Miracle-dz/UniFAR-FTUs huggingface dataset:
Internal Determinants in the Field of RPA Technology Implementation on the Example of Selected Companies in the Context of Industry 4.0 Assumptions

As part of the assumptions of Industry 4.0. many modern technologies are developing. One of them is robotic process automation (RPA). It allows the replacement of human labor with robots, thus increasing the production capacity of enterprises. In practice, the implementation of RPA takes place through two solutions (models): using the services of an external entity offering RPA (service insourcing) or creating your own center of excellence (CoE). The use of both solutions is influenced by numerous conditions (determinants), among which we can mention benefits on the one hand and threats on the other. They are very different and depend on the model used. In this article, attempts were made to identify and determine their impact on the selection of the appropriate model from the point of view of the company (the purpose of the article). The research used two cases of entities implementing RPA on the basis of opposites, i.e., two different models, in which the discussed technology is treated as an important element of their automation. The achieved results indicate that there is no universalism, and their dominant feature is individualism concerning both enterprises and the solutions they implement. This is undoubtedly the effect of the currently too shallow RPA market, as well as the small number of entities using technologies based on intelligent systems. This is a serious research gap, which along with RPA growth will be reduced as a result of more and more intensive research in this field.
Distributed Nyström Kernel Learning with Communications

We study the statistical performance for distributed kernel ridge regression with Nystr¨om (DKRR-NY) and with Nystr¨om and iterative solvers (DKRR-NY-PCG) and successfully derive the optimal learning rates, which can improve the ranges of the number of local processors p to the optimal in existing state-of-art bounds. More precisely, our theoretical analysis show that DKRR-NY and DKRR-NY-PCG achieve the same learning rates as the exact KRR requiring essentially O ( | D | 1 . 5 ) time and O ( | D | ) memory with relaxing the restriction on p in expectation, where | D | is the number of data, which exhibits the average effectiveness of multiple trials. Furthermore, for showing the generalization performance in a single trial, we deduce the learning rates for DKRR-NY and DKRR-NY-PCG in probability. Finally, we propose a novel algorithm DKRR-NY-CM based on DKRR-NY, which employs a communication strategy to further improve the learning performance, whose effectiveness of communications is validated in theoretical and experimental analysis.
PC-MLP: Model-based Reinforcement Learning with Policy Cover Guided Exploration

Model-based Reinforcement Learning (RL) is a popular learning paradigm due to its potential sample efficiency compared to model-free RL. However, existing empirical model-based RL approaches lack the ability to explore. This work studies a computationally and statistically efficient model-based algorithm for both Kernelized Nonlinear Regulators (KNR) and linear Markov Decision Processes (MDPs). For both models, our algorithm guarantees polynomial sample complexity and only uses access to a planning oracle. Experimentally, we first demonstrate the flexibility and efficacy of our algorithm on a set of exploration challenging control tasks where existing empirical model-based RL approaches completely fail. We then show that our approach retains excellent performance even in common dense reward control benchmarks that do not require heavy exploration. Finally, we demonstrate that our method can also perform reward-free exploration efficiently. Our code can be found at https://github.com/yudasong/PCMLP.
Improved Regret Bounds of Bilinear Bandits using Action Space Analysis

We consider the bilinear bandit problem where the learner chooses a pair of arms, each from two different action spaces of dimension d 1 and d 2 , respectively. The learner then receives a reward whose expectation is a bilinear function of the two chosen arms with an unknown matrix parameter Θ ∗ ∈ R d 1 × d 2 with rank r . Despite abundant applications such as drug discovery, the optimal regret rate is unknown for this problem, though it was conjectured to be ˜ O ( (cid:112) d 1 d 2 ( d 1 + d 2 ) rT ) by Jun et al. (2019) where ˜ O ignores polylogarith-mic factors in T . In this paper, we make progress towards closing the gap between the upper and lower bound on the optimal regret. First, we reject the conjecture above by proposing algorithms that achieve the regret ˜ O ( (cid:112) d 1 d 2 ( d 1 + d 2 ) T ) using the fact that the action space dimension O ( d 1 + d 2 ) is signiﬁcantly lower than the matrix parameter di-mension O ( d 1 d 2 ) . Second, we additionally devise an algorithm with better empirical performance than previous algorithms.
Optimal Thompson Sampling strategies for support-aware CVaR bandits

