Reviewerly/exHarmony huggingface dataset:
Processing Entangled Photons in High Dimensions with a Programmable Light Converter

High-dimensional entanglement offers a variety of advantages for both fundamental and applied applications in quantum information science. A central building block for such applications is a programmable processor of entangled states, which is crucial for the certification, manipulation, and distribution of high-dimensional entanglement. The leading technology for processing photons is integrated multiport interferometers. However, such devices are incompatible with structured light and their scaling is challenging. Here, we unlock these limitations by demonstrating a reconfigurable processor of entangled photons in high dimensions that is based on multiplane light conversion (MPLC), a technology that has recently been developed for multiplexing hundreds of spatial modes for classical communication. We use our programmable MPLC platform to certify three-dimensional entanglement in two mutually unbiased bases, perform 400 arbitrary random transformations on entangled photons, and convert the mode basis of entangled photons for entanglement distribution.
Deep reinforcement learning with a critic-value-based branch tree for the inverse design of two-dimensional optical devices

In optical engineering, designing a device or a system with the desired property is an important but challenging task. It is relatively straightforward to compute the physical properties of a given design; however, there is no general method for the reverse, i.e., designing with desired properties. To address this problem with a computational method, this paper proposes a deep reinforcement learning-based inverse design framework consisting of two methods: Inverse DEsign Agent (IDEA) and Critic-Value-based Branch Tree (CVBT) algorithm. IDEA is a deep reinforcement learning model based on the Advantage Actor–Critic (A2C) method using a deep learning simulator as a replacement for a real environment, significantly reducing training time compared to conventional methods. The CVBT algorithm suggests several design candidates using the critic values of IDEA, while conventional methods propose only one candidate. In this study, the proposed framework was applied to a two-dimensional optical device design problem. Experimental results with untrained target properties demonstrated that the proposed model, IDEA-CVBT, achieved state-of-the-art performance in terms of accuracy and stability. For instance, in a scenario with a binary type design, IDEA-CVBT exhibited an accuracy of 91.5%, while conventional deep reinforcement models showed 36.1% and 7.4% accuracies. Extensive analyses verified that IDEA-CVBT can be employed as an assistance system for engineers since IDEA-CVBT suggests several appropriate candidates that satisfy target properties.
Quantifying coherence relative to channels via metric-adjusted skew information

In terms of the metric-adjusted skew information (an important and versatile class of quantum Fisher information), which generalizes the seminal Wigner-Yanase skew information arising naturally from the study of quantum measurement, we propose a family of coherence measures of states relative to quantum channels, and reveal their basic properties such as unitary covariance, convexity, and monotonicity. Furthermore, we evaluate these coherence measures of states relative to several prototypical quantum channels, and make a comparative study for this family of coherence measures with relative entropy of coherence. This provides a general approach to coherence of states relative to quantum channels, which also captures decoherence on the states caused by quantum channels and asymmetry of states relative to quantum channels.
Cross lingual transfer learning for sentiment analysis of Italian TripAdvisor reviews

Over the years, the attention of the scientific world towards the techniques of sentiment analysis has increased considerably, driven by industry. The arrival of the Google BERT language model has confirmed the superiority of models based on a particular structure of artificial neural network called Transformer, from which many variants have resulted. These models are generally pre-trained on large text corpora and only later specialized according to the precise task to be faced on much smaller amounts of data. For these reasons, countless versions were developed to meet the specific needs of each language, especially in the case of languages with relatively few datasets available. At the same time, models that were pre-trained for multiple languages became widespread, providing greater flexibility of use in exchange for lower performance. This study shows how the use of techniques to transfer learning from languages with high resources to languages with low resources provides an important performance increase: a multilingual BERT model fine tuned on a mixed English/Italian dataset (using for the English a literature dataset and for the Italian a reviews dataset created ad-hoc from the well-known platform TripAdvisor), provides much higher performance than models specific to Italian. Overall, the results obtained by comparing the different possible approaches indicate which one is the most promising to pursue in order to obtain the best results in low resource scenarios. • Use transfer learning from high to low resources languages to identify fake reviews. • Propose a novel ad-hoc created dataset of TripAdvisor reviews in Italian. • Identify the best fine-tuning approach improving performance of BERT language model.
Sphinx: Enabling Privacy-Preserving Online Learning over the Cloud

