Reviewerly/exHarmony huggingface dataset:
An open randomized controlled trial of the effects of linguistic simplification and mediation on the comprehension of “easy read” text by people with intellectual disabilities

Abstract Background This trial aimed to measure the effects of extrinsic and intrinsic factors on users’ comprehension of health information provided in adapted written “easy read” material. Method Sixty adults with intellectual disabilities undertook The Easy Read Task, randomly allocated with stratification by reading ability to one of four conditions (with and without simplified language/with and without mediation). Results Neither linguistic complexity of the text nor mediation independently or combined made a significant difference to the understanding of information. Post hoc testing revealed that the group who received simplified language with mediation performed significantly better than the group that had complex text with mediation. None of the other differences between the remaining groups were significant. Conclusions Constructing meaning needs to extend beyond a consideration of form as found in “easy read” documents to recognize the role of individual capacity for language processing.
Guided Cyclegan Via Semi-Dual Optimal Transport for Photo-Realistic Face Super-Resolution

Face super-resolution has been studied for decades, and many approaches have been proposed to upsample low-resolution face images using information mined from paired low-resolution (LR) images and high-resolution (HR) images. However, most of this kind of works only simply sharpen the blurry edges in the upsampled face images and typically no photo-realistic face is reconstructed in the final result. In this paper, we present a GAN-based algorithm for face super-resolution which properly synthesizes photo-realistic super-recovered face. To this end, we introduce semi-dual optimal transport to optimize our model such that the distribution of its generated data can match the distribution of a target domain as much as possible. This way would endow our model with learning the mapping of distribution from unpaired LR images and HR images with desired properties. We demonstrate the robustness of our algorithm by testing it on Color FERET database and show that its performance is considerably superior to all state-of-the-art approaches.
Divide-and-conquer framework for image restoration and enhancement

We develop a novel divide-and-conquer framework for image restoration and enhancement based on their task-driven requirements, which takes advantage of visual importance differences of image contents (i.e., noise versus image, edge-based structures versus smoothing areas, high-frequency versus low-frequency components) and sparse prior differences of image contents for performance improvements. The proposed framework is efficient in implementation of decomposition-processing-integration. An observed image is first decomposed into different subspaces based on considering visual importance of different subspaces and exploiting their prior differences. Different models are separately established for image subspace restoration and enhancement, and existing image restoration and enhancement methods are utilized to deal with them effectively. Then a simple but effective fusion scheme with different weights is used to integrate the post-processed subspaces for the final reconstructed image. Final experimental results demonstrate that the proposed divide-and-conquer framework outperforms several restoration and enhancement algorithms in both subjective results and objective assessments. The performance improvements of image restoration and enhancement can be yielded by using the proposed divide-and-conquer strategy, which greatly benefits in terms of mixed Gaussian and salt-and-pepper noise removal, non-blind deconvolution, and image enhancement. In addition, our divide-and-conquer framework can be simply extensible to other restoration and enhancement algorithms, and can be a new way to promote their performances for image restoration and enhancement.
A Container Scheduling Strategy Based on Machine Learning in Microservice Architecture

Good progress has been made in the microservice technology in recent years due to the development of docker container technology. Although microservice architecture can adjust the number of containers, there is a new problem. That is according to the current load pressure of services, how to adjust the number of containers accurately and quickly in real time, especially when the load pressure of services suddenly increases or decreases. In order to deal with the problem, a container scheduling strategy based on machine learning is proposed in this paper. The paper uses the data set obtained by our experiments to train random forest regression model in advance to predict the required containers of services in the next time window, according to the current load pressure of services. Large amounts of data has been collected to make the data set for the experiment. Based on the data set, random forest regression model has been trained in advance to predict the number of required containers of services in next time window. When adjusting the number of containers to balance load pressure of services, the proposed algorithm saves 50% of the time compared with traditional algorithms. In addition, the accuracy is 10%~38% higher compared with other machine learning algorithms.
Semantic Textual Similarity Measures for Case-Based Retrieval of Argument Graphs

Argumentation is an important sub-field of Artificial Intelligence, which involves computational methods for reasoning and decision making based on argumentative structures. This paper contributes to case-based reasoning with argument graphs in the standardized Argument Interchange Format by improving the similarity-based retrieval phase. We explore a large range of novel approaches for semantic textual similarity measures (both supervised and unsupervised) and use them in the context of a graph-based similarity measure for argument graphs. In addition, the use of an ontology-based semantic similarity measure for argumentation schemes is investigated. With a range of experiments we demonstrate the strengths and weaknesses of the various methods and show that our methods can improve over our previous work. Our code is publicly available on GitHub.
Towards an English Constructicon using patterns and frames

