Reviewerly/exHarmony huggingface dataset:
The Tightness of Multipartite Coherence from Spectrum Estimation

Detecting multipartite quantum coherence usually requires quantum state reconstruction, which is quite inefficient for large-scale quantum systems. Along this line of research, several efficient procedures have been proposed to detect multipartite quantum coherence without quantum state reconstruction, among which the spectrum-estimation-based method is suitable for various coherence measures. Here, we first generalize the spectrum-estimation-based method for the geometric measure of coherence. Then, we investigate the tightness of the estimated lower bound of various coherence measures, including the geometric measure of coherence, the l1-norm of coherence, the robustness of coherence, and some convex roof quantifiers of coherence multiqubit GHZ states and linear cluster states. Finally, we demonstrate the spectrum-estimation-based method as well as the other two efficient methods. We observe that the spectrum-estimation-based method outperforms other methods in various coherence measures, which significantly enhances the accuracy of estimation.
Towards Improving the Trustworthiness of Hardware based Malware Detector using Online Uncertainty Estimation

Hardware-based Malware Detectors (HMDs) using Machine Learning (ML) models have shown promise in detecting malicious workloads. However, the conventional black-box based machine learning (ML) approach used in these HMDs fail to address the uncertain predictions, including those made on zero-day malware. The ML models used in HMDs are agnostic to the uncertainty that determines whether the model “knows what it knows,” severely undermining its trustworthiness. We propose an ensemble-based approach that quantifies uncertainty in predictions made by ML models of an HMD, when it encounters an unknown workload than the ones it was trained on. We test our approach on two different HMDs that have been proposed in the literature. We show that the proposed uncertainty estimator can detect > 90% of unknown workloads for the Power-management based HMD, and conclude that the overlapping benign and malware classes undermine the trustworthiness of the Performance Counter-based HMD.
Design and Implementation of Sunlight Tracking Based on the Internet of Things

Abstract In this article, for smart management and control of the solar pursuing device, an archetype is designed to evaluate or verify administration system and control. The newly-built prototype is designed for several applications to be a powerful platform for understanding the smart solar energy method. The Internet of Stuff integrates ordinary items using the Internet to enter the physical world. The IoT technology is facilitated to track solar PV (photovoltaic) power generation that can improve plant upkeep, tracking, and efficiency. This would include monitoring the solar panel and pointing it into the sunlight. It’s all feasible to utilize LDR sensors. The IoT automatically preserves track of the voltage supply the solar panel detects the towards of sunlight. The suggested system demonstrates the online utilization of solar PV power.
Explanation-Guided Diagnosis of Machine Learning Evasion Attacks

Machine Learning (ML) models are susceptible to evasion attacks. Evasion accuracy is typically assessed using aggregate evasion rate, and it is an open question whether aggregate evasion rate enables feature-level diagnosis on the effect of adversarial perturbations on evasive predictions. In this paper, we introduce a novel framework that harnesses explainable ML methods to guide high-fidelity assessment of ML evasion attacks. Our framework enables explanation-guided correlation analysis between pre-evasion perturbations and post-evasion explanations. Towards systematic assessment of ML evasion attacks, we propose and evaluate a novel suite of model-agnostic metrics for sample-level and dataset-level correlation analysis. Using malware and image classifiers, we conduct comprehensive evaluations across diverse model architectures and complementary feature representations. Our explanation-guided correlation analysis reveals correlation gaps between adversarial samples and the corresponding perturbations performed on them. Using a case study on explanation-guided evasion, we show the broader usage of our methodology for assessing robustness of ML models .
A Quantitative Study on Awareness, Usage and Reservations of Voice Control Interfaces by Elderly People

One third of Germans talk to 'Alexa', 'Siri' and other voice-controlled devices. These devices become omnipresent and change the way how humans interact with digital technologies. We hypothesize, this Human-Computer-Interface can minimize barriers for elderly people in their usage of digital services. But, do elderly people even know about voice-controlled technologies? Are the systems used by elderly and what reservations do they have?
Interpreting clinical latent representations using autoencoders and probabilistic models

Electronic health records (EHRs) are a valuable data source that, in conjunction with deep learning (DL) methods, have provided important outcomes in different domains, contributing to supporting decision-making. Owing to the remarkable advancements achieved by DL-based models, autoencoders (AE) are becoming extensively used in health care. Nevertheless, AE-based models are based on nonlinear transformations, resulting in black-box models leading to a lack of interpretability, which is vital in the clinical setting. To obtain insights from AE latent representations, we propose a methodology by combining probabilistic models based on Gaussian mixture models and hierarchical clustering supported by Kullback-Leibler divergence. To validate the methodology from a clinical viewpoint, we used real-world data extracted from EHRs of the University Hospital of Fuenlabrada (Spain). Records were associated with healthy and chronic hypertensive and diabetic patients. Experimental outcomes showed that our approach can find groups of patients with similar health conditions by identifying patterns associated with diagnosis and drug codes. This work opens up promising opportunities for interpreting representations obtained by the AE-based model, bringing some light to the decision-making process made by clinical experts in daily practice.
An Improved Diracnet Convolutional Neural Network for Haze Visibility Detection