With the growing complexity of deep learning applications, users have started to delegate their data and models to the cloud. Among these applications, online learning services, which involve both training and inference procedures, are widely deployed. To ensure privacy guarantee on the public cloud, researchers have proposed a plethora of privacy-preserving deep learning algorithms with different techniques, ranging from obfuscation mechanisms to cryptographic tools. However, none of them is applicable to online learning services. They either focus only on inference or training procedure while ignoring the other, or require non-colluding or trusted third parties. In this paper, we present Sphinx, an efficient and privacy-preserving online deep learning system without any trusted third parties. Sphinx strikes a balance between model performance, computational efficiency, and privacy preservation with systematical optimizations on both private inference and training protocols. At its core, Sphinx synthesizes homomorphic encryption and differential privacy reciprocally to maintain the model by keeping most of its parameters as plaintexts, enabling fast training and inference protocol designs. Meanwhile, by refining the homomorphic operation behaviors, Sphinx avoids most of the heavyweight homomorphic operations and minimizes the communication cost. As a result, Sphinx is able to reduce the training time significantly while achieving real-time inference without exposing user privacy. In our experiments, we find that compared to the pure homomorphic encryption solution, Sphinx is $35 \times$ faster for training and 4 orders of magnitude faster for inference, providing real-time inference response (0.05 seconds for MNIST and 0.08 seconds for CIFAR-10). Our experiments also demonstrate that Sphinx achieves promising model accuracy under a tight privacy budget (96% accuracy under $\epsilon=2, \delta=10^{-5}$ for MNIST) without a trusted data aggregator, and is more robust against practical reconstruction attacks.
ProTRR: Principled yet Optimal In-DRAM Target Row Refresh

The DRAM substrate is becoming increasingly more vulnerable to Rowhammer as we move to smaller technology nodes. We introduce ProTRR, the first principled in-DRAM Target Row Refresh mitigation with formal security guarantees and low bounds on overhead. Unlike existing proposals that require changes to the memory controllers, the in-DRAM nature of ProTRR enables its seamless integration. However, this means that ProTRR must respect the synchronous nature of the DRAM protocol, which limits the number of DRAM rows that can be protected at any given time. To overcome this challenge, ProTRR proactively refreshes each row that is most likely to observe bit flips in the future. While this strategy catches the rows that are hammered the most, some others may still fly under the radar. We use this observation to construct Feinting, a new Rowhammer attack that we formally prove to be optimal in this setting. We then conFigure ProTRR to be secure against Feinting. To achieve this, ProTRR should keep track of accesses to each row, which is prohibitively expensive to implement in hardware. Instead, ProTRR uses a new frequent item counting scheme that leverages Feinting to provide a provably optimal yet flexible trade-off between the tolerated DRAM vulnerability, the number of counters, and the number of additional refreshes. Our extensive evaluation using an ASIC implementation of ProTRR and cycle-accurate simulation shows that ProTRR can provide principled protection for current and future DRAM technologies with a negligible performance, power, and area impact. ProTRR is fully compatible with DDR4 and the new Refresh Management (RFM) extension in DDR5.
Multi-Server Verifiable Computation of Low-Degree Polynomials

The conflicts between input privacy and efficiency in single-server non-interactive verifiable computation (NIVC) makes it interesting to consider the multi-server models of NIVC. Although the existing multi-server NIVC schemes provide meaningful improvements, they either require the servers to communicate or leave the client’s data unprotected. It has been an open problem to design multi-server NIVC with both input privacy and non-communicating servers. In this paper we define a multi-server verifiable computation (MSVC) model where the client secret-shares its input x among non-communicating servers, each server locally computes a function F to get a partial result, and finally the client reconstructs F(x) from all partial results. We construct five MSVC schemes for outsourcing low-degree polynomials and thus answer the open question for such polynomials. Our schemes are t-private such that any t servers learn no information about x. Our schemes are t-secure such that any t servers cannot persuade the client to output wrong results. The privacy and security can be either information-theoretic or computational. Comparing with the existing schemes, our servers can be at least two orders faster.
Perceptually Unimportant Information Reduction and Cosine Similarity-Based Quality Assessment of 3D-Synthesized Images

