Miracle-dz/UniFAR-FTUs huggingface dataset:
Cooperative Control of Multiple Quadrotors for Transporting a Common Payload

This paper investigates the problem of controlling a team of quadrotors that cooperatively transport a common payload. The main contribution of this study is to propose a cooperative control algorithm based on a decentralized algorithm. This strategy is comprised of two main steps: the first one is calculating the basic control vectors for each quadrotor using Moore–Penrose theory aiming at cooperative transport of an object and the second one is combining these vectors with individual control vectors, which are obtained from a closed-loop non-linear robust controller. In this regard, a nonlinear robust controller is designed based on Second Order Sliding Mode (SOSMC) approach using Extended Kalman-Bucy Filter (EKBF) to estimate the unmeasured states which is capable of facing external disturbances. The distinctive features of this approach include robustness against model uncertainties along with high flexibility in designing the control parameters to have an optimal solution for the nonlinear dynamics of the system. Design of the controller is based on Lyapunov method which can provide the stability of the end-effecter during the tracking of the desired trajectory. Finally, simulation results are given to illustrate the effectiveness of the proposed method for the cooperative quadrotors to transport a common payload in various maneuvers. Review History: Received: 28 March 2018 Revised: 19 October 2018 Accepted: 22 October 2018 Available Online: 25 October 2018
Work performance: is it one thing or many things? The multidimensionality of performance in a Middle Eastern context

This paper aims at exploring the multidimensional nature of performance in a Middle Eastern country. Using eighteen-item scales, 1,000 employees from twenty industrial firms were surveyed, representing the three managerial levels, i.e. top, middle and bottom management. Both self-performance ratings (SPR) and immediatesupervisor-performance ratings (ISPR) were used to examine the dimensionality of performance. The study results revealed that performance is a multifaceted concept, and that it comprises five factors. Both SPR and ISPR were found to be multidimensional constructs. The implications of the results for both researchers and practitioners are also discussed in the paper.
Distributed Learning Algorithms for Opportunistic Spectrum Access in Infrastructure-less Networks

An opportunistic spectrum access (OSA) for the infrastructure-less (or cognitive ad-hoc) network has received significant attention thanks to emerging paradigms such as the Internet of Things (IoTs) and smart grids. Research in this area has evolved from the \r{ho}rand algorithm requiring prior knowledge of the number of active secondary users (SUs) to the musical chair (MC) algorithm where the number of SUs are unknown and estimated independently at each SU. These works ignore the number of collisions in the network leading to wastage of power and bring down the effective life of battery operated SUs. In this paper, we develop algorithms for OSA that learn faster and incurs fewer number of collisions i.e. energy efficient. We consider two types of infrastructure-less decentralized networks: 1) static network where the number of SUs are fixed but unknown, and 2) dynamic network where SUs can independently enter or leave the network. We set up the problem as a multi-player mult-armed bandit and develop two distributed algorithms. The analysis shows that when all the SUs independently implement the proposed algorithms, the loss in throughput compared to the optimal throughput, i.e. regret, is a constant with high probability and significantly outperforms existing algorithms both in terms of regret and number of collisions. Fewer collisions make them ideally suitable for battery operated SU terminals. We validate our claims through exhaustive simulated experiments as well as through a realistic USRP based experiments in a real radio environment.
Personalization Recommendation Algorithm Based on Trust Correlation Degree and Matrix Factorization

The rapid development of the Internet of Things (IoT) and e-commerce has brought a lot of convenience to people’s lives. IoT applications generate a large number of services and user data. It is necessary to design a personalized recommendation technology suitable for the users of IoT services and improve the user experience. In this paper, a recommendation algorithm with trusted relevance combined with matrix factorization is proposed. By establishing an effective trust metric model, the user’s social information is integrated into the recommendation algorithm. First, the social network concentric hierarchical model is used to consider the direct or indirect trust relationship, and more trust information is integrated for the matrix factorization recommendation algorithm. Then, we design the trust relevance, comprehensively considering the trust factors and interest similar factors. Our experiments were performed on the Dianping datasets. The recommendation algorithm using matrix factorization and trusted relevance degree has higher prediction accuracy than the basic matrix decomposition and social matrix factorization in terms of accuracy and stability.
Monitoring Human Visual Behavior during the Observation of Unmanned Aerial Vehicles (UAVs) Videos

