Reviewerly/exHarmony huggingface dataset:
Impact Of Training On Employee Satisfaction And Change Management: A Pragmatic Study With Special Reference To Employees Working In Luxury Hotels Of Mumbai

Background: It was always a challenge for hotel industry to find and retain skilled manpower and most of the hotels depend on in-house training programme to train and develop the employees as per their requirement. It is easy for hotels to retain staff if they are well-trained, they enjoy their work and are satisfied with workplace. COVID-19 pandemic presented one more challenge to hotel industry i.e., to embrace changes as per the expectations of market. Objectives: The main objectives of the study were to find out the impact of training towards employee satisfaction and change management. To establish the influence of employee satisfaction towards change management. Methodology: The factors shortlisted towards employee satisfaction were rewards, nature of work, supervision, benefits, work environment and co-workers. Factors identified for change management were strategic success, technological changes, effective systematic plan, communication, environment for change and change management. The research design for this paper was descriptive. The study was conducted for employee working in four major revenue producing operational departments. Total 16 hotels of city of Mumbai were approached for data collection. Due diligence was taken to collect data from different demographic profiles such as ownership pattern of hotels, department and experience of employees, their qualifications etc. Total 165 samples were collected. Data was corroborated using Cronbach’s alpha and hypotheses were validated using correlation and compare means one-way ANOVA tests with the help of SPSS software. Result: Main findings of study were that nature of work and supervision had moderate effect on work environment. Benefits, rewards and co-workers showed strong correlation. Co-workers and work environment strongly impacted employee satisfaction. Strategic success, environment for change and communication significantly affected change management. Conclusion: Training was found to have significant relationship with employee satisfaction and change management. Employee satisfaction had significant association with change management. However technological change and effective systematic plan (factors of change management) were not significantly associated.
Decomposition of Matrix Product States into Shallow Quantum Circuits

Abstract Tensor networks (TNs) are a family of computational methods built on graph-structured factorizations of large tensors, which have long represented state-of-the-art methods for the approximate simulation of complex quantum systems on classical computers. The rapid pace of recent advancements in numerical computation, notably the rise of GPU and TPU hardware accelerators, have allowed TN algorithms to scale to even larger quantum simulation problems, and to be employed more broadly for solving machine learning tasks. The "quantum-inspired" nature of TNs permits them to be mapped to parametrized quantum circuits (PQCs), a fact which has inspired recent proposals for enhancing the performance of TN algorithms using near-term quantum devices, as well as enabling joint quantum-classical training frameworks which benefit from the distinct strengths of TN and PQC models. However, the success of any such methods depends on efficient and accurate methods for approximating TN states using realistic quantum circuits, something which remains an unresolved question. In this work, we compare a range of novel and previously-developed algorithmic protocols for decomposing matrix product states (MPS) of arbitrary bond dimension into low-depth quantum circuits consisting of stacked linear layers of two-qubit unitaries. These protocols are formed from different combinations of a preexisting analytical decomposition scheme with constrained optimization of circuit unitaries, and all possess efficient classical runtimes. Our experimental results reveal one particular protocol, involving sequential growth and optimization of the quantum circuit, to outperform all other methods, with even greater benefits seen in the setting of limited computational resources. Given these promising results, we expect our proposed decomposition protocol to form a useful ingredient within any joint application of TNs and PQCs, in turn further unlocking the rich and complementary benefits of classical and quantum computation.
Can Offline Reinforcement Learning Help Natural Language Understanding?

Pre-training has been a useful method for learning implicit transferable knowledge and it shows the benefit of offering complementary features across different modalities. Recent work mainly focuses on the modalities such as image and text, for example, studies show that visual features learned from images can help visual-grounded language understanding. In this paper, we consider investigating the potential connection between offline reinforcement learning (RL) and language modeling (LM). Intuitively, RL and LM are similar in predicting the next states based on the current and previous states, which rely on both local and long-range dependency across states. To validate such an assumption, we pre-trained different offline RL tasks using Transformer and then evaluate these models on various language-related tasks. Experimental results show that our RL pre-trained models can give close performance compared with the models using the LM training objective, showing that there exist common useful features across these two modalities. To further explore the potential relationship, we investigate some factors such as Markov property and the sequential nature of RL trajectory.
Receding Horizon Control Using Graph Search for Multi-Agent Trajectory Planning

