Reviewerly/exHarmony huggingface dataset:
Public Service Values and Chatbots in the Public Sector: Reconciling Designer efforts and User Expectations

Chatbots are deployed across a wide range of public services, frequently to manage the increased volumes of online service requests.The appropriateness of many chatbot initiatives is often challenged.One reason for this is these initiatives are largely driven by agency centric goals, often neglecting the expectations of other public stakeholders.A public service value perspectivefounded on the notion of public valueoffers an avenue to represent the views of other public stakeholder groups.We examine the public service values of two key stakeholder groupsdesigners and users and discuss how they can be reconciled.
Social and Telepresence Robots a future of teaching

The robotics industry is advancing at rates faster than ever before. It produces plentiful different types of robots incorporated in other fields like medical, defense, manufacturing, and mining industries. Of course, it is no surprise that the robotics industry managed to make its way to the educational field, and with it came the social and telepresence robots. This paper aims to compare social and telepresence robots in the teaching industry in the near future. Both a literature survey and a comparison segment were produced. It was concluded that the future of educational environments would incorporate both of these technologies since they serve different functions. This is to provide the best experience for as wide of a range of people as possible, wherever they are living and whatever disabilities/ challenges they are experiencing.
Hybrid Rydberg quantum gate for quantum network

The high-fidelity storage, distribution, and processing of quantum information prefers qubits with different physical properties. Thus hybrid quantum gates interfacing different types of qubits are essential for the realization of complex quantum network structures. A Rydberg-atom-based physical quantum controlled-Z (CZ) gate is proposed to hybridly process the polarization-encoded single-photon optical qubit and the ``Schr\"odinger cat'' microwave qubit. The degradation of the fidelity under the influence of various noise channels, such as microwave cavity loss, spontaneous emission of atom states, and nonadiabaticity effect, etc., has been analyzed through detailed theoretical analysis by deriving the input-output relation of qubit fields. The feasibility and the challenges of the protocol within current technology are also discussed by analyzing the possible experimental parameter settings. Our work opens prospects for future large-scale quantum architecture based on hybrid quantum modules.
Easy computation of the Bayes factor to fully quantify Occam’s razor in least-squares fitting and to guide actions

The Bayes factor is the gold-standard figure of merit for comparing fits of models to data, for hypothesis selection and parameter estimation. However, it is little-used because it has been considered to be subjective, and to be computationally very intensive. A simple computational method has been known for at least 30 years, but has been dismissed as an approximation. We show here that all three criticisms are misplaced. The method should be used to complement and augment all least-squares fitting, because it can give very different, and better outcomes than classical methods. It can discriminate between models with equal numbers of parameters and equally good fits to data. It quantifies the Occam's Razor injunction against over-fitting, and it demands that physically-meaningful parameters rejected by classical significance testing be included in the fitting, to avoid spurious precision and incorrect values for the other parameters. It strongly discourages the use of physically-meaningless parameters, thereby satisfying the Occam's Razor injunction to use existing entities for explanation rather than multiplying new ones. More generally, as a relative probability, the Bayes factor combines naturally with other quantitative information to guide action in the absence of certain knowledge.
COVID-19 pandemic

Humanity has been on its way accompanied by epidemics, as man has evolved, he has faced different problems that have affected most of society. In the last 50 years, more viruses have appeared that have affected different regions and multiple countries; but one of the most distributed worldwide is COVID-19. The objective is to offer some information related to this pandemic and its evolution in different countries. The bibliographic review method was used even though some bibliographies are very recent, but it has allowed us to know their behavior and follow-up. The results of the countries most affected by this pandemic are shown, where it could be said that Italy has increasingly affected all of them and some countries in Asia, the United States, Latin America, and the Caribbean have been the most affected.
LIME-Based Data Selection Method for SAR Images Generation Using GAN

Deep learning has obtained remarkable achievements in computer vision, especially image and video processing. However, in synthetic aperture radar (SAR) image recognition, the application of DNNs is usually restricted due to data insufficiency. To augment datasets, generative adversarial networks (GANs) are usually used to generate numerous photo-realistic SAR images. Although there are many pixel-level metrics to measure GAN’s performance from the quality of generated SAR images, there are few measurements to evaluate whether the generated SAR images include the most representative features of the target. In this case, the classifier probably categorizes a SAR image into the corresponding class based on “wrong” criterion, i.e., “Clever Hans”. In this paper, local interpretable model-agnostic explanation (LIME) is innovatively utilized to evaluate whether a generated SAR image possessed the most representative features of a specific kind of target. Firstly, LIME is used to visualize positive contributions of the input SAR image to the correct prediction of the classifier. Subsequently, these representative SAR images can be selected handily by evaluating how much the positive contribution region matches the target. Experimental results demonstrate that the proposed method can ally “Clever Hans” phenomenon greatly caused by the spurious relationship between generated SAR images and the corresponding classes.
Investigation of Metaverse in cryptocurrency

