastructure and many wireless sensor devices. Presently, Radio Frequency Identification embedded (RFID-embedded) smart cards are ubiquitous, used for many things including student ID cards, transportation cards, bank cards, prepaid cards, and citizenship cards. One example of places that require smart cards is libraries. Each library, such as a university library, city library, local library, or community library, has its own card and the user must bring the appropriate card to enter a library and borrow material. However, it is inconvenient to bring various cards to access different libraries. Wireless infrastructure has been well developed and IoT devices are connected through this infrastructure. Moreover, the development of biometric identification technologies has continued to advance. Blockchain methodologies have been successfully adopted in various fields. This paper proposes the BlockMetrics library based on integrated technologies using blockchain and finger-vein biometrics, which are adopted into a library collection management and access control system. The library collection is managed by image recognition, RFID, and wireless sensor technologies. In addition, a biometric system is connected to a library collection control system, enabling the borrowing procedure to consist of only two steps. First, the user adopts a biometric recognition device for user authentication and then performs a collection scan with the RFID devices. All the records are recorded in a personal borrowing blockchain, which is a peer-to-peer transfer system and permanent data storage. In addition, the user can check the status of his collection across various libraries in his personal borrowing blockchain. The BlockMetrics library is based on an integration of technologies that include blockchain, biometrics, and wireless sensor technologies to improve the smart library.
Web Design Visual Comic Communication As A Visual Based Learning Media

In this era of digitalization, there is no denying that all daily activities are made easier by technological advancements. Not spared from the current learning activities have almost used the progress of digitalization in the delivery of information and learning materials efficiently and effectively. But the more rapid and development of learning methods are not matched by unique creations so that learning becomes boring and can reduce the attractiveness of learning. With the existence of visual communication that has developed, the animation media can be one of the efforts to attract and improve reading and learning, comic animation can be relied upon in the current era where it can cover all groups. The method used in this research is data collection and system design. This web design was created using Yii Framework and Adobe Illustrator. The results of this study aim to improve the quality of learning, making it easier for users to create comics, especially for beginners.
A formal universal of natural language grammar

This article proposes that the possible word orders for any natural language construction composed of n elements, each of which selects for the category headed by the next, are universally limited both across and within languages to a subclass of permutations on the 'universal order of command' 1, … , n, as determined by their selectional restrictions.The permitted subclass is known as the 'separable' permutations, and grows in n as the large Schröder series {1, 2, 6, 22, 90, 394, 1806, … }.This universal is identified as formal because it follows directly from the assumptions of combinatory categorial grammar (CCG)-in particular, from the fact that all CCG syntactic rules are subject to a combinatory projection principle that limits them to binary rules applying to contiguous nonempty categories.The article presents quantitative empirical evidence in support of this claim from the linguistically attested orders of the four elements Dem(onstrative), Num(erator), A(djective), N(oun), which have been examined in connection with various versions of Greenberg's putative 20th universal concerning their order.A universal restriction to separable permutation is also supported by word-order variation in the Germanic verb cluster and in the Hungarian verb complex, among other constructions.
Towards model-based approaches for musical instruments making: validation of the model of a Spanish guitar soundboard and characterization features proposal

Nowadays, the virtual prototyping method is widely used for industrial applications and can lead to a powerful tool for musical instruments making and conservation. Nevertheless, physics-based models of musical instruments are barely developed for this purpose and the confrontation between model predictions and experiments have been the focus of very few researches. The objective of this paper is to highlight the predictive capability of physics-based models in vibratory domain, even in presence of variable by nature material and climatic conditions. To this end, a finite element model of the soundboard of a Spanish guitar is developed for model validation purposes. The simulated modal bases are compared with experimental ones from a previous study. Screening and stochastic analyses are performed to rank which are, among material and climatic parameters, the most influential ones on the dynamics of guitar soundboard. Moreover, uncertainties are taken into account to evaluate the dispersion of the response for a given design, and simulations are validated facing experimental data. It is shown that specific elastic parameters of the wood (in longitudinal and radial directions and longitudinal-radial plane) of the top plate are mainly influential with regard to the dynamics of the soundboard, and the relative humidity changes have a non negligible impact. Moreover, test-model correlations have shown that a nominal model with average material parameters is able to predict the vibratory behaviour of a real braced soundboard with an average error on the first eight eigenfrequencies lower than 4%. In addition, when uncertainties are taken into account, the model is able to predict every experimental data. Finally, dynamic features like CFDAC and Fuzzy-FRF are proposed in an innovative way in this application domain.
Avoiding coherent errors with rotated concatenated stabilizer codes

Abstract Coherent errors, which arise from collective couplings, are a dominant form of noise in many realistic quantum systems, and are more damaging than oft considered stochastic errors. Here, we propose integrating stabilizer codes with constant-excitation codes by code concatenation. Namely, by concatenating an [[ n , k , d ]] stabilizer outer code with dual-rail inner codes, we obtain a [[2 n , k , d ]] constant-excitation code immune from coherent phase errors and also equivalent to a Pauli-rotated stabilizer code. When the stabilizer outer code is fault-tolerant, the constant-excitation code has a positive fault-tolerant threshold against stochastic errors. Setting the outer code as a four-qubit amplitude damping code yields an eight-qubit constant-excitation code that corrects a single amplitude damping error, and we analyze this code’s potential as a quantum memory.
Accurate and Robust Feature Importance Estimation under Distribution Shifts

