Reviewerly/exHarmony huggingface dataset:
Development and evaluation of empirical models for the estimation of hourly horizontal diffuse solar irradiance in the United Kingdom

Solar irradiation data is required in many applications to obtain the solar energy output. However, solar irradiation data is not always available, especially horizontal surface diffuse solar irradiation. This has led to the creation of empirical solar models to predict these values. The aim of this paper is to develop an empirical model for the estimation of hourly diffuse solar irradiance on horizontal surfaces. Initially, a set of existing empirical models have been evaluated to test their accuracy. New models have been developed based on ground level measurements of global and diffuse horizontal solar irradiation that have been obtained from three different regions of the United Kingdom: South Yorkshire, Norfolk, and West Sussex for a period between 1982 and 1999. The models created for each region correlate the diffuse fraction (kd) and the clearness index (kt), for the estimation of hourly diffuse solar irradiance on horizontal surfaces. The models developed for the three regions were evaluated to test their accuracy using error histograms and by contrasting different datasets. Finally, a comparison of the new models to the existing empirical models showed that the new developed correlations significantly improved the existing empirical models for the three regions evaluated in this study.
Optimizing the Migration of Virtual Machines in Cloud Data Centers

Dynamic resource allocation of cloud data centers is implemented with the use of virtual machine migration. Selected virtual machines (VM) should be migrated on appropriate destination servers. This is a critical step and should be performed according to several criteria. It is proposed to use the criteria of minimum resource wastage and service level agreement violation. The optimization problem of the VM placement according to two criteria is formulated, which is equivalent to the well-known main assignment problem in terms of the structure, necessary conditions, and the nature of variables. It is suggested to use the Hungarian method or to reduce the problem to a closed transport problem. This allows the exact solution to be obtained in real time. Simulation has shown that the proposed approach outperforms widely used bin-packing heuristics in both criteria.
A Systematic Review of User Acceptance in Industrial Augmented Reality

In the industrial work context, Augmented Reality (AR) can support work processes and employees’ cognitive relief through the location-specific and context-related superimposition of real objects with virtual information. The AR-based support of industrial work processes ranges over product development, manufacturing, assembly, maintenance, and training. In all these areas, numerous location-based AR support functions are being prototypically implemented, aiming to improve work efficiency, communication in mobile work situations, or employee qualification in the work process. In contrast to the increasing number of developed AR solutions in recent years, there is no widespread use of these solutions in industrial practice. AR systems’ successful introduction is closely related to user acceptance, which has not been comprehensively considered over the system development process. In addition to improving AR hardware ergonomic features, usability or user interface design play an essential role in user acceptance. Particularly in the context of employee qualification, increasing employee engagement can be named as a success factor. Previous user studies of industrial AR systems only include individual user acceptance aspects. The use of game elements has not been widely addressed in connection with manual tasks in production environments, including AR-based assistance systems. This paper aims to examine user acceptance of industrial AR systems and the relevant factors for investigating user acceptance, e.g., ease of use or enjoyment, based on a systematic literature review. An analysis of existing review articles on industrial AR systems elaborates the current state of the art and identifies the research gap. This review of 109 scientific articles from 2011 to 2020 provides an overview of the current state of research on the inclusion of user acceptance in industrial AR systems. The identified papers from the scientific databases, Scopus, Web of Science, IEEE Xplore Digital Library, ACM Digital Library, and Science Direct, are evaluated for their relevance and selected for further analysis based on inclusion and exclusion criteria, e.g., year of publication. This review presents the current challenges regarding user acceptance of industrial AR systems and future possibilities for the comprehensive integration of user acceptance factors into the development, evaluation, and implementation process.
Blockchain Applications and Challenges in Smart grid

Recently the interest in renewable energy has been increased worldwide. Therefore, the microgrid is considered more efficient and flexibility to improve the economic and environmental conditions by integrating the traditional distributed energy resources using technology devices and information technology (IT). Blockchain is one of these technologies, which has attracted in microgrid applications to build a sustainable society. The blockchain concept can provides immutability of the transactions in the microgrid such as record the amount of power generation and verify the transaction between the generators and end-users. blockchain based smart contract can be used for audit or solving a transaction dispute between the producers and the users by strip the necessity to link with third parties. In this paper, we present a number of recent research works related with blockchain application in microgrid and the application of the blockchain to improve the transactive resilience in the microgrid using smart contract.
ContrXT: Generating contrastive explanations from any text classifier