Metaverse is an immersive 3D virtual environment, a true virtual artificial community in which avatars act as the user's alter ego and interact with each other. If we do not manage the hype for the metaverse, which has recently been receiving a surge in interest, the metaverse will fail to cross the chasm. This article conducts a comprehensive survey on computational arts, in which seven critical topics are relevant to the metaverse, describing novel artworks in blended virtual-physical realities. The topics first cover the building elements for the metaverse, e.g. Virtual scenes and characters, auditory, textual elements. Next, several remarkable types of novel creations in the expanded horizons of metaverse cyberspace have been reﬂected, such as immersive arts, robotic arts, and other user-centric approaches fuelling contemporary creative outputs.
A Systematic Literature Review of Blockchain Technology Adoption in Bangladesh

The spirit of “blockchain technology” is a distributed database in which saved data is transparent, accountable, public, immutable, and traceable. This base-level disruptive technology can boost the security and privacy-related efficiency of various domains. As Bangladesh is currently aiming for sustainable development, blockchain technology adoption by the local researchers is growing robustly. However, in Bangladesh, the blockchain Technology Acceptance Model (TAM) is not yet well structured which is also limiting the perspective of local developers and researchers. Therefore, sectors like governance, healthcare, security, privacy, farming, information authentication, cryptocurrencies, internet architecture, data, and so on are unable to utilize the full potential of this technology. In this research, the authors conduct an in-depth review of such types of blockchain technology-related research articles that have been published recently and are also solely focused on Bangladesh. From 5 publishers (IEEE Xplore, ACM, ScienceDirect, Taylor &amp; Francis, and SpringerLink) this study analyses 70 articles published during the year 2016-2020. The study results find the top 13 sectors where Bangladeshi researchers are currently focusing on. Those studies identify that the rigid policy by the government, scarcity of expert researchers, and lack of resources are the main reasons why Bangladesh is still struggling to accommodate blockchain extensively. In addition, published papers are mostly based on theoretical concepts without an appropriate implementation. Finally, this study will be a great resource to the developers, entrepreneurs, and technology enthusiasts to determine the strategic plan for adopting blockchain technology in Bangladesh or even to any other developing country.
New Single-Preparation Methods for Unsupervised Quantum Machine Learning Problems

The term "machine learning" especially refers to algorithms that derive mappings, i.e. intput/output transforms, by using numerical data that provide information about considered transforms. These transforms appear in many problems, related to classification/clustering, regression, system identification, system inversion and input signal restoration/separation. We here first analyze the connections between all these problems, in the classical and quantum frameworks. We then focus on their most challenging versions, involving quantum data and/or quantum processing means, and unsupervised, i.e. blind, learning. Moreover, we propose the quite general concept of SIngle-Preparation Quantum Information Processing (SIPQIP). The resulting methods only require a single instance of each state, whereas usual methods have to very accurately create many copies of each fixed state. We apply our SIPQIP concept to various tasks, related to system identification (blind quantum process tomography or BQPT, blind Hamiltonian parameter estimation or BHPE, blind quantum channel identification/estimation, blind phase estimation), system inversion and state estimation (blind quantum source separation or BQSS, blind quantum entangled state restoration or BQSR, blind quantum channel equalization) and classification. Numerical tests show that our framework moreover yields much more accurate estimation than the standard multiple-preparation approach. Our methods are especially useful in a quantum computer, that we propose to more briefly call a "quamputer": BQPT and BHPE simplify the characterization of the gates of quamputers; BQSS and BQSR allow one to design quantum gates that may be used to compensate for the non-idealities that alter states stored in quantum registers, and they open the way to the much more general concept of self-adaptive quantum gates (see longer version of abstract in paper).
Rethinking Why Intermediate-Task Fine-Tuning Works

Supplementary Training on IntermediateLabeled-data Tasks (STILT) is a widely applied technique, which first fine-tunes the pretrained language models on an intermediate task before on the target task of interest.While STILT is able to further improve the performance of pretrained language models, it is still unclear why and when it works.Previous research shows that those intermediate tasks involving complex inference, such as commonsense reasoning, work especially well for RoBERTa-large.In this paper, we discover that the improvement from an intermediate task could be orthogonal to it containing reasoning or other complex skills -a simple real-fake discrimination task synthesized by GPT2 can benefit diverse target tasks.We conduct extensive experiments to study the impact of different factors on STILT.These findings suggest rethinking the role of intermediate fine-tuning in the STILT pipeline.
Fast Panoptic Segmentation with Soft Attention Embeddings