In this paper we study a multi-arm bandit problem in which the quality of each arm is measured by the Conditional Value at Risk (CVaR) at some level alpha of the reward distribution. While existing works in this setting mainly focus on Upper Confidence Bound algorithms, we introduce a new Thompson Sampling approach for CVaR bandits on bounded rewards that is flexible enough to solve a variety of problems grounded on physical resources. Building on a recent work by Riou&Honda (2020), we introduce B-CVTS for continuous bounded rewards and M-CVTS for multinomial distributions. On the theoretical side, we provide a non-trivial extension of their analysis that enables to theoretically bound their CVaR regret minimization performance. Strikingly, our results show that these strategies are the first to provably achieve asymptotic optimality in CVaR bandits, matching the corresponding asymptotic lower bounds for this setting. Further, we illustrate empirically the benefit of Thompson Sampling approaches both in a realistic environment simulating a use-case in agriculture and on various synthetic examples.
PID Accelerated Value Iteration Algorithm

The convergence rate of Value Iteration (VI), a fundamental procedure in dynamic programming and reinforcement learning, for solving MDPs can be slow when the discount factor is close to one. We propose modiﬁcations to VI in order to potentially accelerate its convergence behaviour. The key insight is the realization that the evolution of the value function approximations ( V k ) k ≥ 0 in the VI procedure can be seen as a dynamical system. This opens up the possibility of using techniques from control theory to modify, and potentially accelerate, this dynamics. We present such mod-iﬁcations based on simple controllers, such as PD (Proportional-Derivative), PI (Proportional-Integral), and PID. We present the error dynamics of these variants of VI, and provably (for certain classes of MDPs) and empirically (for more general classes) show that the convergence rate can be signiﬁcantly improved. We also propose a gain adaptation mechanism in order to automatically select the controller gains, and empirically show the effectiveness of this procedure.
6G Green IoT Network: Joint Design of Intelligent Reflective Surface and Ambient Backscatter Communication

Ambient backscatter communication (AmBC) is one of the candidate solutions for the 6G green internet of things (IoT) network. However, the uncontrollability of the radio frequency (RF) environment is one of the main obstacles hindering the popularization of AmBC. The intelligent reflective surface (IRS) can improve the radio frequency environment by adjusting the phase and amplitude of the incident signal, which provides the possibility for the widespread deployment of AmBC. Currently, there is no discussion about the joint optimization of AmBC and IRS. In this paper, we introduce a novel IRS and AmBC joint design method. The purpose of this method is to jointly design the beamforming vector, the IRS phase shift, and the reflection coefficient of AmBC to minimize the AP’s transmit power while ensuring the quality of service of the AmBC system and the primary communication system. Due to the nonconvexity of the problem, the time complexity of solving the problem through exhaustive search will be very high. Therefore, we propose a joint design method based on an iterative beamforming vector, IRS phase shift, and reflection coefficient to minimize the AP’s transmit power. This method can effectively reduce the transmission power of the access point (AP), and the simulation results prove the effectiveness of the method.
Risk-Sensitive Reinforcement Learning with Function Approximation: A Debiasing Approach

