Reviewerly/exHarmony huggingface dataset:
Speak, If You Can: What Are You? An Alternative Approach to the Qualification of Tokens and Initial Coin Offerings

In Act 1, Scene III, of Shakespeare’s Macbeth, the encounter between the protagonist and the witches is marked by a question, that Macbeth addresses to the strange creatures he meets – together with Banquo – in “a heath near Forres”: “Speak, if you can: what are you?”, whereby the witches reply with the three prophecies, triggering the entire development of the Story. A similar issue of identification, and attractiveness-aversion, seems to occur, today, in the area of capital and financial markets regulation, as one stumbles across a wide range of strange, new assets, born out of the depths of the dashing – and mysterious, at least for neophytes - technological developments over these last years. Recently, also regulators and supervisors have been quite active in providing support with regard to the possible classification of crypto-assets. However, the approach taken until today seems to be a bit unsatisfactory, or – rather – doomed to be overcome by the development and gradual dissemination of crypto-trading platforms. Looking at the current ongoing of the debate, the qualification of crypto-assets is focused on the attempt to frame such assets within the standard categories provided for by the overall system of financial regulation, according to what can be defined as a “bottom-up” approach. In this context, areas that are being dealt with are, on the one hand, the regulation of payment services and, on the other, rules on public offerings, prospectus and – obviously – MiFID. We believe, however, that the approach should be combined and integrated with a “top-down” approach, by better framing, into the analysis, the status of the trading platforms where crypto-assets are eventually exchanged on the secondary market. In particular, if tokens are traded on platofrms that present the features of trading venues as defined under MiFID, they should be considered as financial instruments, as long as they have derivative components and may, therefore, qualify as commodity derivatives within the scope of MiFID. This applies, in particular, to utility tokens, that would otherwise fall outside the scope of capital markets regulations according the bottom-up approach. In addition, also the relative trading platforms would fall into the MiFID regime. However, as truly-decentralised trading systems develop in the future, exchange platforms might fall outside the scope of MiFID rules (since centralisation is a core element of the definition of an MTF or an OTF), and utility tokens would, again, be out of the scope of EU capital markets rules. Secondary markets must therefore be taken into account in the attempt to understand the nature of ICOs tokens, and their relevance for current EU capital markets legislation.
DeLighT: Deep and Light-weight Transformer

We introduce a deep and light-weight transformer, DeLighT, that delivers similar or better performance than standard transformer-based models with significantly fewer parameters. DeLighT more efficiently allocates parameters both (1) within each Transformer block using the DeLighT transformation, a deep and light-weight transformation and (2) across blocks using block-wise scaling, that allows for shallower and narrower DeLighT blocks near the input and wider and deeper DeLighT blocks near the output. Overall, DeLighT networks are 2.5 to 4 times deeper than standard transformer models and yet have fewer parameters and operations. Experiments on benchmark machine translation and language modeling tasks show that DeLighT matches or improves the performance of baseline Transformers with 2 to 3 times fewer parameters on average.
Analysis of Service-Oriented Architecture and Scrum Software Development Approach for IIoT

Flexibility and change adoption are key attributes for service-oriented architecture (SOA) and agile software development processes. Although the notion of agility is quite visible on both sides, still the integration of the two diverse concepts (architectural framework and development process) should be well thought of before employing them for a software development project. For this purpose, this study is designed to analyze the two diverse software architectural framework and development approaches, that is, SOA and Scrum process model, respectively, and their integrated environment in software project development setup perspective for Industrial Internet of Things (IIoT). This study also analyzes commonalities among Scrum process model and SOA architectural framework to identify compatibility between Scrum and SOA so that the Scrum process can be constructively used for SOA based projects. This study also examines the proper design and setup of Scrum process suitable for large-scale SOA based projects. For this purpose, an SOA based research and development project is selected as a case study using Scrum as the software development process. The project development and deployment perspective include eight core modules that constitute the overall project framework.
Optimization of the Bi-Axial Tracking System for a Photovoltaic Platform

The article deals with the optimization of the azimuthal tracking mechanism for a photovoltaic (PV) platform, which uses linear actuators as actuation elements for both movements (diurnal and elevation). In the case of diurnal movement, where the platform’s angular field of orientation is large, a mechanism with a relatively simple structure is used for amplifying the actuator’s stroke and avoiding the risk of the system locking itself (by limiting the values of the transmission angle). The optimization study targets the mechanical device, the control device, and the bi-axial tracking program (embodied by the laws of motion in time for the platform’s diurnal and elevation angles) with the purpose of obtaining a high input of solar radiation, with a minimal energy consumption to achieve tracking. The study is carried out by using a virtual prototyping platform, which includes Computer Aided Design (CAD), Multi-Body Systems (MBS), and Design for Control (DFC) computer applications. The mechanical and control devices of the solar tracker are integrated and tested in mechatronic concept. The simulations’ results, which were performed for a set of representative days throughout the year, prove the effectiveness of the proposed design.
FedMix: Approximation of Mixup under Mean Augmented Federated Learning

