Reviewerly/exHarmony huggingface dataset:
Driving as well as on a Sunny Day? Predicting Driver's Fixation in Rainy Weather Conditions via a Dual-Branch Visual Model

Dear Editor, Traffic driving is a dynamic and complicated task in which drivers are required to pay close attention to the important targets or regions to maintain safe margins. Rainy weather conditions make it more challenging with factors such as low visibility, raindrops, pedestrian with umbrellas, wipers, etc. Studies showed that rainy condition affects driving safety significantly [1], [2]. It is reported that, in raining weather condition, the odds for a fatal accident are 3.340 times higher on highways than on streets [3]. An investigation of the relationship between rainfall and fatal crashes in Texas from 1994 to 2018 on fatality analysis reporting system (FARS) database illustrated that rain-related fatal crashes represented about 6.8% of the total fatal crashes on average, moreover, the proportion showed high variability at the annual, monthly, and hourly time scales [4]. Therefore, raining is a complex and critical factor for road safety planning and management. In fact, the traffic environment is a dynamic scene with multiple sources of information, including important targets that are highly relevant to the current driving task as well as irrelevant targets that may distract the driving task [5]. Driven by the visual selective attention mechanism, experienced drivers often focus their attention on the most important regions and only show concern for objects related to driving safety in those salient regions. This selective attention mechanism [6], [7] helps drivers reduce the interference of irrelevant scene information and guarantee the driving safety. Understanding the selective attention mechanism of experienced drivers and then simulating the efficient saliency detection process in rainy conditions may help driving a car in rainy conditions as well as on a sunny day.
A machine learning approach for Bengali handwritten vowel character recognition

Recognition of handwritten characters is complex because of the different shapes and numbers of characters. Many handwritten character recognition strategies have been proposed for both English and other major dialects. Bengali is generally considered the fifth most spoken local language in the world. It is the official and most widely spoken language of Bangladesh and the second most widely spoken among the 22 posted dialects of India. To improve the recognition of handwritten Bengali characters, we developed a different approach in this study using face mapping. It is quite effective in distinguishing different characters. The real highlight is that the recognition results are more efficient than expected with a simple machine learning technique. The proposed method uses the Python library Scikit-Learn, including NumPy, Pandas, Matplotlib, and support vector machine (SVM) classifier. The proposed model uses a dataset derived from the BanglaLekha isolated dataset for the training and testing part. The new approach shows positive results and looks promising. It showed accuracy up to 94% for a particular character and 91% on average for all characters.
Implementation of a Nationwide Knowledge-Based COVID-19 Contact Tracing Training Program, 2020

In the United States, the public health response to control COVID-19 required rapid expansion of the contact tracing workforce from approximately 2200 personnel prepandemic to more than 100 000 during the pandemic. We describe the development and implementation of a free nationwide training course for COVID-19 contact tracers that launched April 28, 2020, and summarize participant characteristics and evaluation findings through December 31, 2020. Uptake of the online asynchronous training was substantial: 90 643 registrants completed the course during the first 8 months. In an analysis of a subset of course participants (n = 13 697), 7724 (56.4%) reported having no prepandemic public health experience and 7178 (52.4%) reported currently serving as case investigators, contact tracers, or both. Most participants who completed a course evaluation reported satisfaction with course utility (94.8%; 59 497 of 62 753) and improved understanding of contact tracing practice (93.0%; 66 107 of 71 048). These findings suggest that the course successfully reached the intended audience of new public health practitioners. Lessons learned from this implementation indicate that an introductory course level is appropriate for a national knowledge-based training that aims to complement jurisdiction-specific training. In addition, offering a range of implementation options can promote course uptake among public health agency staff. This course supported the emerging needs of the public health practice community by training a workforce to fill an important gap during the COVID-19 pandemic and could serve as a feasible model for enhancing workforce knowledge for future and ongoing public health threats.
Toward Benchmarking of Long-Term Spatio-Temporal Maps of Pedestrian Flows for Human-Aware Navigation

Despite the advances in mobile robotics, the introduction of autonomous robots in human-populated environments is rather slow. One of the fundamental reasons is the acceptance of robots by people directly affected by a robot’s presence. Understanding human behavior and dynamics is essential for planning when and how robots should traverse busy environments without disrupting people’s natural motion and causing irritation. Research has exploited various techniques to build spatio-temporal representations of people’s presence and flows and compared their applicability to plan optimal paths in the future. Many comparisons of how dynamic map-building techniques show how one method compares on a dataset versus another, but without consistent datasets and high-quality comparison metrics, it is difficult to assess how these various methods compare as a whole and in specific tasks. This article proposes a methodology for creating high-quality criteria with interpretable results for comparing long-term spatio-temporal representations for human-aware path planning and human-aware navigation scheduling. Two criteria derived from the methodology are then applied to compare the representations built by the techniques found in the literature. The approaches are compared on a real-world, long-term dataset, and the conception is validated in a field experiment on a robotic platform deployed in a human-populated environment. Our results indicate that continuous spatio-temporal methods independently modeling spatial and temporal phenomena outperformed other modeling approaches. Our results provide a baseline for future work to compare a wide range of methods employed for long-term navigation and provide researchers with an understanding of how these various methods compare in various scenarios.
Subjective Attributes in Conversational Recommendation Systems: Challenges and Opportunities