We study function approximation for episodic re-inforcement learning with entropic risk measure. We ﬁrst propose an algorithm with linear function approximation. Compared to existing algorithms, which suffer from improper regularization and regression biases, this algorithm features debiasing transformations in backward induction and regression procedures. We further propose an algorithm with general function approximation, which is shown to perform implicit debiasing transformations. We prove that both algorithms achieve a sublinear regret and demonstrate a tradeoff be-tween generality and efﬁciency. Our analysis provides a uniﬁed framework for function approximation in risk-sensitive reinforcement learning, which leads to the ﬁrst sub-linear regret bounds in the setting.
No-regret Algorithms for Capturing Events in Poisson Point Processes

Inhomogeneous Poisson point processes are widely used models of event occurrences. We address adaptive sensing of Poisson Point processes , namely, maximizing the number of captured events subject to sensing costs. We encode prior assumptions on the rate function by modeling it as a member of a known reproducing kernel Hilbert space (RKHS). By partitioning the domain into separate small regions, and using heteroscedastic linear regression, we propose a tractable estimator of Poisson process rates for two feedback models: count-record , where exact locations of events are observed, and histogram feedback, where only counts of events are observed. We derive provably accurate anytime con-ﬁdence estimates for our estimators for sequentially acquired Poisson count data. Using these, we formulate algorithms based on optimism that provably incur sublinear count-regret. We demonstrate the practicality of the method on problems from crime modeling, revenue maximization as well as environmental monitoring.
Nonparametric Decomposition of Sparse Tensors

Tensor decomposition is a powerful framework for multiway data analysis. Despite the success of existing approaches, they ignore the sparse nature of the tensor data in many real-world applications, explicitly or implicitly assuming dense tensors. To address this model misspeciﬁcation and to exploit the sparse tensor structures, we propose Nonparametric dEcomposition of Sparse Tensors (NEST), which can capture both the sparse structure properties and complex relationships between the tensor nodes to enhance the embedding estimation. Speciﬁcally, we ﬁrst use completely random measures to construct tensor-valued random processes. We prove that the entry growth is much slower than that of the corresponding tensor size, which implies sparsity. Given ﬁnite observations ( i.e., projections), we then propose two nonparametric decomposition models that couple Dirichlet processes and Gaussian processes to jointly sample the sparse entry indices and the entry values (the latter as a nonlinear mapping of the embeddings), so as to encode both the structure properties and nonlinear relationships of the tensor nodes into the embeddings. Finally, we use the stick-breaking construction and random Fourier features to develop a scalable, stochastic variational learning algorithm. We show the advantage of our approach in sparse tensor generation, and entry index and value prediction in several real-world applications.
Deep Continuous Networks

CNNs and computational models of biological vision share some fundamental principles, which opened new avenues of research. However, fruitful cross-field research is hampered by conventional CNN architectures being based on spatially and depthwise discrete representations, which cannot accommodate certain aspects of biological complexity such as continuously varying receptive field sizes and dynamics of neuronal responses. Here we propose deep continuous networks (DCNs), which combine spatially continuous filters, with the continuous depth framework of neural ODEs. This allows us to learn the spatial support of the filters during training, as well as model the continuous evolution of feature maps, linking DCNs closely to biological models. We show that DCNs are versatile and highly applicable to standard image classification and reconstruction problems, where they improve parameter and data efficiency, and allow for meta-parametrization. We illustrate the biological plausibility of the scale distributions learned by DCNs and explore their performance in a neuroscientifically inspired pattern completion task. Finally, we investigate an efficient implementation of DCNs by changing input contrast.
On Recovering from Modeling Errors Using Testing Bayesian Networks

We consider the problem of supervised learning with Bayesian Networks when the used dependency structure is incomplete due to missing edges or missing variable states. These modeling errors induce independence constraints on the learned model that may not hold in the true, data-generating distribution. We provide a uniﬁed treatment of these modeling errors as instances of state-space abstractions. We then identify a class of Bayesian Networks and queries which allow one to fully recover from such modeling errors if one can choose Conditional Probability Tables (CPTs) dynamically based on evidence. We show theoretically that the recently proposed Testing Bayesian Networks (TBNs), which can be trained by compiling them into Testing Arithmetic Circuits (TACs), provide a promising construct for emulating this CPT selection mechanism. Finally, we present empirical results that illustrate the promise of TBNs as a tool for recovering from certain modeling errors in the context of supervised learning.
Cyber Threat Predictive Analytics for Improving Cyber Supply Chain Security

