Reviewerly/exHarmony huggingface dataset:
A Byzantine Fault-Tolerant Raft Algorithm Combined with Schnorr Signature

To optimize the Byzantine fault tolerance of Raft algorithm, a Byzantine fault-tolerant B-Raft algorithm combined with Schnorr signature mechanism is proposed. First, during leader election, followers are required to sign the ballot to prevent the Byzantine node from forging the vote. Second, the client is required to sign the message when sending the message to leader to prevent the Byzantine leader from tampering with the logs. B-Raft combines the signature mechanism with the Raft algorithm and several modifications and additions provide Byzantine fault tolerance. The results of the experiments launched in Hyperledger Fabric show that compared with the Raft algorithm, the safety of B-Raft is greatly improved.
Single-Shot is Enough: Panoramic Infrastructure Based Calibration of Multiple Cameras and 3D LiDARs

The integration of multiple cameras and 3D Li-DARs has become basic configuration of augmented reality devices, robotics, and autonomous vehicles. The calibration of multi-modal sensors is crucial for a system to properly function, but it remains tedious and impractical for mass production. Moreover, most devices require re-calibration after usage for certain period of time. In this paper, we propose a single-shot solution for calibrating extrinsic transformations among multiple cameras and 3D LiDARs. We establish a panoramic infrastructure, in which a camera or LiDAR can be robustly localized using data from single frame. Experiments are conducted on three devices with different camera-LiDAR configurations, showing that our approach achieved comparable calibration accuracy with the state-of-the-art approaches but with much greater efficiency.
Quantum Entanglement and Its Application in Quantum Communication

Abstract Since the concept of quantum entanglement was proposed, quantum entanglement has been paid more and more attention. Especially in recent years, with the interdisciplinary and interdisciplinary development of quantum informatics, the general definition, physical properties and entanglement measurement of quantum entanglement have been comprehensively and deeply studied. Due to its nonlocality, quantum entanglement has been widely used in quantum information, especially in quantum communication. In view of the application status of quantum entanglement in quantum communication, this paper presents a survey of quantum entanglement and its application in quantum communication. In this paper, 230 staff members from 15 Chinese companies involved in quantum communication are selected as the investigation samples, and the proportion of quantum entanglement in the application is obtained, which can promote the quantum communication. The capacity, speed and security of quantum entanglement and ordinary quantum in the application of quantum communication are compared. Through the analysis, it is concluded that each index of quantum entanglement is conducive to the development of quantum communication. Quantum entanglement provides a technical guarantee for the research of quantum communication.
Pseudo-Spectrum Based Track-Before-Detect for Weak Maneuvering Targets in Range-Doppler Plane

Track-before-detect (TBD) of weak maneuvering targets in range-Doppler plane is investigated. The existing TBD methods suffer from performance degradation due to the use of approximate motion models in range-Doppler plane. In this paper, exact time evolving rules of target range and Doppler are presented for constant acceleration (CA) and coordinated turn (CT) motions in Cartesian coordinates, and two novel pseudo-spectrum based TBD approaches are proposed for effective detection of weak CA and CT targets in range-Doppler plane, respectively. In accordance with the common CA and CT motions in Cartesian coordinates, the accurate time evolution equations of range and Doppler are derived for CA and CT targets, respectively, which are nonlinear functions of range-Doppler and several invariable parameters. Then, the pseudo-spectrum based TBD of CA and CT targets in range-Doppler plane is performed by matching these invariants. Each range-Doppler cell is predicted according to the exact evolution equation. A pseudo-spectrum is constructed around the predicted position, and sample values of the pseudo-spectrum are added onto cells for energy integration. This eliminates performance loss caused by model mismatch and target output envelope can be well maintained. To deal with the unknown target parameters, two pseudo-spectrum based matched filter banks are proposed for CA and CT targets, respectively. Analysis and design of the filter banks are provided in detail. Simulations are conducted to evaluate the validity of the proposed methods in handling weak maneuvering targets.
Recency and quality-based ranking question in CQAs: A Stack Overflow case study

