Reviewerly/exHarmony huggingface dataset:
The superconducting quasicharge qubit

The non-dissipative non-linearity of a Josephson junction converts macroscopic superconducting circuits into artificial atoms, enabling some of the best controlled quantum bits (qubits) today. Three fundamental types of superconducting qubits are known, each reflecting a distinct behavior of quantum fluctuations in a Cooper pair condensate: single charge tunneling (charge qubit), single flux tunneling (flux qubit), and phase oscillations (phase qubit). Yet, the dual nature of charge and flux suggests that circuit atoms must come in pairs. Here we introduce the missing one, named "blochnium". It exploits a coherent insulating response of a single Josephson junction that emerges from the extension of phase fluctuations beyond the $2\pi$-interval. Evidence for such effect was found in an out-of-equilibrium dc-transport through junctions connected to high-impedance leads, although a full consensus is absent to date. We shunt a weak junction with an exceptionally high-value inductance -- the key technological innovation behind our experiment -- and measure the rf-excitation spectrum as a function of external magnetic flux through the resulting loop. The junction's insulating character manifests by the vanishing flux-sensitivity of the qubit transition between the ground and the first excited states, which nevertheless rapidly recovers for transitions to higher energy states. The spectrum agrees with a duality mapping of blochnium onto transmon, which replaces the external flux by the offset charge and introduces a new collective quasicharge variable in place of the superconducting phase. Our result unlocks the door to an unexplored regime of macroscopic quantum dynamics in ultrahigh-impedance circuits, which may have applications to quantum computing and quantum metrology of direct current.
BMT-Net: Broad Multitask Transformer Network for Sentiment Analysis

Sentiment analysis uses a series of automated cognitive methods to determine the author's or speaker's attitudes toward an expressed object or text's overall emotional tendencies. In recent years, the growing scale of opinionated text from social networks has brought significant challenges to humans' sentimental tendency mining. The pretrained language model designed to learn contextual representation achieves better performance than traditional learning word vectors. However, the existing two basic approaches for applying pretrained language models to downstream tasks, feature-based and fine-tuning methods, are usually considered separately. What is more, different sentiment analysis tasks cannot be handled by the single task-specific contextual representation. In light of these pros and cons, we strive to propose a broad multitask transformer network (BMT-Net) to address these problems. BMT-Net takes advantage of both feature-based and fine-tuning methods. It was designed to explore the high-level information of robust and contextual representation. Primarily, our proposed structure can make the learned representations universal across tasks via multitask transformers. In addition, BMT-Net can roundly learn the robust contextual representation utilized by the broad learning system due to its powerful capacity to search for suitable features in deep and broad ways. The experiments were conducted on two popular datasets of binary Stanford Sentiment Treebank (SST-2) and SemEval Sentiment Analysis in Twitter (Twitter). Compared with other state-of-the-art methods, the improved representation with both deep and broad ways is shown to achieve a better F1 -score of 0.778 in Twitter and accuracy of 94.0% in the SST-2 dataset, respectively. These experimental results demonstrate the abilities of recognition in sentiment analysis and highlight the significance of previously overlooked design decisions about searching contextual features in deep and broad spaces.
Knowledge Discovery in Cryptocurrency Transactions: A Survey

Cryptocurrencies gain trust in users by publicly disclosing the full creation and transaction history.In return, the transaction history faithfully records the whole spectrum of cryptocurrency user behaviors.This article analyzes and summarizes the existing research on knowledge discovery in the cryptocurrency transactions using data mining techniques.Specifically, we classify the existing research into three aspects, i.e., transaction tracings and blockchain address linking, the analyses of collective user behaviors, and the study of individual user behaviors.For each aspect, we present the problems, summarize the methodologies, and discuss major findings in the literature.Furthermore, an enumeration of transaction data parsing and visualization tools and services is also provided.Finally, we outline several gaps and trends for future investigation in this research area.
IWOSSA: An improved whale optimization salp swarm algorithm for solving optimization problems

In this paper, a hybrid improved whale optimization salp swarm algorithm (IWOSSA) is proposed. The main idea behind IWOSSA is to combine improved Whale Optimization Algorithm (IWOA) and Salp Swarm Algorithm (SSA). First, WOA algorithm is improved by applying exponential relationships instead of linear relationships. Then, the algorithm chooses between either IWOA or SSA depending on a specific condition. To validate the efficiency of the proposed algorithm, IWOSSA is applied to 23 different benchmark functions of different dimensions and results are compared with 8 optimization algorithms including WOA and SSA. As an application to an industrial process and to confirm the good performance of the suggested algorithm, they are applied to tune an adaptive PID controller. The PID controller is used in controlling a divided wall column. Different disturbances are applied. From the simulation results, the enhancement made by the IWOSSA is proved by means of the different performance indexes.
Digital competence of higher education professor according to DigCompEdu. Statistical research methods with ANOVA between fields of knowledge in different age ranges

