Reviewerly/exHarmony huggingface dataset:
Efficient noise mitigation technique for quantum computing

Quantum computers have enabled solving problems beyond the current machines' capabilities. However, this requires handling noise arising from unwanted interactions in these systems. Several protocols have been proposed to address efficient and accurate quantum noise profiling and mitigation. In this work, we propose a novel protocol that efficiently estimates the average output of a noisy quantum device to be used for quantum noise mitigation. The multi-qubit system average behavior is approximated as a special form of a Pauli Channel where Clifford gates are used to estimate the average output for circuits of different depths. The characterized Pauli channel error rates, and state preparation and measurement errors are then used to construct the outputs for different depths thereby eliminating the need for large simulations and enabling efficient mitigation. We demonstrate the efficiency of the proposed protocol on four IBM Q 5-qubit quantum devices. Our method demonstrates improved accuracy with efficient noise characterization. We report up to 88% and 69% improvement for the proposed approach compared to the unmitigated, and pure measurement error mitigation approaches, respectively.
The Effect of the Dark Web on the Security

The elementary interest of every country is to maintain its inner security and stability.To achieve this goal the state must restrict within legal frameworks some fundamental rights of its own citizens.One of these fundamental rights is the right to privacy that can be breached only under certain circumstances.It is easy to see that it is unacceptable for a state not to control within the legal frameworks the communication of its own citizens so they can commit crimes, run terrorist rings, or spy rings or establish drug cartels without any consequences.Of course, the control over the communication is not the only means of the successful investigation but undeniably a vital one.That is why the Janus faced nature of the Dark Web is a real security risk nowadays.Although this new medium is the fruit of the last two decades its presence today is stronger than ever before and its popularity is growing day by day.Its most important features are anonymity, hidden geolocation and freedom from censorship.The Dark Web is very useful when it provides anonymity for political dissidents and whistleblowers, but is very harmful when it provides the same features for arm and drug traffickers and terrorists not to mention for pedophiles and so on.This article aims to shed some light on the effects of the Dark Web on the security and economy of the states especially in the aspects of organized crime and the terrorism.
A Game-Theoretic Analysis of Cross-ledger Swaps with Packetized Payments

We propose a game-theoretic framework to study the outcomes of packetized payments, a cross-ledger transaction protocol, with strategic and possibly malicious agents. We derive the transaction failure rate and demonstrate that without disciplinary mechanisms, packetized payments are likely to be incomplete. Our analysis suggests that collateral deposits can prevent malicious agents from taking advantage of the protocol. We further infer that the deposit amount should depend on the underlying asset price volatility or that it should be dynamically adjusted as the price changes.
A low-noise single-photon detector for long-distance free-space quantum communication

We build and test a single-photon detector based on a Si avalanche photodiode Excelitas 30902SH thermoelectrically cooled to -100 deg. C. Our detector has dark count rate below 1 Hz, 500 um diameter photosensitive area, photon detection efficiency around 50%, afterpulsing less than 0.35%, and timing jitter under 1 ns. These characteristics make it suitable for long-distance free-space quantum communication links, which we briefly discuss. We also report an improved method that we call long-time afterpulsing analysis, used to determine and visualise long trap lifetimes at different temperatures.
SUST TTS Corpus: A phonetically-balanced corpus for Bangla text-to-speech synthesis

This paper presents the Shahjalal University of Science and Technology Text-To-Speech Corpus (SUST TTS Corpus), a phonetically balanced speech corpus for Bangla speech synthesis. Due to the advancement of deep learning techniques, modern speech processing researches such as speech recognition and speech synthesis are being conducted in various deep learning methods. Any state-of-the-art neural TTS system needs a large dataset to be trained efficiently. The lack of such datasets for under-resourced languages like Bangla is a major obstacle for developing TTS systems in those languages. To mitigate this problem and accelerate speech synthesis research in Bangla, we have developed a large-scale, phonetically-balanced speech corpus containing more than 30 hours of speech. Our corpus includes 17,357 utterances spoken by a professional voice talent in a sound-proof audio laboratory. We ensure that the corpus contains all possible Bangla phonetic units in sufficient amounts, making it a phonetically-balanced speech corpus. We describe the process of creating the corpus in this paper. We also train a neural Bangla TTS system with our corpus and obtain a synthetic voice which is comparable to the state-of-the-art TTS systems.
Leveraging Word-Formation Knowledge for Chinese Word Sense Disambiguation

