Reviewerly/exHarmony huggingface dataset:
Decentralized Finance: Regulating Cryptocurrency Exchanges

Global financial markets are in the midst of a transformative movement. The creation of Bitcoin and Facebook’s proposed distribution of Diem mark a watershed moment in the evolution of the financial markets ecosystem. Purportedly, peer-to-peer distributed digital ledger technology eliminates legacy financial market intermediaries such as investment banks, depository banks, exchanges, clearinghouses, and broker-dealers.Yet careful examination reveals that cryptocurrency issuers and the firms that offer secondary market cryptocurrency trading services have not quite lived up to their promise. Notwithstanding cryptoenthusiasts’ calls for disintermediation, evidence reveals that platforms that facilitate cryptocurrency trading frequently employ the long-adopted intermediation practices of their traditional counterparts. In fact, when emerging technologies fail, cryptocoin and token trading platforms partner with and rely on traditional financial services firms. As a result, these platforms face many of the risk-management threats that have plagued conventional financial institutions as well as a host of underexplored threats. Automated or algorithmic trading strategies, accelerated high frequency trading tactics, and sophisticated Ocean’s Eleven-style cyberheists leave crypto investors vulnerable to predatory practices.Early responses to fraud, misconduct, and manipulation emphasize intervention when originators first distribute cryptocurrencies— the initial coin offerings. This Article rejects the dominant regulatory narrative that prioritizes oversight of primary market transactions. Instead, this Article proposes that regulators introduce formal registration obligations for cryptocurrency intermediaries —the exchange platforms that provide a marketplace for secondary market trading. This approach recognizes the dynamic nature of cryptocurrency secondary market actors seeking to achieve disintermediation yet balances the potential benefits of trading intermediaries with normative regulatory goals—protecting investors from fraud, theft, misconduct, and manipulation; enforcing accountability; preserving market integrity; and addressing enterprise and systemic risk management concerns
An Efficient Translation of Tulu to Kannada South Indian Scripts using Optical Character Recognition

Tulu script is not used to write the Tulu language, as it uses the Kannada script for documentation. As Tulu is not an official language of Karnataka, most people are unaware of this language. The Tulu-speaking people are larger in number than speakers of Manipuri and Sanskrit, which have the Eighth Schedule status. To enhance the readability of Tulu documents, there is a need for machine translation of Tulu scripts into Kannada Script. The motivation behind this work is to create software that can proficiently perceive written by a handwritten Tulu character and produces a yield in Kannada character. Tulu Kannada characters include a combination of needs to focus, making them difficult to recognize when written by hand. Besides, interpretation of the south Dravidian language (TULU) is the least investigations in the research field. The south-west of Karnataka state and northern Kerala with some Maharashtra state are speaking around 5 million TULU speakers in India. The programmed acknowledgment of transcribed characters from filtered images assists with changing over characters in a image into the helpful editable and comprehensible structure. This framework is utilized to map TULU to classical Kannada perceives the Tulu characters and reacquaint the precious data store in automatic recognitions for future generations.
Pali Speech Synthesis using HMM

In this paper, we present a Pali (Thai) speech synthesis system using the parametric statistical approach. To develop the system, we recorded 40 Pali chants. Data were extracted and represented by the Mel frequency cepstral coefficients and fundamental frequency (F0), and labeled by force alignment. These parameters were modeled using the hidden Markov model (HMM). To generate synthesized speech, the input text was converted into context-dependent phonemes and generated speech parameters from the trained HMM model. The resulting parameters were used for synthesizing speech using a speech vocoder. In the study, we modeled two speech synthesized models: the first model represents tone in syllable levels (tone-syllable) and the second model represents tone in phoneme levels (tone-phoneme). To evaluate the naturalness of the proposed system, we asked 13 users to participate in listening tests comparing the two synthesized speech models (tone-syllable and tone-phoneme models) and original speech. The results, expressing naturalness in mean opinion score (MOS), were 4.21, 3.25, and 3.32 (from 5) for the original, tone-syllable, and tone-phoneme synthesized speeches, respectively. We also conducted an objective test in which we calculated the cepstral distance between the cepstral coefficients of the original speeches and synthesized speeches. The average distances were 3.67 and 3.60 for the tone-syllable and the tone-phoneme models, respectively.
Testing the fit of relational models

