Reviewerly/exHarmony huggingface dataset:
Indoor Emergency Path Planning Based on the Q-Learning Optimization Algorithm

The internal structure of buildings is becoming increasingly complex. Providing a scientific and reasonable evacuation route for trapped persons in a complex indoor environment is important for reducing casualties and property losses. In emergency and disaster relief environments, indoor path planning has great uncertainty and higher safety requirements. Q-learning is a value-based reinforcement learning algorithm that can complete path planning tasks through autonomous learning without establishing mathematical models and environmental maps. Therefore, we propose an indoor emergency path planning method based on the Q-learning optimization algorithm. First, a grid environment model is established. The discount rate of the exploration factor is used to optimize the Q-learning algorithm, and the exploration factor in the ε-greedy strategy is dynamically adjusted before selecting random actions to accelerate the convergence of the Q-learning algorithm in a large-scale grid environment. An indoor emergency path planning experiment based on the Q-learning optimization algorithm was carried out using simulated data and real indoor environment data. The proposed Q-learning optimization algorithm basically converges after 500 iterative learning rounds, which is nearly 2000 rounds higher than the convergence rate of the Q-learning algorithm. The SASRA algorithm has no obvious convergence trend in 5000 iterations of learning. The results show that the proposed Q-learning optimization algorithm is superior to the SARSA algorithm and the classic Q-learning algorithm in terms of solving time and convergence speed when planning the shortest path in a grid environment. The convergence speed of the proposed Q- learning optimization algorithm is approximately five times faster than that of the classic Q- learning algorithm. The proposed Q-learning optimization algorithm in the grid environment can successfully plan the shortest path to avoid obstacle areas in a short time.
Stochastic Collisional Quantum Thermometry

We extend collisional quantum thermometry schemes to allow for stochasticity in the waiting time between successive collisions. We establish that introducing randomness through a suitable waiting time distribution, the Weibull distribution, allows us to significantly extend the parameter range for which an advantage over the thermal Fisher information is attained. These results are explicitly demonstrated for dephasing interactions and also hold for partial swap interactions. Furthermore, we show that the optimal measurements can be performed locally, thus implying that genuine quantum correlations do not play a role in achieving this advantage. We explicitly confirm this by examining the correlation properties for the deterministic collisional model.
Distributed Zero-Knowledge Proofs Over Networks

Previous chapter Next chapter Full AccessProceedings Proceedings of the 2022 Annual ACM-SIAM Symposium on Discrete Algorithms (SODA)Distributed Zero-Knowledge Proofs Over NetworksAviv Bick, Gillat Kol, and Rotem OshmanAviv Bick, Gillat Kol, and Rotem Oshmanpp.2426 - 2458Chapter DOI:https://doi.org/10.1137/1.9781611977073.97PDFBibTexSections ToolsAdd to favoritesExport CitationTrack CitationsEmail SectionsAboutAbstract Zero knowledge proofs are one of the most influential concepts in theoretical computer science. In the seminal definition due to Goldwasser, Micali and Rackoff dating back to the 1980s, a computationally-bounded verifier interacts with a powerful but untrusted prover, with the goal of becoming convinced that the input is in some language. In addition to the usual requirements of completeness and soundness, in a zero knowledge proof, we protect the prover's knowledge: assuming the prover is honest, anything that the verifier can deduce after interacting with the prover, it could have deduced by itself. Zero knowledge proofs have found many applications within theoretical computer science and beyond, e.g., in cryptography, client-cloud computing, blockchains and cryptocurrencies, electronic voting and auctions, and in the financial industry. We define and study the notion of distributed zero knowledge proofs, reconciling the computational notion of zero-knowledge with the communication-based paradigm of distributed graph algorithms. In our setting, a network of verifiers interacts with an untrusted prover to decide some distributed language. As is usually the case in distributed graph algorithms, we assume that the verifiers have local views of the network and each only knows its neighbors. The prover, on the other hand, is assumed to know the entire network graph, as well as any input that the verifier may possess. As in the computational centralized setting, the protocol we design should protect this knowledge. In particular, due to the dual role of the underlying graph in distributed graph algorithms, serving as both the communication topology and the input to the problem, our protocol must protect the graph itself. We construct communication-efficient distributed zero knowledge proofs for two central problems: the 3-coloring problem, one of the poster children of computational zero-knowledge, and for the spanning-tree verification problem, a fundamental building block for designing graph algorithms. We also give a general scheme for converting proof labeling-schemes to distributed zero-knowledge protocols with related parameters. Our protocols combine ideas from computational complexity, distributed computing, and cryptography. Previous chapter Next chapter RelatedDetails Published:2022eISBN:978-1-61197-707-3 https://doi.org/10.1137/1.9781611977073Book Series Name:ProceedingsBook Code:PRDA22Book Pages:xvii + 3771
Segmentation of Breast Masses in Mammogram Image Using Multilevel Multiobjective Electromagnetism-Like Optimization Algorithm

