Miracle-dz/UniFAR-FTUs huggingface dataset:
How can Older Adults Shop Online in the Future? Developing Design Principles for Virtual-Commerce Stores

The Covid-19 crisis has significantly affected retailers in interacting with their customers. Now more than ever, retailers innovate and strive for effective replication of in-store experiences in an online environment. Virtual reality can be effective in emulating experiential shopping at home. However, particularly older adults (OAs) experience difficulties in using these novel technologies as they undergo several age-related changes, such as deteriorating vision or weakened hand-eye-coordination. Thus, we identify the requirements of OAs and develop seven design principles for virtual-commerce stores for OAs. We follow a design science research approach with a staged research process and iterative cycles. The requirements and design principles are evaluated and refined by conducting semi-structured interviews and focus groups. This study offers prescriptive knowledge in form of design principles for virtual retail and a specific customer segment and contributes to customer-centred design literature. These principles can further guide developers in designing digital systems for OAs.
Disruption-Free Load Balancing for Aerial Access Network

A fundamental issue of 6G networks with aerial access networks (AAN) as a core component is that user devices will send high-volume traffic via AAN to backend servers. As such, it is critical to load balance such traffic such that it will not cause network congestion or disruption and affect users’ experience in 6G networks. Motivated by the success of software-defined networking-based load balancing, this paper proposes a novel system called Tigris, to load balance high-volume AAN traffic in 6G networks. Different from existing load balancing solutions in traditional networks, Tigris tackles the fundamental disruption-resistant challenge in 6G networks for avoiding disruption of continuing flows and the control-path update challenge for limiting the throughput of updating load balancing instructions. Tigris achieves disruption-free and low-control-path-cost load balancing for AAN traffic by developing an online algorithm to compute disruption-resistant, per-flow load balancing policies and a novel bottom-up algorithm to compile the per-flow policies into a highly compact rule set, which remains disruption-resistant and has a low control-path cost. We use extensive evaluation to demonstrate the efficiency and efficacy of Tigris to achieve zero disruption of continuing AAN flows and an extremely low control-path update overhead, while existing load balancing techniques in traditional networks such as ECMP cause high load variance and disrupt almost 100% continuing AAN flows.
Guest Editorial Special Issue on Continual Unsupervised Sensorimotor Learning

HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L’archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d’enseignement et de recherche français ou étrangers, des laboratoires publics ou privés. Guest Editorial Special Issue on Continual Unsupervised Sensorimotor Learning Nicolas Navarro-Guerrero, Sao Mai Nguyen, Erhan Oztop, Junpei Zhong
Learning the Dynamics of Time Delay Systems with Trainable Delays

In this paper, we propose a delay learning algorithm for time delay neural networks (TDNNs) based on mini-batch gradient descent. We show that the proposed algorithm is suitable for learning the dynamics of nonlinear time delay systems using TDNNs with trainable delays. The delays are introduced in the input layer and are learned with the same approach as weights and biases. The learned delays are easy to interpret and they are not restricted to discrete values. We demonstrate the method with an example of learning the dynamics of an autonomous time delay system. We show the performance of two proposed network architectures with trainable delays and compare it to a standard TDNN which has a large number of ﬁxed (non-trainable) input delays. We demonstrate that the networks with trainable input delays achieve signiﬁcantly better performance in neural network simulations compared to the standard TDNN. We also highlight that undesired local minima may appear due to the delays in the networks
X-Posts Explained: Analyzing and Predicting Controversial Contributions in Thematically Diverse Reddit Forums

Most online discussion forums capture user feedback in the form of "likes'' and other similar signals, but limit this to positive feedback. A few forums, most notably Reddit, offer both upvotes and downvotes. Reddit posts that received a large number of both upvotes and downvotes receive an explicit "controversiality'' marker, while heavily downvoted posts are hidden from the standard view of the discussion, and only shown upon explicit clicks. This paper aims at understanding the nature and role of controversial posts in Reddit, considering four subreddits of very different natures: US politics, World politics, Relationships and Soccer. We design a feature space and devise a classifier to predict the occurrence of a controversial post given a prefix of a path in a discussion thread. Our findings include that these classifiers exhibit different behaviors in the four subreddits, and we identify key features for the respective cases. An in-depth analysis indicates that controversial posts do not arise as troll-like behavior, but are often due to a polarizing topic (mostly in US politics), off-topic content, or mentions of individual entities such as soccer players or clubs.
A Socio-Technical Perspective on Organizational Interaction with AI: A Literature Review