The ubiquity of recommender systems has increased the need for higher-bandwidth, natural and efficient communication with users. This need is increasingly filled by recommenders that support natural language interaction, often conversationally. Given the inherent semantic subjectivity present in natural language, we argue that modeling subjective attributes in recommenders is a critical, yet understudied, avenue of AI research. We propose a novel framework for understanding different forms of subjectivity, examine various recommender tasks that will benefit from a systematic treatment of subjective attributes, and outline a number of research challenges.
Risk-Aware Stochastic Shortest Path

We treat the problem of risk-aware control for stochastic shortest path (SSP) on Markov decision processes (MDP). Typically, expectation is considered for SSP, which however is oblivious to the incurred risk. We present an alternative view, instead optimizing conditional value-at-risk (CVaR), an established risk measure. We treat both Markov chains as well as MDP and introduce, through novel insights, two algorithms, based on linear programming and value iteration, respectively. Both algorithms offer precise and provably correct solutions. Evaluation of our prototype implementation shows that risk-aware control is feasible on several moderately sized models.
An Optimal Transport Approach to Deep Metric Learning (Student Abstract)

Capturing visual similarity among images is the core of many computer vision and pattern recognition tasks. This problem can be formulated in such a paradigm called metric learning. Most research in the area has been mainly focusing on improving the loss functions and similarity measures. However, due to the ignoring of geometric structure, existing methods often lead to sub-optimal results. Thus, several recent research methods took advantage of Wasserstein distance between batches of samples to characterize the spacial geometry. Although these approaches can achieve enhanced performance, the aggregation over batches definitely hinders Wasserstein distance's superior measure capability and leads to high computational complexity. To address this limitation, we propose a novel Deep Wasserstein Metric Learning framework, which employs Wasserstein distance to precisely capture the relationship among various images under ranking-based loss functions such as contrastive loss and triplet loss. Our method directly computes the distance between images, considering the geometry at a finer granularity than batch level. Furthermore, we introduce a new efficient algorithm using Sinkhorn approximation and Wasserstein measure coreset. The experimental results demonstrate the improvements of our framework over various baselines in different applications and benchmark datasets.
Dynamic Knowledge Management Capabilities: An Approach to High-Performance Organization

The purpose of this research was to study the effect of dynamic knowledge management capability on high-performance organizations and organizational performance. The data collection was carried out using questionnaires from 4-5-star hotels in Thailand, for a total of 148 hotels. The data analysis was performed using descriptive statistics followed by Pearson’s correlation analysis, confirmatory factor analysis, and structural equation modeling. The research results revealed that dynamic knowledge management capability had a positive direct effect on high-performing organizations and a positive indirect effect on organizational performance. Therefore, executives should emphasize the building of dynamic knowledge management capabilities to improve the performance of high-performance organizations, which will lead to performance strength beyond rivals. Doi: 10.28991/HIJ-2022-03-03-01 Full Text: PDF
Glow-WaveGAN 2: High-quality Zero-shot Text-to-speech Synthesis and Any-to-any Voice Conversion

The zero-shot scenario for speech generation aims at synthesizing a novel unseen voice with only one utterance of the target speaker. Although the challenges of adapting new voices in zero-shot scenario exist in both stages -- acoustic modeling and vocoder, previous works usually consider the problem from only one stage. In this paper, we extend our previous Glow-WaveGAN to Glow-WaveGAN 2, aiming to solve the problem from both stages for high-quality zero-shot text-to-speech and any-to-any voice conversion. We first build a universal WaveGAN model for extracting latent distribution $p(z)$ of speech and reconstructing waveform from it. Then a flow-based acoustic model only needs to learn the same $p(z)$ from texts, which naturally avoids the mismatch between the acoustic model and the vocoder, resulting in high-quality generated speech without model fine-tuning. Based on a continuous speaker space and the reversible property of flows, the conditional distribution can be obtained for any speaker, and thus we can further conduct high-quality zero-shot speech generation for new speakers. We particularly investigate two methods to construct the speaker space, namely pre-trained speaker encoder and jointly-trained speaker encoder. The superiority of Glow-WaveGAN 2 has been proved through TTS and VC experiments conducted on LibriTTS corpus and VTCK corpus.
Decentralized and Privacy Sensitive Data De-Duplication Framework for Convenient Big Data Management in Cloud Backup Systems