The visibility reduction caused by haze is a serious hazard to traffic safety. In this paper, a new DiracNet convolutional neural network is improved, based on which a haze visibility detection method is constructed to overcome the overfitting phenomenon, reduce the training time, and subsequently improve the detection accuracy. Based on the massive data, the validation results show that the mean absolute percentage error (MAPE) value obtained from the test of the improved DiracNet visibility detection algorithm is 2.24%, while the MAPE values of the ResNet-based haze visibility algorithm and the DiracNet-based haze visibility detection algorithms are 5.72% and 4.73%, respectively. The algorithm validation results prove the effectiveness and superiority of the improved DiracNet convolutional neural network algorithm.
The impact of emoji use and feedback medium on perceptions of marker personality in online assessment feedback

This study investigated whether emoji and multimedia delivery in online assessment feedback influenced student perceptions of the marker. Undergraduate students (N = 296) read a faux essay and viewed associated feedback emulating the comments students might receive in higher education. Feedback was manipulated using a 2 (emoji: no, yes) × 3 (medium: written-only, written plus audio, and written plus audio-visual) between-groups design. There were no main effects for the emoji manipulation. However, audio-visual feedback produced higher perceptions of marker intelligence and honesty-humility. In addition, the effect of emoji depended upon medium such that in feedback without emoji, perceptions of marker emotional stability and conscientiousness were lowest for audio feedback and highest for audio-visual feedback. Overall, results revealed that feedback medium and paralinguistic emotional cues interact to influence perceptions of the marker. Educators should consider how this interconnection may affect student engagement with the feedback process when delivering assessment feedback online.
Dynamic balance tip selection algorithm for IOTA

Traditional distributed ledger technology has the disadvantages of low throughput, high resource consumption and poor scalability, which make it difficult to be applied in the field of Internet of things. As a new distributed ledger technology based on directed acyclic graph, IOTA aims to be applied in the field of Internet of things. However, IOTA suffers from the problem of fairness. In IOTA,a large number of honest transactions may not be approved, and the system has the risk of parasitic chain attack and splitting attack. In this paper we proposes a new tip selection algorithm, which can dynamically change the tip selection strategy. Interestingly, the concept of sharpness is proposed firstly to describe the extreme degree of a tangle-fragment in this paper.Experimental results show that our algorithm can make most honest transactions be verified, while always keeping the security of the tangle in an acceptable range.
Building a Mobile AR Engagement Tool: Evaluation of Citizens Attitude Towards a Sustainable Future

Adopting a sustainable way of living in today’s cities requires a high level of citizen empowerment, motivation, and engagement. The mobile Augmented Reality (AR) technology offers an accessible, inexpensive, and rich user experience that has the potential to lead towards this adoption. The widespread smartphones and tablets with unique features (e.g., embedded sensors, cameras, high-speed Internet, accessibility, and portability) provide powerful and ubiquitous platforms for supporting such applications. Mobile technology, and the easiness of immersing a mobile user in AR, impact the interaction of citizens with their environment. We leverage the deep penetration of mobile phones in urban environments and their advanced features to design and develop an AR citizen engagement tool. The tool is employed in a municipality and studied as means to foster citizen engagement in sustainable practices. The Technology Acceptance Model is used to study the acceptance of this application and the factors that may affect its adoption. In total, 127 end users were exposed to the prototype system that allowed 3D, audio, and 2D visualizations as well as interactions with them. Through a web-based survey, we assessed the factors and measures influencing the acceptance of technology and how they can be aligned with the characteristics of the contemporary urban settings and utilize the potentials offered by mobile AR technologies. Providing detailed information regarding the system design we expect that the results of this study will contribute to the discourse on the use of mobile AR as a tool for citizen engagement to guide the development of future efforts.
Bert-based Braille Summarization of Long Documents

Availability of accurate document summaries is of paramount importance in our lives, though there is a dearth of such options for the visually challenged people. With a large volume of existing text summarization work dealing with short documents, this work aims to tackle the problem of large document summarization in braille. We propose a novel domain and genre agnostic approach to document summarization in braille using a BERT extractive summarizer. Experimental evaluation of our approach involved a comparison of the generated and reference summaries using ROUGE metrics. Given the lack of robust convertors from English to Braille, another key contribution of our work is the development of a novel English & Braille interconversion library to facilitate further research in this domain.
MarsExplorer: Exploration of Unknown Terrains via Deep Reinforcement Learning and Procedurally Generated Environments