Panoptic segmentation provides a rich 2D environment representation by unifying semantic and instance segmentation. Most current state-of-the-art panoptic segmentation methods are built upon two-stage detectors and are not suitable for real-time applications, such as automated driving, due to their high computational complexity. In this work, we introduce a novel, fast and accurate single-stage panoptic segmentation network that employs a shared feature extraction backbone and three network heads for object detection, semantic segmentation, instance-level attention masks. Guided by object detections, our new panoptic segmentation head learns instance specific soft attention masks based on spatial embeddings. The semantic masks for stuff classes and soft instance masks for things classes are pixel-wise coherent and can be easily integrated in a panoptic output. The training and inference pipelines are simplified and no post-processing of the panoptic output is necessary. Benefiting from fast inference speed, the network can be deployed in automated vehicles or robotic applications. We perform extensive experiments on COCO and Cityscapes datasets and obtain competitive results in both accuracy and time. On the Cityscapes dataset we achieve 59.7 panoptic quality with an inference speed of more than 10 FPS on high resolution 1024 × 2048 images.
Proposing Posture Recognition System Combining MobilenetV2 and LSTM for Medical Surveillance

This paper proposes a posture recognition system that can be applied for medical surveillance. The proposed method estimates human posture using mobilenetV2 and long short-term memory (LSTM) to extract the important features of an image. The output of the system was a fully estimated skeleton. We used seven human indoor postures, including lying, sitting, crouching, standing, walking, fighting, and falling, and classified them. The output results are the extraction of the human skeleton and the corresponding labels for the poses. We first experiment with classification using machine learning. The system only achieves approximately 88% accuracy because it is not able to classify similar postures, such as standing and walking. This difference can be caused by the extraction of features for static images, and the machine learning classification algorithm has not reached accuracy with training data. Therefore, we proposed the integration of the LSTM model into the proposed system. LSTM learns the features of the skeleton and provides classification results for postures. As a result, our system improved the accuracy by up to 99%. Similar postures, such as standing and walking, have improved accuracy by up to 7%. In addition, we performed the system on the Jetson Nano hardware. The results show that it can run on a low-profile (44% CPU and 2.1 frames per second) that is capable of applications for remote patient monitoring devices.
Research on the Construction of Human-Computer Interaction System Based on a Machine Learning Algorithm

In this paper, we use machine learning algorithms to conduct in-depth research and analysis on the construction of human-computer interaction systems and propose a simple and effective method for extracting salient features based on contextual information. The method can retain the dynamic and static information of gestures intact, which results in a richer and more robust feature representation. Secondly, this paper proposes a dynamic planning algorithm based on feature matching, which uses the consistency and accuracy of feature matching to measure the similarity of two frames and then uses a dynamic planning algorithm to find the optimal matching distance between two gesture sequences. The algorithm ensures the continuity and accuracy of the gesture description and makes full use of the spatiotemporal location information of the features. The features and limitations of common motion target detection methods in motion gesture detection and common machine learning tracking methods in gesture tracking are first analyzed, and then, the kernel correlation filter method is improved by designing a confidence model and introducing a scale filter, and finally, comparison experiments are conducted on a self-built gesture dataset to verify the effectiveness of the improved method. During the training and validation of the model by the corpus, the complementary feature extraction methods are ablated and learned, and the corresponding results obtained are compared with the three baseline methods. But due to this feature, GMMs are not suitable when users want to model the time structure. It has been widely used in classification tasks. By using the kernel function, the support vector machine can transform the original input set into a high-dimensional feature space. After experiments, the speech emotion recognition method proposed in this paper outperforms the baseline methods, proving the effectiveness of complementary feature extraction and the superiority of the deep learning model. The speech is used as the input of the system, and the emotion recognition is performed on the input speech, and the corresponding emotion obtained is successfully applied to the human-computer dialogue system in combination with the online speech recognition method, which proves that the speech emotion recognition applied to the human-computer dialogue system has application research value.
Evaluation of Online Markets Considering Trust and Resilience: A Framework for Predicting Customer Behavior in E-Commerce