The number of customers transferring information to cloud storage has grown significantly, with the rising prevalence of cloud computing. The rapidly rising data volume in the cloud, mostly on one side, is followed by a large replication of data. On the other hand, if there is a single duplicate copy of stored symmetrical information in the de-duplicate cloud backup the manipulation or lack of a single copy may cause untold failure. Thus, the deduplication of files and the auditing of credibility are extremely necessary and how they are achieved safely and effectively must be addressed in academic and commercial contexts urgently. In order to tune in this task by using application recognition, data similitude, and locality to simplify decentralized deduplication with two-tier internode and application deduction, we suggest a flexible direct decentralized symmetry deduplication architecture in a cloud scenario. It first distributes application logic to the contents of the directory through implementation-oriented steering to maintain a deployment location and also attributes the same kind of information to the cloud backup node with the storage node specificity by means of a hand printing-based network model to attain adequate global deduplication performance. We build up a new ownership mechanism during file deduplication to ensure continuity of tagging and symmetrical modeling and verify shared ownership. In addition, we plan an effective ownership policy maintenance plan. In order to introduce a probabilistic key process and reduce key storage capacity, a user-helped key is used for in-user block deduplication. Finally, the protection and efficiency audit demonstrate that the data integrity and accuracy of our system are ensured and symmetrically effective in the management of data ownership.
DEVELOPMENT OF SUNSHINE BASED MODELS FOR ESTIMATING GLOBAL SOLAR RADIATION OVER KANO AND IKEJA, NIGERIA

In this present study, three (3) new sunshine based models were developed and evaluated using observed monthly average daily global solar radiation and sunshine hours meteorological variables spanning through thirty one years (1980-2010). The estimated global solar radiation values obtained from the empirical regression models for the developed models and nine (9) distinctive existing sunshine based models were compared through validation indicators of Mean Bias Error (MBE), Root Mean Square Error (RMSE), Mean Percentage Error (MPE), t – test, Nash – Sutcliffe Equation (NSE), Index of Agreement (IA) and coefficient of determination (R2) allowing the identification and recommendation of the most accurate model suitable for the estimation of global solar radiation in Kano and Ikeja located in the Sahelian and Coastal regions respectively. The results in this present study revealed that the developed exponent exponential and quadratic latitude 2 sunshine based models were found more appropriate for estimation of global solar radiation in Kano (Latitude 12.05 and Longitude 8.20 ) and Ikeja (Latitude 6.58 and Longitude 3.33 ) respectively. The evaluated existing linear (1st order) and quadratic (2nd order) sunshine based models for Kano were found more suitable for the estimation of global solar radiation as compared with those found through literature survey. The empirical regression equations and the values of the validation indicators obtained in this study for each model varies significantly in each of the regions under investigation indicating that the twelve (12) sunshine based models are site – specific. The correlation between the
Towards Intrinsic Common Discriminative Features Learning for Face Forgery Detection Using Adversarial Learning

Existing face forgery detection methods usually treat face forgery detection as a binary classification problem and adopt deep convolution neural networks to learn discriminative features. The ideal discriminative features should be only related to the real/fake labels of facial images. However, we observe that the features learned by vanilla classification networks are correlated to unnecessary properties, such as forgery methods and facial identities. Such phenomenon would limit forgery detection performance especially for the generalization ability. Motivated by this, we propose a novel method which utilizes adversarial learning to eliminate the negative effect of different forgery methods and facial identities, which helps classification network to learn intrinsic common discriminative features for face forgery detection. To leverage data lacking ground truth label of facial identities, we design a special identity discriminator based on similarity information derived from off-the-shelf face recognition model. Extensive experiments demonstrate the effectiveness of the proposed method under both intra-dataset and cross-dataset evaluation settings.
Intelligent System for Detection and Identification of Ground Anomalies for Rescue

The search and identification of people lost in an emergency is a very important activity, it is carried out to assist in human lives in danger, and the unavailability of support technologies. Unmanned aerial vehicles (UAV) act directly in this activity for greater capacity in the coverage of the area in a shorter time of operation. A brief state-of-the-art survey is presented, and specific needs are raised for accuracy and practical application. In this joint work, low-processing image recognition alternatives for use in UAV will be explored to face challenges such as location in large areas, small targets, and orientation. Assessments are performed on real images using Inception, SSD, and Yolo. For tracking, MIL, KCF, and Boosting techniques are applied to empirically observe the results of test missions. Preliminary results show that the proposed method is viable for action in the search and rescue of people.
Managing biomass in semi-arid Burkina Faso: Strategies and levers for better crop and livestock production in contrasted farm systems

