Miracle-dz/UniFAR-FTUs huggingface dataset:
Determinants of Corporate Cash Holdings

Objective: Cash holdings are one of the fundamental financial decisions for the company. This study aims to examine the determinants of cash holdings in agricultural companies on the Indonesia Capital Market. Methods: The population in this study was all agricultural companies on the Indonesia Capital Market from 2012 to 2019. The sample was obtained through a purposive sampling method. Data analysis in this study used multiple regressions. Results: This study found that profitability, liquidity, and operating cash flow positively affect the cash holdings of agricultural companies in Indonesia. This study also finds that leverage, firm growth, and firm size have no effect on cash holdings in agricultural companies on the Indonesia Capital Market. Originality/Relevance: This study emphasizes the importance of profitability, liquidity, and operating cash flow as factors that affect the company's cash holdings for agricultural companies in Indonesia. Theoretical/methodological contributions: The results of this study strengthen and complement the results of previous research on the factors that affect a company's cash holdings. Social/management contributions: The findings of this study are important as an effort to map the condition of cash holdings of agricultural companies in Indonesia. This study also advises managers of agricultural companies in Indonesia to increase the company's cash holdings to anticipate future business risks.
The FOMC's New Individual Economic Projections and Macroeconomic Theories

This paper examines whether the individual economic projections made by the Federal Open Market Committee’s (FOMC) policymakers are consistent with macroeconomic theories: Okun’s law, the Phillips curve, and the Taylor rule. By analyzing the FOMC’s individual economic projections between 2007 and 2014, I find that they are consistent with Okun’s law, revealing a significantly negative relationship between unemployment and output growth projections. On the other hand, the relationship between inflation and unemployment projections associated with the Phillips curve is much weaker and more dispersed. The results on the FOMC’s reaction function, the Taylor rule, are mixed: The response of the projections of the federal funds rate against the inflation gap projections—the deviation of inflation projections from the target—is significantly positive, whereas the response against the corresponding output gap projections varies depending on the specification.
Maintenance Management Research of a Large-span Continuous Rigid Frame Bridge Based on Reliability Assessment by Using Strain Monitored Data

1. Shixing County Bureau of transportation, Shixing County, Shaoguan City, Guangdong Province, 512500, China 2. HARDA (XIAMEN) PLASTIC CO.,LTD, No.37, Huili Zone, TongAn Industrial Area, Xiamen, China 3. Guangdong College of Industry and Commerce, No. 1O98, Guangzhou North Road, Tianhe District, Guangzhou, China 4. 48-13B, Baohe road 81, Long Gang District, Shenzhen, Guangdong, China; 5. 54-28D, Yi Jin Yuan, Long Cheng Street, Long Gang District, Shenzhen, Guangdong, China
Ab Initio Particle-based Object Manipulation

This paper presents Particle-based Object Manipulation (Prompt), a new approach to robot manipulation of novel objects ab initio, without prior object models or pre-training on a large object data set. The key element of Prompt is a particle-based object representation, in which each particle represents a point in the object, the local geometric, physical, and other features of the point, and also its relation with other particles. Like the model-based analytic approaches to manipulation, the particle representation enables the robot to reason about the object's geometry and dynamics in order to choose suitable manipulation actions. Like the data-driven approaches, the particle representation is learned online in real-time from visual sensor input, specifically, multi-view RGB images. The particle representation thus connects visual perception with robot control. Prompt combines the benefits of both model-based reasoning and data-driven learning. We show empirically that Prompt successfully handles a variety of everyday objects, some of which are transparent. It handles various manipulation tasks, including grasping, pushing, etc,. Our experiments also show that Prompt outperforms a state-of-the-art data-driven grasping method on the daily objects, even though it does not use any offline training data.
A novel methodology to predict regulations using Deep Learning

—The current air trafﬁc control system tries to allocate as many ﬂights as possible in a scenario that is expected to be time-efﬁcient, cost-efﬁcient, and safe. To guaranty these safety conditions, it is performed a cyclic process known as Demand-Capacity Balancing. During this process, a specialized Air Trafﬁc Controller analyses the situations where the demand is over the capacity to identify the required corrective actions. These corrective actions are mostly in the form of regulations, and they are necessary to avoid overload during the day of operation. The task of declaring a regulation is complicated, very time-consuming, and based on the Air Trafﬁc Controller’s experience. A massive amount of information must be considered simultaneously, together with a risk maturation process because of the uncertainty and granularity in the information. This paper proposes and evaluates two Deep Learning models able to mimic the current procedure’s behavior, and therefore, helping the specialized Air Trafﬁc Controller to automatically detect the imbalances that will require regulation. Both models, one based on Convolutional Neural Networks, and the second one based on Recurrent Neural Networks, have demonstrated the potential to predict regulations, with an accuracy of 81.45% and 80.73% respectively over the entire MUAC region in 30-minute intervals. This accuracy can be increased by up to 91% by developing specialized models for each airspace sector. Additionally, we performed an in-depth analysis of the most relevant features using SHapley Additive exPlanations .
Grid-connected control of PV-Wind hybrid energy system