Cyber Supply Chain (CSC) system is complex which involves different sub-systems performing various tasks. Security in supply chain is challenging due to the inherent vulnerabilities and threats from any part of the system which can be exploited at any point within the supply chain. This can cause a severe disruption on the overall business continuity. Therefore, it is paramount important to understand and predicate the threats so that organization can undertake necessary control measures for the supply chain security. Cyber Threat Intelligence (CTI) provides an intelligence analysis to discover unknown to known threats using various properties including threat actor skill and motivation, Tactics, Techniques, and Procedure (TT and P), and Indicator of Compromise (IoC). This paper aims to analyse and predicate threats to improve cyber supply chain security. We have applied Cyber Threat Intelligence (CTI) with Machine Learning (ML) techniques to analyse and predict the threats based on the CTI properties. That allows to identify the inherent CSC vulnerabilities so that appropriate control actions can be undertaken for the overall cybersecurity improvement. To demonstrate the applicability of our approach, CTI data is gathered and a number of ML algorithms, i.e., Logistic Regression (LG), Support Vector Machine (SVM), Random Forest (RF), and Decision Tree (DT), are used to develop predictive analytics using the Microsoft Malware Prediction dataset. The experiment considers attack and TTP as input parameters and vulnerabilities and Indicators of compromise (IoC) as output parameters. The results relating to the prediction reveal that Spyware/Ransomware and spear phishing are the most predictable threats in CSC. We have also recommended relevant controls to tackle these threats. We advocate using CTI data for the ML predicate model for the overall CSC cyber security improvement.
Policy Caches with Successor Features

Transfer in reinforcement learning is based on the idea that it is possible to use what is learned in one task to improve the learning process in another task. For transfer between tasks which share transition dynamics but differ in reward function, successor features have been shown to be a useful representation which allows for efﬁcient computation of action-value functions for previously-learned policies in new tasks. These functions induce policies in the new tasks, so an agent may not need to learn a new policy for each new task it encounters, especially if it is allowed some amount of subop-timality in those tasks. We present new bounds for the performance of optimal policies in a new task, as well as an approach to use these bounds to decide, when presented with a new task, whether to use cached policies or learn a new policy.
Dueling Convex Optimization

We address the problem of convex optimization with preference (dueling) feedback. Like the traditional optimization objective, the goal is to ﬁnd the optimal point with the least possible query complexity, however, without the luxury of even a zeroth order feedback. Instead, the learner can only observe a single noisy bit which is win-loss feedback for a pair of queried points based on their function values. The problem is certainly of great practical relevance as in many real-world scenarios, such as recommender systems or learning from customer preferences, where the system feedback is often restricted to just one binary-bit preference information. We consider the problem of online convex optimization (OCO) solely by actively querying { 0 , 1 } noisy-comparison feed-back of decision point pairs, with the objective of ﬁnding a near-optimal point (function minimizer) with the least possible number of queries. For the non-stationary OCO setup, where the underlying convex function may change over time, we prove an impossibility result towards achieving the above objective. We next focus only on the stationary OCO problem, and our main contribution lies in designing a normalized gradient descent based algorithm towards ﬁnding a (cid:15) -best optimal point. Towards this, our algorithm is shown to yield a convergence rate of ˜ O ( dβ / (cid:15)ν 2 ) ( ν being the noise parameter) when the underlying function is β -smooth. Further we show an improved convergence rate of just ˜ O ( dβ / αν 2 log 1 (cid:15) ) when the function is additionally also α -strongly convex.
Robust Learning for Data Poisoning Attacks

