Miracle-dz/UniFAR-FTUs huggingface dataset:
Estimating a Null Model of Scientific Image Reuse to Support Research Integrity Investigations

When there is a suspicious figure reuse case in science, research integrity investigators often find it difficult to rebut authors claiming that "it happened by chance". In other words, when there is a "collision" of image features, it is difficult to justify whether it appears rarely or not. In this article, we provide a method to predict the rarity of an image feature by statistically estimating the chance of it randomly occurring across all scientific imagery. Our method is based on high-dimensional density estimation of ORB features using 7+ million images in the PubMed Open Access Subset dataset. We show that this method can lead to meaningful feedback during research integrity investigations by providing a null hypothesis for scientific image reuse and thus a p-value during deliberations. We apply the model to a sample of increasingly complex imagery and confirm that it produces decreasingly smaller p-values as expected. We discuss applications to research integrity investigations as well as future work.
Distributed Joint Detection and Estimation: A Sequential Approach

We investigate the problem of jointly testing two hypotheses and estimating a random parameter based on data that is observed sequentially by sensors in a distributed network. In particular, we assume the data to be drawn from a Gaussian distribution, whose random mean is to be estimated. Forgoing the need for a fusion center, the processing is performed locally and the sensors interact with their neighbors following the consensus+innovations approach. We design the test at the individual sensors such that the performance measures, namely, error probabilities and mean-squared error, do not exceed predefined levels while the average sample number is minimized. After converting the constrained problem to an unconstrained problem and the subsequent reduction to an optimal stopping problem, we solve the latter utilizing dynamic programming. The solution is shown to be characterized by a set of non-linear Bellman equations, parametrized by cost coefficients, which are then determined by linear programming as to fulfill the performance specifications. A numerical example validates the proposed theory.
What Timing for an Automated Vehicle to Communicate Its Driving Intentions to Pedestrians for Improving Their Perception of Safety?

Although automated driving systems have been used frequently, they are still unpopular in society. To increase the popularity of automated vehicles (AVs), assisting pedestrians to accurately understand the driving intentions of the vehicle and improving their perception of safety when interacting with AVs are considered effective. Therefore, the AV should send information about its driving intention to pedestrians when they interact with each other. However, the following questions should be answered regarding how the AV sends the information to them: 1) What timing for the AV to make the pedestrian understand its driving intentions after it is noticed? 2) What timing for the AV to make the pedestrian feel safe after it is noticed? Thirteen participants were invited to interact with a manually driven vehicle and an AV in an experiment. The participants' gaze information and a subjective evaluation of their understanding of the driving intention as well as their perception of safety were collected. By analyzing the participants' gaze duration on the vehicle with their subjective evaluations, we found that the AV should enable the pedestrian to accurately understand its driving intention within 0.5~6.5 [s] and make the pedestrian feel safe within 0.5~8.0 [s] while the pedestrian is gazing at it.
Optimizing Dynamic Trajectories for Robustness to Disturbances Using Polytopic Projections

This paper focuses on robustness to disturbance forces and uncertain payloads. We present a novel formulation to optimize the robustness of dynamic trajectories. A straightforward transcription of this formulation into a nonlinear programming problem is not tractable for state-of-the-art solvers, but it is possible to overcome this complication by exploiting the structure induced by the kinematics of the robot. The non-trivial transcription proposed allows trajectory optimization frameworks to converge to highly robust dynamic solutions. We demonstrate the results of our approach using a quadruped robot equipped with a manipulator.
V2I Connectivity-Based Dynamic Queue-Jumper Lane for Emergency Vehicles: An Approximate Dynamic Programming Approach