Federated learning (FL) allows edge devices to collectively learn a model without directly sharing data within each device, thus preserving privacy and eliminating the need to store data globally. While there are promising results under the assumption of independent and identically distributed (iid) local data, current state-of-the-art algorithms suffer a performance degradation as the heterogeneity of local data across clients increases. To resolve this issue, we propose a simple framework, \emph{Mean Augmented Federated Learning (MAFL)}, where clients send and receive \emph{averaged} local data, subject to the privacy requirements of target applications. Under our framework, we propose a new augmentation algorithm, named \emph{FedMix}, which is inspired by a phenomenal yet simple data augmentation method, Mixup, but does not require local raw data to be directly shared among devices. Our method shows greatly improved performance in the standard benchmark datasets of FL, under highly non-iid federated settings, compared to conventional algorithms.
Scaling Up Quasi-newton Algorithms: Communication Efficient Distributed SR1

In this paper, we present a scalable distributed implementation of the Sampled Limited-memory Symmetric Rank-1 (S-LSR1) algorithm. First, we show that a naive distributed implementation of S-LSR1 requires multiple rounds of expensive communications at every iteration and thus is inefficient. We then propose DS-LSR1, a communication-efficient variant that: (i) drastically reduces the amount of data communicated at every iteration, (ii) has favorable work-load balancing across nodes, and (iii) is matrix-free and inverse-free. The proposed method scales well in terms of both the dimension of the problem and the number of data points. Finally, we illustrate the empirical performance of DS-LSR1 on a standard neural network training task.
Resolving the Chatbot Disclosure Dilemma: Leveraging Selective Self-Presentation to Mitigate the Negative Effect of Chatbot Disclosure

Chatbots are increasingly able to pose as humans. However, this does not hold true if their identity is explicitly disclosed to users—a practice that will become a legal obligation for many service providers in the imminent future. Previous studies hint at a chatbot disclosure dilemma in that disclosing the non-human identity of chatbots comes at the cost of negative user responses. As these responses are commonly attributed to reduced trust in algorithms, this research examines how the detrimental impact of chatbot disclosure on trust can be buffered. Based on computer-mediated communication theory, the authors demonstrate that the chatbot disclosure dilemma can be resolved if disclosure is paired with selective presentation of the chatbot’s capabilities. Study results show that while merely disclosing (vs. not disclosing) chatbot identity does reduce trust, pairing chatbot disclosure with selectively presented information on the chatbot’s expertise or weaknesses is able to mitigate this negative effect.
Using latent space regression to analyze and leverage compositionality in GANs

In recent years, Generative Adversarial Networks have become ubiquitous in both research and public perception, but how GANs convert an unstructured latent code to coherent, high quality output is still an open question. In this work, we investigate regression into the latent space as a probe to understand the compositional properties of GANs. We find that combining the regressor and a pretrained generator provides a strong image prior, allowing us to create composite images from a collage of random image parts at inference time and leverage the generator to rectify the inconsistencies. To compare compositional properties across different generators, we measure the trade-offs between reconstruction of the unrealistic input and image quality of the regenerated samples. We find that the regression approach enables more localized editing of individual image parts compared to direct editing in the latent space, and we conduct experiments to quantify this independence effect. Our method is agnostic to how image parts are extracted, and does not require explicit labelled knowledge of concepts during training. Because this approach only uses a single feed-forward pass, it can operate in real-time and also perform a number of related applications, such as image inpainting or example-based image editing. We demonstrate its effectiveness on several GANs and datasets.
Linear Mode Connectivity in Multitask and Continual Learning

Continual (sequential) training and multitask (simultaneous) training are often attempting to solve the same overall objective: to find a solution that performs well on all considered tasks. The main difference is in the training regimes, where continual learning can only have access to one task at a time, which for neural networks typically leads to catastrophic forgetting. That is, the solution found for a subsequent task does not perform well on the previous ones anymore. However, the relationship between the different minima that the two training regimes arrive at is not well understood. What sets them apart? Is there a local structure that could explain the difference in performance achieved by the two different schemes? Motivated by recent work showing that different minima of the same task are typically connected by very simple curves of low error, we investigate whether multitask and continual solutions are similarly connected. We empirically find that indeed such connectivity can be reliably achieved and, more interestingly, it can be done by a linear path, conditioned on having the same initialization for both. We thoroughly analyze this observation and discuss its significance for the continual learning process. Furthermore, we exploit this finding to propose an effective algorithm that constrains the sequentially learned minima to behave as the multitask solution. We show that our method outperforms several state of the art continual learning algorithms on various vision benchmarks.
Deep Reinforcement Learning for Quantum Hamiltonian Engineering