The semi-arid zone of Burkina Faso is characterized by strong climate variability and declining soil fertility associated with low biomass production. The main objective of this study was to identify key levers to improve biomass management in semi-arid Burkina Faso for diverse farm types. Farm diversity was captured with a statistical typology complemented by a participatory typology established with local farmers. Biomass management was described for the different farm types based on survey information obtained from 228 households across two villages. Fuzzy Cognitive Mapping (FCM) was conducted to represent biomass production strategies of each farm type. After a sensitivity analysis which revealed model robustness, scenario analysis was performed with the FCMs to explore farm type-specific options for alleviating biomass scarcity. Two contrasting scenarios were built based on observations and insights from the survey and focus group discussions with farmers and included (1) deliberate exchange of crop residue with manure, and (2) a subsidies policy allowing a reduction in prices of 30% for farm inputs coupled with increased off-farm revenue for the subsistence-oriented farms. The statistical typology identified four farm types, distinguishing between subsistence or market-oriented farms and crop or livestock-oriented production. The participatory typology partly confirmed these four main types, even though other criteria of distinction were given by farmers. Different farm types used contrasted strategies in biomass production and management which were mainly driven by the total cultivated area and the herd size. The farm type with the largest herd and smallest cultivated land was the only one to rely on grain inflow from outside the farm to meet its household food requirement. The inflow of crop residue was also largest for this farm type. In contrast, crop residue outflow was mainly observed for the subsistence-oriented crop farm type, which had the smallest fodder needs. The scenario analysis using FCM suggested that biomass exchange had a negligible effect on farm performance but that the subsidy and income diversification scenario positively impacted crop and livestock production, especially for the subsistence-oriented types. Our study pointed out that FCM is a useful tool to not only describe system dynamics but also to reveal levers for improvement through sensitivity and scenario analysis. These levers included income diversification (for subsistence farms especially), improved production and storage of forage (for large herd owners), and investment into equipment and better access to markets (for market-oriented farms).
Conceptual framework for decentralised information management along the entire lifecycle of a built asset.

The construction industry is characterised by a high level of fragmentation, inefficient collaboration and a lack of trust between project stakeholders. Issues due to the fragmented nature of the construction industry are extenuated from centralised Building Information Modelling approaches. Blockchain technology can help address information management issues by providing data traceability, transparency, and immutability. First, this paper reviews centralised and decentralised approaches to lifecycle information management. Second, a conceptual framework for decentralised information management workflow based on blockchain technology and the Inter-Planetary File System is proposed. Smart contracts can improve the information flow between different phases by providing more accountability.
When linguistics meets web technologies. Recent advances in modelling linguistic linked data

This article provides a comprehensive and up-to-date survey of models and vocabularies for creating linguistic linked data (LLD) focusing on the latest developments in the area and both building upon and complementing previous works covering similar territory. The article begins with an overview of some recent trends which have had a significant impact on linked data models and vocabularies. Next, we give a general overview of existing vocabularies and models for different categories of LLD resource. After which we look at some of the latest developments in community standards and initiatives including descriptions of recent work on the OntoLex-Lemon model, a survey of recent initiatives in linguistic annotation and LLD, and a discussion of the LLD metadata vocabularies META-SHARE and lime. In the next part of the paper, we focus on the influence of projects on LLD models and vocabularies, starting with a general survey of relevant projects, before dedicating individual sections to a number of recent projects and their impact on LLD vocabularies and models. Finally, in the conclusion, we look ahead at some future challenges for LLD models and vocabularies. The appendix to the paper consists of a brief introduction to the OntoLex-Lemon model.
COP: Control &amp; Observability-aware Planning

In this research, we aim to answer the question: How to combine Closed-Loop State and Input Sensitivity-based with Observability-aware trajectory planning? These possibly op-posite optimization objectives can be used to improve trajectory control tracking and, at the same time, estimation performance. Our proposed novel Control & Observability-aware Planning (COP) framework is the first that uses these possibly opposing objectives in a Single-Objective Optimization Problem (SOOP) based on the Augmented Weighted Tchebycheff method to perform the balancing of them and generation of Bézier curve-based trajectories. Statistically relevant simulations for a 3D quadrotor unmanned aerial vehicle (UAV) case study produce results that support our claims and show the negative correlation between both objectives. We were able to reduce the positional mean integral error norm as well as the estimation uncertainty with the same trajectory to comparable levels of the trajectories optimized with individual objectives.