Emergency vehicle (EV) service is a key function of cities and is exceedingly challenging due to urban traffic congestion. A key contributor to EV service delay is the lack of communication and cooperation between vehicles blocking EVs. In this paper, we study the improvement of EV service using vehicle-to-vehicle connectivity. We consider the establishment of dynamic queue jumper lanes (DQJLs) based on real-time coordination of connected vehicles. We develop a novel stochastic dynamic programming formulation for the DQJL problem, which explicitly account for the uncertainty of drivers' reaction to approaching EVs. We propose a deep neural network-based approximate dynamic programming (ADP) algorithm that efficiently computes the optimal coordination instructions. We also validate our approach on a micro-simulation testbed using Simulation On Urban Mobility (SUMO).
InfiniWolf: Energy Efficient Smart Bracelet for Edge Computing with Dual Source Energy Harvesting

This work presents InfiniWolf, a novel multi-sensor smartwatch that can achieve self-sustainability exploiting thermal and solar energy harvesting, performing computationally high demanding tasks. The smartwatch embeds both a System-on-Chip (SoC) with an ARM Cortex-M processor and Bluetooth Low Energy (BLE) and Mr. Wolf, an open-hardware RISC-V based parallel ultra-low-power processor that boosts the processing capabilities on board by more than one order of magnitude, while also increasing energy efficiency. We demonstrate its functionality based on a sample application scenario performing stress detection with multi-layer artificial neural networks on a wearable multi-sensor bracelet. Experimental results show the benefits in terms of energy efficiency and latency of Mr. Wolf over an ARM Cortex-M4F micro-controllers and the possibility, under specific assumptions, to be self-sustainable using thermal and solar energy harvesting while performing up to 24 stress classifications per minute in indoor conditions.
Asynchronous Policy Evaluation in Distributed Reinforcement Learning over Networks

This paper proposes a \emph{fully asynchronous} scheme for policy evaluation of distributed reinforcement learning (DisRL) over peer-to-peer networks. Without any form of coordination, nodes can communicate with neighbors and compute their local variables using (possibly) delayed information at any time, which is in sharp contrast to the asynchronous gossip. Thus, the proposed scheme fully takes advantage of the distributed setting. We prove that our method converges at a linear rate $\mathcal{O}(c^k)$ where $c\in(0,1)$ and $k$ increases by one no matter on which node updates, showing the computational advantage by reducing the amount of synchronization. Numerical experiments show that our method speeds up linearly w.r. t. the number of nodes, and is robust to straggler nodes. To the best of our knowledge, our work is the first theoretical analysis for asynchronous update in DisRL, including the \emph{parallel RL} domain advocated by A3C.
Mixed Reinforcement Learning with Additive Stochastic Uncertainty

Reinforcement learning (RL) methods often rely on massive exploration data to search optimal policies, and suffer from poor sampling efficiency. This paper presents a mixed reinforcement learning (mixed RL) algorithm by simultaneously using dual representations of environmental dynamics to search the optimal policy with the purpose of improving both learning accuracy and training speed. The dual representations indicate the environmental model and the state-action data: the former can accelerate the learning process of RL, while its inherent model uncertainty generally leads to worse policy accuracy than the latter, which comes from direct measurements of states and actions. In the framework design of the mixed RL, the compensation of the additive stochastic model uncertainty is embedded inside the policy iteration RL framework by using explored state-action data via iterative Bayesian estimator (IBE). The optimal policy is then computed in an iterative way by alternating between policy evaluation (PEV) and policy improvement (PIM). The convergence of the mixed RL is proved using the Bellman's principle of optimality, and the recursive stability of the generated policy is proved via the Lyapunov's direct method. The effectiveness of the mixed RL is demonstrated by a typical optimal control problem of stochastic non-affine nonlinear systems (i.e., double lane change task with an automated vehicle).
Dynamic Skyline Queries on Encrypted Data Using Result Materialization