In recent times, breast mass is the most diagnostic sign for early detection of breast cancer, where the precise segmentation of masses is important to reduce the mortality rate. This research proposes a new multiobjective optimization technique for segmenting the breast masses from the mammographic image. The proposed model includes three phases such as image collection, image denoising, and segmentation. Initially, the mammographic images are collected from two benchmark datasets like Digital Database for Screening Mammography (DDSM) and Mammographic Image Analysis Society (MIAS). Next, image normalization and Contrast-Limited Adaptive Histogram Equalization (CLAHE) techniques are employed for enhancing the visual capability and contrast of the mammographic images. After image denoising, electromagnetism-like (EML) optimization technique is used for segmenting the noncancer and cancer portions from the mammogram image. The proposed EML technique includes the advantages like enhanced robustness to hold the image details and adaptive to local context. Lastly, template matching is carried out after segmentation to detect the cancer regions, and then, the effectiveness of the proposed model is analysed in light of Jaccard coefficient, dice coefficient, specificity, sensitivity, and accuracy. Hence, the proposed model averagely achieved 92.3% of sensitivity, 99.21% of specificity, and 98.68% of accuracy on DDSM dataset, and the proposed model averagely achieved 92.11% of sensitivity, 99.45% of specificity, and 98.93% of accuracy on MIAS dataset.
Concept Drift Detection in Federated Networked Systems

As next-generation networks materialize, increasing levels of intelligence are required. Federated Learning has been identified as a key enabling technology of intelligent and distributed networks; however, it is prone to concept drift as with any machine learning application. Concept drift directly affects the model's performance and can result in severe consequences considering the critical and emergency services provided by modern networks. To mitigate the adverse effects of drift, this paper proposes a concept drift detection system leveraging the federated learning updates provided at each iteration of the federated training process. Using dimensionality reduction and clustering techniques, a framework that isolates the system's drifted nodes is presented through experiments using an Intelligent Transportation System as a use case. The presented work demonstrates that the proposed framework is able to detect drifted nodes in a variety of non-iid scenarios at different stages of drift and different levels of system exposure.
Enhancing customer satisfaction with chatbots: the influence of anthropomorphic communication styles and anthropomorphised roles

Purpose Chatbots are increasingly used in online retail settings and are becoming a powerful tool for brands to engage customers. However, consumers’ satisfaction with these chatbots is mixed. Thus, this paper aims to investigate how using a social- versus task-oriented anthropomorphic communication style can improve customer satisfaction. Design/methodology/approach The authors explore the link between the anthropomorphic communication style use and customer perceptions/customer satisfaction in online customer service interactions. Two experiment scenarios were developed to test these links. Findings Overall, using a social-oriented communication style boosts customer satisfaction. Warmth perception of the chatbot mediates this effect, while chatbot’s anthropomorphised role (servant versus partner) moderates this effect. Originality/value This paper enriches the bilateral communication literature by extending the investigation on communication style effects to chatbot service interactions and revealing the psychological process driving the impacts. It also adds to the existing literature on chatbots as a customer service and contributes to the prominent topic examining how consumers react to artificial intelligence that is used to establish and maintain a relationship with them. Additionally, the authors also make contribution to the warmth and competence literature by demonstrating that customers can interpret social cues in chatbot service interactions mainly based on the warmth dimension. Thus, the authors further add to the growing chatbot humanness perception literature and respond to the calls for investigating more anthropomorphic design cues to enhance chatbot humanness. Finally, the authors also provide a way to help reconcile seemingly conflicting prior findings.
Storing and Retrieving Secrets on a Blockchain