Relational models generalize log-linear models to arbitrary discrete sample spaces by specifying effects associated with any subsets of their cells. A relational model may include an overall effect, pertaining to every cell after a reparameterization, and in this case, the properties of the maximum likelihood estimates (MLEs) are analogous to those computed under traditional log-linear models, and the goodness-of-fit tests are also the same. If an overall effect is not present in any reparameterization, the properties of the MLEs are considerably different, and the Poisson and multinomial MLEs are not equivalent. In the Poisson case, if the overall effect is not present, the observed total is not always preserved by the MLE, and the likelihood ratio statistic has a form which can be expressed using the Bregman divergence but does not reduce to its Kullback–Leibler version. The asymptotic chi-squared distribution of the Pearson and likelihood ratio statistics holds, but the generality considered here requires extended proofs.
Notice of Violation of IEEE Publication Principles: DP-SIGNSGD: When Efficiency Meets Privacy and Robustness

Notice of Violation of IEEE Publication Principles: <br><br>"DP-SIGNSGD: When Efficiency Meets Privacy and Robustness," <br>by Lingjuan Lyu, <br>in the Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2021, pp. 3070-3074 <br><br> After careful and considered review of the content and authorship of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE’s Publication Principles. <br><br>This paper contains significant portions of content from the paper cited below that were reused with insufficient credit to the following <br><br> “Stochastic-Sign SGD for Federated Learning with Theoretical Guarantees” <br>by Richeng Jin, Yufan Huang, Xiaofan He, Huaiyu Dai, Tianfu Wu <br> posted in ArXiv:2002.10940 <br><br> <br/> Federated learning (FL) has emerged as a promising collaboration paradigm by enabling a multitude of parties to construct a joint model without exposing their private training data. Three main challenges in FL are efficiency, privacy, and robustness. The recently proposed <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SIGN</inf> SGD with majority vote shows a promising direction to deal with efficiency and Byzantine robustness. However, there is no guarantee that <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SIGN</inf> SGD is privacy-preserving. In this paper, we bridge this gap by presenting an improved method called <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">DP-SIGN</inf> SGD, which can meet all the aforementioned properties. We further propose an error-feedback variant of <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">DP-SIGN</inf> SGD to improve accuracy. Experimental results on benchmark image datasets demonstrate the effectiveness of our proposed methods.
Quick detection of product quality based on clustering hypersphere model

With the rapid development of industry 4.0, intelligent methods for detecting product quality have attached considerable interests. In traditional quality control methods, the process data is required to meet the requirements of independent and identical distribution, which limits the industrial applications. In this paper, a fast product quality detection method based on a clustering hypersphere model is proposed. First, to make the detection boundary more flexible, a simplified classification method based on k-means clustering is designed. Next, the smallest closed hypersphere is built for each subset, and its radius and center are calculated by a sequence minimum optimization algorithm. Then, by exploring the data distribution in the feature space, the accurate original image of the mirror sphere center can be obtained. Finally, compared with the traditional methods, the proposed method can achieve lower false-detection rate and faster detection speed according to the simulation and experimental results.
Internet of Things and Internet of Drones in the Renewable Energy Infrastructure Towards Energy Optimization