Recency ranking, in Community-based Question Answering (CQA), would refer to put recent answers in a list’s top positions. To be recent is not related to how new is the date of creation or editing of a given answer, but how current is the content of the answer. A good ranking should also consider the answers’ quality since a current but no quality answer may be useless. Similarly, a high-quality answer, presenting adequate text and references with obsolete information, may be valueless. Combining these two issues (recency and quality) is crucial as users usually hope for current solutions and need to have fast/easy access (top items in the ranking) to the best answers to solve their problems quickly. The CQAs usually provide voting mechanisms so that the users can indicate the best quality answers. However, this method is not concerned with the recency of the answers besides being a slow and subjective process, which does not keep up with new content’s dynamism. Therefore, we propose an automatic approach that, besides the quality, also considers the answer’s recency to generating the ranking. We have used textual and non-textual features that indicate the answers’ quality and recency, extracted from the users’ answers in the CQA environment as a whole. In our approach, quality is used to classify the answers between good and poor, using a threshold value, generating two sets of answers: high quality and low quality. Then, both sets are sorted into recency order. Finally, these sets are concatenated, giving rise to the final ranking, so that the best and most current answers are in the top positions. To verify our proposal’s effectiveness, we have performed a case study in Stack Overflow CQA with a set of experiments, using different combinations of characteristics and different learning to rank Stack Overflow. Then, our main contributions are: (1) an approach to ranking answers of a questions dataset on the recency and quality of an answer; (2) a thorough evaluation of 9 learning to rank algorithms, showing that Coordinate Ascent and LambdaMart have the best performance in this task; (3) a feature analysis, which has shown that features related to the age of the response contributed to improving the ranking performance taking recency and quality into account. Furthermore, as far as we know, it is the first work that considers the recency of an answer in this task.
Snow-Induced PV Loss Modeling Using Production-Data Inferred PV System Models

Snow-induced photovoltaic (PV)-energy losses (snow losses) in snowy and cold locations vary up to 100% monthly and 34% annually, according to literature. Levels that illustrate the need for snow loss estimation using validated models. However, to our knowledge, all these models build on limited numbers of sites and winter seasons, and with limited climate diversity. To overcome this limitation in underlying statistics, we investigate the estimation of snow losses using a PV system’s yield data together with freely available gridded weather datasets. To develop and illustrate this approach, 263 sites in northern Sweden are studied over multiple winters. Firstly, snow-free production is approximated by identifying snow-free days and using corresponding data to infer tilt and azimuth angles and a snow-free performance model incorporating shading effects, etc. This performance model approximates snow-free monthly yields with an average hourly standard deviation of 6.9%, indicating decent agreement. Secondly, snow losses are calculated as the difference between measured and modeled yield, showing annual snow losses up to 20% and means of 1.5–6.2% for winters with data for at least 89 sites. Thirdly, two existing snow loss estimation models are compared to our calculated snow losses, with the best match showing a correlation of 0.73 and less than 1% bias for annual snow losses. Based on these results, we argue that our approach enables studying snow losses for high numbers of PV systems and winter seasons using existing datasets.
Towards a machine learning approach to the analysis of indirect translation

Despite its importance in a globalized world, indirect translation is a peripheral and under-researched topic in translation studies. Existing research on indirect translation is almost exclusively limited to literary translation and focuses mainly on historical aspects. From a methodological perspective, textual analysis based on close reading is the main source of insight into indirect translation, while distant reading using computational approaches remains unexplored. In order to promote methodological innovation, this study gives a replicable demonstration of how to apply supervised machine learning to corpora of indirect translations. The study is based on comparable corpora of proceedings from the European Parliament. Open-access data is used to ensure the replicability of the proposed methodology. Based on the computational findings, the methodological caveats of this approach are discussed.
Security analysis on optical image hiding with silhouette removal based on the interference

In this paper, the security of the cryptosystem with three phase-only masks (POMs) based on the interference has been evaluated. The security strength of the classical interference-based cryptosystem with two POMs has been enhanced by employing a random phase distribution to generate three POMs. This random phase distribution is utilized not only to replace the partial part of two POMs which could be used to retrieve information of the plaintexts directly, but also to modulate and generate an additional POMs; thus, the silhouette problem could be eradicated and key space would be enlarged. It is shown that the security strength of the interference-based cryptosystem has been improved; however, the random phase distribution is irrelative to the plaintexts and two POMs are dependent. Based on this finding, hybrid attacks including a known-plaintext attack (KPA) and phase-retrieval technique-based processes with different constraints have been proposed to crack the interference-based cryptosystem with three POMs and to release its silhouette problem. Numerical simulations have been carried out to validate the feasibility and effectiveness of the proposed attacks.
A Low Power In-DRAM Architecture for Quantized CNNs using Fast Winograd Convolutions