The tasks of the university educators must be linked to the digital demands posed by the more sophisticated professions of the twenty-first century. Faced with this panorama, the objective of this study is to examine and compare the degree of digital competence of Higher Education educators from different fields of knowledge and different age ranges according to the DigCompEdu framework. A non-probabilistic ex post facto sampling was utilized with 2180 university professors from Andalusia (Spain). The main results of the study provide evidence of an intermediate level of digital competence, for men and women. More specifically, the male professors in Architecture and Judicial & Social Sciences, younger and older than 40, possessed a higher level, as compared to the other fields. For the female professors, the highest level was found once again in Judicial and Social Sciences, in this case for both age ranges, without a clear trend found for the rest of the fields. For each dimension of the DigCompEdu instrument, the level of competence follows the same trend with respect to the overall level. More research is recommended to validate these preliminary results, as well as the development of training lines of action that are specific and adapted to each field of knowledge.
UAV Detection and Identification Based on WiFi Signal and RF Fingerprint

The security threats caused by the popularity of Unmanned Aerial Vehicles (UAVs) have received much attention. In this paper, a UAV detection and identification system based on WiFi signal and radio frequency (RF) fingerprint is proposed. The system firstly conducts UAV detection and after the UAV is detected, fractal dimension (FD), axially integrated bispectra (AIB), and square integrated bispectra (SIB) are extracted as UAV RF fingerprints due to their applicability and reliability. Furthermore, we propose weighted AIB and SIB fingerprints to identify UAVs. Since the high dimensionality of AIB and SIB features, the principal component analysis (PCA) algorithm is applied to reduce the dimensionality of these two features. Then the neighborhood component analysis (NCA) algorithm is utilized to weight the dimensions of the two features, AIB and SIB. The extracted UAV fingerprints are stored as training data and test data, respectively. Finally, machine learning algorithms are utilized to identify UAVs. The identification results are as follows: In the indoor scenario, the average identification accuracy of three features (FD, AIB, SIB) are 100%, 97.23%, and 96.11%, respectively. In outdoor scenario, the identification accuracy of three features are 100%, 95.00%, and 93.50%, respectively.
An Efficient Blood-Cell Segmentation for the Detection of Hematological Disorders

The automatic segmentation of blood cells for detecting hematological disorders is a crucial job. It has a vital role in diagnosis, treatment planning, and output evaluation. The existing methods suffer from the issues like noise, improper seed-point detection, and oversegmentation problems, which are solved here using a Laplacian-of-Gaussian (LoG)-based modified highboosting operation, bounded opening followed by fast radial symmetry (BOFRS)-based seed-point detection, and hybrid ellipse fitting (EF), respectively. This article proposes a novel hybrid EF-based blood-cell segmentation approach, which may be used for detecting various hematological disorders. Our prime contributions are: 1) more accurate seed-point detection based on BO-FRS; 2) a novel least-squares (LS)-based geometric EF approach; and 3) an improved segmentation performance by employing a hybridized version of geometric and algebraic EF techniques retaining the benefits of both approaches. It is a computationally efficient approach since it hybridizes noniterative-geometric and algebraic methods. Moreover, we propose to estimate the minor and major axes based on the residue and residue offset factors. The residue offset parameter, proposed here, yields more accurate segmentation with proper EF. Our method is compared with the state-of-the-art methods. It outperforms the existing EF techniques in terms of dice similarity, Jaccard score, precision, and F1 score. It may be useful for other medical and cybernetics applications.
The Economic and Environmental Impact of Bitcoin

The controversies surrounding Bitcoin, one of the most frequently used and advertised cryptocurrency, are focused on identifying its qualities, the advantages and disadvantages of using it and, last but not least, its ability to survive over time and become a viable alternative to the traditional currency, taking into account the effects on the environment of the technology used to extract and trade it. Based on such considerations, this article aims to provide an overview of this cryptocurrency, from the perspective of conducting a systematic review of the literature dedicated to the economic and environmental impact of Bitcoin. Using peer-reviewed articles collected from academic databases, we aimed at synthesizing and critically evaluating the points of view in the scientific literature regarding the doctrinal source of the emergence of Bitcoin, the identity of this cryptocurrency from an economic point of view, following its implications on the economic and social environment. Subsequently, this research offers the opportunity of evaluating the level of knowledge considering the impact of Bitcoin mining process on the environment from the perspective of the energy consumption and CO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> emissions, in order to finally analyze Bitcoin regulation and identify possible solutions to reduce the negative impact on the environment and beyond. The findings suggest that, despite high energy consumption and adverse environmental impact, Bitcoin continues to be an instrument used in the economic environment for a variety of purposes. Moreover, the trend of regulating it in various countries shows that the use of Bitcoin is beginning to gain some legitimacy, despite criticism against this cryptocurrency.
Modulated Periodic Activations for Generalizable Local Functional Representations

