Reviewerly/exHarmony huggingface dataset:
Cluster-Guided Unsupervised Domain Adaptation for Deep Speaker Embedding

Recent studies have shown that pseudo labels can contribute to unsupervised domain adaptation (UDA) for speaker verification. Inspired by the self-training strategies that use an existing classifier to label the unlabeled data for retraining, we propose a cluster-guided UDA framework that labels the target domain data by clustering and combines the labeled source domain data and pseudo-labeled target domain data to train a speaker embedding network. To improve the cluster quality, we train a speaker embedding network dedicated for clustering by minimizing the contrastive center loss. The goal is to reduce the distance between an embedding and its assigned cluster center while enlarging the distance between the embedding and the other cluster centers. Using VoxCeleb2 as the source domain and CN-Celeb1 as the target domain, we demonstrate that the proposed method can achieve an equal error rate (EER) of 8.10% on the CN-Celeb1 evaluation set without using any labels from the target domain. This result outperforms the supervised baseline by 39.6% and is the state-of-the-art UDA performance on this corpus.
Path and Trajectory Planning for UV-C Disinfection Robots

Ultraviolet-C(UV-C) disinfection has been known for long, but its use with robotics is quite new, mainly motivated by the recent coronavirus disease pandemics (COVID-19). In UV-C disinfection, application of the right dose to target surfaces is fundamental to ensure proper disinfection. Hence a common goal is to provide a proper motion plan that ensures effective disinfection. In this letter, we provide a generic framework that generates a shortest path and the corresponding trajectory that ensures effective disinfection while optimizing different cost functions, and then experimentally verify it using a differential drive mobile robot. The trajectories generated by the proposed framework comply with kinodynamic constraints of the robot carrying the UV-C source. We also study non-uniform illumination and present a novel cost function that aims to decrease excessive illumination, in order to reduce UV-C damage of the surfaces. Additionally, we analyze the effect of height of line sources, which can change effectiveness and speed of disinfection considerably.
Development of a Hardware Demonstration Platform for Multispacecraft Reconnaissance of Small Bodies

The next frontier in space exploration involves visiting some of the 2 million small bodies scattered throughout the solar system. However, these missions are expected to be challenging due to the surface irregularities of these bodies and the very low gravity, which makes steps like getting into orbit very complex. For these reasons, reconnaissance is crucial for small-body exploration before taking on ambitious orbital, surface, and sample-return missions. Our previous work developed IDEAS, an automated design software for small-body reconnaissance mission development using spacecraft swarms. A critical challenge to furthering such designs is the lack of hardware demonstration platforms for interplanetary spacecraft operations. In this article, we present multiagent photogrammetry of small bodies (MAPS), a hardware platform to demonstrate critical reconnaissance operations of multispacecraft missions identified by the IDEAS framework. MAPS uses unmanned air vehicles (UAVs) as the autonomous agents that perform reconnaissance operations. The UAVs use their visual feed to generate a 3-D surface map of a small-body mockup, which is encountered along their flight path. In this article, we examine the various design elements of a small-body surface reconstruction mission inside the MAPS testbed. These elements are used for designing reference trajectories of the participating UAVs, which is enforced using a tracking feedback control law. We then formulate the small-body mapping problem as a mixed-integer nonlinear programming problem, which is handled by the Automated Swarm Designer module of the IDEAS framework. The solutions are implemented inside the MAPS, and shape models generated from the UAV feeds are compared.
Cross-Chain Virtual Payment Channels

With the emergence of countless independent blockchain systems in recent years, cross-chain transactions have attracted considerable attention, and lots of solutions have been put forth by both industry and academia. However, most of the existing solutions suffer from either centralization or scalability issues. To mitigate these issues, in this paper, we propose the concept of cross-chain virtual payment channels, which allows two users in different blockchain systems to conduct limitless off-chain transactions with the help of an intermediate node, hence solving the centralization and scalability issues. Furthermore, as the intermediate node is only involved in the channel open and close operations, it further improves the efficiency of cross-chain transactions, and to a certain extent, it even enhances the privacy of the cross-chain transaction. Meanwhile, we also present the first concrete cross-chain virtual payment channel scheme, which only requires one of the blockchain systems supporting the Turing-complete scripting language. The corresponding detailed security analysis in the Universal Composability framework demonstrates that our proposal holds the Consensus on Open, Update, and Close. Finally, we implement and deploy our cross-chain virtual payment channel scheme on the Ethereum and Bitcoin test networks. The extensive experimental results show that our proposal dramatically improves the efficiency of cross-chain transactions, and the advantage becomes more pronounced as the number of transactions increases.
GFANet: Group Fusion Aggregation Network for Real Time Stereo Matching