This paper serves as an overview of IS research on artificial intelligence and the organizational interaction with such systems, from a socio-technical perspective. Therefore, we conduct a structured literature review of relevant outlets (journals and conferences). We show that the literature can be synthesized into four groups: decision-making, interaction, knowledge management, and economic effects of AI. Further focusing on the second group, we identify certain dimensions of interaction research: technological implementation, collaborative factors, interface design, task allocation, task support, process allocation, and learning. From this analysis, a framework is derived. With this article, we contribute to the AI, the socio-technical systems, and the organizational interactions-literature in IS research, but most importantly, we offer a novel socio-technical perspective on AI.
Robust Reinforcement Learning: A Constrained Game-theoretic Approach

Reinforcement learning (RL) methods provide state-of-art performance in complex control tasks. However, it has been widely recognized that RL methods often fail to generalize due to unaccounted uncertainties. In this work, we propose a game theoretic framework for robust reinforcement learning that comprises many previous works as special cases. We formulate robust RL as a constrained minimax game between the RL agent and an environmental agent which represents uncertainties such as model parameter variations and adversarial disturbances. To solve the competitive optimization problems arising in our framework, we propose to use competitive mirror descent (CMD). This method accounts for the interactive nature of the game at each iteration while using Bregman divergences to adapt to the global structure of the constraint set. leveraging Lagrangian duality, we demonstrate an RRL policy gradient algorithm based on CMD. We empirically show that our algorithm is stable for large step sizes, resulting in faster convergence on constrained linear quadratic games.
Evaluation and Prediction of COVID-19 Prevention and Control Strategy Based on the SEIR-AQ Infectious Disease Model

Based on the SEIR model, which takes into account prevention and control measures, prevention and control awareness, and economic level and medical level indicators, this paper proposes an infectious disease model of “susceptible-exposed-infected-removed-asymptomatic-isolated” (short for SEIR-AQ) to assess and predict the development of the COVID-19 pandemic with different prevention and control strategies. The kinetic parameters of the SEIR-AQ model were obtained by fitting, and the parameters of the SEIR-AQ model were solved through the Euler method. Furthermore, the effects of different countries’ prevention and control strategies on the number of infections, the proportion of isolation, the number of deaths, and the number of recoveries were also simulated. The theoretical analysis showed that measures such as isolation for prevention and control and medical tracking isolation had a significant inhibitory effect on the development of the COVID-19 pandemic, among which stratified treatment and enhanced awareness played a key role in the rapid regression of the peak of COVID-19-infected patients. Conclusion of the Simulation. The SEIR-AQ model can be used to evaluate the development status of the COVID-19 epidemic and has some theoretical value for the prediction of COVID-19.
American Politicians Diverge Systematically, Indian Politicians do so Chaotically: Text Embeddings as a Window into Party Polarization

Conversations on polarization are increasingly central to discussions of politics and society, but the schisms between parties and states can be hard to identify systematically in what politicians say. In this paper, we demonstrate the use of representation learning as a window into political dialogue on social media through the tweets authored by politicians on Twitter. We propose to embed politicians in a space such that their output embedding vectors represent the content similarity between the two politicians based on their tweets. We further propose a short-text based embedding technique to overcome some of the shortcomings of the previous methods. The learnt embeddings for politicians of India and the United States show two trends. First, that in the US case, we find a clear distinction between Democrats and Republicans, which is also reflected in the coalescing of the states that lean towards each party placing likewise in a graphical space. However, in the Indian case, the federal structure, multiparty system, and linguistic differences clearly manifest in the coalescing political discourse in the largely monolingual north and the scattered regional states. Our work shows ways in which machine learning methods can offer a window into thinking about how polarized political discourses manifest in what politicians say online.
UNDERSTANDING VALUE CREATION IN DIGITAL COMPANIES – A UNDERSTANDING VALUE CREATION IN DIGITAL COMPANIES – A TAXONOMY OF IOT ENABLED BUSINESS MODELS TAXONOMY OF IOT ENABLED BUSINESS MODELS

The spread of the Internet of Things offers companies the potential to exert a disruptive influence on existing market structures and entire domains. The shift from product to service orientation and the integration of the customer as a value co-creator makes the identification and development of value-adding, IoT-based offerings a central challenge. As a link between strategy and business processes, business models are a suitable tool to meet this challenge. However, present business models lack of consideration of IoT specific characteristics. Against this background we provide a taxonomy for the description of IoT-based business models based on systematic literature research. Furthermore, the taxonomy is applied to 103 business models, demonstrating its usefulness. We also provide insights into the design of business models within two domains. The taxonomy provides a tool for investigating business models, especially how IoT can be incorporated into them and also a conceptual basis for future research.
Social networking platforms to close the gender gap: an analysis of female doctoral students in information systems

