Reviewerly/exHarmony huggingface dataset:
Randomized Ancillary Qubit Overcomes Detector-Control and Intercept-Resend Hacking of Quantum Key Distribution

Practical implementations of quantum key distribution (QKD) have been shown to be subject to various detector side-channel attacks that compromise the promised unconditional security.Most notable is a general class of attacks adopting the use of faked-state photons as in the detector-control and, more broadly, the intercept-resend attacks.In this paper, we present a simple scheme to overcome such class of attacks: A legitimate user, Bob, uses a polarization randomizer at his gateway to distort an ancillary polarization of a phase-encoded photon in a bidirectional QKD configuration.Passing through the randomizer once on the way to his partner, Alice, and again in the opposite direction, the polarization qubit of the genuine photon is immune to randomization.However, the polarization state of a photon from an intruder, Eve, to Bob is randomized and hence directed to a detector in a different path, whereupon it triggers an alert.We demonstrate theoretically and experimentally that, using commercial off-the-shelf detectors, it can be made impossible for Eve to avoid triggering the alert, no matter what faked-state of light she uses.
Automatic System Based on Riesz MV-algebras, for Predictive Maintenance of Bearings of Industrial Equipment Using Temperature Sensors

Predictive maintenance systems take a more and more important place in increasing the reliability of industrial equipment, of productivity and reducing production costs. There’s a constant focus to develop new system architectures and in using the latest numerical methods and algorithms in developing more and more reliable predictive maintenance systems. Based on recent results in using Shepard local approximation operators defined in Riesz MV-Algebras for IoT devices signals processing, in this article we propose a new architecture for a predictive maintenance system by adding an extra layer (denoted Data Validation Layer). This new layer is processing the acquired signals by sensors such that to provide to the predictive algorithms a complete and validated data set. The proposed architecture was validated by implementing and testing a predictive maintenance system that is monitoring the bearings of industrial equipment using temperature sensors.
Mixed Reality Platform Supporting Human-Robot Interaction

Human-Robot Interaction (HRI) is an interdisciplinary research field aiming to study and develop platform in which robots (and in particular collaborative robots, also called cobots) can interact and collaborate with humans to execute dedicated tasks. Usually, cobots are intended as passive robotic devices for direct collaboration with a human operator within a shared workspace. They are designed to be used in open and uncontrolled environments; the robot shall be able to adapt its behaviors to the dynamic input of the surrounding environment. In this optic, Mixed Reality (MR) can play a crucial role supporting the flow of data between the actors (cobot and human) working in the shared environment, it can offer a simply, but remarkably advanced, communication interface between human and robot. Thanks to MR, tools to allow human operators, without particular experience or knowledge of robotics, to easily interact with the cobot can be developed. Our work is focused on development of a MR platform that integrates cutting-edge technologies, i.e. a Head Mounted Display (HMD), and a cobot in a shared environment. The experimental setup includes the Microsoft’s Mixed Reality HMD HoloLens 2 and the Franka Emika Robot System.
A Human-Centred Design Approach for Designing Augmented Reality Enabled Interactive Systems: A Kitchen Machine Case Study

In the ever-changing scenario of technology evolution, designers need to develop new interactive systems that respond to users’ needs. Augmented Reality (AR) could be a tool and an opportunity for designers to create novel interactive systems. AR has proven to be effective in several domains, showing potential for widespread deployment even in everyday life tasks such as the use of household appliances. Thus, leveraging the Human-Centred Design (HCD) approach, we integrate AR into the design and development process of an interactive system for household appliances. Based on our survey results with 463 participants, one of the appliances that could benefit most from AR is the kitchen machine. Starting from a case study, we develop a demo to prove the feasibility of designing interactive systems with the integration of AR technologies, following the HCD approach.
Tintoretto Unveiled: Interactive Virtual Experience for Artworks

Conventionally, visitors of exhibitions cannot directly interact with artworks but remain mainly passive. This work presents a new way to discover paintings through an extended reality application with the aim of enhancing visitors’ engagement. The workflow consists of digitally recreating a painting. All its constitutive elements are contoured and then removed by the artwork. These elements will be unveiled by the visitors with gesture interaction. The discovery of each element is followed by additional information on the subject. The application also proposes an alternative experience where the user paints the components of the artwork with gestures. The hands’ position was tracked by the Leap Motion Controller, an optical sensor by Ultraleap. The process has been applied to one of the Tintoretto’s pieces of art: “L’Annunciazione del Doge Grimani”. The extended reality application has been experienced by over twenty thousand visitors at the dedicated exhibition. Numerous positive comments received from both visitors and guides are encouraging.
Quantifying Temporal Privacy Leakage in Continuous Event Data Publishing