A significant growth in implementing various renewable energy systems is observed throughout the world. Variable renewable electricity (VRE) sources such as solar PV and wind power have gained attractive investments in many countries, resulting in rapid growth in the installed capacity of these green sources of energy. The contribution of these variable renewable electricity energy sources had produced around 8.7% of global electricity as compared to 27.3% of all renewable energy sources at the world level. Therefore, there is an urgent need to improve the power system flexibility as the progress of the integration of variable renewable electricity energy sources. International efforts to meet renewable energy deployment and energy efficiency measures are resulting in a safe and reliable manner of renewable energy, thereby, resulting in minimized environmental, climate impacts, air quality improvement, good public health, and increased jobs and economic growth, increased grid reliability as well as lower energy costs on a household, corporate and national levels, The joint efforts by various institutions, corporations, governments, and non-governmental organizations (NGOs) has resulted in enhancing world level energy efficiency highlighting the potential to significantly minimization of greenhouse gas emissions on the earth. This paper highlights the latest developments in implementing Internet of Things (IoT) GPS and GIS tools and applications in energy sector in various parts of the world.
Gazetteer-Guided Keyphrase Generation from Research Papers

The task of keyphrase generation aims to generate the key phrases that capture the primary content of a document. An external domain-specific gazetteer can assist in generating keyphrases that are literally absent in the document (i.e., do not match any contiguous sub-sequence of source text) but relevant to the content of the document. In this paper, we present a technique to integrate knowledge from a gazetteer in order to improve keyphrase generation from research papers. We also present a copy mechanism that helps our model to utilize the gazetteer vocabulary to deal with the out-of-vocabulary words in keyphrases. Since constructing and maintaining relevant high-quality gazetteer by hand is very expensive, we also propose a method for automatic construction of a gazetteer given the input document, by leveraging similar documents in the training corpus. The thus constructed gazetteer helps focus on corpus-level information carried by other similar documents. Although this external information is crucial, it is never considered in previous studies. Experiments on real world datasets of research papers demonstrate that our proposed approach improves the performance of the state-of-the-art keyphrase generation models.
Teacher-Student Learning With Multi-Granularity Constraint Towards Compact Facial Feature Representation

In this paper, we propose a novel end-to-end feature compression scheme by leveraging the representation and learning capability of deep neural networks, towards intelligent front-end equipped analysis with promising accuracy and efficiency. In particular, the extracted features are compactly coded in an end-to-end manner by optimizing the rate- distortion cost to achieve feature-in-feature representation. The multi-granularity constraint is further imposed, serving as the optimization objective to make the feature compression more "healthier" from the perspective of ultimate utility. More specifically, the analysis accuracy is considered in the coarse granularity level constraint, ensuring the capability of facial analysis with the reconstructed feature. Furthermore, at the fine granularity level the feature fidelity is involved to preserve the original feature quality. Moreover, a latent code level teacher-student enhancement model is proposed to efficiently transfer the low bit-rate representation into a high bit- rate one. Such a strategy further allows us to adaptively shift the representation cost to decoding computations, leading to more flexible feature compression with enhanced decoding capability. We verify the effectiveness of the proposed model with the facial feature, and experimental results reveal better compression performance in terms of rate-accuracy compared with existing models.
SMARTEST: A Surrogate-Assisted Memetic Algorithm for Code Size Reduction

Compiling source code effectively to meet various criteria is a critical task in software engineering. Especially, code size reduction has attracted much attention from both industry and academia due to the requirement of resource utilization. Generally, developers rely on compiler optimization passes to realize code size reduction. However, it is impractical to select a desirable optimization sequence manually since a wide variety of optimization passes are integrated into a compiler. Evolutionary algorithms offer an impressive way to alleviate this problem. Nevertheless, previous approaches fail to balance the exploitation and exploration of the search space. Moreover, the expensive fitness evaluation requires actual compilation, which makes the evolution rather time-consuming. To tackle the challenges, we propose a novel approach SMARTEST, which characterizes the systematic exploitation of a huge volume of historical compilation information. Specifically, SMARTEST comprises two components: 1) a local search operator to enhance the solution quality; and 2) a data-driven surrogate model to avoid expensive fitness evaluation. We evaluate the effectiveness of SMARTEST over the cBench benchmark suite. Experimental results indicate that SMARTEST outperforms the standard level -Os by 2.17% on average, and achieves 1.2 times code size reduction compared with the genetic algorithm. Furthermore, experimental results over the benchmark suite evidently show that SMARTEST gets a better result and takes less actual fitness evaluations than its variants, which demonstrates the contribution of the local search and the surrogate model.
Towards a Comprehensive Expert System for Coronavirus Disease

