Reviewerly/exHarmony huggingface dataset:
Towards employing recommender systems for supporting data and algorithm sharing

Data and algorithm sharing is an imperative part of data- and AI-driven economies. The efficient sharing of data and algorithms relies on the active interplay between users, data providers, and algorithm providers. Although recommender systems are known to effectively interconnect users and items in e-commerce settings, there is a lack of research on the applicability of recommender systems for data and algorithm sharing. To fill this gap, we identify six recommendation scenarios for supporting data and algorithm sharing, where four of these scenarios substantially differ from the traditional recommendation scenarios in e-commerce applications. We evaluate these recommendation scenarios using a novel dataset based on interaction data of the OpenML data and algorithm sharing platform, which we also provide for the scientific community. Specifically, we investigate three types of recommendation approaches, namely popularity-, collaboration-, and content-based recommendations. We find that collaboration-based recommendations provide the most accurate recommendations in all scenarios. Plus, the recommendation accuracy strongly depends on the specific scenario, e.g., algorithm recommendations for users are a more difficult problem than algorithm recommendations for datasets. Finally, the content-based approach generates the least popularity-biased recommendations that cover the most datasets and algorithms.
Thermodynamics of Quantum Switch Information Capacity Activation

We address a new setting where the second law is under question: thermalizations in a quantum superposition of causal orders, enacted by the so-called quantum switch. This superposition has been shown to be associated with an increase in the communication capacity of the channels, yielding an apparent violation of the data-processing inequality and a possibility to separate hot from cold. We analyze the thermodynamics of this information capacity increasing process. We show how the information capacity increase is compatible with thermodynamics. We show that there may indeed be an information capacity increase for consecutive thermalizations obeying the first and second laws of thermodynamics if these are placed in an indefinite order and moreover that only a significantly bounded increase is possible. The increase comes at the cost of consuming a thermodynamic resource, the free energy of coherence associated with the switch.
3D face reconstruction from single image with generative adversarial networks

Traditional reconstruction techniques extract information from the object's geometry or one or more 2D images. On the other hand, the limit of the existing methods is that they generate less precise objects. Thus the lack of robustness towards several face reconstruction problems, such as the position of the head, occlusion, noise, and lighting variation. Therefore, generative neural networks and graphical convolution networks have marked a significant evolution in the field of 3D reconstruction. This paper proposes a model for 3D face reconstruction from a single 2D image. Our model is composed of a generator and a discriminator based on convolutional graphic layers. Indeed, in order to generate a face mesh with expression, our idea is to use the landmarks associated with this image as input to the generator to reconstruct a face geometry with expression and improve the convergence rate. As a result, our model offers an accurate reconstruction of facial geometry with expression; thus, our model outperforms state-of-the-art methods through qualitative and quantitative comparison.
Developing geno-fuzzy controller for suppressing quadrotor slung-load oscillations

Quadrotors with slung loads have many real-life applications, therefore a new fuzzy logic controller (FLC) is proposed to suppress the oscillations of this load. The output of the proposed FLC will be an additional trajectory that is added to the quadrotor original trajectory. The integration of the proposed FLC with the existing quadrotor autopilot such PX4 will be easy and it will not need any modification to the original structure of the autopilot. The rules of the proposed FLC are generated to mimic the performance of the anti-swing controller that depends on the time-delayed feedback of the load swing angles. The optimal distribution of the membership functions is determined by the genetic algorithms technique such that the load swing history is minimal. The simulation results show that the suggested geno-fuzzy controller is able to stabilize the vehicle and reduce the slung load oscillations under different flight conditions. Moreover, it can be implemented directly on the existing flight controllers without making any modification to the code of the original firmware.
Generic Solution-Space Sampling for Multi-domain Product Lines

Validating a configurable software system is challenging, as there are potentially millions of configurations, which makes testing each configuration individually infeasible. Thus, existing sampling algorithms allow to compute a representative subset of configurations, called sample, that can be tested instead. However, sampling on the set of configurations may miss potential error sources on implementation level. In this paper, we present solution-space sampling, a concept that mitigates this problem by allowing to sample directly on the implementation level. We apply solution-space sampling to six real-word, automotive product lines and show that it produces up to 56 % smaller samples, while also covering all potential error sources missed by problem-space sampling.
Preserving Consistency of Interrelated Models during View-Based Evolution of Variable Systems

