Reviewerly/exHarmony huggingface dataset:
Is the readability of abstracts decreasing in management research?

Abstract The readability of scientific texts is critical for the successful distribution of research findings. I replicate a recent study which found that the abstracts of scientific articles in the life sciences became less readable over time. Specifically, I sample 28,345 abstracts from 17 of the leading journals in the field of management and organization over 3 decades, and study two established indicators of readability over time, namely the Flesch Reading Ease and the New Dale–Chall Readability Formula. I find a modest trend towards less readable abstracts, which leads to an increase in articles that are extremely hard to read from 12% in the first decade of the sample to 16% in the final decade of the sample. I further find that an increasing number of authors partially explains this trend, as do the use of scientific jargon and corresponding author affiliations with institutions in English-speaking countries. I discuss implications for authors, reviewers, and editors in the field of management.
Ensemble selection with joint spectral clustering and structural sparsity

Generally, ensemble selection techniques are split into two categories: dynamic and static. Static ensemble selection selects a fixed subset of the original ensemble which improves the space complexity but is not flexible to each test instance. Dynamic ensemble selection selects base learners on-the-fly according to each test instance but it does not significantly improve the complexity. Currently, there is no ensemble selection technique that is robust to the test instances as well as improves space complexity. To narrow this gap, we propose a novel static ensemble selection method, called Ensemble Selection with Joint Spectral Clustering and Structural Sparsity. This method integrates spectral clustering and structural sparsity into a joint framework whose ensemble selection result is robust to test instances and consumes less space. Using 25 datasets from KEEL and UCI, we demonstrate the effectiveness of our proposed algorithm and its promising performance compared to that of other state-of-the-art algorithms.
DISCO: Dynamic and Invariant Sensitive Channel Obfuscation for deep neural networks

Recent deep learning models have shown remarkable performance in image classification. While these deep learning systems are getting closer to practical deployment, the common assumption made about data is that it does not carry any sensitive information. This assumption may not hold for many practical cases, especially in the domain where an individual’s personal information is involved, like healthcare and facial recognition systems. We posit that selectively removing features in this latent space can protect the sensitive information and provide better privacy-utility trade-off. Consequently, we propose DISCO which learns a dynamic and data driven pruning filter to selectively obfuscate sensitive information in the feature space. We propose diverse attack schemes for sensitive inputs & attributes and demonstrate the effectiveness of DISCO against state-of-the-art methods through quantitative and qualitative evaluation. Finally, we also release an evaluation benchmark dataset of 1 million sensitive representations to encourage rigorous exploration of novel attack and defense schemes at https://github.com/splitlearning/InferenceBenchmark.
A framework for applying <scp>ethics‐by‐design</scp> to decision support systems for emergency management

Abstract The development and utilisation of new information and communication technologies presents opportunities and risks, which bring ethical issues to the forefront. Any attempt to minimise the potential negative consequences to individuals, organisations and society resulting from the use of these technologies is challenging. In order to address these challenges, this paper presents an ethics‐by‐design approach that has been developed and implemented in the context of Decision Supports Systems for Emergency Management. Such systems help manage large and cross‐border disasters by supporting decision makers to respond on emergencies in a reasonable way by taking follow‐up actions into account. The approach taken in this paper specifically provides means to support the ethical dimensions of these decisions. Actions taken during disasters can have ramifications that persist long after a disaster has passed. The ethics‐by‐design approach presented here not only informs the design of systems, but also considers the role and training of the decision makers in the design process. The paper builds on the literature on ethics in information systems and makes a contribution to theory by providing a framework to ensure ethical considerations are embedded into the design of systems.
“Won’t We Fix this Issue?” Qualitative characterization and automated identification of wontfix issues on GitHub

: Addressing user requests in the form of bug reports and Github issues represents a crucial task of any successful software project. However, user-submitted issue reports tend to widely differ in their quality, and developers spend a considerable amount of time handling them. : By collecting a dataset of around 6,000 issues of 279 GitHub projects, we observe that developers take significant time (i.e., about five months, on average) before labeling an issue as a wontfix. For this reason, in this paper, we empirically investigate the nature of wontfix issues and methods to facilitate issue management process. : We first manually analyze a sample of 667 wontfix issues, extracted from heterogeneous projects, investigating the common reasons behind a “wontfix decision”, the main characteristics of wontfix issues and the potential factors that could be connected with the time to close them. Furthermore, we experiment with approaches enabling the prediction of wontfix issues by analyzing the titles and descriptions of reported issues when submitted. : Our investigation sheds some light on the wontfix issues’ characteristics, as well as the potential factors that may affect the time required to make a “wontfix decision”. Our results also demonstrate that it is possible to perform prediction of wontfix issues with high average values of precision, recall, and F-measure (90%–93%).
Meta-Mining Discriminative Samples for Kinship Verification