&lt;pre&gt;It is hard to find the global optimum to general nonlinear, nonconvex optimization problems in reasonable time. This paper presents a method to transfer the receding horizon control approach, where nonlinear, nonconvex optimization problems are considered, into graph-search problems. Specifically, systems with symmetries are considered to transfer system dynamics into a finite state automaton. In contrast to traditional graph-search approaches where the search continues until the goal vertex is found, the transfer of a receding horizon control approach to graph-search problems presented in this paper allows to solve them in real-time. We proof that the solutions are recursively feasible by restricting the graph search to end in accepting states of the underlying finite state automaton. The approach is applied to trajectory planning for multiple networked and autonomous vehicles. We evaluate its effectiveness in simulation as well as in experiments in the Cyber-Physical Mobility Lab, an open source platform for networked and autonomous vehicles. We show real-time capable trajectory planning with collision avoidance in experiments on off-the-shelf hardware and code in MATLAB for two vehicles.&lt;/pre&gt;
A Faster Third-Order Masking of Lookup Tables

Masking of S-boxes using lookup tables is an effective countermeasure to thwart side-channel attacks on block ciphers implemented in software. At first and second orders, the Table-based Masking (TBM) schemes can be very efficient and even faster than circuit-based masking schemes. Ever since the customised second-order TBM schemes were proposed, the focus has been on designing and optimising Higher-Order Table-based Masking (HO-TBM) schemes that facilitate masking at arbitrary order. One of the reasons for this trend is that at large orders HO-TBM schemes are significantly slower and consume a prohibitive amount of RAM memory compared to circuit-based masking schemes such as bit-sliced masking, and hence efforts were targeted in this direction. However, a recent work due to Valiveti and Vivek (TCHES 2021) has demonstrated that the HO-TBM scheme of Coron et al. (TCHES 2018) is feasible to be implemented on memory-constrained devices with pre-processing capability and a competitive online execution time. Yet, currently, there are no customised designs for third-order TBM that are more efficient than instantiating a HO-TBM scheme at third order.In this work, we propose a third-order TBM scheme for arbitrary S-boxes that is secure in the probing model and under compositions, i.e., 3-SNI secure. It is very efficient in terms of the overall running time, compared to the third-order instantiations of state-of-the-art HO-TBM schemes. It also supports the pre-processing functionality. For example, the overall running time of a single execution of the third-order masked AES-128 on a 32-bit ARM-Cortex M4 micro-controller is reduced by about 80% without any overhead on the online execution time. This implies that the online execution time of the proposed scheme is approximately eight times faster than the bit-sliced masked implementation at third order, and it is comparable to the recent scheme of Wang et al. (TCHES 2022) that makes use of reuse of shares. We also present the implementation results for the third-order masked PRESENT cipher. Our work suggests that there is a significant scope for tuning the performance of HO-TBM schemes at lower orders.
ReQuSim: Faithfully simulating near-term quantum repeaters

Quantum repeaters have long been established to be essential for distributing entanglement over long distances. Consequently, their experimental realization constitutes a core challenge of quantum communication. However, there are numerous open questions about implementation details for realistic, near-term experimental setups. In order to assess the performance of realistic repeater protocols, we here present ReQuSim, a comprehensive Monte-Carlo based simulation platform for quantum repeaters that faithfully includes loss and models a wide range of imperfections such as memories with time-dependent noise. Our platform allows us to perform an analysis for quantum repeater setups and strategies that go far beyond known analytic results: This refers to being able to both capture more realistic noise models and analyse more complex repeater strategies. We present a number of findings centered around the combination of strategies for improving performance, such as entanglement purification and the use of multiple repeater stations, and demonstrate that there exist complex relationships between them. We stress that numerical tools such as ours are essential to model complex quantum communication protocols aimed at contributing to the quantum internet.
Low Variance Off-policy Evaluation with State-based Importance Sampling