Abstract Recent research in construction grammar has been marked by increasing efforts to create constructicons: detailed inventories of form-meaning pairs to describe the grammar of a given language, following the principles of construction grammar. This paper describes proposals for building a new constructicon of English, based on the combination of the COBUILD Grammar Patterns and the semantic frames of FrameNet. In this case study, the valency information from FrameNet was automatically matched to the verb patterns of COBUILD, in order to identify the frames that each pattern is associated with. We find that the automatic procedure must be complemented by a good deal of manual annotation. We examine the “V that” pattern in particular, illustrating how the frame information can be used to describe this pattern in terms of constructions.
Comparing Scaling Agile Frameworks Based on Underlying Practices

Context: Agile software development is widely-used by small teams and has benefits like increased transparency or faster feedback. However, companies want to benefit from Agile also in the development of big products, where multiple teams are involved. Many Scaling Agile Frameworks exist, but only few can be found in industry, especially SAFe, LeSS, and Nexus. Objective: The aim of this work is to identify commonalities of existing Scaling Agile Frameworks concerning their practices. Method: We extracted and consolidated the practices of twelve frameworks and compared the frameworks based on their practices using a visualization. Results: Frameworks prescribe scaling practices as well as practices on team level. There are practices common to most frameworks like the scaled Scrum events, e.g., a scaled planning meeting or retrospective. Conclusion: Practitioners are enabled to make informed decisions when choosing or tailoring their individual Scaling Agile Framework.
QuADD: QUantifying Accelerator Disaggregated Datacenter Efficiency

In the current era of data explosion accelerators such as GPUs facilitate data-driven applications with requisite compute boost. Availability of GPUs in Public Cloud offerings has expedited their mass adoption. Consequently, varied customer demands and exclusive allocation of GPUs to VMs can leave stranded GPUs across the datacenter. Hardware disaggregation can alleviate this issue to enable a powerefficient datacenter. However, it is important to first quantify the gains associated with this new deployment paradigm. In the absence of real deployments, simulations can be helpful to evaluate the benefits of disaggregation at scale. In this paper, we evaluate the gains associated primarily with disaggregated GPU deployments. For this, we use QUADD-SIM, a simulator we built to model, quantify, and contrast different facets of these emerging GPU deployments. Using QUADD-SIM we model different VM and resource provisioning aspects of disaggregated GPU deployments. We simulate realistic AI workload requests for a period of 3 months with characteristics derived from recent public datacenter traces. Our results attest that disaggregated GPU deployment strategies outperform traditional GPU deployments in terms of failed VM requests and GPU Watt-hours consumption. Overall, 5.14% and 7.90% additional failed VM requests were serviced by disaggregated GPU deployments consuming 10.92% and 3.30% lesser GPU Watt-hours compared to traditional deployment.
Estimating Labeling Quality with Deep Object Detectors

Deep Learning methods are widely applied in Robotics and Automated Driving scenarios. The task of perception for Automated Driving in the real world is particularly challenging and requires a sufficient amount of high quality labeled training data for the algorithms to perform well. However, the means of obtaining real world datasets are limited. It is common practice to have human labelers involved at least to some extent. Regardless of whether the process is partially automated or not, these labels never represent perfectly accurate ground-truth. By investigating the recognition performance of a state-of-the-art object detector as a function of the quality of a labeled real world training set, we study the effect of labeling errors of various types and severity. To this end, the given labels are treated as a reference to which synthetic errors are added systematically in order to determine the performance of the object detector if trained on the erroneous dataset.
Examining Machine Learning Techniques in Business News Headline Sentiment Analysis

Sentiment analysis is a natural language processing task that attempts to predict the opinion, feeling or view of a text. The interest in sentiment analysis has been rising due to the availability of a large amount of sentiment corpus and the enormous potential of sentiment analysis applications. This work attempts to evaluate different machine learning techniques in predicting the sentiment of the readers toward business news headlines. News articles report events that have happened in the world and expert opinions. These are factors that will affect market sentiment, and a headline can be considered as a summary of an article in a single sentence. In this study, we constructed a sentiment analysis corpus which consists of business news headlines. We examined two different approaches, namely text classification and recurrent neural network (RNN) in predicting the sentiment of a business news headline. For text classification approach, multi-layer perceptron (MLP) classifier, multinomial naïve Bayes, complement naïve Bayes and decision trees were experimented. On the other hand, for the RNN approach, we evaluated the typical RNN architecture and the encoder-decoder architecture in predicting the sentiment.
Towards the Modelling of Hybrid Cloud Applications

