Reviewerly/exHarmony huggingface dataset:
Will We Trust What We Don’t Understand? Impact of Model Interpretability and Outcome Feedback on Trust in AI

Despite AI's superhuman performance in a variety of domains, humans are often unwilling to adopt AI systems. The lack of interpretability inherent in many modern AI techniques is believed to be hurting their adoption, as users may not trust systems whose decision processes they do not understand. We investigate this proposition with a novel experiment in which we use an interactive prediction task to analyze the impact of interpretability and outcome feedback on trust in AI and on human performance in AI-assisted prediction tasks. We find that interpretability led to no robust improvements in trust, while outcome feedback had a significantly greater and more reliable effect. However, both factors had modest effects on participants' task performance. Our findings suggest that (1) factors receiving significant attention, such as interpretability, may be less effective at increasing trust than factors like outcome feedback, and (2) augmenting human performance via AI systems may not be a simple matter of increasing trust in AI, as increased trust is not always associated with equally sizable improvements in performance. These findings invite the research community to focus not only on methods for generating interpretations but also on techniques for ensuring that interpretations impact trust and performance in practice.
Context-Aware Probabilistic Models for Predicting Future Sedentary Behaviors of Smartphone Users

Sedentary behaviors are now prevalent as most modern jobs are done while seated. However, such sedentary behaviors have been found to increase the risk of several ailments including diabetes, cardiovascular disease, and all-cause mortality. Current interventions are mostly reactive and are triggered after the user has already been sedentary. Behavior change theory suggests that preventive sedentary interventions, which are triggered before a person becomes sedentary, are more likely to succeed. In this paper, we characterize user patterns of sedentary behaviors by analyzing smartphone-sensor data in a real-world dataset. Our work reveals location types (where), times of day/week (when), and smartphone contexts in which sedentary behaviors are most likely. Leveraging our findings, we then propose a set of context-aware probabilistic models that can predict sedentary behaviors in advance by analyzing smartphone sensor data. Our Context-Aware Predictive (CAP) models leverage smartphone-sensed contextual variables and the user’s history of sedentary behaviors to predict their future sedentary behaviors. We rigorously analyze the performance of our models and discuss the implications of our work.
Blockchain-Based Result Verification for Computation Offloading

Offloading of computation, e.g., to the cloud, is today a major task in distributed systems. Usually, consumers which apply offloading have to trust that a particular functionality offered by a service provider is delivering correct results. While redundancy (i.e., offloading a task to more than one service provider) or (partial) reprocessing help to identify correct results, they also lead to significantly higher cost.Hence, within this paper, we present an approach to verify the results of offchain computations via the blockchain. For this, we apply zero-knowledge proofs to provide evidence that results are correct. Using our approach, it is possible to establish trust between a service consumer and arbitrary service providers. We evaluate our approach using a very well-known example task, i.e., the Traveling Salesman Problem.
Histopathological Cancer Detection Using CNN

Histopathology involves the examination and analysis of diseases associated with human or animal tissues. It mainly comprises scrutinizing a batch of tissues or cells under a microscope, and it is one of the most frequently employed tools in the detection and diagnosis of cancer. The detection of metastatic cancer in pathological scans is an onerous and wearisome task. The possibility of melding artificial intelligence (AI) with this field holds immense potential. In this chapter, we attempt to build a system using a diverse ensemble of convolutional neural network (CNN) techniques, namely Inception Net V3 (or Inception V3), Xception Net, ResNet, and DenseNet, that can accurately analyze digital scans of tissues and identify traces of metastatic cancer in them. Further, based on the detailed dissection of the results, we aim to isolate the best technique from the ensemble. With this research, we aim to find a method that can surpass state-of-the-art methods tackling the same issue.
Identifying diverse metal oxide nanomaterials with lethal effects on embryonic zebrafish using machine learning

