Reviewerly/exHarmony huggingface dataset:
Clinical Text Classification in Cancer Real-World Data in Spanish

Healthcare systems currently store a large amount of clinical data, mostly unstructured textual information, such as electronic health records (EHRs). Manually extracting valuable information from these documents is costly for healthcare professionals. For example, when a patient first arrives at an oncology clinical analysis unit, clinical staff must extract information about the type of neoplasm in order to assign the appropriate clinical specialist. Automating this task is equivalent to text classification in natural language processing (NLP). In this study, we have attempted to extract the neoplasm type by processing Spanish clinical documents. A private corpus of 23, 704 real clinical cases has been processed to extract the three most common types of neoplasms in the Spanish territory: breast, lung and colorectal neoplasms. We have developed methodologies based on state-of-the-art text classification task, strategies based on machine learning and bag-of-words, based on embedding models in a supervised task, and based on bidirectional recurrent neural networks with convolutional layers (C-BiRNN). The results obtained show that the application of NLP methods is extremely helpful in performing the task of neoplasm type extraction. In particular, the 2-BiGRU model with convolutional layer and pre-trained fastText embedding obtained the best performance, with a macro-average, more representative than the micro-average due to the unbalanced data, of 0.981 for precision, 0.984 for recall and 0.982 for F1-score.
Software Aided Approach for Constrained Optimization Based on QAOA Modifications

We present two variants of the QAOA modification for solving constrained combinatorial problems. The results presented in this paper were obtained using the QHyper framework, which we developed specifically for this purpose. More specifically, we use the created framework to compare the QAOA results with its two modifications, namely: Weight-Free QAOA (WF-QAOA) and Hyper QAOA (H-QAOA). Additionally, we compare the Basin-hopping global optimization method for subsequent sampling of the initial points for the proposed QAOA modifications with a simple Random Search. The results obtained for the Knapsack Problem indicate that the proposed solution outperforms the original QAOA algorithm and can be promising for QUBO, where adjusting the relative importance of the cost function and the constraints is a significant challenge.
Multi-objective Quantum-Inspired Genetic Algorithm for Supervised Learning of Deep Classification Models

Quantum decision making is an emerging field that explores how quantum computing can be used to make decisions more efficiently and effectively than classical computing. The main advantage of quantum decision making is the ability to explore multiple possible solutions to a problem simultaneously, using the principles of superposition and entanglement. A quantum-inspired genetic algorithm can improve a quality of a multi-criteria supervised learning of deep classification models. Designed classifiers can be trained by a quantum simulator with Hadamard, CNOT and rotation gates. To demonstrate advantages of the new algorithm, we analyze the Pareto-optimal classifiers for an efficient diagnosis of SARS-CoV-2 infection based on remote analysis of X-rays images with the quantum computing platform QI.
Towards an Earned Value Management Didactic Simulator to Engineering Management Teaching

Agile development (AD) is a methodology that many small businesses have adopted for production convenience, and educators have taken notice of the trend. A need to implement some form of agile development in undergraduate programs at universities is now clear, particularly for undergraduate engineering students who should understand their role in a project focused on AD. This paper presents our preliminary evaluation of user experience (UX) using an Earned Value Management (EVM) simulator, which helps the student understand the team member’s role in an agile development process. The simulator uses a Task-board interface to display task status changes, a burn-down chart to depict the remaining work, and EVM metrics to assess the efficiency of the teamwork. Using the Task-board and EVM models, the simulator offers students different agile project management experimental experiences.
TAQOS: A Benchmark Protocol for Quantum Optimization Systems

The growing availability of quantum computers raises questions about their ability to solve concrete problems. Existing benchmark protocols still lack problem diversity and attempt to summarize quantum advantage in a single metric that measures the quality of found solutions. Unfortunately, the solution quality metric is insufficient for measuring quantum algorithm performance and should be presented along with time and instances coverage metrics. This paper aims to establish the TAQOS protocol to perform a Tight Analysis of Quantum Optimization Systems. The combination of metrics considered by this protocol helps to identify problems and instances liable to produce quantum advantage on Noisy-Intermediate Scale Quantum (NISQ) devices for useful applications. The methodology used for the benchmark process is detailed and an illustrative short case study on the Max-Cut problem is provided.
Quantum Factory Method: A Software Engineering Approach to Deal with Incompatibilities in Quantum Libraries

The current context of Quantum Computing and its available technologies present an extensive variety of tools and lack of methodologies, leading to incompatibilities across platforms, which end up as inconsistencies in the developed solutions. We propose a design called Quantum Factory Method, based on software engineering and design patterns, to solve these issues by integrating different quantum platforms in the same development. We provide example implementations whose results prove the suitability of the design in different cases, and conclude on how this approach can be expanded for future work.
An interview with the Scholcomm Librarian behind ShareYourPaper.org