This paper is an initial endeavor to bridge the gap between powerful Deep Reinforcement Learning methodologies and the problem of exploration/coverage of unknown terrains. Within this scope, MarsExplorer, an openai-gym compatible environment tailored to exploration/coverage of unknown areas, is presented. MarsExplorer translates the original robotics problem into a Reinforcement Learning setup that various off-the-shelf algorithms can tackle. Any learned policy can be straightforwardly applied to a robotic platform without an elaborate simulation model of the robot’s dynamics to apply a different learning/adaptation phase. One of its core features is the controllable multi-dimensional procedural generation of terrains, which is the key for producing policies with strong generalization capabilities. Four different state-of-the-art RL algorithms (A3C, PPO, Rainbow, and SAC) are trained on the MarsExplorer environment, and a proper evaluation of their results compared to the average human-level performance is reported. In the follow-up experimental analysis, the effect of the multi-dimensional difficulty setting on the learning capabilities of the best-performing algorithm (PPO) is analyzed. A milestone result is the generation of an exploration policy that follows the Hilbert curve without providing this information to the environment or rewarding directly or indirectly Hilbert-curve-like trajectories. The experimental analysis is concluded by evaluating PPO learned policy algorithm side-by-side with frontier-based exploration strategies. A study on the performance curves revealed that PPO-based policy was capable of performing adaptive-to-the-unknown-terrain sweeping without leaving expensive-to-revisit areas uncovered, underlying the capability of RL-based methodologies to tackle exploration tasks efficiently.
Lattice (List) Decoding Near Minkowski’s Inequality

Minkowski proved that any <inline-formula> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula>-dimensional lattice of unit determinant has a nonzero vector of Euclidean norm at most <inline-formula> <tex-math notation="LaTeX">$\sqrt {n}$ </tex-math></inline-formula>; in fact, there are <inline-formula> <tex-math notation="LaTeX">$2^{\Omega (n)}$ </tex-math></inline-formula> such lattice vectors. Lattices whose minimum distances come close to Minkowski&#x2019;s bound provide excellent sphere packings and error-correcting codes in <inline-formula> <tex-math notation="LaTeX">${\mathbb {R}}^{n}$ </tex-math></inline-formula>. The focus of this work is a certain family of efficiently constructible <inline-formula> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula>-dimensional lattices due to Barnes and Sloane, whose minimum distances are within an <inline-formula> <tex-math notation="LaTeX">$O(\sqrt {\log n})$ </tex-math></inline-formula> factor of Minkowski&#x2019;s bound. Our primary contribution is a polynomial-time algorithm that <i>list decodes</i> this family to distances approaching <inline-formula> <tex-math notation="LaTeX">$1/\sqrt {2}$ </tex-math></inline-formula> of the minimum distance. The main technique is to decode Reed-Solomon codes under error measured in the Euclidean norm, using the Koetter-Vardy &#x201C;soft decision&#x201D; variant of the Guruswami-Sudan list-decoding algorithm.
Measurement-Driven Navigation in Many-Body Hilbert Space: Active-Decision Steering

The challenge of preparing a system in a designated state spans diverse facets of quantum mechanics. To complete this task of steering quantum states, one can employ quantum control through a sequence of generalized measurements, which direct the system towards the target state. In an active version of this protocol, the obtained measurement readouts are used to adjust the protocol on the go. This enables a sped-up performance relative to the passive version of the protocol, where no active adjustments are included. In this work, we consider such active measurement-driven steering as applied to the challenging case of many-body quantum systems. The target states of highest interest would be those with multipartite entanglement. Such state preparation in a measurement-based protocol is limited by the natural constraints for system-detector couplings. We develop a framework for finding such physically feasible couplings, based on parent Hamiltonian construction. For helpful decision-making strategies, we offer Hilbert-space-orientation techniques, comparable to those used in navigation. The first one is to tie the active-decision protocol to the greedy accumulation of the cost function, such as the target state fidelity. We show the potential of a significant speedup, employing this greedy approach to a broad family of matrix product state targets. For system sizes considered here, an average value of the speedup factor f across this family settles about 20, for some targets even reaching a few thousands. We also identify a subclass of matrix product state targets, including the ground state of the Affleck-Kennedy-Lieb-Tasaki spin chain, for which the value of f increases with system size. In addition to the greedy approach, the second wayfinding technique is to map out the available measurement actions onto a quantum state machine. A decision-making protocol can be based on such a representation, using semiclassical heuristics. This state-machine-based approach can be applied to a more restricted set of targets, where it sometimes offers advantages over the cost-function-based method. We give an example of a W-state preparation, which is accelerated with this method by f≃3.5, outperforming the greedy protocol for this target.3 MoreReceived 24 December 2021Revised 10 October 2022Accepted 10 May 2023DOI:https://doi.org/10.1103/PRXQuantum.4.020347Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasEntanglement productionQuantum controlQuantum state engineeringWeak values & weak measurementsTechniquesApproximation methods for many-body systemsQuantum InformationCondensed Matter, Materials & Applied Physics
Multifaceted Reciprocal Recommendations for Online Dating