This paper advances the state-of-the-art by enhancing an existing provider-independent modelling language towards the complete specification of both serverless and hybrid multi-cloud applications. This extension has been validated by a use case developed in the context of the Functionizer project.
Learning a Lattice Planner Control Set for Autonomous Vehicles

This paper introduces a method to compute a sparse lattice planner control set that is suited to a particular task by learning from a representative dataset of vehicle paths. To do this, we use a scoring measure similar to the Fréchet distance and propose an algorithm for evaluating a given control set according to the scoring measure. Control actions are then selected from a dense control set according to an objective function that rewards improvements in matching the dataset while also encouraging sparsity. This method is evaluated across several experiments involving real and synthetic datasets, and it is shown to generate smaller control sets when compared to the previous state-of-the-art lattice control set computation technique, with these smaller control sets maintaining a high degree of manoeuvrability in the required task. This results in a planning time speedup of up to 4.31x when using the learned control set over the state-of-the-art computed control set. In addition, we show the learned control sets are better able to capture the driving style of the dataset in terms of path curvature.
An Efficient Character Segmentation Algorithm for Recognition of Arabic Handwritten Script

This manuscript proposed a new novel algorithm for vertical segmentation because the accuracy of recognition will be very high if there is a good character segmentation. For the purpose of locate the segmentation points used the word image thinning to fetch the width of stroke of one pixel and to detect the ligatures of Arabic characters geometry and shape are used in the segmentation procedure. The proposed segmentation approach is worked with touching characters, in case of ligatures touching segmentation existing between the characters of consecutive closed and the existence of ligatures within characters in the case of open characters. It evaluated on IFN/ENIT, AHDB and our dataset. It shows higher accuracy with an excellent performance by decreasing over-segmentation problem which showed through open characters segmentation and correct the segmentation in cases of the touching characters or the miss-segmentation errors in images of word.
Deeply Supervised Multimodal Attentional Translation Embeddings for Visual Relationship Detection

Detecting visual relationships, i.e. <; Subject, Predicate, Object> triplets, has been a challenging Scene Understanding task approached in the past via linguistic priors or spatial information in a single feature branch. We introduce a new deeply supervised two-branch architecture, the Multimodal Attentional Translation Embeddings, where the visual features of each branch are driven by a multimodal attentional mechanism that exploits spatio-linguistic similarities in a low-dimensional space. We present a variety of experiments comparing against all related approaches in the literature, as well as by re-implementing and fine-tuning several of them. Results on the commonly employed VRD dataset [1] show that the proposed method clearly outperforms all others, while we also justify our claims both quantitatively and qualitatively.
Polar Coordinate Convolutional Neural Network: From Rotation-Invariance to Translation-Invariance

Convolutional neural network (CNN) has been famous for its translation-invariant ability in feature learning. In order to further encounter rotation-invariant, data augmentation by rotation of training samples should be considered for multiple-branch based structure using maximum operator or average operator. In this paper, a novel Polar Coordinate CNN (PC-CNN) is proposed for rotation-invariant feature learning. Specifically, training samples are first input to a polar coordinate transform layer by which rotation-invariance is converted into translation-invariance. Consequently, rotation-invariance problem in feature learning can be easily encountered by traditional CNNs without the multiple-branch structure. Experimental results over two benchmark data sets demonstrate that the proposed polar transformation is very effective to encounter rotation-invariant into traditional CNNs and outperforms several state-of-the-art rotation-invariant CNNs.
Laconic Conditional Disclosure of Secrets and Applications

In a Conditional Disclosure of Secrets (CDS) a verifier V wants to reveal a message m to a prover P conditioned on the fact that x is an accepting instance of some NP-language L. An honest prover (holding the corresponding witness w) always obtains the message m at the end of the interaction. On the other hand, if x ∉ L we require that no PPT P* can learn the message m. We introduce laconic CDS, a two round CDS protocol with optimal computational cost for the verifier V and optimal communication cost. More specifically, the verifier's computation and overall communication grows with poly(|x|; λ; log(T)), where λ is the security parameter and T is the verification time for checking that x ϵ L (given w). We obtain constructions of laconic CDS under standard assumptions, such as CDH or LWE. Laconic CDS serves as a powerful tool for maliciousifying semi-honest protocols while preserving their computational and communication complexities. To substantiate this claim, we consider the setting of non-interactive secure computation: Alice wants to publish a short digest corresponding to a private large input x on her web page such that (possibly many) Bob, with a private input y, can send a short message to Alice allowing her to learn C(x; y) (where C is a public circuit). The protocol must be reusable in the sense that Bob can engage in arbitrarily many executions on the same digest. In this context we obtain the following new implications. 1) UC Secure Bob-optimized 2PC: We obtain a UC secure protocol where Bob's computational cost and the communication cost of the protocol grows with poly(|x|; |y|; λ; d), where d is the depth of the computed circuit C. 2) Malicious Laconic Function Evaluation: Next, we move on to the setting where Alice's input x is large. For this case, UC secure protocols must have communication cost growing with |x|. Thus, with the goal of achieving better efficiency, we consider a weaker notion of malicious security. For this setting, we obtain a protocol for which Bob's computational cost and the communication cost of the protocol grows with poly(|y|; λ; d), where d is the depth of the computed circuit C.
Extensional Paramodulation for Higher-Order Logic and Its Effective Implementation Leo-III