With increasing reliance on the outcomes of black-box models in critical applications, post-hoc explainability tools that do not require access to the model internals are often used to enable humans understand and trust these models. In particular, we focus on the class of methods that can reveal the influence of input features on the predicted outputs. Despite their wide-spread adoption, existing methods are known to suffer from one or more of the following challenges: computational complexities, large uncertainties and most importantly, inability to handle real-world domain shifts. In this paper, we propose PRoFILE (Producing Robust Feature Importances using Loss Estimates), a novel feature importance estimation method that addresses all these challenges. Through the use of a loss estimator jointly trained with the predictive model and a causal objective, PRoFILE can accurately estimate the feature importance scores even under complex distribution shifts, without any additional re-training. To this end, we also develop learning strategies for training the loss estimator, namely contrastive and dropout calibration, and find that it can effectively detect distribution shifts. Using empirical studies on several benchmark image and non-image data, we show significant improvements over state-of-the-art approaches, both in terms of fidelity and robustness.
Meta-Analysis of Artificial Intelligence Works in Ubiquitous Learning Environments and Technologies

Ubiquitous learning (u-learning) refers to anytime and anywhere learning.U-learning has progressed to be considered a conventional teaching and learning approach in schools and is adopted to continue with the school curriculum when learners cannot attend schools for face-to-face lessons.Computer Science, namely the field of Artificial Intelligence (AI) presents tools and techniques to support the growth of u-learning and provide recommendations and insights to academic practitioners and AI researchers.Aim: The aim of this study was to conduct a meta-analysis of Artificial Intelligence works in ubiquitous learning environments and technologies to present state from the plethora of research.Method: The mining of related articles was devised according to the technique of Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA).The complement of included research articles was sourced from the broadly used databases, namely, Science Direct, Springer Link, Semantic Scholar, Academia, and IEEE.Results: A total of 16 scientific research publications were shortlisted for this study from 330 articles identified through database searching.Using random-effects model, the estimated pooled estimate of artificial intelligence works in ubiquitous learning environments and technologies reported was 10% (95% CI: 3%, 22%; I 2 = 99.46%,P = 0.00) which indicates the presence of considerable heterogeneity.Conclusion: It can be concluded based on the experimental results from the sub group analysis that machine learning studies [18% (95% CI: 11%, 25%), I 2 = 99.83%] was considerably more heterogeneous (I 2 = 99.83%)than intelligent decision support systems, intelligent systems and educational data mining.However, this does not mean that intelligent decision support systems, intelligent systems and educational data mining is not efficient.
Uncertainty-Matching Graph Neural Networks to Defend Against Poisoning Attacks

Graph Neural Networks (GNNs), a generalization of neural networks to graph-structured data, are often implemented using message passes between entities of a graph. While GNNs are effective for node classification, link prediction and graph classification, they are vulnerable to adversarial attacks, i.e., a small perturbation to the structure can lead to a non-trivial performance degradation. In this work, we propose Uncertainty Matching GNN (UM-GNN), that is aimed at improving the robustness of GNN models, particularly against poisoning attacks to the graph structure, by leveraging epistemic uncertainties from the message passing framework. More specifically, we propose to build a surrogate predictor that does not directly access the graph structure, but systematically extracts reliable knowledge from a standard GNN through a novel uncertainty-matching strategy. Interestingly, this uncoupling makes UM-GNN immune to evasion attacks by design, and achieves significantly improved robustness against poisoning attacks. Using empirical studies with standard benchmarks and a suite of global and target attacks, we demonstrate the effectiveness of UM-GNN, when compared to existing baselines including the state-of-the-art robust GCN.
Pengembangan Buku Teks Matematika Kelas 8 Dengan Model ADDIE

Textbook is one of the primary resources to support teaching and learning. From the survey of the target school, the current secondary mathematics textbook does not match with Curriculum 2013 and heavy in size. Based on that survey, need analysis was conducted and it was recommended that the requirement of the textbook aligned with 2013 Curriculum, using proper English, and light in size. This research purpose is to produce the secondary mathematics textbook that matches the school’s need. The method of research is Research and Development with model of ADDIE. The study was conducted in a private school in Jakarta with 96 students of grade 8 and 10 teachers in 2018. Instruments used are need analysis, validity expert, and test. Analysis of data use description of expert judgment based on content, language and design criteria and descriptive statistic for pre-test and post-test. The result was the textbook is valid and matches with the needs of that school.
Understand the Turnover Intention among Kindergarten Teachers in Chinese Mainland

In recent years, teachers in kindergarten from Chinese mainland face many new challenges after ECE promoting policy in 2010 by Chinses Ministry of Education. Therefore high-quality KG teachers with excellent job performance and long retention are necessary. However, due to the high workload and low salary the turnover intention among KG teachers in China is high. Once turnover happens, both direct cost and indirect cost would occur, which would bring negative influence on ECE quality. Due to the professional nature of KG teachers the replacement is hard therefore it is important to understand how to achieve retention of KG teachers. Therefore, workload and work-life conflict are identified as independent variables to predict the turnover intention among KG teachers in Chinese mainland mediated by job stress and moderated by family-supportive supervisor. At last, through data analysis by SmartPLS workload and work-life conflict are positively related to turnover intention among KG teachers in Chinese mainland mediated by job stress. However, family-supportive supervisor does not have a moderating effect on the relationship between workload, work-life conflict and job stress. This study gives new insights for understanding the factors that are related to turnover intention among KG teachers in Chinese mainland.