In off-policy reinforcement learning, a behaviour policy performs exploratory interactions with the environment to obtain state-action-reward samples which are then used to learn a target policy that optimises the expected return. This leads to a problem of off-policy evaluation, where one needs to evaluate the target policy from samples collected by the often unrelated behaviour policy. Importance sampling is a traditional statistical technique that is often applied to off-policy evaluation. While importance sampling estimators are unbiased, their variance increases exponentially with the horizon of the decision process due to computing the importance weight as a product of action probability ratios, yielding estimates with low accuracy for domains involving long-term planning. This paper proposes state-based importance sampling, which drops the action probability ratios of sub-trajectories with ``negligible states'' -- roughly speaking, those for which the chosen actions have no impact on the return estimate -- from the computation of the importance weight. Theoretical results show this reduces the ordinary importance sampling variance from $O(\exp(H))$ to $O(\exp(X))$ where $X < H$ is the largest subtrajectory with non-negligible states. To identify negligible states, two search algorithms are proposed, one based on covariance testing and one based on state-action values. We formulate state-based variants of ordinary importance sampling, weighted importance sampling, per-decision importance sampling, incremental importance sampling, doubly robust off-policy evaluation, and stationary density ratio estimation. Experiments in four distinct domains show that state-based methods consistently yield reduced variance and improved accuracy compared to their traditional counterparts.
A parallelizable model-based approach for marginal and multivariate clustering

This paper develops a clustering method that takes advantage of the sturdiness of model-based clustering, while attempting to mitigate some of its pitfalls. First, we note that standard model-based clustering likely leads to the same number of clusters per margin, which seems a rather artificial assumption for a variety of datasets. We tackle this issue by specifying a finite mixture model per margin that allows each margin to have a different number of clusters, and then cluster the multivariate data using a strategy game-inspired algorithm to which we call Reign-and-Conquer. Second, since the proposed clustering approach only specifies a model for the margins -- but leaves the joint unspecified -- it has the advantage of being partially parallelizable; hence, the proposed approach is computationally appealing as well as more tractable for moderate to high dimensions than a `full' (joint) model-based clustering approach. A battery of numerical experiments on artificial data indicate an overall good performance of the proposed methods in a variety of scenarios, and real datasets are used to showcase their application in practice.
A Systematic Literature Review On Privacy Of Deep Learning Systems

The last decade has seen a rise of Deep Learning with its applications ranging across diverse domains. But usually, the datasets used to drive these systems contain data which is highly confidential and sensitive. Though, Deep Learning models can be stolen, or reverse engineered, confidential training data can be inferred, and other privacy and security concerns have been identified. Therefore, these systems are highly prone to security attacks. This study highlights academic research that highlights the several types of security attacks and provides a comprehensive overview of the most widely used privacy-preserving solutions. This relevant systematic evaluation also illuminates potential future possibilities for study, instruction, and usage in the fields of privacy and deep learning.
Cross-view Geo-localization via Learning Disentangled Geometric Layout Correspondence

Cross-view geo-localization aims to estimate the location of a query ground image by matching it to a reference geo-tagged aerial images database. As an extremely challenging task, its difficulties root in the drastic view changes and different capturing time between two views. Despite these difficulties, recent works achieve outstanding progress on cross-view geo-localization benchmarks. However, existing methods still suffer from poor performance on the cross-area benchmarks, in which the training and testing data are captured from two different regions. We attribute this deficiency to the lack of ability to extract the spatial configuration of visual feature layouts and models' overfitting on low-level details from the training set. In this paper, we propose GeoDTR which explicitly disentangles geometric information from raw features and learns the spatial correlations among visual features from aerial and ground pairs with a novel geometric layout extractor module. This module generates a set of geometric layout descriptors, modulating the raw features and producing high-quality latent representations. In addition, we elaborate on two categories of data augmentations, (i) Layout simulation, which varies the spatial configuration while keeping the low-level details intact. (ii) Semantic augmentation, which alters the low-level details and encourages the model to capture spatial configurations. These augmentations help to improve the performance of the cross-view geo-localization models, especially on the cross-area benchmarks. Moreover, we propose a counterfactual-based learning process to benefit the geometric layout extractor in exploring spatial information. Extensive experiments show that GeoDTR not only achieves state-of-the-art results but also significantly boosts the performance on same-area and cross-area benchmarks.
Re-purposing Perceptual Hashing based Client Side Scanning for Physical Surveillance

Content scanning systems employ perceptual hashing algorithms to scan user content for illegal material, such as child pornography or terrorist recruitment flyers. Perceptual hashing algorithms help determine whether two images are visually similar while preserving the privacy of the input images. Several efforts from industry and academia propose to conduct content scanning on client devices such as smartphones due to the impending roll out of end-to-end encryption that will make server-side content scanning difficult. However, these proposals have met with strong criticism because of the potential for the technology to be misused and re-purposed. Our work informs this conversation by experimentally characterizing the potential for one type of misuse -- attackers manipulating the content scanning system to perform physical surveillance on target locations. Our contributions are threefold: (1) we offer a definition of physical surveillance in the context of client-side image scanning systems; (2) we experimentally characterize this risk and create a surveillance algorithm that achieves physical surveillance rates of >40% by poisoning 5% of the perceptual hash database; (3) we experimentally study the trade-off between the robustness of client-side image scanning systems and surveillance, showing that more robust detection of illegal material leads to increased potential for physical surveillance.
A Novel Hierarchical-Classification-Block Based Convolutional Neural Network for Source Camera Model Identification