Kinship verification aims to find out whether there is a kin relation for a given pair of facial images. Kinship verification databases are born with unbalanced data. For a database with N positive kinship pairs, we naturally obtain N(N − 1) negative pairs. How to fully utilize the limited positive pairs and mine discriminative information from sufficient negative samples for kinship verification remains an open issue. To address this problem, we propose a Discriminative Sample Meta-Mining (DSMM) approach in this paper. Unlike existing methods that usually construct a balanced dataset with fixed negative pairs, we propose to utilize all possible pairs and automatically learn discriminative information from data. Specifically, we sample an unbalanced train batch and a balanced meta-train batch for each iteration. Then we learn a meta-miner with the meta-gradient on the balanced meta-train batch. In the end, the samples in the unbalanced train batch are re-weighted by the learned meta-miner to optimize the kinship models. Experimental results on the widely used KinFaceW-I, KinFaceW-II, TSKinFace, and Cornell Kinship datasets demonstrate the effectiveness of the proposed approach.
Pilot-Edge: Distributed Resource Management Along the Edge-to-Cloud Continuum

Many science and industry IoT applications necessitate data processing across the edge-to-cloud continuum to meet performance, security, cost, and privacy requirements. However, diverse abstractions and infrastructures for managing resources and tasks across the edge-to-cloud scenario are required. We propose Pilot-Edge as a common abstraction for resource management across the edge-to-cloud continuum. Pilot-Edge is based on the pilot abstraction, which decouples resource and workload management, and provides a Function-as-a-Service (FaaS) interface for application-level tasks. The abstraction allows applications to encapsulate common functions in high-level tasks that can then be configured and deployed across the continuum. We characterize Pilot-Edge on geographically distributed infrastructures using machine learning workloads (e. g., k-means and auto-encoders). Our experiments demonstrate how Pilot-Edge manages distributed resources and allows applications to evaluate task placement based on multiple factors (e. g., model complexities, throughput, and latency).
Dance Motion Capture Based on Data Fusion Algorithm and Wearable Sensor Network

In this paper, through an in-depth study and analysis of dance motion capture algorithms in wearable sensor networks, the extended Kalman filter algorithm and the quaternion method are selected after analysing a variety of commonly used data fusion algorithms and pose solving algorithms. In this paper, a sensor-body coordinate system calibration algorithm based on hand-eye calibration is proposed, which only requires three calibration poses to complete the calibration of the whole-body sensor-body coordinate system. In this paper, joint parameter estimation algorithm based on human joint constraints and limb length estimation algorithm based on closed joint chains are proposed, respectively. The algorithm is an iterative optimization algorithm that divides each iteration into an expectation step and a great likelihood step, and the best convergence value can be found efficiently according to each iteration step. The feature values of each pose action are fed into the algorithm for model learning, which enables the training of the model. The trained model is then tested by combining the collected gesture data with the algorithmic model to recognize and classify the gesture data, observe its recognition accuracy, and continuously optimize the model to achieve accurate recognition of human gesture actions.
Memetic algorithms for mapping <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="d1e2012" altimg="si269.svg"><mml:mi>p</mml:mi></mml:math>-body interacting systems in effective quantum 2-body Hamiltonians

Quantum computing is an emerging research area which promises to offer a revolution in the computing performance. The world’s first commercially available quantum computer has been the D-Wave machine which aims at solving complex problems by representing them in terms of Ising Hamiltonians. This formulation allows addressing several combinatorial optimization problems since generally it is possible to map any problem to the Hamiltonian of the Ising model. However, D-Wave’s architecture restricts the Ising Hamiltonian to the case with only 2-body interactions. Therefore, in order to face problems mapped on systems with p-body interactions (p≥2), it is necessary to implement a procedure to compute 2-body effective Hamiltonians of p-body interacting systems. Due to the complexity of this task, recently, meta-heuristic methods have been applied with promising results. The aim of this paper is to implement a procedure to convert from p-body to 2-body Hamiltonians by means of memetic algorithms. As shown in the experimental session involving the ferromagnetic p-spin model as a case study, the proposed approach improves by 60% on average over the state-of-the-art meta-heuristic approaches.
More precise runtime analyses of non-elitist EAs in uncertain environments