The present article describes an experimental study towards the examination of human visual behavior during the observation of unmanned aerial vehicles (UAVs) videos. Experimental performance is based on the collection and the quantitative & qualitative analysis of eye tracking data. The results highlight that UAV flight altitude serves as a dominant specification that affects the visual attention process, while the presence of sky in the video background seems to be the less affecting factor in this procedure. Additionally, the main surrounding environment, the main size of the observed object as well as the main perceived angle between UAV’s flight plain and ground appear to have an equivalent influence in observers’ visual reaction during the exploration of such stimuli. Moreover, the provided heatmap visualizations indicate the most salient locations in the used UAVs videos. All produced data (raw gaze data, fixation and saccade events, and heatmap visualizations) are freely distributed to the scientific community as a new dataset (EyeTrackUAV) that can be served as an objective ground truth in future studies.
The value of country-level perceived ethics to entrepreneurs around the world

The actions behind the ‘Great Recession’ have done a lot of damage to the ability of the average investor to trust both corporate executives and Wall Street. Given the riskiness of investing in private equity, private firms are particularly vulnerable to the risk aversion of investors. For this reason, maintaining trust may be particularly useful to private firms. Using a dataset that spans 33 countries from 1998 to 2004, this paper examines the impact of the perception of ethical behavior at the country level on the performance and outcome of private firms. Using two separate sources for country-level perception of ethics, this paper finds that both the performance and the outcome of the private firm are positively influenced by the level of perceived ethics in their country. The paper further finds that these benefits come without adding cost to the entrepreneurial firm (as well as its pre-issue investors) when it goes public.
Organization of ML-based product development as per ISO 26262

Machine learning (ML) applications generate a continuous stream of success stories from various domains. ML enables many novel applications, also in a safety-related context. With the advent of Autonomous Driving, ML gets used in automotive domain. In such a context, ML-based systems are safety-related. In the automotive industry, the applicable functional safety standard is ISO 26262, which it does not cover specific aspects of ML. In a safety-related ML project, all ISO 26262 work products are typically necessary and have to be delivered. However, specific aspects of ML (like data set requirements, special analyses for ML) must be addressed within some work products. In this paper, we propose how the organization of a ML project could be done according to ISO 26262 phases, sub-phases and work-products.
Old Dog Learns New Tricks: Randomized UCB for Bandit Problems

We propose $\tt RandUCB$, a bandit strategy that uses theoretically derived confidence intervals similar to upper confidence bound (UCB) algorithms, but akin to Thompson sampling (TS), uses randomization to trade off exploration and exploitation. In the $K$-armed bandit setting, we show that there are infinitely many variants of $\tt RandUCB$, all of which achieve the minimax-optimal $\widetilde{O}(\sqrt{K T})$ regret after $T$ rounds. Moreover, in a specific multi-armed bandit setting, we show that both UCB and TS can be recovered as special cases of $\tt RandUCB. $ For structured bandits, where each arm is associated with a $d$-dimensional feature vector and rewards are distributed according to a linear or generalized linear model, we prove that $\tt RandUCB$ achieves the minimax-optimal $\widetilde{O}(d \sqrt{T})$ regret even in the case of infinite arms. We demonstrate the practical effectiveness of $\tt RandUCB$ with experiments in both the multi-armed and structured bandit settings. Our results illustrate that $\tt RandUCB$ matches the empirical performance of TS while obtaining the theoretically optimal regret bounds of UCB algorithms, thus achieving the best of both worlds.
Searching for super-Eddington quasars using a photon trapping accretion disc model

Accretion on to black holes at rates above the Eddington limit has long been discussed in the context of supermassive black hole (SMBH) formation and evolution, providing a possible explanation for the presence of massive quasars at high redshifts (z ≳ 7), as well as having implications for SMBH growth at later epochs. However, it is currently unclear whether such ‘super-Eddington’ accretion occurs in SMBHs at all, how common it is, or whether every SMBH may experience it. In this work, we investigate the observational consequences of a simplistic model for super-Eddington accretion flows – an optically thick, geometrically thin accretion disc where the inner-most parts experience severe photon-trapping, which is enhanced with increased accretion rate. The resulting spectral energy distributions (SEDs) show a dramatic lack of rest-frame UV, or even optical, photons. Using a grid of model SEDs spanning a wide range in parameter space (including SMBH mass and accretion rate), we find that large optical quasar surveys (such as SDSS) may be missing most of these luminous systems. We then propose a set of colour selection criteria across optical and infrared colour spaces designed to select super-Eddington SEDs in both wide-field surveys (e.g. using SDSS, 2MASS, and WISE) and deep and narrow-field surveys (e.g. COSMOS). The proposed selection criteria are a necessary first step in establishing the relevance of advection-affected super-Eddington accretion on to SMBHs at early cosmic epochs.
Autonomous Shuttles for Last-Mile Connectivity