Quality assessment of 3D-synthesized images has traditionally been based on detecting specific categories of distortions such as stretching, black-holes, blurring, etc. However, such approaches have limitations in accurately detecting distortions entirely in 3D synthesized images affecting their performance. This work proposes an algorithm to efficiently detect the distortions and subsequently evaluate the perceptual quality of 3D synthesized images. The process of generation of 3D synthesized images produces a few pixel shift between reference and 3D synthesized image, and hence they are not properly aligned with each other. To address this, we propose using morphological operation (opening) in the residual image to reduce perceptually unimportant information between the reference and the distorted 3D synthesized image. The residual image suppresses the perceptually unimportant information and highlights the geometric distortions which significantly affect the overall quality of 3D synthesized images. We utilized the information present in the residual image to quantify the perceptual quality measure and named this algorithm as Perceptually Unimportant Information Reduction (PU-IR) algorithm. At the same time, the residual image cannot capture the minor structural and geometric distortions due to the usage of erosion operation. To address this, we extract the perceptually important deep features from the pre-trained VGG-16 architectures on the Laplacian pyramid. The distortions in 3D synthesized images are present in patches, and the human visual system perceives even the small levels of these distortions. With this view, to compare these deep features between reference and distorted image, we propose using cosine similarity and named this algorithm as Deep Features extraction and comparison using Cosine Similarity (DF-CS) algorithm. The cosine similarity is based upon their similarity rather than computing the magnitude of the difference of deep features. Finally, the pooling is done to obtain the objective quality scores using simple multiplication to both PU-IR and DF-CS algorithms. Our source code is available online: https://github.com/sadbhawnathakur/3D-Image-Quality-Assessment.
Towards Human-Like Educational Question Generation with Large Language Models

We investigate the utility of large pretrained language models (PLMs) for automatic educational assessment question generation. While PLMs have shown increasing promise in a wide range of natural language applications, including question generation, they can generate unreliable and undesirable content. For high-stakes applications such as educational assessments, it is not only critical to ensure that the generated content is of high quality but also relates to the specific content being assessed. In this paper, we investigate the impact of various PLM prompting strategies on the quality of generated questions. We design a series of generation scenarios to evaluate various generation strategies and evaluate generated questions via automatic metrics and manual examination. With empirical evaluation, we identify the prompting strategy that is most likely to lead to high-quality generated questions. Finally, we demonstrate the promising educational utility of generated questions using our concluded best generation strategy by presenting generated questions together with human-authored questions to a subject matter expert, who despite their expertise, could not effectively distinguish between generated and human-authored questions.
Transparency through Explanations and Justifications in Human-Robot Task-Based Communications

Transparent task-based communication between human instructors and robot instructees requires robots to be able to determine whether a human instruction can and should be carried out, i.e., whether the human is authorized, and whether the robot can and should do it. If the instruction is not appropriate, the robot needs to be able to reject it in a transparent manner by including its reasons for the rejection. In this article, we provide a brief overview of our work on natural language understanding and transparent communication in the Distributed Integrated Affect Reflection Cognition (DIARC) architecture and demonstrate how the robot can perform different inferences based on context to determine whether it should reject a human instruction. Specifically, we discuss four task-based dialogues and show videos of the interactions with fully autonomous robots that are able to reject human commands and provide succinct explanations and justifications for their rejection. The proposed approach can form the basis of further algorithmic developments for adapting the robot's level of transparency for different interlocutors and contexts.
People Count

An introduction to the technology of contact tracing and its usefulness for public health, considering questions of efficacy, equity, and privacy. How do you stop a pandemic before a vaccine arrives? Contact tracing is key, the first step in a process that has proven effective: trace, test, and isolate. Smartphones can collect some of the information required by contact tracers—not just where you've been but also who's been near you. Can we repurpose the tracking technology that we carry with us—devices with GPS, Wi-Fi, Bluetooth, and social media connectivity—to serve public health in a pandemic? In People Count, cybersecurity expert Susan Landau looks at some of the apps developed for contact tracing during the COVID-19 pandemic, finding that issues of effectiveness and equity intersect. Landau explains the effectiveness (or ineffectiveness) of a range of technological interventions, including dongles in Singapore that collect proximity information; India's biometric national identity system; Harvard University's experiment, TraceFi; and China's surveillance network. Other nations rejected China-style surveillance in favor of systems based on Bluetooth, GPS, and cell towers, but Landau explains the limitations of these technologies. She also reports that many current apps appear to be premised on a model of middle-class income and a job that can be done remotely. How can they be effective when low-income communities and front-line workers are the ones who are hit hardest by the virus? COVID-19 will not be our last pandemic; we need to get this essential method of infection control right.
Estimation of cost and development effort in Scrum-based software projects considering dimensional success factors