In parataxis languages like Chinese, word meanings are constructed using specific wordformations, which can help to disambiguate word senses.However, such knowledge is rarely explored in previous word sense disambiguation (WSD) methods.In this paper, we propose to leverage word-formation knowledge to enhance Chinese WSD.We first construct a large-scale Chinese lexical sample WSD dataset with word-formations.Then, we propose a model FormBERT to explicitly incorporate word-formations into sense disambiguation.To further enhance generalizability, we design a word-formation predictor module in case word-formation annotations are unavailable.Experimental results show that our method brings substantial performance improvement over strong baselines.
Biometrics based Smart and Secured Electronic Voting Machine

The Electronic Voting Machine (EVM) is a basic electronic contraption that has been used to testify a polling form as opposed to surveying structure credentials and boxes that were previously worn in conformist popularity based organisations. The reason of a vote-based framework is shaped by critical accuracy in projecting a polling form or only equitable in races. All previous choices, whether status races or central races, required a balloter to project their main entrant by placing the squash against their name and then imploding the democratic structure thesis as indicated by a supported strategy and dropping them in ballot package. This is a long, drawn-out cycle that is particularly prone to blunders. The current circumstances continued till political race scene were replaced by electronic popularity based machine. Surveying structure paper, casting a ballot stations, venturing, etc are combined into a fundamental sachet called surveying structure unit of the electronic majority rule machine. Since bio-metric identifiers can't be easily lost, made, or mutual, they are viewed as more strong for singular affirmation than standard slip or data based methodologies. Henceforth, the electronic popularity based system should be additionally evolved subject to the current advances in bio-metric structure. This article analyzes absolute assessment about projecting a voting form devices, topic and relationship among the majority rule procedures and bio-metric EVM. We moreover propose astute and secure popularity based structure subject to cloud technique which prompts speedy reporting of results.
Anomalous spectral response of plasmon-exciton strong coupling beyond J-C model

The strong coupling between plasmons and excitons has aroused tremendous interest in recent years, due to its implications for diverse important quantum technologies. Understanding the spectral response properties of these strongly coupled systems is crucial for both the fundamental physics science and the practical applications. Here, we numerically and theoretically demonstrate the spectrum responses of plasmon-exciton strong coupling beyond the J-C model based upon the Au nanorod/dye molecules hybrid system. With increasing the number or oscillator strength of dye molecules, a third/fourth mode in the normal Rabi splitting emerges. These anomalous spectrum Rabi splitting beyond the J-C model are revealed by a unified quantum theory model, which gives the quantified contributions from the plasmon and exciton channels to the total photon absorption spectrum of the hybrid system. Our work not only enriches the understanding of the plasmon-exciton strong coupling, but also may stimulate further researches in this field.
On the Complex and Chaotic Dynamics of Standard Logistic Sine Square Map

Abstract In this article, we set up a new nonlinear dynamical system which is derived by combining logistic map and sine square map in Mann orbit (a two step feedback process) for ameliorating the stability performance of chaotic system and name it Standard Logistic Sine Square Map (SLSSM). The purpose of this paper is to study the whole dynamical behavior of the proposed map (SLSSM) through various introduced aspects consisting fixed point and stability analysis, time series representation, bifurcation diagram and Lyapunov exponent. Moreover, we show that our map is significantly superior than existing other one dimensional maps. We investigate that the chaotic and complex behavior of SLSSM can be controlled by selecting control parameters carefully. Also, the range of convergence and stability can be made to increase drastically. This new system (SLSSM) might be used to achieve better results in cryptography and to study chaos synchronization.
Identification of Tor Anonymous Network Traffic Based on Machine Learning

In order to identify Tor anonymous network traffic which was generated by the most widely used anonymous network in the world, analyzing the features that can be used to recognize Tor traffic based on meek pluggable transport, and proposing a method based on machine learning to classify Tor traffic. Tor traffic identification aimed at Tor-Meek traffic which using Meek traffic confusion technology in Tor network. To determine the flow characteristics that can be used to identify Tor traffic from the original feature set, RandomForest feature selection method is used to evaluate the importance of these features, and select the available feature subset. The Tor traffic classifier is constructed by using C4.5, RandomForest and KNN algorithms to identify Tor traffic. Experiment shows that Tor traffic identification methods based on three classification algorithms can effectively identify Tor anonymous network traffic, for different versions of Tor client, the precise and recall are all greater than 94% when identify Tor traffic.
CARES: Context-Aware Trust Estimation for Realtime Crowdsensing Services in Vehicular Edge Networks

The growing number of smart vehicles makes it possible to envision a crowdsensing service where vehicles can share video data of their surroundings for seeking out traffic conditions and car accidents ahead. However, the service may need to deal with situations like malicious vehicles propagating false information to divert other vehicles to arrive at destinations earlier or lead them to dangerous locations. This article proposes a context-aware trust estimation scheme that can allow roadside units in a vehicular edge network to provide real-time crowdsensing services in a reliable manner by selectively using information from trustworthy sources. Our proposed scheme is novel in that its trust estimation does not require any prior knowledge of vehicles on roads but quickly obtains the accurate trust value of each vehicle by leveraging transfer learning. and its Q-learning-based dynamic adjustment scheme autonomously estimates trust levels of oncoming vehicles with the aim of detecting malicious vehicles and accordingly filtering out untrustworthy input from them. Based on an extensive simulation study, we prove that the proposed scheme outperforms existing ones in terms of malicious vehicle detection accuracy.
How Do Vision Transformers Work?