This paper describes an autonomous shuttle which targets providing last-mile transportation. Often, this involves operation in crowded areas with high levels of pedestrian traffic, and little to no lane markings or traffic control. We aim to create a functional shuttle to be improved upon in the future as new robust solutions are developed to replace the current components. An initial implementation of such a shuttle presented, detailing the overall architecture, controller structure, waypoint following, obstacle detection and avoidance, LiDAR based sign detection, and pedestrian communication. The performance of each component is evaluated, and future improvements are discussed.
Training-Free Uncertainty Estimation for Neural Networks

Uncertainty estimation is an essential step in the evaluation of the robustness for deep learning models in computer vision, especially when applied in risk-sensitive areas. However, most state-of-the-art deep learning models either fail to obtain uncertainty estimation or need significant modification (e.g., formulating a proper Bayesian treatment) to obtain it. None of the previous methods are able to take an arbitrary model off the shelf and generate uncertainty estimation without retraining or redesigning it. To address this gap, we perform the first systematic exploration into training-free uncertainty estimation. We propose three simple and scalable methods to analyze the variance of output from a trained network under tolerable perturbations: infer-transformation, infer-noise, and infer-dropout. They operate solely during inference, without the need to re-train, re-design, or fine-tune the model, as typically required by other state-of-the-art uncertainty estimation methods. Surprisingly, even without involving such perturbations in training, our methods produce comparable or even better uncertainty estimation when compared to other training-required state-of-the-art methods. Last but not least, we demonstrate that the uncertainty from our proposed methods can be used to improve the neural network training.
Keyphrase Generation: A Multi-Aspect Survey

Extractive keyphrase generation research has been around since the nineties, but the more advanced abstractive approach based on the encoder-decoder framework and sequence-to-sequence learning has been explored only recently. In fact, more than a dozen of abstractive methods have been proposed in the last three years, producing meaningful keyphrases and achieving state-of-the-art scores. In this survey, we examine various aspects of the extractive keyphrase generation methods and focus mostly on the more recent abstractive methods that are based on neural networks. We pay particular attention to the mechanisms that have driven the perfection of the later. A huge collection of scientific article metadata and the corresponding keyphrases is created and released for the research community. We also present various keyphrase generation and text summarization research patterns and trends of the last two decades.
Syntactic Interpolation for Tense Logics and Bi-Intuitionistic Logic via Nested Sequents

We provide a direct method for proving Craig interpolation for a range of modal and intuitionistic logics, including those containing a "converse" modality. We demonstrate this method for classical tense logic, its extensions with path axioms, and for bi-intuitionistic logic. These logics do not have straightforward formalisations in the traditional Gentzen-style sequent calculus, but have all been shown to have cut-free nested sequent calculi. The proof of the interpolation theorem uses these calculi and is purely syntactic, without resorting to embeddings, semantic arguments, or interpreted connectives external to the underlying logical language. A novel feature of our proof includes an orthogonality condition for defining duality between interpolants.
Group, Extract and Aggregate: Summarizing a Large Amount of Finance News for Forex Movement Prediction

Incorporating related text information has proven successful in stock market prediction. However, it is a huge challenge to utilize texts in the enormous forex (foreign currency exchange) market because the associated texts are too redundant. In this work, we propose a BERT-based Hierarchical Aggregation Model to summarize a large amount of finance news to predict forex movement. We firstly group news from different aspects: time, topic and category. Then we extract the most crucial news in each group by the SOTA extractive summarization method. Finally, we conduct interaction between the news and the trade data with attention to predict the forex movement. The experimental results show that the category based method performs best among three grouping methods and outperforms all the baselines. Besides, we study the influence of essential news attributes (category and region) by statistical analysis and summarize the influence patterns for different currency pairs.
Estimation of Utility-Maximizing Bounds on Potential Outcomes

Estimation of individual treatment effects is often used as the basis for contextual decision making in fields such as healthcare, education, and economics. However, in many real-world applications it is sufficient for the decision maker to have upper and lower bounds on the potential outcomes of decision alternatives, allowing them to evaluate the trade-off between benefit and risk. With this in mind, we develop an algorithm for directly learning upper and lower bounds on the potential outcomes under treatment and non-treatment. Our theoretical analysis highlights a trade-off between the complexity of the learning task and the confidence with which the resulting bounds cover the true potential outcomes; the more confident we wish to be, the more complex the learning task is. We suggest a novel algorithm that maximizes a utility function while maintaining valid potential outcome bounds. We illustrate different properties of our algorithm, and highlight how it can be used to guide decision making using two semi-simulated datasets.
Prediction-based Resource Allocation using Bayesian Neural Networks and Minimum Cost and Maximum Flow Algorithm