Process mining employs event data extracted from different types of information systems to discover and analyze actual processes. Event data often contain highly sensitive information about the people who carry out activities or the people for whom activities are performed. Therefore, privacy concerns in process mining are receiving increasing attention. To alleviate privacy-related risks, several privacy preservation techniques have been proposed. Differential privacy is one of these techniques which provides strong privacy guarantees. However, the proposed techniques presume that event data are released in only one shot, whereas business processes are continuously executed. Hence, event data are published repeatedly, resulting in additional risks. In this paper, we demonstrate that continuously released event data are not independent, and the correlation among different releases can result in privacy degradation when the same differential privacy mechanism is applied to each release. We quantify such privacy degradation in the form of temporal privacy leakages. We apply continuous event data publishing scenarios to real-life event logs to demonstrate privacy leakages.
Fantastic Morphisms and Where to Find Them

Structured recursion schemes have been widely used in constructing, optimizing, and reasoning about programs over inductive and coinductive datatypes. Their plain forms, catamorphisms and anamorphisms, are restricted in expressivity. Thus many generalizations have been proposed, which further led to several unifying frameworks of structured recursion schemes. However, the existing work on unifying frameworks typically focuses on the categorical foundation, and thus is perhaps inaccessible to practitioners who are willing to apply recursion schemes in practice but are not versed in category theory. To fill this gap, this expository paper introduces structured recursion schemes from a practical point of view: a variety of recursion schemes are motivated and explained in contexts of concrete programming examples. The categorical duals of these recursion schemes are also explained.
How to Verifiably Encrypt Many Bits for an Election?

The verifiable encryption of bits is the main computational step that is needed to prepare ballots in many practical voting protocols. Its computational load can also be a practical bottleneck, preventing the deployment of some protocols or requiring the use of computing clusters. We investigate the question of producing many verifiably encrypted bits in an efficient and portable way, using as a baseline the protocol that is in use in essentially all modern voting systems and libraries supporting homomorphic voting, including ElectionGuard, a state-of-the-art open source voting SDK deployed in government elections. Combining fixed base exponentiation techniques and new encryption and ZK proof mechanisms, we obtain speed-ups by more than one order of magnitude against standard implementations. Our exploration requires balancing conflicting optimization strategies, and the use of asymptotically less efficient protocols that turn out to be very effective in practice. Several of our proposed improvements are now on the ElectionGuard roadmap.
Information Flow Control-by-Construction for an Object-Oriented Language

In security-critical software applications, confidential information must be prevented from leaking to unauthorized sinks. Static analysis techniques are widespread to enforce a secure information flow by checking a program after construction. A drawback of these systems is that incomplete programs during construction cannot be checked properly. The user is not guided to a secure program by most systems. We introduce IFbCOO, an approach that guides users incrementally to a secure implementation by using refinement rules. In each refinement step, confidentiality or integrity (or both) is guaranteed alongside the functional correctness of the program, such that insecure programs are declined by construction. In this work, we formalize IFbCOO and prove soundness of the refinement rules. We implement IFbCOO in the tool CorC and conduct a feasibility study by successfully implementing case studies.
Machine Learning Based Automated Process for Predicting the Anomaly in AIS Data

In this paper, we present an automated process for detecting the anomaly in Automatic Identification System (AIS) data. Machine learning approaches have been employed to automatically detect anomalies in the AIS data. The opensource AIS data is been used to evaluate the performance of the proposed approach. Supervised machine learning approaches like K Nearest Neighbor, Random Forest, Support Vector Machine classifier is employed to predict the anomalies in the AIS data. The AIS data does not contain the ground truth labels and supervised learning algorithms need labelling data, to address this issue, we employed an unsupervised approach to label the data based on the prior information and characteristics of the AIS data. The labelled data is then used to train the supervised machine learning models. The proposed approach with support vector machine classifier has classified the AIS data into normal and anomaly with an accuracy of 96.5%.
Breast Cancer Prediction Using Auto-Encoders

Breast cancer is one of the most common forms of cancer that is diagnosed in most women and some rare cases even men. In recent years, breast cancer survival rates have increased significantly, due to factors such as earlier detection. Treatment for breast cancer largely depends on identifying the type of mass of tissue formed, which is known as a tumor. If normal cells grow in an uncontrollable manner the tumor is called benign (non-cancerous). But, if the cells’ growth is out of control and their behavior is abnormal, then the tumor is called malignant (cancerous). During the invasive, (i.e. curable) stage of cancer, only 10–15% part of the breast contains cancerous cells. Therefore, it is difficult to diagnose it using mammography. However, the development of machine learning techniques has allowed early detection of breast cancer in clinical trials. This paper presents a comparative study of different machine learning techniques for detecting breast cancer. It also presents an auto-encoder model that performs breast cancer detection in an unsupervised manner. It attempts to identify a compact representation of features that are strongly related to breast cancer. The techniques are tested on the Breast Cancer Wisconsin (Diagnostic) Dataset, which is publicly available on Kaggle. The auto-encoder beats its competitors with a precision and recall of 98.4%.
Efficient Unique Ring Signatures from Lattices