In recent years, the performance and memory bandwidth bottlenecks associated with memory intensive applications are encouraging researchers to explore Processing in Memory (PIM) architectures. In this paper, we focus on DRAM-based PIM architecture for Convolutional Neural Network (CNN) inference. The close proximity of the computation units and the memory cells in a PIM architecture reduces the data movement costs and improves the overall energy efficiency. In this context, CNN inference requires efficient implementations of the area-intensive arithmetic multipliers near the highly dense DRAM regions. Additionally, the multiplication units increase the overall latency and power consumption. Due to this, most previous works in this domain uses binary or ternary weights, which replaces the complicated multipliers with bitwise logical operations resulting in efficient implementations. However, it is well known that the binary and ternary weight networks considerably affect the accuracy and hence can be used only for limited applications.
An improved Gaussian distribution based quantum-behaved particle swarm optimization algorithm for engineering shape design problems

In this article, an improved Gaussian distribution based quantum-behaved particle swarm optimization (IG-QPSO) algorithm is proposed to solve engineering shape design problems with multiple constraints. In this algorithm, the Gaussian distribution is employed to generate the sequence of random numbers in the QPSO algorithm. By decreasing the variance of the Gaussian distribution linearly, the algorithm is able not only to maintain its global search ability during the early search stages, but can also obtain gradually enhanced local search ability in the later search stages. Additionally, a weighted mean best position in the IG-QPSO is employed to achieve a good balance between local search and global search. The proposed algorithm and some other well-known PSO variants are tested on ten standard benchmark functions and six well-studied engineering shape design problems. Experimental results show that the IG-QPSO algorithm can optimize these problems effectively in terms of precision and robustness compared to its competitors.
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Let R=Fpm[u]∕〈u3〉 be the finite commutative chain ring with unity, where p is a prime, m is a positive integer and Fpm is a finite field with pm elements. In this study, we investigate σ-constacyclic codes of length ps over R, that is, ideals of the ring R[x]∕〈xps−σ〉, where σ is a nonzero element of the field Fpm. First, we classify all cyclic codes of length ps over R and obtain the number of codewords in each type of those cyclic codes. Furthermore, by using the ring isomorphism we completely determine the structure of σ-constacyclic codes of length ps over R.
DLWIoT: Deep Learning-based Watermarking for Authorized IoT Onboarding

The onboarding of IoT devices by authorized users constitutes both a challenge and a necessity in a world, where the number of IoT devices and the tampering attacks against them continuously increase. Commonly used onboarding techniques today include the use of QR codes, pin codes, or serial numbers. These techniques typically do not protect against unauthorized device access-a QR code is physically printed on the device, while a pin code may be included in the device packaging. As a result, any entity that has physical access to a device can onboard it onto their network and, potentially, tamper it (e.g., install malware on the device). To address this problem, in this paper, we present a framework, called Deep Learning-based Watermarking for authorized IoT onboarding (DLWIoT), featuring a robust and fully automated image watermarking scheme based on deep neural networks. DLWIoT embeds user credentials into carrier images (e.g., QR codes printed on IoT devices), thus enables IoT onboarding only by authorized users. Our experimental results demonstrate the feasibility of DLWIoT, indicating that authorized users can onboard IoT devices with DLWIoT within 2.5-3sec.
Dynamic Acoustic Unit Augmentation with BPE-Dropout for Low-Resource End-to-End Speech Recognition

With the rapid development of speech assistants, adapting server-intended automatic speech recognition (ASR) solutions to a direct device has become crucial. Researchers and industry prefer to use end-to-end ASR systems for on-device speech recognition tasks. This is because end-to-end systems can be made resource-efficient while maintaining a higher quality compared to hybrid systems. However, building end-to-end models requires a significant amount of speech data. Another challenging task associated with speech assistants is personalization, which mainly lies in handling out-of-vocabulary (OOV) words. In this work, we consider building an effective end-to-end ASR system in low-resource setups with a high OOV rate, embodied in Babel Turkish and Babel Georgian tasks. To address the aforementioned problems, we propose a method of dynamic acoustic unit augmentation based on the BPE-dropout technique. It non-deterministically tokenizes utterances to extend the token's contexts and to regularize their distribution for the model's recognition of unseen words. It also reduces the need for optimal subword vocabulary size search. The technique provides a steady improvement in regular and personalized (OOV-oriented) speech recognition tasks (at least 6% relative WER and 25% relative F-score) at no additional computational cost. Owing to the use of BPE-dropout, our monolingual Turkish Conformer established a competitive result with 22.2% character error rate (CER) and 38.9% word error rate (WER), which is close to the best published multilingual system.
A Comparative Study of STPA-Extension and the UFoI-E Method for Safety and Security Co-analysis