Engineering desired Hamiltonians in quantum many-body systems is essential for applications such as quantum simulation, computation, and sensing. Many widely used quantum Hamiltonian engineering sequences are designed using human intuition based on perturbation theory, which may not describe the optimal solution and is unable to accommodate complex experimental imperfections. Here we numerically search for Hamiltonian engineering sequences using deep reinforcement learning (DRL) techniques and experimentally demonstrate that they outperform celebrated decoupling sequences on a solid-state nuclear magnetic resonance quantum simulator. As an example, we aim at decoupling strongly interacting spin-1/2 systems. We train DRL agents in the presence of different experimental imperfections and verify robustness of the output sequences both in simulations and experiments. Surprisingly, many of the learned sequences exhibit a common pattern that had not been discovered before, to our knowledge, but has a meaningful analytical description. We can thus restrict the searching space based on this control pattern, allowing us to search for longer sequences, ultimately leading to sequences that are robust against dominant imperfections in our experiments. Our results not only demonstrate a general method for quantum Hamiltonian engineering, but also highlight the importance of combining black-box artificial intelligence with an understanding of the physical system in order to realize experimentally feasible applications.
Complex Neural Networks for Estimating Epicentral Distance, Depth, and Magnitude of Seismic Waves

Taking advantage of the latest advances in deep learning for seismology, we address earthquake characterization from a data-driven perspective. Many of the usual procedures for extracting information from seismograms require processing a large volume of data using empirical and physics rule-based techniques. In this letter, we propose a novel approach for estimating epicentral distance, depth, and magnitude directly from individual raw three-component seismograms of 1-min length observed by single stations. Our convolutional neural network-based method is able to handle complex-valued representations of the seismic data in the time–frequency domain by using dedicated convolutional and activation functions. In this way, our method benefits both from extracting relevant information through time–frequency domain analysis and from designing a single architecture that deals with complex information. The proposed method achieves a mean absolute error of 4.51 km for epicentral distance, 6.15 km for depth, and 0.26 for magnitude estimation. The experiments were conducted over a publicly available and large database, STanford EArthquake data set (STEAD), and the comparisons with current state-of-the-art approaches show the effectiveness of the proposed approach. Source code and best model are available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/ristea/</uri> stead-earthquake-cnn.
Toward developing tangling noise removal and blind inpainting mechanism based on total variation in image processing

In the field of image processing, tangling noise and artefacts elimination of objects are two essential tasks. Tangling noise and lack of intensity in certain applications also occur at the same time. In this paper, a new variational model is proposed based on total variation and l0 the norm for simultaneously removing the tangling noise, estimating the location of missing pixels, and filling in them. To be specific, the total variation is used to regularize the estimated image and use the l0 norm to make the missing pixel to be sparse. Moreover, the data fidelity term is given by a new forward description about the degraded process and the gamma noise assumption. Finally, an algorithm based on the alternating direction multiplier method is exploited to solve the model. By conducting simulated and real experiments, the damaged images can be effectively restored by the proposed method. In qualitative and quantitative terms, this approach works better.
TLS-Covid19: A New Annotated Corpus for Timeline Summarization

The rise of social media and the explosion of digital news in the web sphere have created new challenges to extract knowledge and make sense of published information. Automated timeline generation appears in this context as a promising answer to help users dealing with this information overload problem. Formally, Timeline Summarization (TLS) can be defined as a subtask of Multi-Document Summarization (MDS) conceived to highlight the most important information during the development of a story over time by summarizing long-lasting events in a timely ordered fashion. As opposed to traditional MDS, TLS has a limited number of publicly available datasets. In this paper, we propose TLS-Covid19 dataset, a novel corpus for the Portuguese and English languages. Our aim is to provide a new, larger and multi-lingual TLS annotated dataset that could foster timeline summarization evaluation research and, at the same time, enable the study of news coverage about the COVID-19 pandemic. TLS-Covid19 consists of 178 curated topics related to the COVID-19 outbreak, with associated news articles covering almost the entire year of 2020 and their respective reference timelines as gold-standard. As a final outcome, we conduct an experimental study on the proposed dataset over two extreme baseline methods. All the resources are publicly available at https://github.com/LIAAD/tls-covid19 .
A Machine Learning Resource Allocation Solution to Improve Video Quality in Remote Education