Multi-Layer Perceptrons (MLPs) make powerful functional representations for sampling and reconstruction problems involving low-dimensional signals like images, shapes and light fields. Recent works have significantly improved their ability to represent high-frequency content by using periodic activations or positional encodings. This often came at the expense of generalization: modern methods are typically optimized for a single signal. We present a new representation that generalizes to multiple instances and achieves state-of-the-art fidelity. We use a dual-MLP architecture to encode the signals. A synthesis network creates a functional mapping from a low-dimensional input (e.g. pixel-position) to the output domain (e.g. RGB color). A modulation network maps a latent code corresponding to the target signal to parameters that modulate the periodic activations of the synthesis network. We also propose a local-functional representation which enables generalization. The signal’s domain is partitioned into a regular grid, with each tile represented by a latent code. At test time, the signal is encoded with high-fidelity by inferring (or directly optimizing) the latent code-book. Our approach produces generalizable functional representations of images, videos and shapes, and achieves higher reconstruction quality than prior works that are optimized for a single signal.
Comparing Methods for Large-Scale Agile Software Development: A Systematic Literature Review

Following the highly pervasive and effective use of agile methods at the team level, many software organisations now wish to replicate this success at the organisational level, adopting large-scale agile methods such as SAFe, Scrum-at-Scale, and others. However, this has proven significantly challenging. An analysis of the extant literature reveals a disparate set of studies across each individual method, with no cross-method comparison based on empirical evidence. This systematic literature review compares the main large-scale agile methods, namely SAFe, LeSS, Scrum-at-Scale, DAD, and the Spotify model. It is the first study to analyse and compare each of the method’s principles, practices, tools, and metrics in a standardised manner. For each method, it presents not just the original method specifications but also all extensions and modifications to each method proposed by subsequent empirical research. It includes in this comparison not just commercial large-scale methods but also those that have been custom-built in organisations such as Nokia, Ericsson, and others. Based on the findings reported in this study, practitioners can make a more informed decision as to which commercial method or method component or, indeed, custom-built method is better suited to their needs. Our study reveals a number of theoretical and practical issues in the current literature, such as an emphasis on the practices of commercial frameworks at the expense of their underlying principles, or indeed any of the custom method. A set of challenges and success factors associated with the use of large-scale agile methods are identified. The study also identifies a number of research gaps to be addressed across methods.
Hands-Free Human–Robot Interaction Using Multimodal Gestures and Deep Learning in Wearable Mixed Reality

This study proposes a novel hands-free interaction method using multimodal gestures such as eye gazing and head gestures and deep learning for human-robot interaction (HRI) in mixed reality (MR) environments. Since human operators hold some objects for conducting tasks, there are many constrained situations where they cannot use their hands for HRI interactions. To provide more effective and intuitive task assistance, the proposed hands-free method supports coarse-to-fine interactions. Eye gazing-based interaction is used for coarse interactions such as searching and previewing of target objects, and head gesture interactions are used for fine interactions such as selection and 3D manipulation. In addition, deep learning-based object detection is applied to estimate the initial positioning of physical objects to be manipulated by the robot. The result of object detection is then combined with 3D spatial mapping in the MR environment for supporting accurate initial object positioning. Furthermore, virtual object-based indirect manipulation is proposed to support more intuitive and efficient control of the robot, compared with traditional direct manipulation (e.g., joint-based and end effector-based manipulations). In particular, a digital twin, the synchronized virtual robot of the real robot, is used to provide a preview and simulation of the real robot to manipulate it more effectively and accurately. Two case studies were conducted to confirm the originality and advantages of the proposed hands-free HRI: (1) performance evaluation of initial object positioning and (2) comparative analysis with traditional direct robot manipulations. The deep learning-based initial positioning reduces much effort for robot manipulation using eye gazing and head gestures. The object-based indirect manipulation also supports more effective HRI than previous direct interaction methods.
Fingerprinting the Fingerprinters: Learning to Detect Browser Fingerprinting Behaviors