The success of multi-head self-attentions (MSAs) for computer vision is now indisputable. However, little is known about how MSAs work. We present fundamental explanations to help better understand the nature of MSAs. In particular, we demonstrate the following properties of MSAs and Vision Transformers (ViTs): (1) MSAs improve not only accuracy but also generalization by flattening the loss landscapes. Such improvement is primarily attributable to their data specificity, not long-range dependency. On the other hand, ViTs suffer from non-convex losses. Large datasets and loss landscape smoothing methods alleviate this problem; (2) MSAs and Convs exhibit opposite behaviors. For example, MSAs are low-pass filters, but Convs are high-pass filters. Therefore, MSAs and Convs are complementary; (3) Multi-stage neural networks behave like a series connection of small individual models. In addition, MSAs at the end of a stage play a key role in prediction. Based on these insights, we propose AlterNet, a model in which Conv blocks at the end of a stage are replaced with MSA blocks. AlterNet outperforms CNNs not only in large data regimes but also in small data regimes. The code is available at https://github.com/xxxnell/how-do-vits-work.
Anonymous conference key agreement in linear quantum networks

Sharing multi-partite quantum entanglement between parties allows for diverse secure communication tasks to be performed. Among them, conference key agreement (CKA), an extension of key distribution to multiple parties, has received much attention recently. Interestingly, CKA can also be performed in a way that protects the identities of the participating parties, therefore providing anonymity. In this work, we propose an anonymous CKA protocol for three parties that is implemented in a highly practical network setting. Specifically, a line of quantum repeater nodes is used to build a linear cluster state among all nodes, which is then used to anonymously establish a secret key between any three of them. The nodes need only share maximally entangled pairs with their neighbours, therefore avoiding the necessity of a central server sharing entangled states. This repeater setup makes our protocol an excellent candidate for implementation in future quantum networks. We explicitly prove that our protocol protects the identities of the participants from one another and perform an analysis of the key rate in the finite regime, contributing to the quest of identifying feasible quantum communication tasks for network architectures beyond point-to-point.
Annotators with Attitudes: How Annotator Beliefs And Identities Bias Toxic Language Detection

The perceived toxicity of language can vary based on someone's identity and beliefs, but this variation is often ignored when collecting toxic language datasets, resulting in dataset and model biases. We seek to understand the who, why, and what behind biases in toxicity annotations. In two online studies with demographically and politically diverse participants, we investigate the effect of annotator identities (who) and beliefs (why), drawing from social psychology research about hate speech, free speech, racist beliefs, political leaning, and more. We disentangle what is annotated as toxic by considering posts with three characteristics: anti-Black language, African American English (AAE) dialect, and vulgarity. Our results show strong associations between annotator identity and beliefs and their ratings of toxicity. Notably, more conservative annotators and those who scored highly on our scale for racist beliefs were less likely to rate anti-Black language as toxic, but more likely to rate AAE as toxic. We additionally present a case study illustrating how a popular toxicity detection system's ratings inherently reflect only specific beliefs and perspectives. Our findings call for contextualizing toxicity labels in social variables, which raises immense implications for toxic language annotation and detection.
Federated Fingerprint Learning with Heterogeneous Architectures

Recent studies on federated learning (FL) have sought to solve the system heterogeneity issue by designing customized local models for different clients. However, public dataset introduction, sensitive information exchange, non-trivial computational cost, or particular architecture requirement limit the applicability of most of them in real scenarios. This paper presents a novel federated fingerprint learning model for making full use of the computing power of each client with the customized local models for improving the FL convergence, while keeping the data and sensitive information safe and local. First, we decompose the parameters of each local model into two types of parameters: rigid ones that have fixed model architecture for ensuring the convergence of global model training and elastic ones that contain customized model structure and size for allowing to make full use of the computing power of each client based on individual data scale. Second, we adopt the standard FL scheme to update and aggregate the local rigid parameters. We introduce a Gaussian distribution as auxiliary input and output K local fingerprints respectively for the elastic parameters of all K local models. The server aggregates K local fingerprints into a global one and sends it back to the clients. A fingerprint-based aggregation strategy makes the local models indirectly receive the aggregated elastic parameters through the aggregation of K local fingerprints while fixing data locally. Last but not least, we design a parameter masking method to mask the rigid parameters irrelevant to the local classification task in the local models. We develop a parameter separation method to guarantee that the combination of unmasked rigid parameters in all local models are able to cover all the rigid parameters as many as possible, for further raising the utilization rate of each rigid parameter.
RTCoInfer: Real-Time Collaborative CNN Inference for Stream Analytics on Ubiquitous Images