The current global pandemic crisis has unquestionably disrupted the higher education sector, forcing educational institutions to rapidly embrace technology-enhanced learning. However, the COVID-19 containment measures that forced people to work or stay at home, have determined a significant increase in the Internet traffic that puts tremendous pressure on the underlying network infrastructure. This affects negatively content delivery and consequently user perceived quality, especially for video-based services. Focusing on this problem, this paper proposes a machine learning-based resource allocation solution that improves the quality of video services for increased number of viewers. The solution is deployed and tested in an educational context, demonstrating its benefit in terms of major quality of service parameters for various video content, in comparison with existing state of the art. Moreover, a discussion on how the technology is helping to mitigate the effects of massively increasing Internet traffic on the video quality in an educational context is also presented.
Neural Networks and Deep Learning

Neural networks, which provide the basis for deep learning, are a class of machine learning methods that are being applied to a diverse array of fields in business, health, technology, and research. In this chapter, we survey some of the key features of deep neural networks and aspects of their design and architecture. We give an overview of some of the different kinds of networks and their applications and highlight how these architectures are used for business applications such as recommender systems. We also provide a summary of some of the considerations needed for using neural network models and future directions in the field.
Mitigating Political Bias in Language Models through Reinforced Calibration

Current large-scale language models can be politically biased as a result of the data they are trained on, potentially causing serious problems when they are deployed in real-world settings. In this paper, we describe metrics for measuring political bias in GPT-2 generation and propose a reinforcement learning (RL) framework for mitigating political biases in generated text. By using rewards from word embeddings or a classifier, our RL framework guides debiased generation without having access to the training data or requiring the model to be retrained. In empirical experiments on three attributes sensitive to political bias (gender, location, and topic), our methods reduced bias according to both our metrics and human evaluation, while maintaining readability and semantic coherence.
Automatically differentiable quantum circuit for many-qubit state preparation

Constructing quantum circuits for efficient state preparation belongs to the central topics in the field of quantum information and computation. As the number of qubits grows fast, methods to derive large-scale quantum circuits are strongly desired. In this work, we propose the automatically differentiable quantum circuit (ADQC) approach to efficiently prepare arbitrary quantum many-qubit states. A key ingredient is to introduce the latent gates whose decompositions give the unitary gates that form the quantum circuit. The circuit is optimized by updating the latent gates using back propagation to minimize the distance between the evolved and target states. Taking the ground states of quantum lattice models and random matrix product states as examples, with the number of qubits where processing the full coefficients is unlikely, ADQC obtains high fidelities with small numbers of layers $N_L \sim O(1)$. Superior accuracy is reached compared with the existing state-preparation approach based on the matrix product disentangler. The parameter complexity of MPS can be significantly reduced by ADQC with the compression ratio $r \sim O(10^{-3})$. Our work sheds light on the "intelligent construction" of quantum circuits for many-qubit systems by combining with the machine learning methods.
Analysis of Optimized Machine Learning and Deep Learning Techniques for Spam Detection

Spam and non-spam email identification are one of the most challenging tasks for both email service providers and consumers. The spammers try to spread misleading facts through irritating messages by attracting user&#x0027;s attention. Several spam identification-models have previously been proposed and tested but the recorded accuracy has shown that further work in this direction is needed to achieve improved accuracy, low training time, and less error rate. In this research work, we have proposed a model that classifies the e-mail into spam and ham. DBSCAN and Isolation Forest are used to identify the extreme values outside of the specific range. Heatmap, Recursive Feature Elimination, and Chi-Square feature selection techniques are used to select the effective features. The proposed model is implemented in both machine learning and deep learning to establish a comparative analysis. Multinomial Na&#x00EF;ve Bayes (MNB), Random Forest (RF), K-Nearest Neighbor (KNN), Gradient Boosting (GB) are used to introduce ensemble method in machine learning implementation. Recurrent Neural Network (RNN), Gradient Descent (GD), Artificial Neural Network (ANN) for deep learning implementation. An ensemble method is constructed to combine multiple classifiers&#x0027; output. The ensemble methods allow producing better prediction accuracy compared to a single classifier. Our proposed model obtained an accuracy of 100&#x0025;, AUC&#x003D;100, MSE error &#x003D; 0 and RMSE error &#x003D; 0 for machine learning implementation and accuracy of 99&#x0025;, loss value&#x003D; 0.0165 for deep learning implementation based on an email spam base dataset collected from the UCI machine learning repository.