This month, we released ShareYourPaper.org for Libraries. Leila Sterman co-designed ShareYourPaper.org and the version for libraries as the Scholarly Communication Librarian and an Assistant Professor at Montana State University Library. As 2020 came to an end, Jake Orlowitz caught up with her. &lt;strong&gt;How did you get started in librarianship?&lt;/strong&gt;
Translating Constraints into QUBOs for the Quadratic Knapsack Problem

One of the first fields where quantum computing will likely show its use is optimisation. Many optimisation problems naturally arise in a quadratic manner, such as the quadratic knapsack problem. The current state of quantum computers requires these problems to be formulated as a quadratic unconstrained binary optimisation problem, or QUBO. Constrained quadratic binary optimisation can be translated into QUBOs by translating the constraint. However, this translation can be made in several ways, which can have a large impact on the performance when solving the QUBO. We show six different formulations for the quadratic knapsack problem and compare their performance using simulated annealing. The best performance is obtained by a formulation that uses no auxiliary variables for modelling the inequality constraint.
Talking ShareYourPaper.org with EIFL

The team at EIFL (Electronic Information for Libraries) and their coalition at large have been doing stelar work in the repository and Open Access world for many years.
HEFactory: A symbolic execution compiler for privacy-preserving Deep Learning with Homomorphic Encryption

Homomorphic Encryption (HE) allows computing operations on encrypted data, and it is a potential solution to enable Deep Learning (DL) in privacy-enforcing scenarios (e.g., sending private data to cloud services). However, HE remains a complex technology with multiple challenges that prevent successful application by non-experts. In this work, we present HEFactory, a program compiler that effectively assists in building HE applications in Python for both general-purpose and Deep Learning applications, focusing on non-expert data scientists. HEFactory relies on a layered architecture that deals with challenges such as automatic parameter selection and specific data representation of HE applications. Our benchmarks show that HEFactory substantially lowers the programming complexity (i.e., a reduction of 80% in the number of lines of code) with negligible performance overhead over programs written by experts using native HE frameworks.
Investigating rational perfect nonlinear functions

Abstract Perfect nonlinear (PN) functions over a finite field, whose study is also motivated by practical applications to Cryptography, have been the subject of several recent papers where the main problems, such as effective constructions and non-existence results, are considered. So far, all contributions have focused on PN functions represented by polynomials, and their constructions. Unfortunately, for polynomial PN functions, the approach based on Hasse–Weil type bounds applied to function fields can only provide non-existence results in a small degree regime. In this paper, we investigate the non-existence problem of rational perfect nonlinear functions over a finite field. Our approach makes it possible to use deep results about the number of points of algebraic varieties over finite fields. Our main result is that no PN rational function f / g with $$f,g\in \mathbb {F}_q[X]$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mi>f</mml:mi><mml:mo>,</mml:mo><mml:mi>g</mml:mi><mml:mo>∈</mml:mo><mml:msub><mml:mi>F</mml:mi><mml:mi>q</mml:mi></mml:msub><mml:mrow><mml:mo>[</mml:mo><mml:mi>X</mml:mi><mml:mo>]</mml:mo></mml:mrow></mml:mrow></mml:math> exists when certain mild arithmetical conditions involving the degree of f ( X ) and g ( X ) are satisfied.
Neural Network Approximation for Time Splitting Random Functions

In this article we present the multivariate approximation of time splitting random functions defined on a box or RN,N∈N, by neural network operators of quasi-interpolation type. We achieve these approximations by obtaining quantitative-type Jackson inequalities engaging the multivariate modulus of continuity of a related random function or its partial high-order derivatives. We use density functions to define our operators. These derive from the logistic and hyperbolic tangent sigmoid activation functions. Our convergences are both point-wise and uniform. The engaged feed-forward neural networks possess one hidden layer. We finish the article with a great variety of applications.
SiamCCF: Siamese visual tracking via cross‐layer calibration fusion

Abstract Siamese networks have attracted wide attention in visual tracking due to their competitive accuracy and speed. However, the existing Siamese trackers usually leverage a fixed linear aggregation of feature maps, which does not effectively fuse the different layers of features with attention. Besides, most of Siamese trackers calculate the similarity between the template and the search region through a cross‐correlation operation between the features of the last blocks from the two branches, which might introduce the redundant noise information. In order to solve these problems, this study proposes a novel Siamese visual tracking method via cross‐layer calibration fusion, termed SiamCCF. An attention‐based feature fusion module is employed using local attention and non‐local attention to fuse the features from the deep and shallow layers, so as to capture both local details and high‐level semantic information. Moreover, a cross‐layer calibration module can use the fused features to calibrate the features of the last network blocks and build the cross‐layer long‐range spatial and inter‐channel dependencies around each spatial location. Extensive experiments demonstrate that the proposed method has achieved competitive tracking performance compared with state‐of‐the‐art trackers on challenging benchmarks, including OTB100, OTB2013, UAV123, UAV20L, and LaSOT.
Analysis Method of App Software User Experience Based on Multisource Information Fusion