We investigate the robustness of stochastic approximation approaches against data poisoning attacks. We focus on two-layer neural networks with ReLU activations and show that under a specific notion of separability in the RKHS induced by the infinite-width network, training (finitewidth) networks with stochastic gradient descent is robust against data poisoning attacks. Interestingly, we find that in addition to a lower bound on the width of the network, which is standard in the literature, we also require a distributiondependent upper bound on the width for robust generalization. We provide extensive empirical evaluations that support and validate our theoretical results.
Framework, Tools and Good Practices for Cybersecurity Curricula

Cybersecurity education and training are essential prerequisites of achieving a secure and privacy-friendly digital environment. Both professionals and the general public widely acknowledge the need for high-quality university education programs and professional training courses. However, guides, recommendations, practical tools, and good examples that could help institutions design appropriate cybersecurity programs are still missing. In particular, a comprehensive method to identify skills needed by cybersecurity work roles offered on the job market is missing. This paper aims to provide practical tools and strategies to help higher education providers design good cybersecurity curricula. First, we analyze the content of 89 existing study programs worldwide, collect recommendations of renowned institutions within and outside the EU, and provide a comprehensive survey accompanied by a dynamic web application called Education Map. Based on the knowledge about the current state in cybersecurity education, we design the SPARTA Cybersecurity Skills Framework that provides the currently missing link between work roles and required expertise and shows how to develop a curriculum that reflects job market requirements. Finally, we provide a practical tool that implements the framework and helps education and training providers design new study programs and analyze existing ones by considering the requirements of cybersecurity work roles.
Approximating a Distribution Using Weight Queries

We consider a novel challenge: approximating a distribution without the ability to randomly sample from that distribution. We study how such an approximation can be obtained using weight queries. Given some data set of examples, a weight query presents one of the examples to an oracle, which returns the probability, according to the target distribution, of observing examples similar to the presented example. This oracle can represent, for instance, counting queries to a database of the target population, or an interface to a search engine which returns the number of results that match a given search. We propose an interactive algorithm that iteratively selects data set examples and performs corresponding weight queries. The algorithm finds a reweighting of the data set that approximates the weights according to the target distribution, using a limited number of weight queries. We derive an approximation bound on the total variation distance between the reweighting found by the algorithm and the best achievable reweighting. Our algorithm takes inspiration from the UCB approach common in multi-armed bandits problems, and combines it with a new discrepancy estimator and a greedy iterative procedure. In addition to our theoretical guarantees, we demonstrate in experiments the advantages of the proposed algorithm over several baselines. A python implementation of the proposed algorithm and of all the experiments can be found at https: //github.com/Nadav-Barak/AWP
A Survey on Beam Steering Techniques in Printed Antennas

Beam steering antennas is essential part of various WCN (Wireless Communication Networks) such as radar and satellite communication. The increasing demand of high data rate, high SNR (Signal to Noise Ratio) and high gain, therefore pattern reconfigurable antennas are required to improve such properties. Beam steering is the technique of changing the main lobe direction of radiation. The constructive and distractive interference are used to steer the lobe in specific direction. Beam steering is necessary in various communications such as localization, satellite, tracking system and airborne application. Beam steering antenna decreases interference and power consumption and also increases directivity and gain. Beam steering antenna transmit and receive signals in specific direction. Beam steering antenna decreases interference and power consumption and also increases directivity and gain. The researchers are interested to find optimal beam steering solution for single and multi-point application. In past various techniques are used to achieve beam reconfigurable antennas. This paper presents, beam steering principle and their theory, beam steering techniques such as; Mechanical Steering, Beamforming, Switching Pin Diodes, Reflector and array antenna (Reflectarray), Parasitic Steering, Phase Shifters, Switched Beam Antennas, Metamaterial Antennas, Traveling Wave Antennas, Retro-directive Antennas, Integrated Lens Antennas (ILAS), merits and demerits and comparison among various techniques.