Manufacturers of nanomaterial-enabled products need models of endpoints that are relevant to human safety to support the “safe by design” paradigm and avoid late-stage attrition. Increasingly, embryonic zebrafish ( Danio Rerio ) are recognised as a key human safety relevant in vivo test system. Hence, machine learning models were developed for identifying metal oxide nanomaterials causing lethality to embryonic zebrafish up to 24 hours post-fertilisation, or excess lethality in the period of 24–120 hours post-fertilisation, at concentrations of 250 ppm or less. Models were developed using data from the Nanomaterial Biological-Interactions Knowledgebase for a dataset of 44 diverse, coated and uncoated metal or, in one case, metalloid oxide nanomaterials. Different modelling approaches were evaluated using nested cross-validation on this dataset. Models were initially developed for both lethality endpoints using multiple descriptors representing the composition of the core, shell and surface functional groups, as well as particle characteristics. However, interestingly, the 24 hours post-fertilisation data were found to be harder to predict, which could reflect different exposure routes. Hence, subsequent analysis focused on the prediction of excess lethality at 120 hours-post fertilisation. The use of two data augmentation approaches, applied for the first time in nano-QSAR research, was explored, yet both failed to boost predictive performance. Interestingly, it was found that comparable results to those originally obtained using multiple descriptors could be obtained using a model based upon a single, simple descriptor: the Pauling electronegativity of the metal atom. Since it is widely recognised that a variety of intrinsic and extrinsic nanomaterial characteristics contribute to their toxicological effects, this is a surprising finding. This may partly reflect the need to investigate more sophisticated descriptors in future studies. Future studies are also required to examine how robust these modelling results are on truly external data, which were not used to select the single descriptor model. This will require further laboratory work to generate comparable data to those studied herein.
Research on Human Motion Data Filtering Based on Unscented Kalman Filter Algorithm

With the continuous development of economy in China, manual production has long been unable to meet the current social needs, and more and more companies have begun to use robots instead of manual operations to carry out changes[1]. In the manufacturing field, human-machine collaborative manufacturing can effectively combine the advantages of industrial robots and workers, reduce production costs, improve product quality, make manufacturing more flexible and individualized, and develop in the direction of high speed, high precision and high efficiency. A key factor in the manufacturing of human-machine collaboration is the safety and security, namely industrial robots operate with a staff job at the same time in the working environment, not harm, directly or indirectly to the staff. Therefore, how to ensure that the industrial robot can accurately obtain the current position of the staff and predict the future position, ensure the safety of the staff, and clarify the intention of the staff is an urgent problem to be solved. In this article, a method aimed at improving the accuracy of Kinect v2 sensor skeleton data is proposed. This method uses the kinematic filtering method of unscented Kalman filter to try to achieve consistency by combining observation data with the motion prediction data being executed. Finally, the Kinect v2 SDK is corrected by extracting motion information and constraining the length of human bones. Some errors occurred in the measurement data. The data source of this experiment is measured by Kinect v2 SDK. The algorithm is realized by Python, and the result of this experiment is compared with the data of Kinect v2 SDK.
Audio-based spam call detection

Spam communications are organized attempts of falsified claims with the purpose of marketing, spreading false information and deceiving the end recipient. Phone spam is an international nuisance, with the U. S. among the most spammed countries in the world in 2020. In addition to the agitating nature of these calls, criminal scams are defrauding subscribers of billions of dollars every year. Therefore, it is necessary to develop automated systems for the identification of spam calls to minimize fraud and reduce the displeasure of receiving them. The call origin, call duration and other Call Detail Records can be used to assess whether a call is fraudulent or not, but the actual audio content is overlooked. This work focuses on extracting acoustic features from voicemail recordings containing speech, which are used to train Machine Learning models that identify spam calls. Both local and global feature descriptors are used, including Mel-Frequency Cepstral Coefficients and Log-Mel Spectrum, and their efficacy for distinguishing spam from non-spam calls is explored. We demonstrate that a spam voice call can be detected while relying only on the acoustic information of the call. A further analysis of the temporal and spectral features that are most informative for the task is also presented.
Traditional vs. the blockchain-based architecture of 5G healthcare

Chapter Contents: 6.1 Introduction 6.1.1 Motivations 6.1.2 Structure of the chapter 6.2 5G-based smart healthcare industry: challenges, benefits, and use cases 6.2.1 5G healthcare challenges 6.2.2 5G healthcare benefits 6.2.3 5G healthcare use cases 6.3 Traditional 5G healthcare architecture 6.3.1 5G healthcare overall architecture 6.3.2 5G healthcare infrastructure architecture 6.3.3 5G healthcare RAN architecture 6.3.4 5G healthcare core network architecture 6.4 Blockchain-based 5G healthcare architecture 6.4.1 Blockchain 6.4.2 The components of a blockchain 6.4.3 The components of blockchain block 6.4.4 The blockchain-based architecture of 5G healthcare 6.5 Comparative analysis: traditional vs. blockchain-based architecture of 5G healthcare 6.5.1 Healthcare requirements 6.5.2 5G opportunities for healthcare requirements 6.5.3 Blockchain opportunities for healthcare requirements 6.5.4 Blockchain to support 5G healthcare architecture functions 6.5.5 Blockchain-based 5G healthcare architecture use cases 6.6 Conclusion References
Distinguishing Multispin Interactions from Lower-Order Effects

Multispin interactions can be engineered with artificial quantum spins. However, it is challenging to verify such interactions experimentally. Here, we describe two methods to characterize the $n$-local coupling of $n$ spins. First, we analyze the variation of the transition energy of the static system as a function of local spin fields. Standard measurement techniques are employed to distinguish $n$-local interactions between up to five spins from lower-order contributions in the presence of noise and spurious fields and couplings. Second, we show a detection technique that relies on time-dependent driving of the coupling term. Generalizations to larger system sizes are analyzed for both static and dynamic detection methods and we find that the dynamic method is asymptotically optimal when increasing the system size. The proposed methods enable robust exploration of multispin interactions across a broad range of both coupling strengths and qubit modalities.
AUGMENTED REALITY FOR DISTANCE EDUCATION: A SYSTEMATIC LITERATURE REVIEW

The COVID-19 pandemic brought about a change in the style and dynamics of teaching, placing a great responsibility on distance education. Limitations of this teaching modality, such as little engagement, low adherence rates, and high dropout rates, have already been raised in several works. Incorporating tools and technologies that support and help teachers improve the learning experience can be one solution to these problems. In recent research in distance education, augmented reality technology is listed as one of the most effective ways to help achieve digital inclusion. Thus, technology could be incorporated into distance classes to increase student interaction, interest, and learning immersion. Therefore, this work aims to carry out a literature review on augmented reality applications for distance education, raising the main areas of application, target audience, technologies, research’s temporal and territorial distribution, the main advantages, and the challenges. From the 15 works raised in the Web of Science, it is concluded that it is a current and world-class research topic. The application areas are extensive, but the most common are medicine, biology, physics, materials science, and computer science. The primary use is through the cell phone using markers. The advantages and challenges of each application were raised. This work contributes to stimulating the generation of related works and spreading application ideas to achieve more dynamic and less traditional learning.
Combining Multi-ratio Undersampling and Metric Learning for Imbalanced Classification

In classification, class imbalance is a factor that degrades the classification performance of many classification methods. Resampling is one widely accepted approach to the class imbalance; however, it still suffers from an insufficient data space, which also degrades performance. To overcome this, in this paper, an undersampling-based imbalanced classification framework, MMEnsemble, is proposed that incorporates metric learning into a multi-ratio undersampling-based ensemble. This framework also overcomes a problem with determining the appropriate sampling ratio in the multi-ratio ensemble method. It was evaluated by using 12 real-world datasets. It outperformed the state-of-the-art approaches of metric learning, undersampling, and oversampling in recall and ROC-AUC, and it performed comparably with them in terms of Gmean and F-measure metrics.
An improved spline adaptive filter for nonlinear system identification under impulsive noise environment

Aiming at the negative influence of impulse noise in conventional spline adaptive filtering (SAF) algorithm in nonlinear system identification, an improved spline adaptive filtering algorithm is proposed. The algorithm mainly improves on two aspects. First, the cost function is constructed by using the hyperbolic tangent function. When the independent variable fluctuates sharply, the cost function will only approach a finite value, which improves the robustness of the filter in an impulse noise environment. In addition, a pre-filtering observation strategy is proposed to further improve the convergence speed of the algorithm and reduce the steady-state error caused by the iterative process in the system. Through the analysis of the amplitude of the noise in the time domain and the frequency domain, the update rule of the weight is determined. The simulation results show that it has better performance than the existing SAF algorithm.
Does corporate social responsibility initiative dissuade the increasing electoral violence in Sub-Saharan Africa? Evidence from Nigeria’s oil producing region

Purpose The purpose of this paper is to critically examine the multinational oil companies (MOCs) corporate social responsibility (CSR) initiatives in Nigeria. Its special focus is to investigate the impact of the global memorandum of understanding (GMoU) on reducing incidents of electoral violence in oil-producing communities. Design/methodology/approach This paper adopts a survey technique, aimed at gathering information from a representative sample of the population, as it is essentially cross-sectional, describing and interpreting the current situation. A total of 1,200 households were sampled across the Niger Delta region of Nigeria. Findings The results from the use of a combined propensity score matching and logit model indicate that the GMoU model made a significant impact in deterring occurrences of electoral violence when interventions on cluster development boards are designed to mitigate the intricate of political clashes in the region. Practical implications This implies that CSR interventions of MOCs play a vital role in reducing incidents of electoral violence in Nigeria’s oil producing region. Social implications Reducing the increasing electoral violence in the oil host communities, will, in turn, create an enabling environment for more extensive and responsible business of Multinational Corporation in Sub-Saharan Africa. Originality/value This paper extends and contributes to the literature on CSR initiatives of multinational enterprises in developing countries and the rationale for demands for social projects by host communities. It concludes that business has an obligation to help in solving problems of public concern.
Developing and Optimizing a Model for Creating a Learning Chatbot Using the Example of Technical Support