With the rapid development and popularization of intelligent terminals, app software has also developed rapidly. The research and practical value of mining user experience (UX) of app software form interaction information are becoming increasingly prominent. The interactive information of app software is multisource homogeneous and heterogeneous. In order to obtain more accurate and more comprehensive app software UX results, the fused multisource information should be analyzed. In this paper, the app software UX analysis method based on multisource information fusion is proposed. First, feature engineering is carried out to extract the features. Then, the feature combination tree is constructed after feature correlation mining. Finally, the multisource app software interactive data are fused, and the result is further analyzed to obtain the information of app software UX. The experiments clearly show that the method can effectively fuse multisource app software interaction data and help to comprehensively mine the app software UX embodied in the data.
Optimal resource optimisation based on multi‐layer monitoring

Abstract The satisfaction of the Quality of Service (QoS) levels during an entire service life‐cycle is one of the key targets for Service Providers (SP). To achieve this in an optimal way, it is required to predict the exact amount of the needed physical and virtual resources, for example, CPU and memory usage, for any possible combination of parameters that affect the system workload, such as number of users, duration of each request, etc. To solve this problem, the authors introduce a novel architecture and its open‐source implementation that a) monitors and collects data from heterogeneous resources, b) uses them to train machine learning models and c) tailors them to each particular service type. The candidate solution is validated in two real‐life services showing very good accuracy in predicting the required resources for a large number of operational configurations where a data augmentation method is also applied to further decrease the estimation error up to 32%.
Deep Lifelong Learning Optimization Algorithm in Dense Region Fusion

Deep lifelong learning models can learn new information continuously while minimizing the impact on previously acquired knowledge, and thus adapt to changing data. However, existing optimization approaches for deep lifelong learning cannot simultaneously satisfy the following conditions: unrestricted learning of new data, no use of old data, and no increase in model parameters. To address this problem, a deep lifelong learning optimization algorithm based on dense region fusion (DLLO-DRF) is proposed. This algorithm first obtains models for each stage of lifelong learning, and divides the model parameters for each stage into multiple regions based on the parameter values. Then, based on the dispersion of the parameter distribution, dense regions are dynamically obtained from the divided regions, and the parameters of the dense regions are averaged and fused to optimize the model. Finally, extensive experiments are conducted on the self-labeled transmission line defect dataset, and the results show that DLLO-DRF has the best performance among various comparative algorithms.
Transitive and Intransitive Verb Analysis for Idiomatic Expression Understanding: An NLP-based Framework

Idiomatic expressions pose challenges for language learners and require knowl- edge of their underlying structure, including transitive and intransitive verbs. This paper presents a framework for understanding idiomatic expressions through transitive and intransitive verb analysis using natural language pro- cessing (NLP) techniques. The framework involves identifying the idiom, its transitive and intransitive verbs, analyzing their dependencies, and deter- mining the meaning of the idiom. The proposed framework is designed to be applicable to idiomatic expressions in various languages and genres of text. A case study involving a selection of English idiomatic expressions demon- strated the effectiveness of the framework in accurately identifying the verbs and providing insights into their meaning. The proposed framework has the potential to contribute to language learning, computational linguistics, and natural language processing research
Carbon emission-aware job scheduling for Kubernetes deployments

Abstract Decreasing carbon emissions of data centers while guaranteeing Quality of Service (QoS) is one of the major challenges for efficient resource management of large-scale cloud infrastructures and societal sustainability. Previous works in the area of carbon reduction mostly focus on decreasing overall energy consumption, replacing energy sources with renewable ones, and migrating workloads to locations where lower emissions are expected. These measures do not consider the energy mix of the power used for the data center. In other words, all KWh of energy are considered the same from the point of view of emissions, which is rarely the case in practice. In this paper, we overcome this deficit by proposing a novel practical CO 2 -aware workload scheduling algorithm implemented in the Kubernetes orchestrator to shift non-critical jobs in time. The proposed algorithm predicts future CO 2 emissions by using historical data of energy generation, selects time-shiftable jobs, and creates job schedules utilizing greedy sub-optimal CO 2 decisions. The proposed algorithm is implemented using Kubernetes’ scheduler extender solution due to its ease of deployment with little overheads. The algorithm is evaluated with real-world workload traces and compared to the default Kubernetes scheduling implementation on several actual scenarios.
Towards Universal Industrial Augmented Reality: Implementing a Modular IAR System to Support Assembly Processes