Existing 3D stereo networks with 4D volumes are computationally expensive but precise while 2D stereo networks are efficient but poor performance. In this letter, we present a novel group fusion aggregation (GFA) for 2D convolutions cost aggregation based on 4D volumes to reduce computational costs. Group-wise disparity aggregation block (GDAB) and group-wise channel fusion block (GCFB) are proposed to fuse geometry and context information of different group cost volumes in GFA, respectively. Further, we employ channel-dimension-first cost volume transformation and disparity-dimension-first cost volume transformation to convert 4D cost volumes into 3D tensors for GDAB and GCFB input in GFA. We evaluate our method on two popular public benchmark datasets. Experimental results from the KITTI official website show that our method can achieve similar accuracy with other 3D stereo networks (PSMNet, GCNet, GwcNet, etc.) at a low computing consumption. The ablation studies further demonstrate the facticity and reasonability of our proposed GFA.
Adaptive Multispectral Encoding Network for Image Demoiréing

Moiré often appears when photographing textured objects, which can seriously degrade the quality of captured photos. Due to the wide distribution of moiré and the dynamic properties of moiré, it is a challenge to effectively remove moiré patterns. For this purpose, we present an adaptive multi-spectral encoding network (AMSDM) for image demoiréing. In AMSDM, we leverage a multiscale network structure to process moiré images at different spatial resolutions, which can relieve the issue of moiré with distributed frequency spectrum. To solve the issue of dynamic properties of moiré, we design an adaptive multi-spectral encoding (AMSE) module to encode moiré patterns adaptively, which helps AMSDM restore moiré images clearly. Besides, a demoiréing convolutional network block (DMCNB) in the AMSE module makes AMSDM have the adaptability and the long-range correlation; thus, it can learn both global and local information about moiré images. Extensive experimental results indicate that our proposed AMSDM significantly outperforms state-of-the-art methods and achieves a great balance between performance and efficiency.
Human-Flow-Aware Long-Term Mobile Robot Task Planning Based on Hierarchical Reinforcement Learning

The difficulty in finding long-term planning policies for a mobile robot increases when operating in crowded and dynamic environments. State-of-the-art approaches do not consider cues of human-robot-shared dynamic environments. Aiming to fill this gap, we present a novel Human-Flow-Aware Guided Hierarchical Dyna-Q (HA-GHDQ) algorithm, which solves long-term robot task planning problems by using human motion patterns encoded in Maps of Dynamics (MoDs). To tackle the complexity of long-term robot operation in dynamic environments, we propose a combination of symbolic planning and Hierarchical Reinforcement Learning (HRL) that generates robot policies considering cost information derived from MoDs. We evaluated HA-GHDQ in a factory environment with two simulation and one real-world datasets to complete a transportation-and-assembly task. Our approach outperforms the baselines with respect to sample efficiency and final plan quality. Moreover, we show that it is more adaptable and robust against environmental changes than the baselines.
Improving Machine Translation and Summarization with the Sinkhorn Divergence

Important natural language processing tasks such as machine translation and document summarization have made enormous strides in recent years. However, their performance is still partially limited by the standard training objectives, which operate on single tokens rather than on more global features. Moreover, such standard objectives do not explicitly consider the source documents, potentially affecting their alignment with the predictions. For these reasons, in this paper, we propose using an Optimal Transport (OT) training objective to promote a global alignment between the model's predictions and the source documents. In addition, we present an original implementation of the OT objective based on the Sinkhorn divergence between the final hidden states of the model's encoder and decoder. Experimental results over machine translation and abstractive summarization tasks show that the proposed approach has been able to achieve statistically significant improvements across all experimental settings compared to our baseline and other alternative objectives. A qualitative analysis of the results also shows that the predictions have been able to better align with the source sentences thanks to the supervision of the proposed objective.
The Seven Capital Sins in the Governance of Blockchain Ecosystems

In recent years, blockchain has gained traction as a technology to increase the security and transparency of firms' transactions.There is a widespread misconception that blockchains, once rolled out, are fully decentralized and transparent in terms of governance.The practice of many blockchain initiatives tells a different story: the development of a robust and reliable governance structure for a blockchain ecosystem requires balancing the power, accountability, and incentives of each blockchain member.This is a highly complex balancing act.It is important that CEOs, COOs and other top managers become deeply aware of the complexity of setting up and governing an (envisioned or existing) blockchain, to increase its success chances.In this article, we discuss what we consider are the seven capital sins of the governance of an enterprise blockchain that can hinder the successful implementation of this technology.These insights are based on three years of research in which we studied a large number of blockchain ecosystems.
Leveraging Large Language Models for End-User Website Generation