• In this article, all 36 dimensional success factors are considered to estimate the required effort and cost to develop any Agile-Scrum based software project. • The work presented in the article computes the required time, effort and cost of projects mentioned in a data set on the basis of different inputs to the system. • For computation purposes, we divided the projects into three categories as Low, Medium and High level project for the unbiased estimation. • The datasets considered for computation purposes, also validated by experienced professionals from software industries. • It is found that the results obtained from the extended algorithm are cost-effective and less effort-oriented to a great extent as a compared to the existing algorithm. Effort and cost estimation is essential processes during software development. For agile methodologies as well, several approaches have been proposed so far to estimate the required effort and cost to develop any software system. Genuine assessment of software products is not an easy task as it requires the constant attention of the product owner. For effective estimation, we need to consider various success factors associated with all possible dimensions. In this article, we look at Scrum-based Agile projects that are developed over multiple sprints. We proposed an extension to the existing algorithm based on a total of 36 success factors; Which estimates the development effort and cost required to complete the project. For assessment and calculation, we have taken a data-set of a total of 30 projects categorized into three groups as low, medium, and high-level projects. This dataset is further validated by experienced professionals. We also compared our results with the existing approach and found that our results are cost-effective and require less effort even after considering more success factors.
RegTech in public and private sectors: the nexus between data, technology and regulation

Abstract Higher regulatory compliance requirements, fast and continuous changes in regulations and high digital dynamics in the financial markets are powering RegTech (regulatory technology), defined as technology‐enabled innovation applied to the world of regulation, compliance, risk management, reporting and supervision. This work builds on a systematic literature review and a bibliometric analysis of the literature on RegTech, its influential papers and authors, its main areas of research, its past and its future. The resulting multi-dimensional framework bridges across four main dimensions, starting with regulation and technology, where one or more regulations, not necessarily financial ones, are addressed with the support of technologies (e.g. artificial intelligence, DLT, blockchain, smart contracts, API). Data play a central role, as sharing them enables data ecosystems, where additional value can be attained by each market participant, while data automation and machine-readable regulations empower regulators to pull data directly from the banks’ systems and combine these data with data obtained directly from customers or other external sources. Several applications emerge, both for regulated entities, covering matters of compliance, monitoring, risk management, reporting and operations, as well as for authorities, which can leverage on RegTech (SupTech) solutions to make policies, to undertake their authorising, supervising and enforcement operations, for monitoring and controlling purposes, and even to issue fines automatically. As a consequence, stakeholders can reap a series of benefits, such as higher efficiency and effectiveness, accuracy, transparency and lower compliance costs but also risks, such as cyber risk, algorithmic biases, and dehumanization.
Inter-cluster and intra-cluster joint optimization for unsupervised cross-domain person re-identification

The purpose of unsupervised cross-domain (UCD) person re-identification (re-ID) is to adapt the well pre-trained model on the labeled source domain to the unlabeled target domain, which tackles a more realistic problem. Most successful UCD methods are devoted to exploring the relationship at instance level in the target domain, but neglect the relationship at cluster level. However, person re-ID is the task of retrieving samples belonging to the same identity. Thus, exploring the cluster level relationship is critical to improve the performance of re-ID. In this paper, a novel inter-cluster and intra-cluster joint optimization (IcJO) model is proposed. Specifically, for the inter-cluster relationship, a novel centroid-based contrastive learning strategy is designed, making the sample close to its cluster centroid and far away from the heterogeneous centroids. In order to alleviate the influence of clustering noise, a cluster-based uncertainty estimation coefficient is introduced to guide the contrastive learning process. As for the intra-cluster relationship, a centroid-based maximum mean discrepancy (cMMD) loss is adopted to narrow the intra-cluster distribution. Moreover, in order to enhance the feature invariance of each identity, a centroid-based similarity loss is developed. Extensive experiments are conducted on three standard benchmarks. The results demonstrate that our IcJO model performs favorably against the state-of-the-art UCD person re-ID methods.
Quantifying information scrambling via classical shadow tomography on programmable quantum simulators

We develop techniques to probe the dynamics of quantum information and implement them experimentally on an IBM superconducting quantum processor. Our protocols adapt shadow tomography for the study of time-evolution channels rather than of quantum states and rely only on single-qubit operations and measurements. We identify two unambiguous signatures of quantum information scrambling, neither of which can be mimicked by dissipative processes, and relate these to many-body teleportation. By realizing quantum chaotic dynamics in experiment, we measure both signatures and support our results with numerical simulations of the quantum system. We additionally investigate operator growth under this dynamics and observe behavior characteristic of quantum chaos. As our methods require only a single quantum state at a time, they can be readily applied on a wide variety of quantum simulators.