The need for explanations of ML systems is growing as new models outperform their predecessors while becoming more complex and less comprehensible for their end-users. Though several XAI methods have been proposed in recent years, not enough attention was paid to explaining how models change their behaviour in contrast with previous ones (e.g., due to retraining). In such cases, an XAI system should explain why the model changes its predictions concerning past outcomes. Capturing and understanding such differences is crucial, as the need for trust is key in any application to support human-AI decision-making processes. This is the idea of ContrXT, a novel approach that (i) traces the decision criteria of a black box text classifier by encoding the changes in the decision logic through Binary Decision Diagrams. Then (ii) it provides global, model-agnostic, Time-Contrastive (T-contrast) explanations in natural language, estimating why – and to what extent – the model has modified its behaviour over time. We implemented and evaluated ContrXT over several supervised ML models trained on a benchmark dataset and a real-life application, showing it is effective in catching majorly changed classes and in explaining their variation through a user study. The approach has been implemented, and it is available to the community both as a python package and through REST API, providing contrastive explanations as a service.
WORLD-SPACE SPATIOTEMPORAL RESERVOIR REUSE FOR RAY-TRACED GLOBAL ILLUMINATION

Path-traced global illumination of scenes with complex lighting remains particularly challenging at real-time framerates. Reservoir-based resampling methods for light sampling allow for significant noise reduction at the cost of very few shadow rays per pixel. However, current image-space approaches to reservoir reuse do not scale to sample lighting at further bounces, as is required for efficiently evaluating indirect illumination.
The Impact of Instrumental Stakeholder Management on Blockchain Technology Adoption Behavior in Agri-Food Supply Chains

Coffee is the second most important commodity in terms of global trade value, with its global market value exceeding $460 billion in 2020. Its supply networks, which encompass multiple stakeholders, are complex and nontransparent. Blockchain is a trust technology, and some coffee firms have embraced this technology to provide trust attributes to consumers while making their supply chain more transparent. For businesses to gain the expected productivity advantages, a technology must be adopted and used. As theoretical and empirical research on blockchain technology adoption is scarce, this article attempts to identify behavioral intentions of stakeholders in the supply network toward its adoption. Based on exploratory interviews, this article develops a blockchain technology adoption model based on factors relevant to individuals’ use behavior. The results provide evidence that a normative stakeholder management approach positively impacts use behavior. Managers can use the model to benchmark and improve their corporate social responsibility strategy to obtain better returns on blockchain investments. This study closes a research gap as, to the best of the authors’ knowledge, no research has been conducted so far on the impact of an instrumental stakeholder management approach on blockchain technology adoption behavior. Understanding how stakeholder management can compensate for the lack of consensus mechanisms in private and consortium blockchains, as well as understanding the factors influencing behavioral intentions toward the use of a technology, can provide for managerial guidance toward the development of an effective stakeholder management strategy, which eventually can result in a competitive advantage.
Explaining Deep Learning Models for Tabular Data Using Layer-Wise Relevance Propagation

Trust and credibility in machine learning models are bolstered by the ability of a model to explain its decisions. While explainability of deep learning models is a well-known challenge, a further challenge is clarity of the explanation itself for relevant stakeholders of the model. Layer-wise Relevance Propagation (LRP), an established explainability technique developed for deep models in computer vision, provides intuitive human-readable heat maps of input images. We present the novel application of LRP with tabular datasets containing mixed data (categorical and numerical) using a deep neural network (1D-CNN), for Credit Card Fraud detection and Telecom Customer Churn prediction use cases. We show how LRP is more effective than traditional explainability concepts of Local Interpretable Model-agnostic Explanations (LIME) and Shapley Additive Explanations (SHAP) for explainability. This effectiveness is both local to a sample level and holistic over the whole testing set. We also discuss the significant computational time advantage of LRP (1–2 s) over LIME (22 s) and SHAP (108 s) on the same laptop, and thus its potential for real time application scenarios. In addition, our validation of LRP has highlighted features for enhancing model performance, thus opening up a new area of research of using XAI as an approach for feature subset selection.
Overcoming Barriers to Including Agricultural Workers in the Co‐Design of New AgTech: Lessons from a COVID‐19‐Present World