This work introduces an innovative approach that harnesses the power of large language models (LLMs) to facilitate the creation of websites by end users through natural language specifications. Our key contribution lies in a user-oriented method that utilizes prompt engineering, compelling the LLM response to adhere to a specific template, which in turn enables direct parsing of the model’s responses, allowing users to focus on refining the generated website without concerning themselves with the underlying code. The engineered prompt ensures model efficiency by implementing a modification strategy that preserves context and tokens generated in the LLM responses, updating only specific parts of the code rather than rewriting the entire document, thereby minimizing unnecessary code revisions. Moreover, our approach empowers LLMs to generate multiple documents, augmenting the user experience. We showcase a proof-of-concept implementation where users submit textual descriptions of their desired website features, prompting the LLM to produce corresponding HTML and CSS code. This paper underscores the potential of our approach to democratize web development and enhance its accessibility for non-technical users. Future research will focus on conducting user studies to ascertain the efficacy of our method within existing low-code/no-code platforms, ultimately extending its benefits to a broader audience.
Public health research using cell phone derived mobility data in sub-Saharan Africa: Ethical issues

The movements of humans have a significant impact on population health. While studies of such movements are as old as public health itself, the COVID-19 pandemic has raised the profile of mobility research using digital technologies to track transmission routes and calculate the effects of health policies, such as lockdowns. In sub-Saharan Africa, the high prevalence of cell phone and smartphone use is a source of potentially valuable mobility data for public health purposes. Researchers can access call data records, passively collected in real time from millions of clients by cell phone companies, and associate these records with other data sets to generate insights, make predictions or draw possible policy implications. The use of mobility data from this source could have a range of significant benefits for society, from better control of infectious diseases, improved city planning, more efficient transportation systems and the optimisation of health resources. We discuss key ethical issues raised by public health studies using mobility data from cell phones in sub-Saharan Africa and identify six key ethical challenge areas: autonomy, including consent and individual or group privacy; bias and representativeness; community awareness, engagement and trust; function creep and accountability; stakeholder relationships and power dynamics; and the translation of mobility analyses into health policy. We emphasise the ethical importance of narrowing knowledge gaps between researchers, policymakers and the general public. Given that individuals do not really provide valid consent for the research use of phone data tracking their movements, community understanding and input will be crucial to the maintenance of public trust. Significance: Mobility data derived from cell phones are being increasingly used for health research and public health purposes in sub-Saharan Africa, with minimal individual consent and largely without public awareness. While such data can have significant potential public health benefits, risks and concerns related to their collection and use in sub-Saharan African contexts have not been widely discussed. Innovative community engagement initiatives, which are appropriate and responsive to sub-Saharan African contexts, need to be developed to address ethical challenge areas and help warrant public trust in mobility research.
A novel hand-eye calibration method for industrial robot and line laser vision sensor

Purpose The purpose of this study is solving the hand–eye calibration issue for line structured light vision sensor. Only after hand–eye calibration the sensor measurement data can be applied to robot system. Design/methodology/approach In this paper, the hand–eye calibration methods are studied, respectively, for eye-in-hand and eye-to-hand. Firstly, the coordinates of the target point in robot system are obtained by tool centre point (TCP), then the robot is controlled to make the sensor measure the target point in multiple poses and the measurement data and pose data are obtained; finally, the sum of squared calibration errors is minimized by the least square method. Furthermore, the missing vector in the process of solving the transformation matrix is obtained by vector operation, and the complete matrix is obtained. Findings On this basis, the sensor measurement data can be easily and accurately converted to the robot coordinate system by matrix operation. Originality/value This method has no special requirement for robot pose control, and its calibration process is fast and efficient, with high precision and has practical popularized value.
Measuring digital competences. Ten common methodological challenges