Coping with different and changing requirements leads to concurrent products (variability in space) and subsequent revisions (variability in time). Moreover, products consist of interrelated models that represent different kinds of artifacts. Dependencies and redundancies between interrelated models within a product and across products can quickly lead to inconsistencies during evolution. Thus, dealing with both variability dimensions uniformly while preserving consistency of interrelated models is a major challenge when developing large and long-living variable systems. Recent research addresses uniform management of variability in space and time by unifying concepts and operations from software product line engineering and software configuration management. However, consistency preservation for interrelated models, which is a major research topic in model-driven software development, has hardly been considered in variability management. We propose an approach that builds on recent efforts for unifying variability in space and time and leverages view-based consistency preservation for systems comprised of different kinds of interrelated models. We evaluate our approach by applying it to two real-world case studies: the well-known ArgoUML-SPL, that is based on the UML modeling tool ArgoUML, and MobileMedia, a mobile application for media management. Our results show that, by manually evolving only the Java models of products, other interrelated models (i.e.,~UML class diagrams) and the remaining affected products can be kept consistent fully automatically.
Property-Based Testing: Climbing the Stairway to Verification

Property-based testing (PBT) is a powerful tool that is widely available in modern programming languages. It has been used to reduce formal software verification effort. We demonstrate how PBT can be used in conjunction with formal verification to incrementally gain greater assurance in code correctness by integrating PBT into the verification framework of Cogent---a programming language equipped with a certifying compiler for developing high-assurance systems components. Specifically, for PBT and formal verification to work in tandem, we structure the tests to mirror the refinement proof that we used in Cogent's verification framework: The expected behaviour of the system under test is captured by a functional correctness specification, which mimics the formal specification of the system, and we test the refinement relation between the implementation and the specification. We exhibit the additional benefits that this mutualism brings to developers and demonstrate the techniques we used in this style of PBT, by studying two concrete examples.
Digital Crochet: Toward a Visual Language for Pattern Description

Crochet is still purely manual. While other crafts such as knitting or weaving have received technical support, the process of creating instructions for new crochet patterns is barely supported by domain-specific, digital tools. Those tools are constrained by their underlying crochet pattern languages that are either ambiguous or limited in their expressiveness. As a result, creating crochet instructions requires substantial manual effort and can result in incomplete or ambiguous instructions after all.
Armchair travel through video games: stories from elsewheres and elsewhens

COVID-19 has caused stress, negative emotions, and a reduction in physical and social activity due to the lockdown measures that were necessary for public health. During this period, many had to negotiate professional and familial responsibilities and were challenged to carve out time for leisure indoors. Using autoethnography as a method of inquiry, this research explores ‘armchair travel’ through video games as a leisure activity. Therefore, the article discusses memories and feelings of escaping the confines of domestic space from the perspective of self. Armchair travel via video games may provide an endless array of sights and sensations, bringing up feelings of leisure and contentment when real-world isolation and restriction are the norms.
Parametric Synthesis of Computational Circuits for Complex Quantum Algorithms in the Transport and Road Sphere

Since quantum computing technologies are still in their early development phase, quantum circuits are created mainly by manual placement of logic elements. This development approach has the drawback of becoming inefficient due to lack of human comprehension when analyzing large circuits that correspond to complex algorithms. Because, even a slight increase in the number of operations in a quantum algorithm, could lead to the significant increase in size of its corresponding quantum circuit. Therefore, the purpose of creating Quantum Circuit Synthesizer "Naginata" is to improve the development and debugging processes of quantum circuits by introducing dynamically scalable compositions for common operations such as: the adder, multiplier, digital comparator (comparison operator), etc., turning them into building blocks for quantum programs, as well as providing a stable platform for creating more of these compositions. This way, our quantum synthesizer is opening an opportunity to implement quantum algorithms using higher-level commands. The programmer could implement a quantum algorithm with these generic blocks, and the quantum synthesizer would create a suitable circuit for this algorithm, in a format that is supported by the chosen quantum computation environment. With the help of simple command logging and using coding for building quantum circuits, this approach has the potential to significantly simplify and enhance the development and debugging processes of quantum circuits. The proposed approach for implementing quantum algorithms could have a potential application in the field of machine learning, in this regard, we provided an example of creating a circuit for training a simple neural network. Neural networks have a significant impact on the technological development of the transport and road complex, and there is a potential for improving the reliability and efficiency of their learning process by utilizing quantum computation.
Entropy regularization methods for parameter space exploration

Entropy regularization is an important approach to improve exploration and enhance policy stability for reinforcement learning. However, in previous study, entropy regularization is applied only to action spaces. In this paper, we apply entropy regularization to parameter spaces. We use learnable noisy layers to parameterize the policy network to obtain a learnable entropy. Also, we derive the expression for the entropy of the noisy parameter and an upper bound on the joint entropy. Based on these, we propose a model-free method named deep pseudo deterministic policy gradients based on entropy regularization (DPGER). This method maximizes the entropy of each noisy parameter in the early learning process to promote exploration, and minimizes the joint entropy of the noisy parameters in the later learning process to facilitate the formation of stable policies. We test our method on four Mujoco environments with five random seeds. The results show that our method brings better performance compared to previous methods.
Can recurrent neural networks learn process model structure?