Emerging challenges in cyber-physical systems (CPSs) have been encouraging the development of safety and security co-analysis methods. These methods aim at mitigating the new risks associated with the convergence of safety-related systemic flaws and security-related cyber-attacks that have led to major losses in CPSs. Although several studies have reviewed existing safety and security co-analysis methods, only a few empirical studies have attempted to compare their strengths and limitations to guide risk analysis in practice. This paper bridges the gap between two novel safety and security co-analysis methods and their practical implementations. Namely, this paper compares a novel extension of the System-Theoretic Process Analysis (STPA-Extension) and the Uncontrolled Flows of Information and Energy (UFoI-E) method through a common case study. In our case study, the CPS under analysis is a conceptual autonomous ship. We conducted our comparative study as two independent teams to guarantee that the implementation of one method did not influence the other method. Furthermore, we developed a comparative framework that evaluates the relative completeness and the effort required in each analysis. Finally, we propose a tailored combination of these methods, exploiting their unique strengths to achieve more complete and cost-effective risk analysis results.
Vulnerability Detection in C/C++ Source Code With Graph Representation Learning

An open challenge in software vulnerability detection is how to identify potential vulnerabilities of source code at a fine-grained level automatically. This paper proposes an approach to automate vulnerability detection in source code at the software function level based on graph representation learning without the efforts of security experts. The proposed approach firstly represents software functions as Simplified Code Property Graphs (SCPG), which can conserve syntactic and semantic information of source code while keeping itself small enough for computing. It then utilizes graph neural network and multi layer perceptrons to learn graph representations and extract features automatically, saving efforts of feature engineering. The comparison experiments demonstrate the effectiveness of the proposed approach.
LDA-based term profiles for expert finding in a political setting

A common task in many political institutions (i.e. Parliament) is to find politicians who are experts in a particular field. In order to tackle this problem, the first step is to obtain politician profiles which include their interests, and these can be automatically learned from their speeches. As a politician may have various areas of expertise, one alternative is to use a set of subprofiles, each of which covers a different subject. In this study, we propose a novel approach for this task by using latent Dirichlet allocation (LDA) to determine the main underlying topics of each political speech, and to distribute the related terms among the different topic-based subprofiles. With this objective, we propose the use of fifteen distance and similarity measures to automatically determine the optimal number of topics discussed in a document, and to demonstrate that every measure converges into five strategies: Euclidean, Dice, Sorensen, Cosine and Overlap. Our experimental results showed that the scores of the different accuracy metrics of the proposed strategies tended to be higher than those of the baselines for expert recommendation tasks, and that the use of an appropriate number of topics has proved relevant.
Urban furniture in digital placemaking: Adapting a storytelling payphone across Los Angeles

A growing number of urban practitioners and scholars are interested in using digital storytelling to strengthen neighborhood connections to shared culture and build a coherent sense of place. This article contributes to this discussion by investigating how ‘urban furniture’ can sustain social capacity for digital placemaking. While traditional ‘urban furniture’ in public space is purely physical, digital-physical hybrids are emerging, from benches that tell stories to bus stops that play videos. This extended case follows the travels of an Afrofuturist piece of urban furniture: a community-hacked payphone called Sankofa Red. Our analysis triangulates findings across three installations to show how placemaking can be sustained as a social process: as part of a successful makeover of a community plaza, featured in a neighborhood history game, and in an art exhibition on race and ethnicity. We identify promising practices to adapt urban furniture and retain design collectives beyond a single placemaking installation. As a way for cities to build capacity, we propose that rotating one kind urban furniture (e.g., payphones) across neighborhoods can build the social capacity for placemaking around a shared technical foundation, while still prioritizing local needs and culture.
Structural Economic Assessment of Polymetallic Nodules Mining Project with Updates to Present Market Conditions

This paper presents the economic structure, assumptions, and relations of deep-sea mining project assessment and the results of its evaluation, based on exploration activities and research in the field of geology, mining technology, processing technology, and environmental and legislative studies. The Interoceanmetal Joint Organization (IOM) and cooperating organizations conducted a study incorporating those elements of the project that are recognized as most important for commercial viability. On the basis of formulated financial flow of operating and capital expenses of one processing technology the possible market unit price of polymetallic nodules was estimated and the result is presented in this paper. The rapidly changing economic situation, affected inter alia by the COVID-19 pandemic, is reflected in the study and updated results are based on recent changes in metal prices. Although assumptions related to mining costs need to be confirmed during pilot mining tests, promising results have been shown in the case of the use of high-pressure acid leaching processing technology (HPAL) as well as in the case of raw ore sales. A pre-feasibility study of the project will focus on the two most promising variants of the model.