Abstract Collaborative design (co‐design) is a design strategy for generating relevant and socially acceptable technologies, and is inherently political by nature of its inclusion of particular groups and interests. This paper explores how to ethically and responsibly prepare for, notice and overcome barriers to including agricultural workers in the co‐design of new agricultural technologies. Drawing from feminist science and technology studies (STS), we offer response‐able mattering as an analytic tool to explore how the COVID‐19 pandemic caused and illuminated existing barriers to inclusion within an Aotearoa New Zealand‐based co‐design project. We argue that addressing barriers to inclusion requires prioritizing relationships and relationship building in technology design projects. This prioritization must account for a multiplicity of relationship building tempos (e.g., the time/pace necessary to ethically establish and maintain research relationships), temporal tensions (e.g., the pace of technology development versus the ability to meaningfully include collaborators), and un/intended relational cuts (e.g., boundaries or barriers affecting relationship building).
Path planning of UUV based on HQPSO algorithm with considering the navigation error

In the traditional path planning problem of Unmanned Underwater Vehicle (UUV), it is generally assumed that the navigation position information of UUV is accurate. However, the cumulative navigation position error caused by navigation system error of UUV cannot be ignored in practical applications. To get a safe path of UUV with considering navigation error, we propose the Hybrid Quantum-behaved Particle Swarm Optimization (HQPSO) algorithm to solve this problem. We improve the position and attitude error transfer model of UUV under integrated navigation, and give the mathematical models of collision avoidance constraints corresponding to regular obstacles and irregular obstacles respectively. At the same time, the terrain matching area and the terminal position error are used as constraints modification strategy of the HQPSO algorithm. Considering the influence of navigation error divergence rate and terrain matching aided navigation on path planning, two groups of comparative verification experiments are designed. The experimental results show that path planning method based on the HQPSO algorithm can effectively use the correction effect of terrain matching area on navigation error. This ensures that UUV can effectively avoid the adverse effects of navigation error, and get a path with the shortest navigation time. The method proposed in this paper shows strong engineering applicability.
A machine learning investigation of factors that contribute to predicting cognitive performance: Difficulty level, reaction time and eye-movements

Predicting accuracy in cognitively challenging tasks has potential applications in education and industry. Task demand has been linked with increases in response time and variations in reaction time and eye-tracking metrics, however, machine learning research has not been used to predict performance on tasks with multiple levels of difficulty. We report data on adult participants who performed tasks of mental attentional capacity with six levels of difficulty and use machine learning methods to predict accuracy scores considering metrics associated with task difficulty, reaction time and eye movements. Results show that machine learning models can robustly predict performance with reaction times and difficulty level being the strongest predictors. Eye-tracking indices can also predict accuracy independently, with the most important features of the model driven by the number of fixations, number of saccades, duration of the current fixation and pupil size. Practical and theoretical implications of the results are discussed.
An analysis of heuristic metrics for classifier ensemble pruning based on ordered aggregation

Classifier ensemble pruning is a strategy through which a subensemble can be identified via optimizing a predefined performance criterion. Choosing the optimum or suboptimum subensemble decreases the initial ensemble size and increases its predictive performance. In this article, a set of heuristic metrics will be analyzed to guide the pruning process. The analyzed metrics are based on modifying the order of the classifiers in the bagging algorithm, with selecting the first set in the queue. Some of these criteria include general accuracy, the complementarity of decisions, ensemble diversity, the margin of samples, minimum redundancy, discriminant classifiers, and margin hybrid diversity. The efficacy of those metrics is affected by the original ensemble size, the required subensemble size, the kind of individual classifiers, and the number of classes. While the efficiency is measured in terms of the computational cost and the memory space requirements. The performance of those metrics is assessed over fifteen binary and fifteen multiclass benchmark classification tasks, respectively. In addition, the behavior of those metrics against randomness is measured in terms of the distribution of their accuracy around the median. Results show that ordered aggregation is an efficient strategy to generate subensembles that improve both predictive performance as well as computational and memory complexities of the whole bagging ensemble.
Let’s Play for Action: Recognizing Activities of Daily Living by Learning from Life Simulation Video Games