Emerging intelligent applications based on accurate and timely stream analytics require real-time CNN inference of massive data continuously generated at the pervasive end devices. Due to the resource constraints, neither computing locally at end devices nor transmitting to remote servers is competent for computation-intensive CNN inference on large-volume images in real-time. Therefore, Collaborative Inference (CI), which conducts inference sequentially from the local device to the remote server with compressed intermediate inference data, is rapidly promoted. Due to the essential communication in collaboration, the CI efficiency is sensitive to network conditions, and will degrade under the unpredictable network fluctuations in practice, which may cause a severe delay in CI and degrade the responsiveness of stream analytics. For accurate and timely stream analytics in practical fluctuating networks, we present RTCoInfer, the real-time CI framework with run-time transmission adaption considering the network conditions. Specifically, we propose a novel Switchable CNN integrating CNNs with different compression rates on the partition layer for the run-time transmission adjustment, and construct a real-time controller determining the compression rate to maintain the real-time CI for stream analytics. Extensive experiments show that, compared with state-of-the-art methods, RTCoInfer achieves better efficiency and unprecedented resilience in real-time stream analytics.
Improved local models and new Bell inequalities via Frank-Wolfe algorithms

In Bell scenarios with two outcomes per party, we algorithmically consider the two sides of the membership problem for the local polytope: constructing local models and deriving separating hyperplanes, that is, Bell inequalities. We take advantage of the recent developments in so-called Frank-Wolfe algorithms to significantly increase the convergence rate of existing methods. As an application, we study the threshold value for the nonlocality of two-qubit Werner states under projective measurements. Here, we improve on both the upper and lower bounds present in the literature. Importantly, our bounds are entirely analytical; moreover, they yield refined bounds on the value of the Grothendieck constant of order three: $1.4367\leqslant K_G(3)\leqslant1.4546$. We also demonstrate the efficiency of our approach in multipartite Bell scenarios, and present the first local models for all projective measurements with visibilities noticeably higher than the entanglement threshold. We make our entire code accessible as a Julia library called BellPolytopes.jl.
Agile Software Development and Reuse Approach with Scrum and Software Product Line Engineering

Agile methods and software product line engineering (SPLE) are widely recognized as practical approaches for delivering high-quality software, adapting to evolving stakeholder needs, and tackling complex problems. This study proposes a hybrid agile software development and reuse approach called SPLE-Scrum based on the activities of software product line engineering and Scrum. Within the SPLE process, we incorporate requirement engineering and design practices to create a reference architecture with reusable components called core assets by introducing a product management meeting. The core assets are reused to build a series of applications with various product lines. The product increments are delivered in each Sprint with the review and retrospective meetings based on Scrum lifecycle and practices. We present a case study involving a blockchain online store to demonstrate the practical application of SPLE-Scrum, highlighting the benefits of integrating Scrum and software product line engineering. The research hypotheses of the proposed approach were validated through a study of structured interviews with 5 experts and 44 software practitioners, showing that the key factors of product management, project requirements, and product architecture in the SPLE-Scrum approach have a beneficial impact on project success. The SPLE-Scrum approach provides valuable insights and practical guidance for organizations seeking to optimize their software engineering practices while incorporating agile development and software reuse capabilities.
A Declarative Service-Based Method for Adaptive Aggregation of Sensor Streams

The dynamic interventions among sensor streams bring new challenges for the Internet of Things applications to derive meaningful information from a large amount of sensor streams. This paper proposes a declarative service-based method for capturing meaningful events from dynamic sensor streams based on our previous service abstraction, which can increase the value density of primitive sensor streams and raise the accessibility for higher level business applications. For aggregating related events from different sources, we set up declarative rules and compose cascaded events through service collaboration. In addition, for dealing with dynamic interventions among sensor streams, we propose a declarative rule evolution method to realize adaptive service collaboration at runtime. This paper also reports the tryout use of our method in the China Power Grid for detecting abnormal situations of power quality. Through a series of experiments based on real sensor data in the Power Grid, we verified that our method can capture events with high and steady precision.