Received Oct 14, 2020 Revised Mar 22, 2021 Accepted Apr 23, 2021 This paper presented a strategy for modeling, simulation and control of a hybrid grid connected power system which is in fact a rather complex system. In this work, we study how to use two renewable energies in an efficient manner without any disturbing of the main network. Our hybrid energy system (HES) is composed by two renewable energy sources, the photovoltaic source and wind source. It is better for these two systems to work at their maximum power in order to return the investment cost of the system. The proposed solution is to connect those generators to the electrical grid via the AC bus with trackers. Where the photovoltaic generator (PVG) is followed by a DC/DC boost converter, controlled by a perturb and observe (P&O) tracker, then followed by a three-phase voltage inverter (3-ph-VSI) which is controlled by the watt-var decoupled method. The wind system is based on permanent magnet synchronous machine (PMSM) which is used as a variable speed generator and directly connected to the turbine (without gearbox) followed by back-to-back converters. The grid side converter (GSC) allows us to control the DC bus voltage and unity power factor, while the machine side converter (MSC) ensures us to control the PMSM speed. Knowing that pitch angle control is not considered in our study. The MPPT control strategy, which is based on the optimum peak speed ratio (OTSR), is used to ensure the most energy efficiency despite variations in wind speed. The coupling of the two systems is done via the Point of Common Collecting (PCC). Finally, simulation results show the feasibility of our solution and the good performances of the proposed control strategy, applied for mediumsized HES in power generation. They also demonstrate that such system topology is very advantageous.
Music Recommender System

This thesis deals with the field of personalized recommendation of music. Modern approaches are described and analyzed, especially the methods of collaborative filtering. The main focus is the processing of temporal dimension data of user actions and its usage to improve recommendation systems. The most important part is the analysis of models predicting the activity of users. Prediction accuracy and efficiency of solutions are compared with emphasis on the usability in practice. This thesis contains experimental results of presented methods tested on real-world data.
Neural Posterior Domain Randomization

: Combining domain randomization and reinforcement learning is a widely used approach to obtain control policies that can bridge the gap between simulation and reality. However, existing methods make limiting assumptions on the form of the domain parameter distribution which prevents them from utilizing the full power of domain randomization. Typically, a restricted family of probability distributions (e.g., normal or uniform) is chosen a priori for every parameter. Furthermore, straightforward approaches based on deep learning require differentiable simulators, which are either not available or can only simulate a limited class of systems. Such rigid assumptions diminish the applicability of domain randomization in robotics. Building upon recently proposed neural likelihood-free inference methods, we introduce Neural Posterior Domain Randomization (NPDR), an algorithm that alternates between learning a policy from a randomized simulator and adapting the posterior distribution over the simulator’s parameters in a Bayesian fashion. Our approach only requires a parameterized simulator, coarse prior ranges, a policy (optionally with optimization routine), and a small set of real-world observations. Most importantly, the domain parameter distribution is not restricted to a speciﬁc family, parameters can be correlated, and the simulator does not have to be differentiable. We show that the presented method is able to efﬁciently adapt the posterior over the domain parameters to closer match the observed dynamics. Moreover, we demonstrate that NPDR can learn transferable policies using fewer real-world rollouts than comparable algorithms.
On the joint use of Artificial Intelligence and Brain-Imaging Techniques in Technology-enhanced Learning Environments: A Systematic Literature Review

Recent meta-analysis and literature reviews support that adaptive learning systems are components of effective instruction. These exciting results are motivating researchers to explore new technologies that provide relevant students’ information to promote a better-personalized experience for students to achieve better learning outcomes in technology-enhanced learning environments. A new trend is related to the studies that use brain-imaging techniques to provide relevant students’ information for educational systems, aiming to enable an enhanced personalized experience. Some of these studies are making use of artiﬁcial intelligence to provide real-time monitoring of students’ cognitive phenomena supplied by brain-imaging techniques such as electroencephalography and functional magnetic resonance imaging. Therefore, considering the relevance of the application of artiﬁcial intelligence in studies that use brain-imaging techniques combined with technology-enhanced learning environments and the lack of a current understanding of how these techniques have been used in this context, we present a systematic literature review (SLR) that aims to explore which artiﬁcial intelligence algorithms have been adopted, what are their purposes in studies that apply brain-imaging techniques in educational technologies and which were the results reported in these studies related to the use of artiﬁcial intelligence algorithms. The systematic literature review was conducted according to the recommendation of a well-accepted guideline to perform a rigorous review of the current literature. The search was conducted in seven academic databases in January 2020 and resulted in a total of 6089 studies that was reduced to 20 studies for the ﬁnal analysis.
Analysis and design of class E-LCCL compensation circuit topology circuit topology for capacitive power transfer system