Browser fingerprinting is an invasive and opaque stateless tracking technique. Browser vendors, academics, and standards bodies have long struggled to provide meaningful protections against browser fingerprinting that are both accurate and do not degrade user experience. We propose FP-Inspector, a machine learning based syntactic-semantic approach to accurately detect browser fingerprinting. We show that FP-Inspector performs well, allowing us to detect 26% more fingerprinting scripts than the state-of-the-art. We show that an API-level fingerprinting countermeasure, built upon FP-Inspector, helps reduce website breakage by a factor of 2. We use FP-Inspector to perform a measurement study of browser fingerprinting on top-100K websites. We find that browser fingerprinting is now present on more than 10% of the top-100K websites and over a quarter of the top-10K websites. We also discover previously unreported uses of JavaScript APIs by fingerprinting scripts suggesting that they are looking to exploit APIs in new and unexpected ways.
Pyramid: A Layered Sharding Blockchain System

Sharding can significantly improve the blockchain scalability, by dividing nodes into small groups called shards that can handle transactions in parallel. However, all existing sharding systems adopt complete sharding, i.e., shards are isolated. It raises additional overhead to guarantee the atomicity and consistency of cross-shard transactions and seriously degrades the sharding performance. In this paper, we present Pyramid, the first layered sharding blockchain system, in which some shards can store the full records of multiple shards thus the cross-shard transactions can be processed and validated in these shards internally. When committing cross-shard transactions, to achieve consistency among the related shards, a layered sharding consensus based on the collaboration among several shards is presented. Compared with complete sharding in which each cross-shard transaction is split into multiple sub-transactions and cost multiple consensus rounds to commit, the layered sharding consensus can commit cross-shard transactions in one round. Furthermore, the security, scalability, and performance of layered sharding with different sharding structures are theoretically analyzed. Finally, we implement a prototype for Pyramid and its evaluation results illustrate that compared with the state-of-the-art complete sharding systems, Pyramid can improve the transaction throughput by 2.95 times in a system with 17 shards and 3500 nodes.
Digital Quantum Simulation of Open Quantum Systems Using Quantum Imaginary–Time Evolution

Quantum simulation on emerging quantum hardware is a topic of intense interest. While many studies focus on computing ground state properties or simulating unitary dynamics of closed systems, open quantum systems are an interesting target of study owing to their ubiquity and rich physical behavior. However, their non-unitary dynamics are also not natural to simulate on digital quantum devices. Here, we report algorithms for the digital quantum simulation of the dynamics of open quantum systems governed by a Lindblad equation using adaptations of the quantum imaginary time evolution (QITE) algorithm. We demonstrate the algorithms on IBM Quantum's hardware with simulations of the spontaneous emission of a two level system and the dissipative transverse field Ising model. Our work advances efforts to simulate the dynamics of open quantum systems on quantum hardware.
Towards asynchronous federated learning for heterogeneous edge-powered internet of things

The advancement of the Internet of Things (IoT) brings new opportunities for collecting real-time data and deploying machine learning models. Nonetheless, an individual IoT device may not have adequate computing resources to train and deploy an entire learning model. At the same time, transmitting continuous real-time data to a central server with high computing resource incurs enormous communication costs and raises issues in data security and privacy. Federated learning, a distributed machine learning framework, is a promising solution to train machine learning models with resource-limited devices and edge servers. Yet, the majority of existing works assume an impractically synchronous parameter update manner with homogeneous IoT nodes under stable communication connections. In this paper, we develop an asynchronous federated learning scheme to improve training efficiency for heterogeneous IoT devices under unstable communication network. Particularly, we formulate an asynchronous federated learning model and develop a lightweight node selection algorithm to carry out learning tasks effectively. The proposed algorithm iteratively selects heterogeneous IoT nodes to participate in the global learning aggregation while considering their local computing resource and communication condition. Extensive experimental results demonstrate that our proposed asynchronous federated learning scheme outperforms the state-of-the-art schemes in various settings on independent and identically distributed (i.i.d.) and non-i.i.d. data distribution.
SeaDronesSee: A Maritime Benchmark for Detecting Humans in Open Water

Unmanned Aerial Vehicles (UAVs) are of crucial importance in search and rescue missions in maritime environments due to their flexible and fast operation capabilities. Modern computer vision algorithms are of great interest in aiding such missions. However, they are dependent on large amounts of real-case training data from UAVs, which is only available for traffic scenarios on land. Moreover, current object detection and tracking data sets only provide limited environmental information or none at all, neglecting a valuable source of information. Therefore, this paper introduces a large-scaled visual object detection and tracking benchmark (SeaDronesSee) aiming to bridge the gap from land-based vision systems to sea-based ones. We collect and annotate over 54,000 frames with 400,000 instances captured from various altitudes and viewing angles ranging from 5 to 260 meters and 0 to 90° degrees while providing the respective meta information for altitude, viewing angle and other meta data. We evaluate multiple state-of-the-art computer vision algorithms on this newly established benchmark serving as baselines. We provide an evaluation server where researchers can upload their prediction and compare their results on a central leaderboard <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .