Reviewerly/exHarmony huggingface dataset:
Artificial intelligence (AI) library services innovative conceptual framework for the digital transformation of university education

Purpose Artificial intelligence (AI) is one of the latest digital transformation (DT) technological trends the university library can use to provide library users with alternative educational services. AI can foster intelligent decisions for retrieving and sharing information for learning and research. However, extant literature confirms a low adoption rate by the university libraries in using AI to provide innovative alternative services, as this is missing in their strategic plan. The research develops (AI-LSICF) an artificial intelligence library services innovative conceptual framework to provide new insight into how AI technology can be used to deliver value-added innovative library services to achieve digital transformation. It will also encourage library and information professionals to adopt AI to complement effective service delivery. Design/methodology/approach This study adopts a qualitative content analysis to investigate extant literature on how AI adoption fosters innovative services in various organisations. The study also used content analysis to generate possible solutions to aid AI service innovation and delivery in university libraries. Findings This study uses its findings to develop an Artificial Intelligence Library Services Innovative Conceptual Framework (AI-LSICF) by integrating AI applications and functions into the digital transformation framework elements and discussed using a service innovation framework. Research limitations/implications In research, AI-LSICF helps increase an understanding of AI by presenting new insights into how the university library can leverage technology to actualise innovation in service provision to foster DT. This trail will be valuable to scholars and academics interested in addressing the application pathways of AI library service innovation, which is still under-explored in digital transformation. Practical implications In practice, AI-LSICF could reform the information industry from its traditional brands into a more applied and resolutely customer-driven organisation. This reformation will awaken awareness of how librarians and information professionals can leverage technology to catch up with digital transformation in this age of the fourth industrial revolution. Social implications The enlightenment of AI-LSICF will motivate library professionals to take advantage of AI's potential to enhance their current business model and achieve a unique competitive advantage within their community. Originality/value AI-LSICF development serves as a revelation, motivating university libraries and information professionals to consider AI in their strategic plan to enable technology to support university education. This act will enable alternative service delivery in the face of unforeseen circumstances like technological disruption and the present global COVID-19 pandemic that requires non-physical interaction.
Artificial intelligence and knowledge management: A partnership between human and AI

Emerging artificial intelligence (AI) capabilities will likely pervade nearly all organizational contours and activities, including knowledge management (KM). This article aims to uncover opportunities associated with the implementation of emerging systems empowered by AI for KM. In doing so, we explicate the potential role of AI in supporting fundamental dimensions of KM: creation, storage and retrieval, sharing, and application of knowledge. We then propose practical ways to build the partnership between humans and AI in supporting organizational KM activities and provide several implications for the development and management of AI systems based on the components of people, infrastructures, and processes.
An interpretable probabilistic model for short-term solar power forecasting using natural gradient boosting

PV power forecasting models are predominantly based on machine learning algorithms which do not provide any insight into or explanation about their predictions (black boxes). Therefore, their direct implementation in environments where transparency is required, and the trust associated with their predictions may be questioned. To this end, we propose a two stage probabilistic forecasting framework able to generate highly accurate, reliable, and sharp forecasts yet offering full transparency on both the point forecasts and the prediction intervals (PIs). In the first stage, we exploit natural gradient boosting (NGBoost) for yielding probabilistic forecasts, while in the second stage, we calculate the Shapley additive explanation (SHAP) values in order to fully comprehend why a prediction was made. To highlight the performance and the applicability of the proposed framework, real data from two PV parks located in Southern Germany are employed. Comparative results with two state-of-the-art algorithms, namely Gaussian process and lower upper bound estimation, manifest a significant increase in the point forecast accuracy and in the overall probabilistic performance. Most importantly, a detailed analysis of the model's complex nonlinear relationships and interaction effects between the various features is presented. This allows interpreting the model, identifying some learned physical properties, explaining individual predictions, reducing the computational requirements for the training without jeopardizing the model accuracy, detecting possible bugs, and gaining trust in the model. Finally, we conclude that the model was able to develop complex nonlinear relationships which follow known physical properties as well as human logic and intuition.
Layer-Wise Analysis of a Self-Supervised Speech Representation Model

Recently proposed self-supervised learning approaches have been successful for pre-training speech representation models. The utility of these learned representations has been observed empirically, but not much has been studied about the type or extent of information encoded in the pre-trained representations themselves. Developing such insights can help understand the capabilities and limits of these models and enable the research community to more efficiently develop their usage for downstream applications. In this work, we begin to fill this gap by examining one recent and successful pre-trained model (wav2vec 2.0), via its intermediate representation vectors, using a suite of analysis tools. We use the metrics of canonical correlation, mutual information, and performance on simple downstream tasks with non-parametric probes, in order to (i) query for acoustic and linguistic information content, (ii) characterize the evolution of information across model layers, and (iii) understand how fine-tuning the model for automatic speech recognition (ASR) affects these observations. Our findings motivate modifying the fine-tuning protocol for ASR, which produces improved word error rates in a low-resource setting.
Machine Learning in Disaster Management: Recent Developments in Methods and Applications

Recent years include the world’s hottest year, while they have been marked mainly, besides the COVID-19 pandemic, by climate-related disasters, based on data collected by the Emergency Events Database (EM-DAT). Besides the human losses, disasters cause significant and often catastrophic socioeconomic impacts, including economic losses. Recent developments in artificial intelligence (AI) and especially in machine learning (ML) and deep learning (DL) have been used to better cope with the severe and often catastrophic impacts of disasters. This paper aims to provide an overview of the research studies, presented since 2017, focusing on ML and DL developed methods for disaster management. In particular, focus has been given on studies in the areas of disaster and hazard prediction, risk and vulnerability assessment, disaster detection, early warning systems, disaster monitoring, damage assessment and post-disaster response as well as cases studies. Furthermore, some recently developed ML and DL applications for disaster management have been analyzed. A discussion of the findings is provided as well as directions for further research.
Towards Vivid and Diverse Image Colorization with Generative Color Prior

Colorization has attracted increasing interest in recent years. Classic reference-based methods usually rely on external color images for plausible results. A large image database or online search engine is inevitably required for retrieving such exemplars. Recent deep-learning-based methods could automatically colorize images at a low cost. However, unsatisfactory artifacts and incoherent colors are always accompanied. In this work, we aim at recovering vivid colors by leveraging the rich and diverse color priors encapsulated in a pretrained Generative Adversarial Networks (GAN). Specifically, we first "retrieve" matched features (similar to exemplars) via a GAN encoder and then incorporate these features into the colorization process with feature modulations. Thanks to the powerful generative color prior and delicate designs, our method could produce vivid colors with a single forward pass. Moreover, it is highly convenient to obtain diverse results by modifying GAN latent codes. Our method also inherits the merit of interpretable controls of GANs and could attain controllable and smooth transitions by walking through GAN latent space. Extensive experiments and user studies demonstrate that our method achieves superior performance than previous works.
Lifelong generative modeling

Lifelong learning is the problem of learning multiple consecutive tasks in a sequential manner, where knowledge gained from previous tasks is retained and used to aid future learning over the lifetime of the learner. It is essential towards the development of intelligent machines that can adapt to their surroundings. In this work we focus on a lifelong learning approach to unsupervised generative modeling, where we continuously incorporate newly observed distributions into a learned model. We do so through a student-teacher Variational Autoencoder architecture which allows us to learn and preserve all the distributions seen so far, without the need to retain the past data nor the past models. Through the introduction of a novel cross-model regularizer, inspired by a Bayesian update rule, the student model leverages the information learned by the teacher, which acts as a probabilistic knowledge store. The regularizer reduces the effect of catastrophic interference that appears when we learn over sequences of distributions. We validate our model's performance on sequential variants of MNIST, FashionMNIST, PermutedMNIST, SVHN and Celeb-A and demonstrate that our model mitigates the effects of catastrophic interference faced by neural networks in sequential learning scenarios.
Adaptive Semi-Supervised Classifier Ensemble for High Dimensional Data Classification

High dimensional data classification with very limited labeled training data is a challenging task in the area of data mining. In order to tackle this task, we first propose a feature selection-based semi-supervised classifier ensemble framework (FSCE) to perform high dimensional data classification. Then, we design an adaptive semi-supervised classifier ensemble framework (ASCE) to improve the performance of FSCE. When compared with FSCE, ASCE is characterized by an adaptive feature selection process, an adaptive weighting process (AWP), and an auxiliary training set generation process (ATSGP). The adaptive feature selection process generates a set of compact subspaces based on the selected attributes obtained by the feature selection algorithms, while the AWP associates each basic semi-supervised classifier in the ensemble with a weight value. The ATSGP enlarges the training set with unlabeled samples. In addition, a set of nonparametric tests are adopted to compare multiple semi-supervised classifier ensemble (SSCE)approaches over different datasets. The experiments on 20 high dimensional real-world datasets show that: 1) the two adaptive processes in ASCE are useful for improving the performance of the SSCE approach and 2) ASCE works well on high dimensional datasets with very limited labeled training data, and outperforms most state-of-the-art SSCE approaches.
ICDAR 2019 Competition on Document Image Binarization (DIBCO 2019)

DIBCO 2019 is the international Competition on Document Image Binarization organized in conjunction with the ICDAR 2019 conference. The general objective of the contest is to identify current advances in document image binarization of machine-printed and handwritten document images using performance evaluation measures that are motivated by document image analysis and recognition requirements. This paper describes the competition details including the evaluation measures used as well as the performance of the 24 submitted methods along with a brief description of each method.
A comparison study of harmony search and genetic algorithm for the max-cut problem

The max-cut problem is one of well-known NP-complete problems and has applications in various fields such as the design process for VLSI (Very-Large-Scale Integration) chips and spin glass theory in statistical physics. In this paper, a harmony search algorithm for the max-cut problem is proposed. Compared to genetic algorithm, harmony search algorithm has advantages of generating a new vector after considering all of the existing vectors and requiring only a few number of parameters to be determined before the run of the algorithm. For 31 benchmark graphs of various types, the proposed harmony search algorithm is compared with a newly developed genetic algorithm and another genetic algorithm taken from the recent literature are presented. The proposed harmony search algorithm produced significantly better results than the two genetic algorithms.
Reliable Re-Detection for Long-Term Tracking

In long-term object tracking, severe occlusion and deformation could happen to the targets. Due to the accumulation and propagation of estimation errors, even a few frames of full occlusion in a video sequence could lead to the failure of the tracking. Recently, correlation filter-based trackers have received lots of attention and gained great success in real-time tracking. However, most of them ignore the reliability of the tracked results and lack an effective mechanism to refine the unreliable results. To cope with these issues, in this paper, we propose a long-term tracking framework composed of both tracking-by-detection and re-detection modules. The tracking-by-detection part is built on the discriminative correlation filter (DCF) integrated with a color-based model. The re-detection module filters a large number of detection candidates and refines the tracking results. With the proposed re-detection refinement, detected results in each frame were re-evaluated and re-detection is carried out when necessary. Besides, the reliability estimation in the re-detection module also helps adaptively update the object detector and keep it from corruption. The proposed re-detection module can be integrated into correlation filter-based trackers to consistently boost the performance. Extensive experiments on the OTB-2015, Temple-Color, and VOT-2015 benchmarks show that the proposed method performs favorably against the state-of-the-art methods while still running faster than 40 f/s.
A Deep Active Survival Analysis approach for precision treatment recommendations: Application of prostate cancer

Survival analysis has been developed and applied in the number of areas including manufacturing, finance, economics and healthcare. In healthcare domain, usually clinical data are high-dimensional, sparse and complex and sometimes there exists few amount of time-to-event (labeled) instances. Therefore building an accurate survival model from electronic health records is challenging. With this motivation, we address this issue and provide a new survival analysis framework using deep learning and active learning with a novel sampling strategy. First, our approach provides better representation with lower dimensions from clinical features using labeled (time-to-event) and unlabeled (censored) instances and then actively trains the survival model by labeling the censored data using an oracle. As a clinical assistive tool, we introduce a simple effective treatment recommendation approach based on our survival model. In the experimental study, we apply our approach on SEER-Medicare data related to prostate cancer among African–Americans and white patients. The results indicate that our approach outperforms significantly than baseline models.
CSP-E2: An abuse-free contract signing protocol with low-storage TTP for energy-efficient electronic transaction ecosystems

In an increasingly connected world, energy efficiency has become a significant problem. Online electronic transactions are booming based on shopping convenience, fast payments, and lower prices. Electronic trading systems are one of the main sources of energy consumption because the so-called contract signing protocol (CSP) is a crucial part of e-commerce. Therefore, the energy consumption is largely determined by the efficiency of the CSP. To this end, an abuse-free CSP with low-storage TTP is proposed to eliminate the enormous energy-consumption problem originating from the unfit protocol design. This protocol exploits a proxy key scheme and introduces a lightweight TTP storage policy that greatly reduces the TTP burden by integrating symmetric encryption aiming at constructing an energy-efficient electronic transaction ecosystem. Theoretical analysis and comparative analysis show that the storage requirement of TTP is reduced by more than 50% while ensuring fairness, optimism and abuse-freeness in our protocol. Moreover, the exchange steps of our protocol are reduced so that the protocol efficiency is improved.
Toward Interpretable Deep Reinforcement Learning with Linear Model U-Trees

Deep Reinforcement Learning (DRL) has achieved impressive success in many applications. A key component of many DRL models is a neural network representing a Q function, to estimate the expected cumulative reward following a state-action pair. The Q function neural network contains a lot of implicit knowledge about the RL problems, but often remains unexamined and uninterpreted. To our knowledge, this work develops the first mimic learning framework for Q functions in DRL. We introduce Linear Model U-trees (LMUTs) to approximate neural network predictions. An LMUT is learned using a novel on-line algorithm that is well-suited for an active play setting, where the mimic learner observes an ongoing interaction between the neural net and the environment. Empirical evaluation shows that an LMUT mimics a Q function substantially better than five baseline methods. The transparent tree structure of an LMUT facilitates understanding the network's learned strategic knowledge by analyzing feature influence, extracting rules, and highlighting the super-pixels in image inputs. Code related to this paper is available at: https://github.com/Guiliang/uTree_mimic_mountain_car .
Is your virtual self as sensational as your real? Virtual Reality: The effect of body consciousness on the experience of exercise sensations

Past research has shown that Virtual Reality (VR) is an effective method for reducing the perception of pain and effort associated with exercise. As pain and effort are subjective feelings, they are influenced by a variety of psychological factors, including one's awareness of internal body sensations, known as Private Body Consciousness (PBC). The goal of the present study was to investigate whether the effectiveness of VR in reducing the feeling of exercise pain and effort is moderated by PBC. Eighty participants were recruited to this study and were randomly assigned to a VR or a non-VR control group. All participants were required to maintain a 20% 1RM isometric bicep curl, whilst reporting ratings of pain intensity and perception of effort. Participants in the VR group completed the isometric bicep curl task whilst wearing a VR device which simulated an exercising environment. Participants in the non-VR group completed a conventional isometric bicep curl exercise without VR. Participants' heart rate was continuously monitored along with time to exhaustion. A questionnaire was used to assess PBC. Participants in the VR group reported significantly lower pain and effort and exhibited longer time to exhaustion compared to the non-VR group. Notably, PBC had no effect on these measures and did not interact with the VR manipulation. Results verified that VR during exercise could reduce negative sensations associated with exercise regardless of the levels of PBC.
OTPaaS—One Time Password as a Service

Conventional password-based authentication is considered inadequate by users as many online services started to affect each other. Online credentials are used to recover other credentials and complex attacks are directed to the weakest one of many of these online credentials. As researchers are looking for new authentication techniques, one time passwords, which is a two-factor authentication scheme, looks like a natural enhancement over conventional username/password schemes. The manuscript places the OTP verifier to the cloud to ease adoption of its usage by cloud service providers. When the OTP verifier is placed on the cloud as a service, other cloud service providers could outsource their OTP deployments as well as cloud users could activate their respective account on the OTP provider on several cloud services. This enables them to use several cloud services without the difficulty of managing several OTP accounts for each cloud service. On the other hand, OTP service provision saves inexperienced small to medium enterprises from spending extra costs for OTP provisioning hardware, software, and employers. The paper outlines architecture to build a secure, privacy-friendly, and sound OTP provider in the cloud to outsource the second factor of authentication. Cloud user registration to OTP provider, service provider activation, and authentication phases are inspected. The security and privacy considerations of the proposed architecture are defined and analyzed. Attacks from outsiders, unlinkability properties of user profiles, attacks from curious service providers or OTP verifiers are mitigated within the given assumptions. The proposed solution, which locates the OTP provider in the cloud, is rendered robust and sound as a result of the analysis.