Khairul Kamarudin Hasan, Shakir Saat, Yusmarnita Yusop, Masmaria Abdul Majid, Mohd Sufian Ramli Faculty of Electrical Engineering, Universiti Teknologi MARA (UiTM), Cawangan Johor, Kampus Pasir Gudang, Johor, Malaysia Faculty of Electronic and Computer Engineering, Universiti Teknikal Malaysia, Melaka, Malaysia School of Business and Social Sciences, Albukhary International University, Kedah Darul Aman, Malaysia
Personalized Diversity in MDP-Based Recommendation Engines

Diversity is an important aspect of recommender systems. Recommendations that are diverse encourage users to explore new interests and avoid boredom over time. However, common recommender systems designed to fit users’ dominant interests tend to make narrow recommendations and are likely to form filter bubbles. Furthermore, uniformly enforcing diversity among all users is likely to harm the utility of recommendations. Therefore, we adopt a dynamic recommender model that adjusts individual recommendation diversity according to their tolerance to diversity. We pose the recommendation problem as a Markov Decision Process with modified reward function, which favors recommendations that relevant and satisfies users’ preferred level of diversity. We evaluate our method against a diversity-driven approach and a relevancy-driven approach in a synthetic setting. We compare the relevancy and diversity of recommendations using standard evaluation metrics. Using our quantitative results, we will provide qualitative results on the trade-off between relevancy and diversity and on the prevention of preference polarization.
Information Warfare: Memes and their Attack on the Most Valued Critical Infrastructure—the Democratic Institution Itself

Social media has become a potent vector for the spread of disinformation. Content initially posted by bots, trolls, or malicious actors is often picked up and magnified by ordinary users, greatly extending its influence and reach. In order to combat disinformation online, it is important to understand how users interact with and spread this type of content, unwittingly or not. We studied patterns in the sharing of propaganda and disinformation on social media through political image-based memes. Initially, we began with a selection of 12 memes. In our first survey, we narrowed this down from 12 memes to six based on the responses received with respect to the message it was trying to convey and representation of varying viewpoints along the ideological spectrum. Ultimately, we chose a selection of six memes. Four of them involved climate-change with two considered left-leaning and the other two right-leaning, and the remaining two were focused on particular politicians and also split along ideological lines. Next, we conducted a second survey in order to better understand the behavior of ordinary users as they interact with propaganda and disinformation on social media. Particular attention was paid to differences based on political affiliation and psychological factors, including personality and trait affect. Negative types of affect appear to dominate the level of engagement Republicans and Independents have with memes, while positive types of affect and extraversion do the same for Democrats.
Towards Integrating Fairness Transparently in Industrial Applications

Numerous Machine Learning (ML) bias-related failures in recent years have led to scrutiny of how companies incorporate aspects of transparency and accountability in their ML life-cycles. Companies have a responsibility to monitor ML processes for bias and mitigate any bias detected, ensure business product integrity, preserve customer loyalty, and protect brand image. Challenges speciﬁc to industry ML projects can be broadly categorized into principled documentation, human oversight, and need for mechanisms that enable information reuse and improve cost efﬁciency. We highlight speciﬁc road-blocks and propose conceptual solutions on a per-category basis for ML practitioners and organizational subject matter experts. Our systematic approach tackles these challenges by integrating mechanized and human-in-the-loop components in bias detection, mitigation, and documentation of projects at various stages of the ML lifecycle. To motivate the implementation of our system—SIFT (System to Integrate Fairness Transparently)—we present its structural primitives with an example real-world use case on how it can be used to identify potential biases and determine appropriate mitigation strategies in a participatory manner.
Challenges in the development of the wine industry: an exploratory study

Milkovic, S. J., Zmaic, K. & Sudaric, T. (2021). Challenges in the development of the wine industry: an exploratory study. Bulg. J. Agric. Sci., 27 (2), 271–278 The objective of this paper is to investigate and identify the structure of winemakers in Eastern Croatia and the problems and obstacles they face in their business. Survey was the method used to collect the data, and survey questionnaire was used as the instrument. The survey was performed on a sample of n=30 winemakers from five Eastern Croatian counties (OsijekBaranja, Vukovar-Srijem, Virovitica-Podravina, Požega-Slavonija and Brod-Posavina Counties). Survey results pinpoint institutional factors, expensive production inputs, and insufficient oenological support and expertise as the obstacles winemakers face in further development of this industry. Survey results can be used as a framework for future research of this type and as the basis for the development of future measures and policies concerning wine production.
Analysing antecedence of an intelligent voice assistant use intention and behaviour