Unique ring signatures (URS) were introduced by Franklin and Zhang (FC 2012) as a unification of linkable and traceable ring signatures. In URS, each member within a ring can only produce, on behalf of the ring, at most one signature for a message. Applications of URS potentially are e–voting systems and e–token systems. In blockchain technology, URS have been implemented for mixing contract. However, existing URS schemes are based on the Discrete Logarithm Problem, which is insecure in the post-quantum setting. In this paper, we design a new lattice-based URS scheme where the signature size is logarithmic in number of ring members. The proposed URS exploits a Merkle tree-based accumulator as building block in the lattice setting. Our scheme is secure under the Short Integer Solution and Learning With Rounding assumptions in the random oracle model.
Fuzzy Authenticated Key Exchange with Tight Security

Fuzzy authenticated key exchange (FAKE) enables two parties to agree on a shared key with the help of their fuzzy sources, like biometric features [3, 9], physical unclonable functions (PUFs) [16], etc. In FAKE, each user will generate public strings from its own fuzzy source, and register public strings to the system. In the interactive protocol of FAKE, the user will again employ their own fuzzy sources to accomplish key agreement and achieve authenticity and privacy for the agreed session keys. The advantage of FAKE is that users do not have to store them in the devices, hence do not worry about key leakage due to bad key management in devices. In this paper, We propose a generic construction of FAKE from three building blocks including secure sketch (SS), key encapsulation scheme (KEM) and a digital signature (SIG). We also define authenticity for users and pseudo-randomness for session keys to formalize the security of FAKE in the multi-user multi-challenge setting. We prove the security of our FAKE construction with tight security reductions to the building blocks in the random oracle model. Given the available choices for SS, tightly secure KEM and tightly secure SIG schemes, we obtain a bunch of FAKE schemes with tight security in the multi-user multi-challenge setting.
YUDH: Multi-utility Tool to Reassure Serenity in the COVID-19 Pandemic

COVID-19, the virus that has affected current living standards, has undergone mutation, and the second wave has caused a much more devastating situation in India. In such a scenario, the alert of a third wave by the authorities has alarmingly increased concern in the nation. After being declared as an international emergency, the development of its vaccine has been conducted by different countries. India among other countries is also pursuing to develop much more efficient variants of the vaccine. The situation still persists to be hostile and maintaining the current precaution measures and maximizing the distribution of the vaccines is the only solution in hand. A necessity arises for a user-friendly app to reduce social interaction while assisting in medical support. In this paper, we have proposed an android application named YUDH, which focuses on the overall service that an individual requires from booking test centers, vaccine slot notification to home sanitization. The user can book COVID-19 testing centers and can arrange sanitization service after recovery with the provision of place, date, and time. In addition to booking test centers, swab testing at the doorstep is also available. The user also gets regular notifications on COVID vaccine slot availability in accordance with CoWIN portal and users’ preferences. This deployment is aimed at the safety of the user and their privacy safeguard. The application also assists the government to maintain a database more efficiently.
A Hybrid Product Recommendation System Based on Weather Analysis

Product recommendation becomes to be one most revenue-generating technologies that every e-commerce website is using. For enhancing the purchase rate and user engagement, various product recommendations are available in the e-commerce website. Most of the external contexts are also taken into consideration for the product recommendation. This type of external context analysis sometimes will provide better recommendations when compared to the user-generated data. This paper proposes an idea of product recommendation using the weather. Here we predict the weather for the coming days using logistic regression and analyze the weather using big data analytics. Based on the analysis, we will sort the products and recommend a product in the same weather.
Public Cloud Data Auditing Revisited: Removing the Tradeoff Between Proof Size and Storage Cost