Skyline computation is an increasingly popular query, with broad applicability in domains such as healthcare, travel and finance. Given the recent trend to outsource databases and query evaluation, and due to the proprietary and sometimes highly sensitivity nature of the data (e.g., in healthcare), it is essential to evaluate skylines on encrypted datasets. Several research efforts acknowledged the importance of secure skyline computation, but existing solutions suffer from at least one of the following shortcomings: (i) they only provide ad-hoc security; (ii) they are prohibitively expensive; or (iii) they rely on unrealistic assumptions, such as the presence of multiple non-colluding parties in the protocol. Inspired from solutions for secure nearest-neighbors (NN) computation, we conjecture that the most secure and efficient way to compute skylines is through result materialization. However, this approach is significantly more challenging for skylines than for NN queries. We exhaustively study and provide algorithms for pre-computation of skyline results, and we perform an in-depth theoretical analysis of this process. We show that pre-computing results while minimizing storage overhead is NP-hard, and we provide dynamic programming and greedy heuristics that solve the problem more efficiently, while maintaining storage at reasonable levels. Our algorithms are novel and applicable to plain-text skyline computation, but we focus on the encrypted setting where materialization reduces the cost of skyline computation from hours to seconds. Extensive experiments show that we clearly outperform existing work in terms of performance, and our security analysis proves that we obtain a smaller (and quantifiable) data leakage than competitors.
On the Pinned Distances Problem over Finite Fields

We study the Erdos distinct distance conjecture in the plane over an arbitrary field $\mathbb{F}$, proving that any $A$ with $|A|\leq\mathrm{char}(\mathbb{F})^{4/3}$ in positive characteristic, either determines $\gg |A|^{2/3}$ distinct pairwise distances from some point of $A$ to its other points, or the set $A$ lies on an isotropic line. We also establish for the special case of the prime residue field $\mathbb{F}_p$ that the condition $|A|\geq p^{5/4}$ suffices for $A$ to determine a positive proportion of the feasible $p$ distances. This significantly improves prior results on the problem.
Quantisation of $\kappa$-deformed Dirac equation

In this paper, we study the quantisation of Dirac field theory in the $\kappa$-deformed space-time. We adopt a quantisation method that uses only equations of motion for quantising the field. Starting from $\kappa$-deformed Dirac equation, valid up to first order in the deformation parameter $a$, we derive deformed unequal time anti-commutation relation between deformed field and its adjoint, leading to undeformed oscillator algebra. Exploiting the freedom of imposing a deformed unequal time anti-commutation relations between $\kappa$-deformed spinor and its adjoint, we also derive a deformed oscillator algebra. We show that deformed number operator is the conserved charge corresponding to global phase transformation symmetry. We construct the $\kappa$-deformed conserved currents, valid up to first order in $a$, corresponding to parity and time-reversal symmetries of $\kappa$-deformed Dirac equation also. We show that these conserved currents and charges have a mass-dependent correction, valid up to first order in $a$. This novel feature is expected to have experimental significance in particle physics. We also show that it is not possible to construct a conserved current associated with charge conjugation, showing that the Dirac particle and its anti-particle satisfy different equations in $\kappa$-space-time.
CNN Hyperparameter tuning applied to Iris Liveness Detection

The iris pattern has significantly improved the biometric recognition field due to its high level of stability and uniqueness. Such physical feature has played an important role in security and other related areas. However, presentation attacks, also known as spoofing techniques, can be used to bypass the biometric system with artifacts such as printed images, artificial eyes, and textured contact lenses. To improve the security of these systems, many liveness detection methods have been proposed, and the first Internacional Iris Liveness Detection competition was launched in 2013 to evaluate their effectiveness. In this paper, we propose a hyperparameter tuning of the CASIA algorithm, submitted by the Chinese Academy of Sciences to the third competition of Iris Liveness Detection, in 2017. The modifications proposed promoted an overall improvement, with an 8.48% Attack Presentation Classification Error Rate (APCER) and 0.18% Bonafide Presentation Classification Error Rate (BPCER) for the evaluation of the combined datasets. Other threshold values were evaluated in an attempt to reduce the trade-off between the APCER and the BPCER on the evaluated datasets and worked out successfully.
Say As You Wish: Fine-Grained Control of Image Caption Generation With Abstract Scene Graphs