The evolution of electronic commerce has led to expanding online markets due to convenience, online incentives, greater access to information, broader selections, competition, pricing, product quality, and lead time, i.e., the time that it takes to deliver the products after an order is placed. However, online markets have captured less attention in environments where consumers are concerned about the security and privacy of their personal data. Such concerns can be due to several reasons, from the reluctance in using electronic commerce to the lack of trust and reliability of web vendors. In this paper, we study trust and resilience engineering in such online markets and identify the most and least significant factors impacting online shopping systems. To tackle the inherent uncertainties of data used in our analysis, we propose a Fuzzy Data Envelopment Analysis (FDEA) model and validate our findings. Numerical examples show the efficiency of the proposed method in evaluating online and offline shopping systems with respect to trust and resilience.
Shrinking Bigfoot: Reducing wav2vec 2.0 footprint

Wav2vec 2.0 is a state-of-the-art speech recognition model which maps speech audio waveforms into latent representations. The largest version of wav2vec 2.0 contains 317 million parameters. Hence, the inference latency of wav2vec 2.0 will be a bottleneck in production, leading to high costs and a significant environmental footprint. To improve wav2vec's applicability to a production setting, we explore multiple model compression methods borrowed from the domain of large language models. Using a teacher-student approach, we distilled the knowledge from the original wav2vec 2.0 model into a student model, which is 2 times faster, 4.8 times smaller than the original model. More importantly, the student model is 2 times more energy efficient than the original model in terms of CO2 emission. This increase in performance is accomplished with only a 7% degradation in word error rate (WER). Our quantized model is 3.6 times smaller than the original model, with only a 0.1% degradation in WER. To the best of our knowledge, this is the first work that compresses wav2vec 2.0.
Deep Multi-View Stereo Gone Wild

Deep multi-view stereo (MVS) methods have been developed and extensively compared on simple datasets, where they now outperform classical approaches.In this paper, we ask whether the conclusions reached in controlled scenarios are still valid when working with Internet photo collections.We propose a methodology for evaluation and explore the influence of three aspects of deep MVS methods: network architecture, training data, and supervision.We make several key observations, which we extensively validate quantitatively and qualitatively, both for depth prediction and complete 3D reconstructions.First, complex unsupervised approaches cannot train on data in the wild.Our new approach makes it possible with three key elements: upsampling the output, softmin based aggregation and a single reconstruction loss.Second, supervised deep depthmap-based MVS methods are state-of-the art for reconstruction of few internet images.Finally, our evaluation provides very different results than usual ones.This shows that evaluation in uncontrolled scenarios is important for new architectures.
Variational Algorithms for Workflow Scheduling Problem in Gate-Based Quantum Devices

In this paper we consider the combinatorial optimization problem known as workflow scheduling. We compare three encoding schemes of varying density: one-hot, binary, and domain wall, and test their performance against two well-known hybrid quantum-classical algorithms: Quantum Approximate Optimization Algorithm (QAOA) and Variational Quantum Eigensolver (VQE). In an attempt to obtain the best results possible, we investigate various parameters of the algorithms and test out other state-of-the-art improvements, such as dedicated QAOA mixers. Ultimately, we prove that, despite its popularity, one-hot encoding is not always the best, and using a denser encoding scheme, such as binary or domain wall, can allow for solving larger instances of workflow scheduling. Additionally, combining the above-mentioned encodings with dedicated QAOA mixers reduces the number of infeasible solutions, leading to better results.
Transformation of Audit Methodology with the Use of Blockchain and DLT Technologies

The development of blockchain and distributed ledger technology (DLT) technologies stimulates diametrically opposite points of view about the prospects of traditional institutions in the digital economy: from their complete disappearance to a cardinal transformation. This matter also applies to the field of accounting and auditing. The purpose of the study is not only to identify trends in the transformation of the audit methodology but also in an accountant/auditor profession itself by using blockchain and DLT. The conduct of this research is based on the use of the following methods: analysis and synthesis, comparison, systemic and logical approach, analogies and grouping. The paper contains an overview of the implementation practice of blockchain technology by the Big Four audit companies, as well as the composition of the main developers and the software products they offer based on the blockchain technology and DLT used in audit activities. There have been identified the key trends in the development of an accountant/auditor professions in connection with the use of blockchain technology and DLT. The conclusion is made about the need for a significant transformation of the audit methodology, as well as the profession of an accountant/auditor in connection with the use of blockchain and DLT, but it is not about replacing the audit institution with these technologies. The authors have formulated proposals and defined the most significant innovations of the classical methodology for conducting an audit of the business processes effectiveness based on the use of blockchain technology and DLT.