Abstract A model for building a chatbot to automate technical support work was developed, the model was optimized and the developed chatbot was tested. This development will help to reduce the load on the existing technical support system and to solve users’ routine tasks without human intervention.
Combining Chains of Bayesian Models with Markov Melding

A challenge for practitioners of Bayesian inference is specifying a model that incorporates multiple relevant, heterogeneous data sets. It may be easier to instead specify distinct submodels for each source of data, then join the submodels together. We consider chains of submodels, where submodels directly relate to their neighbours via common quantities which may be parameters or deterministic functions thereof. We propose chained Markov melding, an extension of Markov melding, a generic method to combine chains of submodels into a joint model. One challenge we address is appropriately capturing the prior dependence between common quantities within a submodel, whilst also reconciling differences in priors for the same common quantity between two adjacent submodels. Estimating the posterior of the resulting overall joint model is also challenging, so we describe a sampler that uses the chain structure to incorporate information contained in the submodels in multiple stages, possibly in parallel. We demonstrate our methodology using two examples. The first example considers an ecological integrated population model, where multiple data sets are required to accurately estimate population immigration and reproduction rates. We also consider a joint longitudinal and time-to-event model with uncertain, submodel-derived event times. Chained Markov melding is a conceptually appealing approach to integrating submodels in these settings.
A new trust management framework based on the experience of users in industrial cloud computing using multi-criteria decision making

Purpose Nowadays, cloud platforms are used in many fields, including e-commerce, web applications, data storage, healthcare, gaming, mobile social networks, etc. However, security and privacy are still two significant concerns in this area. The target of this paper is to present a system for trust management in industrial cloud computing using the multi-criteria decision making (MCDM) approach. MCDM techniques have been developed to accommodate a wide range of applications. As a result, hundreds of approaches have been generated with even minor variations on current approaches spawning new study fields. Design/methodology/approach Cloud computing provides a fully scalable, accessible and flexible computing platform for various applications. Due to the multiple applications that cloud computing has found in numerous life features, users and providers have considered providing security in cloud communications. Due to its distributive nature, dynamic space and lack of transparency in performing cloud computing, it faces many challenges in providing security. For security improvement, trust management can play a very influential role. This paper proposes a generic analytical methodology that uses a series of assessment criteria to evaluate current trust management testing prototypes in industrial cloud computing and related fields. The authors utilize a MCDM approach in the present article. Due to the multi-dimensionality of the sustainability objective and the complexities of socio-economic and biophysical processes, MCDM approaches have become progressively common in decision-making for sustainable energy. Findings The results of comparing and evaluating the performance of this model show its ability to manage trust and the ability to adapt to changes in the behavior of service providers quickly. Using a simulation, all results are confirmed. The results of simulations and evaluation of the present paper indicate that the proposed model provides a more accurate evaluation of the credibility of cloud service providers than other models. Practical implications The number of cloud services and customers is vast and extremely competitive in cloud environments, where novel cloud services and customers can join at any time, while others can withdraw whenever they want. Because of cloud services' highly dynamic and dispersed design, trust management mechanisms must be highly flexible to obtain feedback and update trust outcomes as quickly as possible. The model presented in this article tries to improve users' trust in the cloud industry. Originality/value Using a method (MCDM) to find the best trust management solution based on user experience in industrial cloud computing is the novelty of this paper.
Grafted and vanishing random subspaces

The random subspace method (RSM) is an ensemble procedure in which each constituent learner is constructed using a randomly chosen subset of the data features. Regression trees are ideal candidate learners in RSM ensembles. By constructing trees upon different feature subsets, RSM reduces correlation between trees resulting in a stronger ensemble. Furthermore, it lessens computational burden by only considering a subset of the features when building each tree. Despite its apparent advantages, RSM has a notable drawback. In some instances a randomly chosen subspace may lack informative features. This is especially true in situations in which the number of truly informative variables is small relative to the total number of variables. Trees that are constructed using feature subsets lacking informative features can be damaging to the ensemble. Here we present grafted random subspace (GRS) and vanishing random subspace (VRS), two novel ensemble procedures designed to remedy the aforementioned drawback by reusing information across trees. Both techniques borrow from RSM by growing individual trees on randomly selected feature subsets. For each tree in a GRS ensemble, the most important variable is identified and guaranteed inclusion into the next q feature subsets. This allows GRS to recycle a promising feature from one tree across several successive trees, effectively grafting the variable into the next q active subsets. In the VRS procedure the least important feature is guaranteed exclusion from the next q feature subsets. This creates a more enriched pool of candidate variables from which the successive feature subsets are drawn.