Humans are able to describe image contents with coarse to fine details as they wish. However, most image captioning models are intention-agnostic which cannot generate diverse descriptions according to different user intentions initiatively. In this work, we propose the Abstract Scene Graph (ASG) structure to represent user intention in fine-grained level and control what and how detailed the generated description should be. The ASG is a directed graph consisting of three types of abstract nodes (object, attribute, relationship) grounded in the image without any concrete semantic labels. Thus it is easy to obtain either manually or automatically. From the ASG, we propose a novel ASG2Caption model, which is able to recognise user intentions and semantics in the graph, and therefore generate desired captions following the graph structure. Our model achieves better controllability conditioning on ASGs than carefully designed baselines on both VisualGenome and MSCOCO datasets. It also significantly improves the caption diversity via automatically sampling diverse ASGs as control signals. Code will be released at \url{https://github.com/cshizhe/asg2cap}.
Characterisation of rational and NURBS developable surfaces in Computer Aided Design

In this paper we provide a characterisation of rational developable surfaces in terms of the blossoms of the bounding curves and three rational functions $\Lambda$, $M$, $\nu$. Properties of developable surfaces are revised in this framework. In particular, a closed algebraic formula for the edge of regression of the surface is obtained in terms of the functions $\Lambda$, $M$, $\nu$, which are closely related to the ones that appear in the standard decomposition of the derivative of the parametrisation of one of the bounding curves in terms of the director vector of the rulings and its derivative. It is also shown that all rational developable surfaces can be described as the set of developable surfaces which can be constructed with a constant $\Lambda$, $M$, $\nu$ . The results are readily extended to rational spline developable surfaces.
Regularisation Can Mitigate Poisoning Attacks: A Novel Analysis Based on Multiobjective Bilevel Optimisation

Machine Learning (ML) algorithms are vulnerable to poisoning attacks, where a fraction of the training data is manipulated to deliberately degrade the algorithms' performance. Optimal poisoning attacks, which can be formulated as bilevel optimisation problems, help to assess the robustness of learning algorithms in worst-case scenarios. However, current attacks against algorithms with hyperparameters typically assume that these hyperparameters remain constant ignoring the effect the attack has on them. We show that this approach leads to an overly pessimistic view of the robustness of the algorithms. We propose a novel optimal attack formulation that considers the effect of the attack on the hyperparameters by modelling the attack as a multiobjective bilevel optimisation problem. We apply this novel attack formulation to ML classifiers using $L_2$ regularisation and show that, in contrast to results previously reported, $L_2$ regularisation enhances the stability of the learning algorithms and helps to mitigate the attacks. Our empirical evaluation on different datasets confirms the limitations of previous strategies, evidences the benefits of using $L_2$ regularisation to dampen the effect of poisoning attacks and shows how the regularisation hyperparameter increases with the fraction of poisoning points.
Superannuation Fees and Asset Allocation

There is considerable debate about the size of fees charged by superannuation funds. This paper investigates both investment fees and administration fees and shows that there are economically valid reasons why most investment fees are set at their current level. Our results show that on average, retail funds are charging both higher investment fees and administration fees than other funds. A defence of higher administration fees can be easily achieved given the opacity that surrounds this fee type. However, it is difficult to justify why investment fees are higher, on average, when the cost (i.e., riskiness) of asset classes is incorporated into our analysis.
Feeder Weeder for Autonomous Farming