Real-world optimisation problems often involve uncertainties. In the past decade, several rigorous analysis results for evolutionary algorithms (EAs) on discrete problems show that EAs can cope with low-level uncertainties, and sometimes benefit from uncertainties. Using non-elitist EAs with large population size is a promising approach to handle higher levels of uncertainties. However, the performance of non-elitist EAs in some common fitness-uncertainty scenarios is still unknown.
A Survey and Taxonomy of Electronics Toolkits for Interactive and Ubiquitous Device Prototyping

Over the past two decades, many toolkits for prototyping interactive and ubiquitous electronic devices have been developed. Although their technical specifications are often easy to look up, they vary greatly in terms of design, features and target audience, resulting in very real strengths and weaknesses depending on the intended application. These less technical characteristics are often reported inconsistently, if at all. In this paper we provide a comprehensive survey of interactive and ubiquitous device prototyping toolkits, systematically analysing their characteristics within the framework of a new taxonomy that we present. In addition to the specific characteristics we cover, we introduce a way to evaluate toolkits more holistically, covering user needs such as 'ease of construction' and 'ease of moving from prototype to product' rather than features. We also present results from an online survey which offers new insights on how the surveyed users prioritize these characteristics during prototyping, and what techniques they use to move beyond prototyping. We hope our analysis will be valuable for others in the community who need to build and potentially scale out prototypes as part of their research. We end by identifying gaps that have not yet been addressed by existing offerings and discuss opportunities for future research into electronics prototyping toolkits.
Introduction to artificial intelligence and machine learning

Artificial intelligence is the term used to describe the automation of intellectual tasks. Tasks that are commonly performed by a human are called as intellectual tasks. Machine learning (ML) is a set of techniques that describe methods used by computers to learn how to do the tasks. Deep learning is a specific subset of ML focused on layered techniques. This chapter reviews some typical ML tasks, the standard project workflow, and further important considerations: from feature generation to model selection and uncertainty quantification.
MFC-Net : Multi-feature fusion cross neural network for salient object detection

Although methods based on the fully convolutional neural networks (FCNs) have shown strong advantages in the field of salient object detection, the existing methods still have two challenging issues: insufficient multi-level feature fusion ability and boundary blur. To overcome these issues, we propose a novel salient object detection method based on a multi-feature fusion cross network (denoted MFC-Net). Firstly, to overcome the issue of insufficient multi-level feature fusion ability, inspired by the connection mode of human brain neurons, we propose a novel cross network framework, combined with contextual feature transfer modules (CFTMs) to integrate, enhance and transmit multi-level feature information in an iterative manner. Secondly, to address the issue of blurred boundaries, we effectively enhance the edge features of saliency map by a simple edge enhancement strategy. Thirdly, to reduce the loss of information caused by the saliency map generated by multi-level feature fusion, we use feature fusion modules (FFMs) to learn contextual feature information from multiple angles and then output the resulting saliency map. Finally, a hybrid loss function fully supervises the network at the pixel and object level, optimizing the network performance. The proposed MFC-Net has been evaluated using five benchmark datasets. The performance evaluation demonstrates that the proposed method outperforms other state-of-the-art methods, which proves the superiority of MFC-Net approach.
Error-feedback stochastic modeling strategy for time series forecasting with convolutional neural networks

Despite the superiority of convolutional neural networks demonstrated in time series modeling and forecasting, it has not been fully explored on the design of the neural network architecture and the tuning of the hyper-parameters. Inspired by the incremental construction strategy for building a random multilayer perceptron, we propose a novel Error-feedback Stochastic Modeling (ESM) strategy to construct a random Convolutional Neural Network (ESM-CNN) for time series forecasting task, which builds the network architecture adaptively. The ESM strategy suggests that random filters and neurons of the error-feedback fully connected layer are incrementally added to steadily compensate the prediction error during the construction process, and then a filter selection strategy is introduced to enable ESM-CNN to extract the different size of temporal features, providing helpful information at each iterative process for the prediction. The performance of ESM-CNN is justified on its prediction accuracy of one-step-ahead and multi-step-ahead forecasting tasks respectively. Comprehensive experiments on both the synthetic and real-world datasets show that the proposed ESM-CNN not only outperforms the state-of-art random neural networks, but also exhibits stronger predictive power and less computing overhead in comparison to trained state-of-art deep neural network models.
Generative Classifiers as a Basis for Trustworthy Image Classification