Background: As the use of intelligent voice assistant applications becomes more prevalent, a growing body of studies are examining individuals' interactions with intelligent voice assistants. However, very limited research has focused on comparing the antecedents of both use and non-use behaviour of individuals, based on the technology acceptance models. To fill this gap, the present study investigated antecedents of intelligent voice assistance use and use intention in a cross-sectional setting. Additionally, to go one step beyond the existing literature on technology acceptance models and theories, a new construct termed perceived needs, as well as the moderating role of perceived privacy concerns and perceived awareness, are introduced. Method: A quantitative, cross-sectional research design was utilised using a nonprobability sampling strategy through the online networking platforms. Total of 277 (n = 155 users vs n = 122 non-users) international adults age between 20-74 years (79.6% female, 20.4 % male) contributed to the study. Ordinary least squares (OLS) linear regression and Bivariate logistic regression analyses for non-users and users were conducted, respectively. Results: Both analyses revealed that peer influence and perceived needs related to the intention to use intelligent voice assistants for non-users, which applied to the current intelligent voice assistance users where privacy concerns were considered. Surprisingly, the key determinants of technology acceptance and use theories, such as perceived ease of use and perceived usefulness, did not hold for intelligent voice assistance usage. Conclusion: The current research contributed to the field by validating new constructs of perceived needs and the moderation role of perceived privacy concerns. However, in order to build on an existing body of knowledge, future studies should further examine the moderation role of perceived privacy concerns, perceived ease of use, and perceived usefulness in the same domain.
HJB-RL: Initializing Reinforcement Learning with Optimal Control Policies Applied to Autonomous Drone Racing

—In this work we present a planning and control method for a quadrotor in an autonomous drone race. Our method combines the advantages of both model-based optimal control and model-free deep reinforcement learning. We consider a single drone racing on a track marked by a series of gates, through which it must maneuver in minimum time. Firstly we solve the discretized Hamilton-Jacobi-Bellman (HJB) equation to produce a closed-loop policy for a simpliﬁed, reduced order model of the drone. Next, we train a deep network policy in a supervised fashion to mimic the HJB policy. Finally, we further train this network using policy gradient reinforcement learning on the full drone dynamics model with a low-level feedback controller in the loop. This gives a deep network policy for controlling the drone to pass through a single gate. In a race course, this policy is applied successively to each new oncoming gate to guide the drone through the course. The resulting policy completes a high-ﬁdelity AirSim drone race with 12 gates in 34.89s (on average), outracing a model-based HJB policy by 33.20s, a supervised learning policy by 1.24s, and a trajectory planning policy by 12.99s, while a model-free RL policy was never able to complete the race.
Regulatory Assessment of Off-Target Changes and Spurious DNA Insertions in Gene-Edited Organisms for Agri-Food Use

Worldwide, an increasing number of regulatory systems have begun to consider applications for the authorization of activities involving gene-edited organisms for agri-food use. Although a handful of countries have made advances in establishing regulatory criteria and gathering practical experience in this regard, there is still a general need for regulatory cooperation concerning capacity building and development of harmonized criteria. Consequently, many biotechnology regulators need to quickly become more acquainted with the numerous technological possibilities enclosed under the concept of "gene editing", and to incorporate criteria for their regulatory assessment. This article contains a simplified introduction to the state of the art in genome editing, described from a regulatory perspective. In particular, two issues of higher practical importance are covered in detail, namely, off-target effects and unintended DNA insertions. The detailed review of current evidence regarding those issues serves as the basis for proposing concrete regulatory criteria to address them. doi: 10.21423/jrs-v09i1lema
Development and Evolution of Agile Changes in a World of Change

1,2 Agile software development is an approach first codified in the Agile Manifesto in 2001. This was a statement of core values that became associated with a set of principles and practices. Key ideas include early and constant customer involvement, self-organizing teams that embrace change, rapid delivery of value, short timeboxed iterations coordinated by a shared list of items—a product backlog and driven by user stories and use cases, clean code, test-driven development, and continuous integration. The values, principles, and practices have permeated the technical and business world, translated and modified to fit many domains, affecting both production and management. But as with any good idea, agility can be misinterpreted, or used when inappropriate. Even a proper implementation must be tempered with good understanding of the domain, overall context, and appropriateness of selected agile practices, and modified to fit the enterprise, the domain, and the problem. In this paper, we briefly trace the evolution of agile methods, placing them within a wider organizational framework, and offer guidelines for their use.