Recognizing Activities of Daily Living (ADL) is a vital process for intelligent assistive robots, but collecting large annotated datasets requires time-consuming temporal labeling and raises privacy concerns, e.g., if the data is collected in a real household. In this work, we explore the concept of constructing training examples for ADL recognition by playing life simulation video games and introduce the SIMS4ACTION dataset created with the popular commercial game THE SIMS 4. We build SIMS4ACTION by specifically executing actions-of-interest in a "top-down" manner, while the gaming circumstances allow us to freely switch between environments, camera angles and subject appearances. While ADL recognition on gaming data is interesting from the theoretical perspective, the key challenge arises from transferring it to the real-world applications, such as smart-homes or assistive robotics. To meet this requirement, SIMS4ACTION is accompanied with a GAMING→REAL benchmark, where the models are evaluated on real videos derived from an existing ADL dataset. We integrate two modern algorithms for video-based activity recognition in our framework, revealing the value of life simulation video games as an inexpensive and far less intrusive source of training data. However, our results also indicate that tasks involving a mixture of gaming and real data are challenging, opening a new research direction. We will make our dataset publicly available at https://github.com/aroitberg/sims4action.
A Novel Multi-Neural Ensemble Approach for Cancer Diagnosis

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Privacy-Preserving Tampering Detection in Automotive Systems

Modern auto-vehicles are built upon a vast collection of sensors that provide large amounts of data processed by dozens of Electronic Control Units (ECUs). These, in turn, monitor and control advanced technological systems providing a large palette of features to the vehicle’s end-users (e.g., automated parking, autonomous vehicles). As modern cars become more and more interconnected with external systems (e.g., cloud-based services), enforcing privacy on data originating from vehicle sensors is becoming a challenging research topic. In contrast, deliberate manipulations of vehicle components, known as tampering, require careful (and remote) monitoring of the vehicle via data transmissions and processing. In this context, this paper documents an efficient methodology for data privacy protection, which can be integrated into modern vehicles. The approach leverages the Fast Fourier Transform (FFT) as a core data transformation algorithm, accompanied by filters and additional transformations. The methodology is seconded by a Random Forest-based regression technique enriched with further statistical analysis for tampering detection in the case of anonymized data. Experimental results, conducted on a data set collected from the On-Board Diagnostics (OBD II) port of a 2015 EUR6 Skoda Rapid 1.2 L TSI passenger vehicle, demonstrate that the restored time-domain data preserves the characteristics required by additional processing algorithms (e.g., tampering detection), showing at the same time an adjustable level of privacy. Moreover, tampering detection is shown to be 100% effective in certain scenarios, even in the context of anonymized data.
Balancing Learning Model Privacy, Fairness, and Accuracy With Early Stopping Criteria

As deep learning models mature, one of the most prescient questions we face is: what is the ideal tradeoff between accuracy, fairness, and privacy (AFP)? Unfortunately, both the privacy and the fairness of a model come at the cost of its accuracy. Hence, an efficient and effective means of fine-tuning the balance between this trinity of needs is critical. Motivated by some curious observations in privacy-accuracy tradeoffs with differentially private stochastic gradient descent (DP-SGD), where fair models sometimes result, we conjecture that fairness might be better managed as an indirect byproduct of this process. Hence, we conduct a series of analyses, both theoretical and empirical, on the impacts of implementing DP-SGD in deep neural network models through gradient clipping and noise addition. The results show that, in deep learning, the number of training epochs is central to striking a balance between AFP because DP-SGD makes the training less stable, providing the possibility of model updates at a low discrimination level without much loss in accuracy. Based on this observation, we designed two different early stopping criteria to help analysts choose the optimal epoch at which to stop training a model so as to achieve their ideal tradeoff. Extensive experiments show that our methods can achieve an ideal balance between AFP.
A General Task and Motion Planning Framework For Multiple Manipulators

Many manipulation tasks combine high-level discrete planning over actions with low-level motion planning over continuous robot motions. Task and motion planning (TMP) provides a powerful general framework to combine discrete and geometric reasoning, and solvers have been previously proposed for single-robot problems. Multi-robot TMP expands the range of TMP problems that can be solved but poses significant challenges when considering scalability and solution quality. We present a general TMP framework designed for multiple robotic manipulators. This is based on two contributions. First, we propose an optimal task planner designed to support simultaneous discrete actions. Second, we introduce an intermediate scheduler layer between task planner and motion planner to evaluate alternate robot assignments to these actions. This aggressively explores the search space and typically reduces the number of expensive task planning calls. Several benchmarks with a rich set of actions for two manipulators are evaluated. We show promising results in scalability and solution quality of our TMP framework with the scheduler for up to six objects. A demonstration indicates scalability to up to five robots.