The development of digital competences is currently one of the key issues not only for media pedagogy, but also for other fields where information and communication technologies (ICTs) are at the centre of human activity. This article is part of a methodological discussion on issues of measuring digital competences. The aim of the article fills the gap related to the ordering of diagnostic bases related to the measurement of the efficiency of using ICTs among different age and occupational groups, showing at the same time the most frequent methodological shortcomings in the literature. Referring to the Bolesaw Niemierkos Polish concept of didactic measurement, it is noted that contemporary media educators are exposed to ten errors affecting the adequate measurement of digital competences, such as inconsistency of theoretical framework, problematic selection of diagnostic indicators, one-dimensional measurement, inadequate construction of measurement scales, subjectivity of measurement, too infrequent use of knowledge and skills tests, use of non-standardised tools, uncritical inclusion of available questionnaires in the research process, inadequacy of measurement tools to the examined professions, and lack of longitudinal studies. This article was prepared as part of the international project REMEDIS Rethinking Media Literacy and Digital Skills in Europe (funded by the National Science Centre, 021/03/Y/HS6/00275).
Proposed Framework to Manage Non-Functional Requirements in Agile

Agile Software Development (ASD) is a type of iterated software development that strives to maximize productivity, effectiveness, and quick delivery through the minimization of documents and needless procedures within constrained timeframes. Agile software development has a number of advantages. There are still some difficulties. For instance, during the development lifecycle, non-functional requirements (NFRs) are disregarded and not given first-class artifacts. This results in several issues, including customer dissatisfaction and a great deal of rework, which impacts time and cost. In this paper, a proposed framework for handling non-functional requirements in Agile is explained. The framework supports the several primary activities of requirements engineering including requirements elicitation, analysis, documentation, and validation. In addition, the framework handles non-functional recommendations. Results of the suggested solution validation showed that it could address the problems with non-functional requirements in Agile.
A Novel PCA-Based Method for PRNU Distillation to the Benefit of Source Camera Identification

Photo response non-uniformity (PRNU) is a widely accepted inherent fingerprint that has been used in source camera identification (SCI). However, the reference PRNU noise is limited by the performance of PRNU noise extraction techniques and is easily contaminated by interfering noise from image content. The existing methods mainly suppressed the interference noise of the reference PRNU noise in the spectral domain, but there was still interference noise related to the image content in the low-frequency region. We considered that this interference noise of the reference PRNU noise could be removed by further operations in the spatial domain. In this paper, we proposed a scheme to distil the reference PRNU by removing the interference noise with the help of principal component analysis (PCA) technology. Specifically, the reference PRNU noise was modelled as white Gaussian noise, whereas the interfering noise caused correlation between pixels and their neighbourhoods in the reference PRNU noise. In the local pixel area, we modelled a pixel and its neighbours as a vector and used block matching to select PCA training samples with similar contents. Next, PCA transformation estimated the interference noise in the local pixel area, and we performed coefficient shrinkage in the PCA domain to better estimate interference noise. The experimental results on the “Dresden” and “VISION” datasets showed that the proposed scheme achieved better receiver operating characteristic curves and the Kappa statistic than state-of-the-art works.
Robust and Fast Quantum State Transfer on Superconducting Circuits

Quantum computation attaches importance to high-precision quantum manipulation, where the quantum state transfer with high fidelity is necessary. Here, we propose a new scheme to implement the quantum state transfer of high fidelity and long distance, by adding on-site potential into the qubit chain and enlarging the proportion of the coupling strength between the two ends and the chain. In the numerical simulation, without decoherence, the transfer fidelities of 9 and 11 qubit chain are 0.999 and 0.997, respectively. Moreover, we give a detailed physical realization scheme of the quantum state transfer in superconducting circuits, and discuss the tolerance of our proposal against decoherence. Therefore, our scheme will shed light on quantum computation with long chain and high-fidelity quantum state transfer.
A quantum resistant universal designated verifier signature proof

&lt;abstract&gt;&lt;p&gt;In order to ensure that only the designated person can verify the signer's signature on the message, Steinfeld et al. introduced the concept of Universal Designated Verifier Signature (UDVS), which enables a designator who has obtained a signature on a message from the signer to designate the signature to any desired designated verifier. This idea was developed to address the privacy concerns of the signature holder at the time of certificate distribution. They are appropriate for applications that demand the designer's secrecy. The fact that the designated verifier must generate a public key with regard to the signer's public parameter for signature verification is a significant drawback of UDVS methods. In cases where the verifier is unable to begin the key generation procedure, this constraint is inapplicable. Baek et al. developed the idea of "Universal Designated Verifier Signature Proof (UDVSP)", which does not require the verifier's public key for verification, to get around this restriction. All existing UDVSP constructions are based on a discrete logarithm problem, which is vulnerable to quantum computer attacks. As a result, an efficient quantum resistant UDVSP is built on a hard problem in coding theory, as suggested by NIST reports. The scheme's security against forgeability and impersonation attacks is examined using the random oracle model.&lt;/p&gt;&lt;/abstract&gt;