The gender gap poses a major challenge in all professions and industries related to information systems. Despite many initiatives, the number of female workers in these fields is still low, raising the question of how the information systems itself could help narrow this gap. This paper builds on prior research that has shown that networking, interaction and role models contribute positively to the advancement of women. By focusing on female doctoral students in the information systems field, it explores how social media could contribute in this endeavor. Based on a questionnaire and systematic platform review, the paper identifies requirements for social networking sites (SNS) and presents an analysis of existing platforms. It reveals that a single platform fails to cover the requirements of the target group, therefore making it necessary to integrate different platforms. Recommendations are proposed to support the conecpt of a future platform for female doctoral students.
Effects of Ultrasound Treatment, UV Irradiation and Avishan-e-Denaei Essential Oil on Oxidative Stability of Sunflower Oil

Abstract Avishan-e-Denaei is usually used as herbal tea, flavoring agents and medicinal plants. In this study, effects of Avishan-e-Denaei essential oil (EO) extracted by ohmic ultrasound extractor (0.08 %) and synthetic antioxidant (BHT) on oxidative stability of sunflower oil at different accelerated condition including ultrasound (30 KHz, 100 W, 30 s) and Ultraviolet (UV) Irradiation (30 min) were compared. GC-MS analyses of EO revealed , the major components were thymol (62.4 %), carvacrol (14.8 %) and γ-terpinene (4.5 %). The results of peroxide value (PV), anisidine value (AV), thiobarbituric acid (TBA) value, free fatty acid (FFA), iodine value (IV) and induction period (IP) showed sunflower oil samples supplemented with EO (treated or no treated by ultrasound and UV) were more stable in comparison to BHT against oxidation. In addition, EO was able to decrease the stable 2, 2-diphenyl-1-picrylhydrazyl free radical (DPPH) with a 50 % inhibition concentration (IC50 ) of 22.3 ± 1.2 µg/mL. The results indicate that ultrasound and UV could accelerate lipid degradation and the addition of the EO as a natural antioxidant, significantly reduced lipid oxidation and improved oil stability.
Changes in cervical sagittal alignment and the effects on cervical parameters in patients with cervical spondylotic myelopathy after laminoplasty

Objective: To monitor changes in cervical parameters before and after laminoplasty surgery. Cervical parameters and health-related quality-of-life (HRQOL) values that may be affected after laminoplasty were examined before and after surgery. The clinical and radiological course of these values was monitored, and their interaction with all spinal radiological parameters was revealed. Materials and Methods: Nineteen patients who underwent clinical and radiological evaluation for 2 years were followed in this study. Neck disability index, visual analog scale, and short form 36 scores were determined to evaluate HRQOL. For radiological parameters, the C0-C2 angle, C2-C7 angle, cervical sagittal vertical axis, T1 slope angle, neck tilt (NT) and thoracic inlet angle were used. The results of the 4-month, 1 year and 2-year follow-ups were statistically evaluated. Results: Both the HRQOL and cervical radiological parameters deteriorated in the first 4 months and returned to normal in the 2nd year. Statistically, all parameters were meaningful (P < 0.05), except for NT. Conclusion: Cervical parameters and HRQOL values, which deteriorated in the early period, recovered in the late period in the long-term follow-up of patients undergoing laminoplasty. The important point is that preoperative cervical parameters suitable for laminoplasty should be present, and spinopelvic parameters should be normal.
Effect of different diets on left ventricle and area postrema in adult male albino rats predisposed to prolonged intake of high salt