Predictive business process monitoring aims at providing predictions about running instances by analyzing logs of completed cases in a business process. Recently, a lot of research focuses on increasing productivity and efficiency in a business process by forecasting potential problems during its executions. However, most of the studies lack suggesting concrete actions to improve the process. They leave it up to the subjective judgment of a user. In this paper, we propose a novel method to connect the results from predictive business process monitoring to actual business process improvements. More in detail, we optimize the resource allocation in a non-clairvoyant online environment, where we have limited information required for scheduling, by exploiting the predictions. The proposed method integrates the offline prediction model construction that predicts the processing time and the next activity of an ongoing instance using Bayesian Neural Networks (BNNs) with the online resource allocation that is extended from the minimum cost and maximum flow algorithm. To validate the proposed method, we performed experiments using an artificial event log and a real-life event log from a global financial organization.
Deducibility of Identicals, Reflection Principle and Synthetic Connectives

Sambin et al (2000) introduced Basic Logic as an uniform framework for various logics. At the same time, they also introduced the principle of reflection as a criterion for being a connective of Basic Logic. We make explicit a relationship between Hacking's deducibility of identicals condition (Hacking, 1979) and the principle of reflection by proving the equivalence between them. Moreover, despite Sambin et al.'s conjecture that only six connectives satisfy the principle of reflection, we show the following; a logical connective satisfies the principle of reflection if and only if the connective is Girard's synthetic connective.
The Detection of Distributional Discrepancy for Text Generation

The text generated by neural language models is not as good as the real text. This means that their distributions are different. Generative Adversarial Nets (GAN) are used to alleviate it. However, some researchers argue that GAN variants do not work at all. When both sample quality (such as Bleu) and sample diversity (such as self-Bleu) are taken into account, the GAN variants even are worse than a well-adjusted language model. But, Bleu and self-Bleu can not precisely measure this distributional discrepancy. In fact, how to measure the distributional discrepancy between real text and generated text is still an open problem. In this paper, we theoretically propose two metric functions to measure the distributional difference between real text and generated text. Besides that, a method is put forward to estimate them. First, we evaluate language model with these two functions and find the difference is huge. Then, we try several methods to use the detected discrepancy signal to improve the generator. However the difference becomes even bigger than before. Experimenting on two existing language GANs, the distributional discrepancy between real text and generated text increases with more adversarial learning rounds. It demonstrates both of these language GANs fail.
Land Conflicts, Property Rights, and the Rise of the Export Economy in Colombia, 1850–1925

The poor performance of the Colombian economy in the world markets during the late nineteenth and early twentieth centuries are attributable, in part, to the weakness of settlers’ property rights in frontier lands. To examine this issue, we collected data on production of agricultural exports at county (municipal) level in 1892, coffee production in 1925, and of public land allocation and land conflicts during the nineteenth and twentieth centuries. The quantitative analysis suggests that in the absence of land conflicts, the per capita production of agricultural exports would have been in 1925 at least twice as much as that observed.
Nonredundant Information Collection in Rescue Applications via an Energy-Constrained UAV

Unmanned aerial vehicles (UAVs) are emerging as promising devices to provide valuable information in rescue applications, which can be dispatched to take photographs for points of interests in disaster areas where humans are hard to approach. Most existing studies focused on the limited energy capacity issue of UAVs when they take photographs, which however ignored an important fact, that is, the photographs taken by the UAVs usually are highly redundant. In this paper we study a novel monitoring quality maximization problem to find a flying tour for an energy-constrained UAV, such that the amount of nonredundant information of the photographs taken by the UAV in its tour is maximized. Due to NP-hardness of the problem, we first propose an approximation algorithm with a quasi-polynomial time complexity. We then devise a fast yet scalable heuristic algorithm for the problem. We finally evaluate the performance of the proposed algorithms via both a real dataset and extensive simulations. Experimental results show that the proposed algorithms are very promising. Especially, the amounts of nonredundant information by the proposed approximation and heuristic algorithms are about 11% and 8% larger than that by the state-of-the-art, respectively. To the best of our knowledge, we are the first to consider the novel problem of collecting nonredundant information with an energy-constrained UAV.