With the maturing of deep learning systems, trustworthiness is becoming increasingly important for model assessment. We understand trustworthiness as the combination of explainability and robustness. Generative classifiers (GCs) are a promising class of models that are said to naturally accomplish these qualities. However, this has mostly been demonstrated on simple datasets such as MNIST and CIFAR in the past. In this work, we firstly develop an architecture and training scheme that allows GCs to operate on a more relevant level of complexity for practical computer vision, namely the ImageNet challenge. Secondly, we demonstrate the immense potential of GCs for trustworthy image classification. Explainability and some aspects of robustness are vastly improved compared to feed-forward models, even when the GCs are just applied naively. While not all trustworthiness problems are solved completely, we observe that GCs are a highly promising basis for further algorithms and modifications. We release our trained model for download in the hope that it serves as a starting point for other generative classification tasks, in much the same way as pretrained ResNet architectures do for discriminative classification.Code: github.com/VLL-HD/trustworthy_GCs
Unqomp: synthesizing uncomputation in Quantum circuits

A key challenge when writing quantum programs is the need for uncomputation: temporary values produced during the computation must be reset to zero before they can be safely discarded. Unfortunately, most existing quantum languages require tedious manual uncomputation, often leading to inefficient and error-prone programs. We present Unqomp, the first procedure to automatically synthesize uncomputation in a given quantum circuit. Unqomp can be readily integrated into popular quantum languages, allowing the programmer to allocate and use temporary values analogously to classical computation, knowing they will be uncomputed by Unqomp. Our evaluation shows that programs leveraging Unqomp are not only shorter (-19% on average), but also generate more efficient circuits (-71% gates and -19% qubits on average).
Variational Pedestrian Detection

Pedestrian detection in a crowd is a challenging task due to a high number of mutually-occluding human instances, which brings ambiguity and optimization difficulties to the current IoU-based ground truth assignment procedure in classical object detection methods. In this paper, we develop a unique perspective of pedestrian detection as a variational inference problem. We formulate a novel and efficient algorithm for pedestrian detection by modeling the dense proposals as a latent variable while proposing a customized Auto-Encoding Variational Bayes (AEVB) algorithm. Through the optimization of our proposed algorithm, a classical detector can be fashioned into a variational pedestrian detector. Experiments conducted on CrowdHuman and CityPersons datasets show that the proposed algorithm serves as an efficient solution to handle the dense pedestrian detection problem for the case of single-stage detectors. Our method can also be flexibly applied to two-stage detectors, achieving notable performance enhancement.
Liquid Tensor Experiment

I propose a formalization challenge. The text below is a slightly edited version of the blog post Xena Project – Liquid Tensor Experiment, and I have kept its informal style.
A Performance Analysis of Fault Recovery in Stream Processing Frameworks

Distributed stream processing frameworks have gained widespread adoption in the last decade because they abstract away the complexity of parallel processing. One of their key features is built-in fault tolerance. In this work, we dive deeper into the implementation, performance, and efficiency of this critical feature for four state-of-the-art frameworks. We include the established Spark Streaming and Flink frameworks and the more novel Spark Structured Streaming and Kafka Streams frameworks. We test the behavior under different types of faults and settings: master failure with and without high-availability setups, driver failures for Spark frameworks, worker failure with or without exactly-once semantics, application and task failures. We highlight differences in behavior during these failures on several aspects, e.g., whether there is an outage, downtime, recovery time, data loss, duplicate processing, accuracy, and the cost and behavior of different message delivery guarantees. Our results highlight the impact of framework design on the speed of fault recovery and explain how different use cases may benefit from different approaches. Due to their task-based scheduling approach, the Spark frameworks can recover within 30 seconds and in most cases without necessitating an application restart. Kafka Streams has only a few seconds of downtime, but is slower at catching up on delays. Finally, Flink can offer end-to-end exactly-once semantics at a low cost but requires job restarts for most failures leading to high recovery times of around 50 seconds.
Ontology-Based Feature Selection: A Survey

The Semantic Web emerged as an extension to the traditional Web, adding meaning (semantics) to a distributed Web of structured and linked information. At its core, the concept of ontology provides the means to semantically describe and structure information, and expose it to software and human agents in a machine and human-readable form. For software agents to be realized, it is crucial to develop powerful artificial intelligence and machine-learning techniques, able to extract knowledge from information sources, and represent it in the underlying ontology. This survey aims to provide insight into key aspects of ontology-based knowledge extraction from various sources such as text, databases, and human expertise, realized in the realm of feature selection. First, common classification and feature selection algorithms are presented. Then, selected approaches, which utilize ontologies to represent features and perform feature selection and classification, are described. The selective and representative approaches span diverse application domains, such as document classification, opinion mining, manufacturing, recommendation systems, urban management, information security systems, and demonstrate the feasibility and applicability of such methods. This survey, in addition to the criteria-based presentation of related works, contributes a number of open issues and challenges related to this still active research topic.