Recommender Systems (RS) aim to filter relevant items from the huge pool of information available to users and assist users by predicting their future preferences. In traditional item-to-user based RS, items are recommended to user and preferences of the user are considered for generating recommendations. Reciprocal Recommender Systems (RRS) are people-to-people systems which recommend users to each other. Thus, the preferences of both the users should be satisfied in order to produce successful recommendations. In RRS, two unilateral user-to-user preference scores are aggregated to generate reciprocal recommendations that should be accepted by both the users involved. In this paper, we propose a reciprocal recommendation algorithm that computes unilateral preference scores based on multiple aspects including multi-criteria preferences of a user, popularity-awareness, demographic information and availability of users. Experimental study conducted with speed-dating experiment data set demonstrate the effectiveness of the proposed approach.
Strong and Tight Security Guarantees Against Integral Distinguishers

Integral attacks belong to the classical attack vectors against any given block ciphers. However, providing arguments that a given cipher is resistant against those attacks is notoriously difficult. In this paper, based solely on the assumption of independent round keys, we develop significantly stronger arguments than what was possible before: our main result is that we show how to argue that the sum of ciphertexts over any possible subset of plaintext is key-dependent, i.e., the non existence of integral distinguishers.
Detection of Username Enumeration Attack on SSH Protocol: Machine Learning Approach

Over the last two decades (2000–2020), the Internet has rapidly evolved, resulting in symmetrical and asymmetrical Internet consumption patterns and billions of users worldwide. With the immense rise of the Internet, attacks and malicious behaviors pose a huge threat to our computing environment. Brute-force attack is among the most prominent and commonly used attacks, achieved out using password-attack tools, a wordlist dictionary, and a usernames list—obtained through a so-called an enumeration attack. In this paper, we investigate username enumeration attack detection on SSH protocol by using machine-learning classifiers. We apply four asymmetrical classifiers on our generated dataset collected from a closed-environment network to build machine-learning-based models for attack detection. The use of several machine-learners offers a wider investigation spectrum of the classifiers’ ability in attack detection. Additionally, we investigate how beneficial it is to include or exclude network ports information as features-set in the process of learning. We evaluated and compared the performances of machine-learning models for both cases. The models used are k-nearest neighbor (K-NN), naïve Bayes (NB), random forest (RF) and decision tree (DT) with and without ports information. Our results show that machine-learning approaches to detect SSH username enumeration attacks were quite successful, with KNN having an accuracy of 99.93%, NB 95.70%, RF 99.92%, and DT 99.88%. Furthermore, the results improve when using ports information.
Survey on Asymmetric Cryptography Algorithms

Recent years have seen the importance of ensuring the safety and confidentiality of communication and digital data storage. Thus, secure cryptosystems are needed to guarantee security issues. Among them are asymmetric cryptography algorithms, which have been widely applied for carrying out key distribution and digital signature. This paper focuses on the current mainstream types of asymmetric cryptography, including the Rivest-Shamir-Adleman (RSA) algorithm, the Elgamal algorithm, and the Elliptic curve cryptography (ECC) algorithm, and discuss their characteristics, advantages as well as prospects, respectively.
Categorizing Disaster Tweets into Actionable Classes for Disaster Managers: An Empirical Analysis on Cyclone Data

The Popularity of Social Media has exponentially increased for both personal as well as official usage. Public often expresses their views, asks for help and raises queries and concerns to competent authorities regarding any disaster situations. Twitter, a microblogging platform is widely used for such information exchange by public and government authorities due to its enhanced outreach. Extracting only relevant information out of huge amounts of regularly generated data in timely manner is a challenging task. An automated system for categorizing disaster tweets into actionable classes for disaster managers will be very helpful to act upon. The training on past disaster tweets can be helpful in classifying tweets of future disaster events. The paper presents comparative analysis among different machine learning, deep learning, and transfer learning techniques applied for categorization of tweets related to Cyclone AMPHAN and NISARGA. The research shows that classification using pre-trained Bidirectional Encoder Representations from Transformers (BERT) [1] model achieves better results than various machine learning classifiers and deep learning models. These pre-trained models are trained on large amounts of data which can be fine-tuned for such classification problems and also work well even on small labelled dataset.