Various methods using machine and deep learning have been proposed to tackle different tasks in predictive process monitoring, forecasting for an ongoing case e.g. the most likely next event or suffix, its remaining time, or an outcome-related variable. Recurrent neural networks (RNNs), and more specifically long short-term memory nets (LSTMs), stand out in terms of popularity. In this work, we investigate the capabilities of such an LSTM to actually learn the underlying process model structure of an event log. We introduce an evaluation framework that combines variant-based resampling and custom metrics for fitness, precision and generalization. We evaluate 4 hypotheses concerning the learning capabilities of LSTMs, the effect of overfitting countermeasures, the level of incompleteness in the training set and the level of parallelism in the underlying process model. We confirm that LSTMs can struggle to learn process model structure, even with simplistic process data and in a very lenient setup. Taking the correct anti-overfitting measures can alleviate the problem. However, these measures did not present themselves to be optimal when selecting hyperparameters purely on predicting accuracy. We also found that decreasing the amount of information seen by the LSTM during training, causes a sharp drop in generalization and precision scores. In our experiments, we could not identify a relationship between the extent of parallelism in the model and the generalization capability, but they do indicate that the process' complexity might have impact.
Semi-supervised Learning for Mars Imagery Classification and Segmentation

With the progress of Mars exploration, numerous Mars image data are being collected and need to be analyzed. However, due to the severe train-test gap and quality distortion of Martian data, the performance of existing computer vision models is unsatisfactory. In this article, we introduce a semi-supervised framework for machine vision on Mars and try to resolve two specific tasks: classification and segmentation. Contrastive learning is a powerful representation learning technique. However, there is too much information overlap between Martian data samples, leading to a contradiction between contrastive learning and Martian data. Our key idea is to reconcile this contradiction with the help of annotations and further take advantage of unlabeled data to improve performance. For classification, we propose to ignore inner-class pairs on labeled data as well as neglect negative pairs on unlabeled data, forming supervised inter-class contrastive learning and unsupervised similarity learning. For segmentation, we extend supervised inter-class contrastive learning into an element-wise mode and use online pseudo labels for supervision on unlabeled areas. Experimental results show that our learning strategies can improve the classification and segmentation models by a large margin and outperform state-of-the-art approaches.
Drug review sentimental analysis based on modular lexicon generation and a fusion of bidirectional threshold weighted mapping <scp>CNN‐RNN</scp>

Summary In drug review sentimental analysis (SA), users can share their experiences after consuming the drugs, which provides an accurate decision about the safety of the drug and public health. Patient‐written medical and health‐care reviews are among the most valuable and informative textual content on social media, but researchers in the areas of natural language processing (NLP) and data mining have not researched them thoroughly. These reviews provide insight into patients' interactions with doctors, treatment, and satisfaction or dissatisfaction with health services. The existing approaches have some problems like exploding/vanishing gradients and do not have sequential modeling. When learning long reviews, the exploding and vanishing gradient problems occurs. This problem makes it hard to tune parameters and learn in the network. The existing methods do not have sequential modeling because they fail to extract long dependencies for long reviews in both backward and forward directions. To overcome these issues, we proposed a Modular Lexicon Generation and a Fusion of Bidirectional threshold weighted mapping CNN‐RNN (MLBTWCR) for classifying drug reviews based on users opinions. The Aspect based Modular Lexicon generation using the Advanced Dragon Fly Algorithm (AMLDA) is used to generate the score values for the lexicon and labels based on aspect. The Bidirectional Dropout Long and Short‐Term Memory (Bi‐DLSTM) and Bidirectional Gated Recurrent Unit (Bi‐GRU) used for extracting long dependencies and for performing the sequence of arbitrary length in both backward and forward directions. The experimental results are evaluated using Drugslib.com and Drugs.com datasets. Based on evaluation result, the proposed MLBTWCR gives accuracy of 93.02%, recall of 88.72%, error rate of 11.2, false positive rate (FPR) of 11.3, false negative rate (FNR) of 13.6, running time of 15 s, and convergence speed of 0.2 and F‐measure of 92.64%. Hence, our method performs well for the drug reviews classification based on aspects.
Federated data processing and learning for collaboration in the physical sciences