While Industrial Augmented Reality (IAR) has many applications across the whole product lifecycle, most IAR applications today are custom-built for specific use-cases in practice. This contribution builds upon a scoping literature review of IAR data representations to present a modern, modular IAR architecture. The individual modules of the presented approach are either responsible for user interface and user interaction or for data processing. They are use-case neutral and independent of each other, while communicating through a strictly separated application layer. To demonstrate the architecture, this contribution presents an assembly process that is supported once with a pick-to-light system and once using in situ projections. Both are implemented on top of the novel architecture, allowing most of the work on the individual models to be reused. This IAR architecture, based on clearly separated modules with defined interfaces, particularly allows small companies with limited personnel resources to adapt IAR for their specific use-cases more easily than developing single-use applications from scratch.
Communication-Efficient Collaborative Best Arm Identification

We investigate top-m arm identification, a basic problem in bandit theory, in a multi-agent learning model in which agents collaborate to learn an objective function. We are interested in designing collaborative learning algorithms that achieve maximum speedup (compared to single-agent learning algorithms) using minimum communication cost, as communication is frequently the bottleneck in multi-agent learning. We give both algorithmic and impossibility results, and conduct a set of experiments to demonstrate the effectiveness of our algorithms.
Learning deep feature fusion for traffic light detection

Traffic light detection in real-world conditions is challenging because of the positioning of lights, variety in shapes and scales, and similarity with other objects. The paper presents a deep learning-based traffic light detection system by learning the fusion of handcrafted features. The handcrafted features for object detection focus on specific attributes such as shape, color, or texture. The objective of this work is to incorporate handcrafted features into the network learning process such that the resulting detector parameters are robust to input variations, sensor noise, and atmospheric noise. The proposed detection framework is based on the latest You only look once (YOLO) architecture, trained with the fusion of different information channels in the Integral Channel Features (ICF). The approach demonstrates a qualitative approach for identifying the optimal layer for additional feature injection in the network, and the selection of ICF channels to be applied for fusion. The validation of the proposed detector on the Bosch small traffic light dataset achieved the best mAP score of 55.7% on the testing set. Further, a qualitative comparison of the proposed detector’s performance with that of other recent methods is presented, along with an analysis using auxiliary experiments.
Process and Pitfalls of Online Teaching and Learning with Design Study “Lite” Methodology: A Retrospective Analysis

Abstract Design studies are an integral method of visualization research with hundreds of instances in the literature. Although taught as a theory, the practical implementation of design studies is often excluded from visualization pedagogy due to the lengthy time commitments associated with such studies. Recent research has addressed this challenge and developed an expedited design study framework, the Design Study “Lite” Methodology (DSLM), which can implement design studies with novice students within just 14 weeks. The framework was developed and evaluated based on five semesters of in‐person data visualization courses with 30 students or less and was implemented in conjunction with Service‐Learning (S‐L). With the growth and popularity of the data visualization field—and the teaching environment created by the COVID‐19 pandemic—more academic institutions are offering visualization courses online. Therefore, in this paper, we strengthen and validate the epistemological foundations of the DSLM framework by testing its (1) adaptability to online learning environments and conditions and (2) scalability to larger classes with up to 57 students. We present two online implementations of the DSLM framework, with and without Service‐Learning (S‐L), to test the adaptability and scalability of the framework. We further demonstrate that the framework can be applied effectively without the S‐L component. We reflect on our experience with the online DSLM implementations and contribute a detailed retrospective analysis using thematic analysis and grounded theory methods to draw valuable recommendations and guidelines for future applications of the framework. This work verifies that DSLM can be used successfully in online classes to teach design study methodology. Finally, we contribute novel additions to the DSLM framework to further enhance it for teaching and learning design studies in the classroom. The preprint and supplementary materials for this paper can be found at https://osf.io/6bjx5/.
Characterizing a Time–Memory Tradeoff Against PudgyTurtle

Abstract PudgyTurtle is not a cipher, but rather an alternative way to utilize the keystream in binary-additive stream-cipher cryptosystems. Instead of modulo-2 adding the keystream to the plaintext, PudgyTurtle uses the keystream to encode 4-bit groups of plaintext, and then to encipher each codeword. One goal of PudgyTurtle is to make time–memory tradeoff attacks more difficult. Here, we investigate one such attack (a modification of the well-known Babbage–Golić method), and show that its time-complexity is harder on average than an analogous tradeoff attack against a standard binary-additive stream cipher; may approach that of a ’brute-force’ attack; can be reduced by certain parameter choices; and can be formulated in terms of a probability distribution which is amenable to simulation.