Automation of classical higher-order logic faces various theoretical and practical challenges. On a theoretical level, powerful calculi for effective equality reasoning from first-order theorem proving cannot be lifted to the higher-order domain in a simple manner. Practically, implementations of higher-order reasoning systems have to incorporate procedures that often have high time complexity or are not decidable in general. In my dissertation, both the theoretical and the practical challenges of designing an effective higher-order reasoning system are studied. The resulting system, the automated theorem prover Leo-III, is one of the most effective and versatile systems, in terms of supported logical formalisms, to date.
TEA-DNN: the Quest for Time-Energy-Accuracy Co-optimized Deep Neural Networks

Embedded deep learning platforms have witnessed two simultaneous improvements. First, the accuracy of convolutional neural networks (CNNs) has been significantly improved through the use of automated neural-architecture search (NAS) algorithms to determine CNN structure. Second, there has been increasing interest in developing hardware accelerators for CNNs that provide improved inference performance and energy consumption compared to GPUs. Such embedded deep learning platforms differ in the amount of compute resources and memory-access bandwidth, which would affect performance and energy consumption of CNNs. It is therefore critical to consider the available hardware resources in the network architecture search. To this end, we introduce TEA-DNN, a NAS algorithm targeting multi-objective optimization of execution time, energy consumption, and classification accuracy of CNN workloads on embedded architectures. TEA-DNN leverages energy and execution time measurements on embedded hardware when exploring the Pareto-optimal curves across accuracy, execution time, and energy consumption and does not require additional effort to model the underlying hardware. We apply TEA-DNN for image classification on actual embedded platforms (NVIDIA Jetson TX2 and Intel Movidius Neural Compute Stick). We highlight the Pareto-optimal operating points that emphasize the necessity to explicitly consider hardware characteristics in the search process. To the best of our knowledge, this is the most comprehensive study of Pareto-optimal models across a range of hardware platforms using actual measurements on hardware to obtain objective values.
How to go global: A transformative process model for the transition towards globally distributed software development projects

We employ design science research to develop a process model for the management of globally distributed software development (GDSD) projects. The model provides guidance for transforming established project frameworks in organizations to cope with additional challenges that a global project scale entails. The model integrates extant GDSD project management knowledge and makes it actionable for project managers, especially those with limited GDSD experience. The model was evaluated and found to be effective in transforming an actual GDSD project with teams in Germany, Poland, and India. Interviews with project managers with and without GDSD experience support the model's wider applicability to other contexts and IT project types. The model can also be used in GDSD project retrospectives to identify areas for improvement. In a research perspective, our model constitutes a novel type and instance of a social meta-artefact intended to transform existing social artefacts to retain their usefulness in evolving contexts.
Role of immersive visualization tools in renewable energy system development

Hybrid renewable energy systems (RESs) are being widely utilized as an alternate source of energy for mitigating the rapidly increasing energy demand. Explicit representation of the results obtained from the impact analysis made on a newly proposed or an existing hybrid RES is complex, and it requires powerful visualization tools. Over the years, various visualization techniques were developed towards addressing this problem. Therefore, the use of visualization techniques are continuously growing and have been the focus of many researchers across the world. This review article presents a comprehensive analysis of the advancements in the use of different immersive visualization (IV) tools in state-of-the-art RES development. A total of 41 software packages and a collection of recently published research articles in the field of RES development incorporated with advanced IV tools was identified and critically reviewed based on its use-case, accessibility, complexity, robustness, immersivity, and adaptability. Finally, a list of fit-for-purpose software packages that could be used at different stages of the RES development is recommended. A summary of the current advancements in the use of the IV tools in RES development is presented to highlight the broader potential of multidisciplinary applications of the advanced IV tools in RES development.