Abstract Property analysis and prediction is a challenging topic in fields such as chemistry, nanotechnology and materials science, and often suffers from lack of data. Federated learning (FL) is a machine learning (ML) framework that encourages privacy-preserving collaborations between data owners, and potentially overcomes the need to combine data that may contain proprietary information. Combining information from different data sets within the same domain can also produce ML models with more general insight and reduce the impact of the selection bias inherent in small, individual studies. In this paper we propose using horizontal FL to mitigate these data limitation issues and explore the opportunity for data-driven collaboration under these constraints. We also propose FedRed, a new dimensionality reduction method for FL, that allows faster convergence and accounts for differences between individual data sets. The FL pipeline has been tested on a collection of eight different data sets of metallic nanoparticles, and while there are expected losses compared to a combined data set that does not preserve the privacy of the collaborators, we obtained extremely good result compared to local training on individual data sets. We conclude that FL is an effective and efficient method for the physical science domain that could hugely reduce the negative effect of insufficient data.
Robot Navigation in Complex Workspaces Using Conformal Navigation Transformations

Navigation functions provide both path and motion planning, which can be used to ensure obstacle avoidance and convergence in the sphere world. When dealing with complex and realistic scenarios, constructing a transformation to the sphere world is essential and, at the same time, challenging. This work proposes a novel transformation termed the conformal navigation transformation to achieve collision-free navigation of a robot in a workspace populated with obstacles of arbitrary shape. The properties of the conformal navigation transformation, including uniqueness, invariance of navigation properties, and no angular deformation, are investigated, which contribute to the solution of the robot navigation problem in complex environments. Based on navigation functions and the proposed transformations, feedback controllers are derived for the automatic guidance and motion control of kinematic and dynamic mobile robots. Moreover, an iterative method is proposed to construct the conformal navigation transformation in a multiply-connected workspace, which transforms the multiply-connected problem into multiple simply connected problems to achieve fast convergence. In addition to the analytic guarantees, simulation studies verify the effectiveness of the proposed methodology in workspaces with non-trivial obstacles.
CADLAD: Device-aware Bitrate Ladder Construction for HTTP Adaptive Streaming

In this paper, we introduce a CMCD-Aware per-Device bitrate LADder construction (CADLAD) that leverages the Common Media Client Data (CMCD) standard to address the above issues. CADLAD comprises components at both client and server sides. The client calculates the top bitrate (tb) — a CMCD parameter to indicate the highest bitrate that can be rendered at the client — and sends it to the server together with its device type and screen resolution. The server decides on a suitable bitrate ladder, whose maximum bitrate and resolution are based on CMCD parameters, to the client device with the purpose of providing maximum QoE while minimizing delivered data. CADLAD has two versions to work in Video on Demand (VoD) and live streaming scenarios. Our CADLAD is client agnostic; hence, it can work with any players and ABR algorithms at the client. The experimental results show that CADLAD is able to increase the QoE by 2.6x while saving 71% of delivered data, compared to an existing bitrate ladder of an available video dataset. We implement our idea within CAdViSE — an open-source testbed for reproducibility.
Aspect-based Sentiment Analysis with Dependency Relation Graph Convolutional Network

Aspect-based sentiment analysis (ABSA) aims to judge the sentiment polarity of specific aspects in text reviews, and is a fine-grained sentiment analysis task. In the current e-commerce era, ABSA based on user reviews is of great significance to consumers, producers and sellers. In order to make full use of the dependency information in the text, we propose a dependency graph convolutional network model for ABSA. Two graph convolutional networks are used to encode the dependency edge and the dependency tag respectively, and then a biaffine module is used to realize the interaction between the two. The experimental results show that the proposed model outperforms all other comparison models on 8 datasets in Chinese and English, including CMPR, SemEval 2014 Task4, Twitter, etc.
A Multi-label Classification Algorithm for Non-standard Text

To solve the difficulty of extracting semantic features from non-standard text, this paper proposes an algorithm combining deep learning to generate multi-labels and calculating text similarities to copy multi-labels. The algorithm idea is firstly to find the most similar text through calculating the similarities between all texts in the corpus and the target text. If the similarity of the most similar text is higher than the set threshold, the multi-labels of the text will directly copy to the target text; Otherwise, the labels generated by the multi-labels generation algorithm based on ALBERT+CNN are used as the output of the target text. The advantage of our algorithm is that it can not only take advantage of the accuracy and convenience of method to copy labels of similar texts, but also avoid the bias in the similarity calculation of under standardized text. The experimental results show that the algorithm has better performance in dealing with the multi-label text classification problem of non-standard text.