Public cloud data auditing allows any third party to check the integrity of data stored on untrusted cloud servers without retrieving the data. The challenge is how to audit the proof of storage with efficient communications. In ACM CCS 2007, Ateniese et al. described the first practical public cloud data auditing scheme based on RSA, in which the proof of storage consists of one RSA element and one hash value and the storage cost for generating the proof can be as short as $$1\%$$ of the stored file. Soon after, in Asiacrypt 2008, Shacham and Waters gave another public cloud data auditing scheme based on bilinear pairing, in which the generated proof of storage can be as short as 320 bits for 80-bit security (71 $$\%$$ less compared to Ateniese et al.’s scheme). However, Shacham and Waters’ scheme must trade off the storage cost, where the storage overhead for generating the proof of storage must be $$100\%$$ of the stored file. Surprisingly, until today, the tradeoff between the proof size (namely proof of storage) and the storage cost (namely storage overhead) in cloud data auditing remains an open problem. In this paper, we introduce a completely new public cloud data auditing mechanism. The proof of storage is not computed from block tags directly, but from evolution tags that are still unforgeable and evolved from bunch tags. We propose a concrete public cloud data auditing scheme based on this mechanism, in which the proof size is 240 bits for 80-bit security (25 $$\%$$ less compared to Shacham and Waters’ scheme) and the storage cost can be as efficient as Ateniese et al.’s scheme. The core of our technique is the feasibility of tag aggregations within this new mechanism. Our scheme is provably secure in the random oracle model.
Back-and-Forth in Space: On Logics and Bisimilarity in Closure Spaces

We adapt the standard notion of bisimilarity for topological models to closure models and refine it for quasi-discrete closure models. We also define an additional, weaker notion of bisimilarity that is based on paths in space and expresses a form of conditional reachability in a way that is reminiscent of Stuttering Equivalence on transition systems. For each bisimilarity we provide a characterisation with respect to a suitable spatial logic.
Preserving bilateral view structural information for subspace clustering

Subspace clustering algorithms have been found successful in various applications that involve two-dimensional data, i.e., each example of the data is a matrix. However, most of the existing methods transform the matrix-type examples to vectors in a pre-processing step, which omits and severely damages the inherent structural information of such data. In this paper, we propose a novel subspace clustering method for two-dimensional data, which is capable of extracting the most representative structural information from the data to recover the underlying grouping relationships of the data. The structural features are extracted from two views of the data and the numbers of feature spaces in both views are automatically determined by optimization. Extensive experiments confirm the effectiveness of the proposed method.
Online active learning for an evolving fuzzy neural classifier based on data density and specificity

Evolving fuzzy neural classifiers are incremental, adaptive models that use new samples to update the architecture and parameters of the models with new incoming data samples, typically occurring in form of data streams for classification problems. Most of the techniques assume that the target labels are permanently given as updating their structures and parameters in a fully supervised manner. This paper aims to implement ideas based on the concept of active learning in order to select the data most relevant for updating the model. This may greatly reduce annoying and costly labeling efforts for users/operators in an online system. Therefore, we propose an online active learning (oAL) methodology, which is closely linked to the internal evolving learning engine for fuzzy neurons, which is based on incremental data-cloud formation. It is thus based on the evaluation of the specificity of the current clouds, and especially by the change in their specificity with new (unsupervised) samples, in order to identify those samples carrying relevant information to the update of previously formed clouds. This is combined with the unsupervised cloud evolution criterion, which upon its fulfillment indicates a new knowledge contained in the data for which the class response needs to be known (thus should be selected for labeling feedback). In synergy to the evolving fuzzy neural classifier, it acts in an incremental single-pass manner, not using any past samples, which makes it extremely fast, as only fuzzy neurons attached to a new sample need to be checked for the degree of their specificity change. To prove the technique's efficiency, tests with binary classification streams commonly used by the machine learning community were conducted for evaluation purposes. The number of supervised samples for model updates could be significantly reduced with a low or even negligible decrease in the classification accuracy trends, while a random selection of samples (with the same percentages as selected by our oAL approach) showed large performance downtrends. Furthermore, a very similar number of rule evolution trends could be observed with different percentages of selected samples, which indicates good robustness of our method with respect to knowledge extraction (as non-changing).
High diopter spectacle using a flexible Fresnel lens with a combination of grooves

In this study, the Fresnel lens was investigated as a potential candidate for vision correction in patients with myopia. A few previous studies have suggested this idea; however, Fresnel lenses are limited by their aesthetics and quality. Therefore, we designed a combination of Fresnel lens grooves with a constant height and pitch of 13 µm and 0.1 mm, respectively, to overcome the limitations caused by ultra-precision machining with a tool nose radius of 30 µm. A thin replicated Fresnel lens with a power of -5 diopter was procured and applied directly as spectacles that are unattached to the normal lens. The optical performance and image quality of the Fresnel lens were compared with those of a conventional lens possessing the same power in both near and far vision. These results extend the applicability for the use of Fresnel lenses as vision-correcting ophthalmological lenses and imaging systems.