Digital security has been an active area of research interest due to the rapid adaptation of internet infrastructure, the increasing popularity of social media, and digital cameras. Due to inherent differences in working principles to generate an image, different camera brands left behind different intrinsic processing noises which can be used to identify the camera brand. In the last decade, many signal processing and deep learning-based methods have been proposed to identify and isolate this noise from the scene details in an image to detect the source camera brand. One prominent solution is to utilize a hierarchical classification system rather than the traditional single-classifier approach. Different individual networks are used for brand-level and model-level source camera identification. This approach allows for better scaling and requires minimal modifications for adding a new camera brand/model to the solution. However, using different full-fledged networks for both brand and model-level classification substantially increases memory consumption and training complexity. Moreover, extracted low-level features from the different network's initial layers often coincide, resulting in redundant weights. To mitigate the training and memory complexity, we propose a classifier-block-level hierarchical system instead of a network-level one for source camera model classification. Our proposed approach not only results in significantly fewer parameters but also retains the capability to add a new camera model with minimal modification. Thorough experimentation on the publicly available Dresden dataset shows that our proposed approach can achieve the same level of state-of-the-art performance but requires fewer parameters compared to a state-of-the-art network-level hierarchical-based system.
Quantum statistical effect induced through conditioned post-processing procedures with unitary $t$-designs

We propose a few-body quantum phenomenon, which manifests itself through stochastic state preparations and measurements followed by a conditioned post-processing procedure. We show two experimental protocols to implement these phenomena with existing quantum computers, and examine their feasibility by using simulations. Our simulation results suggest that the experimental demonstration is feasible if we repeat the state preparations and measurements about thirty thousand times to three-qubit systems.
evosax: JAX-based Evolution Strategies

The deep learning revolution has greatly been accelerated by the 'hardware lottery': Recent advances in modern hardware accelerators and compilers paved the way for large-scale batch gradient optimization. Evolutionary optimization, on the other hand, has mainly relied on CPU-parallelism, e.g. using Dask scheduling and distributed multi-host infrastructure. Here we argue that also modern evolutionary computation can significantly benefit from the massive computational throughput provided by GPUs and TPUs. In order to better harness these resources and to enable the next generation of black-box optimization algorithms, we release evosax: A JAX-based library of evolution strategies which allows researchers to leverage powerful function transformations such as just-in-time compilation, automatic vectorization and hardware parallelization. evosax implements 30 evolutionary optimization algorithms including finite-difference-based, estimation-of-distribution evolution strategies and various genetic algorithms. Every single algorithm can directly be executed on hardware accelerators and automatically vectorized or parallelized across devices using a single line of code. It is designed in a modular fashion and allows for flexible usage via a simple ask-evaluate-tell API. We thereby hope to facilitate a new wave of scalable evolutionary optimization algorithms.
A Novel Efficient Signcryption Scheme for Resource-Constrained Smart Terminals in Cyber-Physical Power Systems

Most of the existing signcryption schemes generate pseudonym by key generation center (KGC) and usually choose bilinear pairing to construct authentication schemes. The drawback is that these schemes not only consume heavy computation and communication costs during information exchange, but also can not eliminate security risks due to not updating pseudonym, which do not work well for resource-constrained smart terminals in cyber-physical power systems (CPPSs). The main objective of this paper is to propose a novel efficient signcryption scheme for resource-constrained smart terminals. First, a dynamical pseudonym self-generation mechanism (DPSGM) is explored to achieve privacy preservation and avoid the source being linked. Second, combined with DPSGM, an efficient signcryption scheme based on certificateless cryptography (CLC) and elliptic curve cryptography (ECC) is designed, which reduces importantly computation and communication burden. Furthermore, under random oracle model (ROM), the confidentiality and non-repudiation of the proposed signcryption scheme are transformed into elliptic curve discrete logarithm and computational Diffie-Hellman problems that cannot be solved in polynomial time, which guarantees the security. Finally, the effectiveness and feasibility of the proposed signcryption scheme are confirmed by experimental analyses.