Agriculture is known as backbone of Indian economy. A large number of rural households depend upon agriculture as their primary source of livelihood. Recent advances in technology have made it possible to increase output as well as reduce dependence on manual labour. Fertilizing plants and removing weeds is very important in agriculture. Weeds are traditionally removed by manually plucking them. The solution we derived is to use grid system for sowing plants. This way it is easier to differentiate between plants and weeds. By using robot module we can fertilize only the required area around root cavity. (This avoids human error of spilling). Because of the grid system the positions of plants is known before so that any other plants can be considered as weeds and removed. In the proposed system Feeder-Weeder, we have considered a scenario where all of the work done on farms is automated by a group of autonomous robots working together in sync with each other. In this paper, concepts such as microcontroller programming, communication, path planning, sensor interfacing are used. An agricultural field with many crops growing in it is considered. Here some of these crops need to be fed with two different fertilizers. In addition to this, there are some weeds growing in the field which need to uprooted and disposed outside the field. In order to complete these tasks two Fertilizer Robots and a Weeding Robot are deployed in the field. To make this system work more efficiently, the robots communicate between each other to share information and coordinate with each other. The concept used in this paper is adopted from Swarm robotics where different robots communicate with each other using wireless Zigbee module and get the specified task done.
Machine Learning Ranks ECG as an Optimal Wearable Biosignal for Assessing Driving Stress

The demand for wearable devices that can detect anxiety and stress when driving is increasing. Recent studies have attempted to use multiple biosignals to detect driving stress. However, collecting multiple biosignals can be complex and is associated with numerous challenges. Determining the optimal biosignal for assessing driving stress can save lives. To the best of our knowledge, no study has investigated both longitudinal and transitional stress assessment using supervised and unsupervised ML techniques. Thus, this study hypothesizes that the optimal signal for assessing driving stress will consistently detect stress using supervised and unsupervised machine learning (ML) techniques. Two different approaches were used to assess driving stress: longitudinal (a combined repeated measurement of the same biosignals over three driving states) and transitional (switching from state to state such as city to highway driving). The longitudinal analysis did not involve a feature extraction phase while the transitional analysis involved a feature extraction phase. The longitudinal analysis consists of a novel interaction ensemble (INTENSE) that aggregates three unsupervised ML approaches: interaction principal component analysis, connectivity-based clustering, and K-means clustering. INTENSE was developed to uncover new knowledge by revealing the strongest correlation between the biosignal and driving stress marker. These three MLs each have their well-known and distinctive geometrical basis. Thus, the aggregation of their result would provide a more robust examination of the simultaneous non-causal associations between six biosignals: electrocardiogram (ECG), electromyogram, hand galvanic skin resistance, foot galvanic skin resistance, heart rate, respiration, and the driving stress marker. INTENSE indicates that ECG is highly correlated with the driving stress marker. The supervised ML algorithms confirmed that ECG is the most informative biosignal for detecting driving stress, with an overall accuracy of 75.02%.
Sliding Mode Load Frequency Control for Two Area Power Systems

Power system involves utilities interconnected in it. Power is shared between the utilities using tie-lines. The interconnection speed of power system is measured in frequency. When generation and load mismatch the frequency deviates. Large frequency deviation leads to the overload and failure in power grid. Therefore the regulation goal is, balancing the load frequency error. Until now various concepts over the designing of the load frequency controller are applied. This paper presents the developed sliding mode load frequency controller. The proposed method is effective compared to other and can ensure the system stable. The proposed sliding mode load frequency controller can achieve good performance in reference to the results.
Modelling & Simulation Of Pv System With Fuzzy Mppt Based Converter

In order to cope with increased demand of energy, an efficient photovoltaic (PV) power system is to be developed which extract the maximum power from the solar model by using Maximum Power Point Tracker (MPPT). This research is focused on the implementation of Fuzzy Logic based MPPT which is compared with conventional P&O MPPT. In first stage, PV Module with Boost Converter with Fuzzy Logic based MPPT and P&O MPPT are simulated in MATLAB/Simulink. From the simulation it is founded that FMPPT can track the Maximum Power Point (MPP) with more precisely and firstly. In second stage Quadratic Boost Converter (QBC) & Boost converter with fuzzy logic based MPPT controller (FMPPT) in different solar irradiation is simulated in MATLAB/Simulink. From simulation it is observed that QBC has high voltage conversion ratio & less oscillation in transient state compared to Boost converter.