Background Excess dietary salt is the most common environmental factor that contributes to the pathogenesis of hypertension. High-sugar and high-fat intakes in hypertension accelerate left ventricular hypertrophy and heart failure and increase the incidence of strokes and infarctions. Aim This work aimed to assess the correlation of long-term high-salt intake with hypertension in adult male albino rats and to evaluate the effect of different dietary regimens on left ventricle and area postrema of those rats. Materials and methods A total of 24 adult male albino rats were used in this study. Rats were divided into group I (control), which included six rats that received standard diet, and group II (experimental), which included 18 rats that received high-salt diet for 8 weeks, which was then classified as subgroup IIA (reversal), IIB (high sugar), and IIC (high fat). Systolic blood pressure was measured at baseline, after 8 weeks, and after 12 weeks from the start of the experiment. Specimens from left ventricle of the heart and area postrema were subjected to histological and immunohistochemical examinations. Data of the body weight, heart weight, and thickness of the left ventricle were analyzed statistically. Results The current study revealed increased weight gain, heart weight, left ventricular thickness, and systolic blood pressure in different experimental groups. These groups showed disturbed histological architecture of left ventricle of the heart and area postrema, but in subgroup IIA, partial improvement of the histological findings was found. Conclusion There is a correlation between high-salt diet and hypertension. Salt, sugar, and fat in high doses have hazardous effects on the histological architecture of the left ventricle and area postrema of adult male albino rats. Restriction of salt in diet leads to some improvement of the adverse effects.
ON THE IDENTIFIABILITY AND ESTIMABILITY OF LATENT CLASS MODELS: A BAYESIAN ANALYSIS

Latent Class Models (LCMs) assume that the population, from which the observed sample is taken, is composed of m mutually exclusive latent classes. The parameters of interest are the probabilities with which a randomly chosen subject belongs to each of the latent classes. For each person, L dichotomous measurements are made. In LCM it is assumed that, for each item, every class has a specific probability of positive responses: these are the parameters of interest in the conditional model. To ensure a coherent inference on the structural parameters, their identifiability is needed. Different solutions to this problem can be found in the psychometric literature. A first group can be found in the early works of Andersen (1954), McHugh (1956), Madansky (1960), Goodman (1974) and Clogg and Goodman (1984). These works are all related since they are based on linear equation systems defined on the marginal probabilities of response patterns of the individuals from the population as a whole. Another approach to analyze this identification problem is motivated by the hierarchical structure underlying LCMs. The key idea is to relate the identifiability of the statistical model with the identifiability of both the conditional model and the marginal latent model. A heuristic analysis in this line can be found in Maris (1999). This paper is motivated by the last approach. At the identification level, we establish that, under prior independence of the parameters of interest, the identification of the marginal model is a necessary condition for the identification of the statistical model. A second result we explore in this paper deals with the consistency of a Bayesian estimator of the probabilities of correct responses given the latent class, denoted by P. More specifically, we show that the conditional expectation of the P given the observations and the latent class probabilities converges to P. A similar result is established for the probabilities of latent classes.
OUP CORRECTED AUTOPAGE PROOFS – FINAL, 24/10/20, SPi

Steven J. Cooke, Christine L. Madliger, Jordanna N. Bergman, Vivian M. Nguyen, Sean J. Landsman, Oliver P. Love, Jodie L. Rummer, and Craig E. Franklin, Optimism and opportunities for conservation physiology in the Anthropocene: a synthesis and conclusions In: Conservation Physiology: Applications for Wildlife Conservation and Management. Edited by: Christine L. Madliger, Craig E. Franklin, Oliver P. Love, and Steven J. Cooke, Oxford University Press (2021). © Oxford University Press. DOI: 10.1093/oso/9780198843610.003. 0019 CHAPTER 19
Bioherbicidal Potential of Eucalyptus and Clove Oil and their Combinations on Four Weedy Species

Essential oils of eucalyptus leaves and clove buds were isolated and tested for their bioherbicidal potential on different annual weed species. Gas ChromatographyMass Spectrophotometry analysis (GC-MS) identified thirteen compounds representing around 87.11% of the total isolated eucalyptus oil. The main constituent was 1,8-cineole, which accounted for 68.15% of the total identified compounds. As for clove oil, eleven compounds were identified, representing 90.03% of the total compounds. Eugenol was the dominant compound and accounted for 73.89%. The bioherbicidal efficacy of the two oils and their combinations by three concentrations (2.5, 5, and 10%) were tested on four weedy species, namely Chenopodium album, Raphanus raphanistrum, Melilotus indicus, and Sisymbrium irio. The isolated oils significantly affected the dry weight and inhibited the seedling growth of the four weedy species. Using the two essential oils, individually or within their combinations, showed the highest bioherbicidal efficacy against the targeted weeds in comparison with the synthetic herbicide. In addition, utilization of the two essential oils as natural bioherbicides caused an electrolyte leakage as a result of membrane disruption and loss of integrity of weed seedling tissues. The results showed the possibility of using eucalyptus and clove essential oils individually or in combination as a desirable alternative to synthetic herbicides for sustainable weed management.