A secret sharing scheme enables one party to distribute shares of a secret to n parties and ensures that an adversary in control of t out of n parties will learn no information about the secret. However, traditional secret sharing schemes are often insufficient, especially for applications in which the set of parties who hold the secret shares might change over time. To achieve security in this setting, dynamic proactive secret sharing (DPSS) is used. DPSS schemes proactively update the secret shares held by the parties and allow changes to the set of parties holding the secrets. We propose FaB-DPSS (FAst Batched DPSS) – a new and highly optimized batched DPSS scheme. While previous work on batched DPSS [BDLO15] focuses on a single client submitting a batch of secrets and does not allow storing and releasing secrets independently, we allow multiple different clients to dynamically share and release secrets. FaB-DPSS is the most efficient robust DPSS scheme that supports the highest possible adversarial threshold of $$\frac{1}{2}$$ . We prove FaB-DPSS secure and implement it. All operations complete in seconds, and we outperform a prior state-of-the-art DPSS scheme [MZW+19] by over $$6\times $$ . Additionally, we propose new applications of DPSS in the context of blockchains. Specifically, we propose a protocol that uses blockchains and FaB-DPSS to provide conditional secret storage. The protocol allows parties to store secrets along with a release condition, and once a (possibly different) party satisfies this release condition, the secret is privately released to that party. This functionality is similar to extractable witness encryption. While there are numerous compelling applications (e.g., time-lock encryption, one-time programs, and fair multi-party computation) which rely on extractable witness encryption, there are no known efficient constructions (or even constructions based on any well-studied assumptions) of extractable witness encryption. However, by utilizing blockchains and FaB-DPSS, we can easily build all those applications. We provide an implementation of our conditional secret storage protocol as well as several applications building on top of it.
Less is more

Reviewing source code from a security perspective has proven to be a difficult task. Indeed, previous research has shown that developers often miss even popular and easy-to-detect vulnerabilities during code review. Initial evidence suggests that a significant cause may lie in the reviewers' mental attitude and common practices.
Experimental violations of Leggett-Garg inequalities on a quantum computer

Leggett-Garg's inequalities predict sharp bounds for some classical correlation functions that address the quantum or classical nature of real-time evolutions. We experimentally observe the violations of these bounds on single- and multiqubit systems, in different settings, exploiting the IBM Quantum platform. In the multiqubit case, we introduce the Leggett-Garg-Bell's inequalities as an alternative to the previous ones. Measuring these correlation functions, we find quantum error mitigation to be essential to spot inequalities violations. Accessing only two qubit readouts, we assess Leggett-Garg-Bell's inequalities to emerge as the most efficient quantum coherence witnesses to be used for investigating quantum hardware, among those introduced. Our analysis highlights the limits of current quantum platforms, showing that the above-mentioned correlation functions deviate from theoretical prediction as the number of qubits and the depth of the circuit grow.
Noise-assisted quantum coherence protection in a hierarchical environment

In this paper, we investigate coherence protection of a quantum system coupled to a hierarchical environment by utilizing noise. As an example, we solve the Jaynes-Cummings model in the presence of both a classical and a quantized noise. The master equation is derived beyond the Markov approximation, where the influence of memory effects from both noises is taken into account. More importantly, we find that the performance of the coherence protection sensitively depends on the non-Markovian properties of both noises. By analyzing the mathematical mechanism of the coherence protection, we show the decoherence caused by a non-Markovian noise with longer memory time can be suppressed by another Markovian noise with shorter memory time. Last but not least, as an outlook, we try to analyze the connection between the atom-cavity entanglement and the atomic coherence, then discuss the possible clue to search the required noise. The results presented in this paper show the possibility of protecting coherence by utilizing noise and may open an alternative path to design noise-assisted coherence protection schemes.
The Recurrent Reinforcement Learning Crypto Agent

We demonstrate a novel application of online transfer learning for a digital assets trading agent.This agent uses a powerful feature space representation in the form of an echo state network, the output of which is made available to a direct, recurrent reinforcement learning agent.The agent learns to trade the XBTUSD (Bitcoin versus US Dollars) perpetual swap derivatives contract on BitMEX on an intraday basis.By learning from the multiple sources of impact on the quadratic risk-adjusted utility that it seeks to maximise, the agent avoids excessive over-trading, captures a funding profit, and can predict the market's direction.Overall, our crypto agent realises a total return of 350%, net of transaction costs, over roughly five years, 71% of which is down to funding profit.The annualised information ratio that it achieves is 1.46.
Automatic Twitter Crime Prediction Using Hybrid Wavelet Convolutional Neural Network with World Cup Optimization

Social media are digitally mediated platforms that allow people to create and exchange content, professional interests, ideas, and other forms of expression through virtual networks. Users often utilize web-based programs on their PCs and laptops to visit social media sites, or they download programs that provide their devices social media capabilities. As users connect with these platforms, groups, organizations, and individuals can upload, co-create, discuss, engage in, and update self-curated or user-generated information. Although the platforms such as Facebook, Twitter, Instagram, etc., aid in the communication purposes, it also has some demerits like cyber-crime, hacking, etc. The growing number of crimes through these platforms needs to be deducted by predicting the crimes. For the crime prediction, the data acquired from Twitter is pre-processed for the data cleansing process. Later the features are extracted using various techniques like bag of words (BoW), Glove, term frequency-inverse document frequency (TF-IDF), and feature hashing. The feature selection is done using a modified tree growth algorithm (MTGA) and clustering is performed using the fuzzy manta ray foraging (FMRF). Finally, the crime detection is done using hybrid wavelet convolutional neural network with world cup organization (WCNN-WCO). The PYTHON tool is used for the implementation and the Twitter user dataset is used for analysis. The results showed that the proposed method outperforms the existing method in terms of precision, accuracy, [Formula: see text]1 measure, and recall.
<tt>Auto-Icon+</tt> : An Automated End-to-End Code Generation Tool for Icon Designs in UI Development

Approximately 50% of development resources are devoted to user interface (UI) development tasks [ 9 ]. Occupying a large proportion of development resources, developing icons can be a time-consuming task, because developers need to consider not only effective implementation methods but also easy-to-understand descriptions. In this article, we present Auto-Icon+ , an approach for automatically generating readable and efficient code for icons from design artifacts. According to our interviews to understand the gap between designers (icons are assembled from multiple components) and developers (icons as single images), we apply a heuristic clustering algorithm to compose the components into an icon image. We then propose an approach based on a deep learning model and computer vision methods to convert the composed icon image to fonts with descriptive labels, thereby reducing the laborious manual effort for developers and facilitating UI development. We quantitatively evaluate the quality of our method in the real-world UI development environment and demonstrate that our method offers developers accurate, efficient, readable, and usable code for icon designs, in terms of saving 65.2% implementing time.
Reducing g<sup>(2)</sup>(0) of a parametric down-conversion source via photon-number resolution with superconducting nanowire detectors

Multiphoton contributions pose a significant challenge for the realisation of heralded single-photon sources (HSPS) based on nonlinear processes. In this work, we improve the quality of single photons generated in this way by harnessing the photon-number resolving (PNR) capabilities of commercial superconducting nanowire single-photon detectors (SNSPDs). We report a 13 ± 0.4% reduction of g(2)(τ = 0), even with a collection efficiency in the photon source of only 29.6%. Our work demonstrates the first application of the PNR capabilities of SNSPDs and shows improvement in the quality of an HSPS with widely available technology.
Simultaneous exercise recognition and evaluation in prescribed routines: Approach to virtual coaches

Home-based physical therapies are effective if the prescribed exercises are correctly executed and patients adhere to these routines. This is specially important for older adults who can easily forget the guidelines from therapists. Inertial Measurement Units (IMUs) are commonly used for tracking exercise execution giving information of patients’ motion data. In this work, we propose the use of Machine Learning techniques to recognize which exercise is being carried out and to assess if the recognized exercise is properly executed by using data from four IMUs placed on the person limbs. To the best of our knowledge, both tasks have never been addressed together as a unique complex task before. However, their combination is needed for the complete characterization of the performance of physical therapies. We evaluate the performance of six machine learning classifiers in three contexts: recognition and evaluation in a single classifier, recognition of correct exercises, excluding the wrongly performed exercises, and a two-stage approach that first recognizes the exercise and then evaluates it. We apply our proposal to a set of 8 exercises of the upper-and lower-limbs designed for maintaining elderly people health status. To do so, the motion of 30 volunteers were monitored with 4 IMUs. We obtain accuracies of 88.4 % and the 91.4 % in the two initial scenarios. In the third one, the recognition provides an accuracy of 96.2 %, whereas the exercise evaluation varies between 93.6 % and 100.0 %. This work proves the feasibility of IMUs for a complete monitoring of physical therapies in which we can get information of which exercise is being performed and its quality, as a basis for designing virtual coaches.
CleftNet: Augmented Deep Learning for Synaptic Cleft Detection From Brain Electron Microscopy

Detecting synaptic clefts is a crucial step to investigate the biological function of synapses. The volume electron microscopy (EM) allows the identification of synaptic clefts by photoing EM images with high resolution and fine details. Machine learning approaches have been employed to automatically predict synaptic clefts from EM images. In this work, we propose a novel and augmented deep learning model, known as CleftNet, for improving synaptic cleft detection from brain EM images. We first propose two novel network components, known as the feature augmentor and the label augmentor, for augmenting features and labels to improve cleft representations. The feature augmentor can fuse global information from inputs and learn common morphological patterns in clefts, leading to augmented cleft features. In addition, it can generate outputs with varying dimensions, making it flexible to be integrated in any deep network. The proposed label augmentor augments the label of each voxel from a value to a vector, which contains both the segmentation label and boundary label. This allows the network to learn important shape information and to produce more informative cleft representations. Based on the proposed feature augmentor and label augmentor, We build the CleftNet as a U-Net like network. The effectiveness of our methods is evaluated on both external and internal tasks. Our CleftNet currently ranks #1 on the external task of the CREMI open challenge. In addition, both quantitative and qualitative results in the internal tasks show that our method outperforms the baseline approaches significantly.