Today, the world is facing an unprecedented health crisis due to the pandemic of the coronavirus.Although enormous efforts are being made by all continents to find the appropriate solution, the situation remains worrying.This fact is justified on one hand by the millions of cases with the number of deaths exceeding one and half million on a global scale, and on other hand by the successive waves of lockdown in almost all countries.This situation was exacerbated December 2020 with the arrival in England of a new strain of the more transmissible coronavirus.This great challenge facing all the world's health systems requires actors in the medical domain (health agencies, researchers, and pharmaceutical laboratories, the World Health Organization) to draw and gather knowledge to identify the path for effective treatment.The objective of this work is to provide an operational expert system aimed at helping decision-makers, health workers, and hospital nursing staff in their task of analysis, diagnosis and care of Covid-19 patients.The tool may be extended to other respiratory illnesses.
Design of a multifuntional nursing wheelchair

Abstract This paper designs a wheelchair with multi-function nursing ability. With Raspberry Pi as the main control board, it has functions such as assisting in getting in and out of bed, term inal monitoring, intelligent risk avoidance. It also has the ability of rehabilitation training for th e elderly with stroke or similar conditions, to prevent muscle atrophy and other problems cause d by long-term inability to exercise independently. Finally, the lightweight aluminum profile m aterial is used as the main body to complete the prototype production and function verificatio n. The prototype test shows that this new type of intelligent nursing wheelchair has a reasonabl e design, and the integration of multiple functions has brought great convenience to the caregiv ers and users. It has the prospect of large-scale promotion and marketing.
Rotational Adjoint Methods for Learning-Free 3D Human Pose Estimation from IMU Data

We present a new framework for learning-free 3D human pose estimation from Inertial Measurement Unit (IMU) data. The proposed method does not rely on a full motion sequence to calculate a pose for any particular time point and thus can operate in real-time. A cost function based only on joint rotations is used, removing the need for frequent transformations between rotations and 3D Cartesian coordinates. A Jacobian that preserves skeleton structure is derived using adjoint methods from Variational Data Assimilation. To facilitate further research in IMU-based Motion Capture, we provide a dataset that combines RGB and depth images from an Intel RealSense camera, marker-based motion capture from an Optitrack system and Xsens IMU data. We have evaluated our method on both our dataset and the Total Capture dataset, showing an average error across 24 joints of 0.45 and 0.48 radians respectively.
Appliancizer: Transforming Web Pages into Electronic Devices

Prototyping electronic devices that meet today’s consumer standards is a time-consuming task that requires multi-domain expertise. Consumers expect electronic devices to have visually appealing interfaces with both tactile and screen-based interfaces. Appliancizer, our interactive computational design tool, exploits the similarities between graphical and tangible interfaces, allowing web pages to be rapidly transformed into physical electronic devices. Using a novel technique we call essential interface mapping, our tool converts graphical user interface elements (e.g., an HTML button) into tangible interface components (e.g., a physical button) without changing the application source code. Appliancizer automatically generates the PCB and low-level code from web-based prototypes and HTML mock-ups. This makes the prototyping of mixed graphical-tangible interactions as easy as modifying a web page and allows designers to leverage the well-developed ecosystem of web technologies. We demonstrate how our technique simplifies and accelerates prototyping by developing two devices with Appliancizer.
Evaluating VR Sickness in VR Locomotion Techniques

VR Sickness is a form of motion sickness in Virtual Reality that affects 25-60% of the population. It is typically caused by exposure to mismatches between real and virtual motion, which happens in most VR Locomotion techniques. Hence, VR Locomotion and VR Sickness are intimately related, but this relationship is not reflected in the state of VR Sickness assessment. In this work we highlight the importance of understanding and quantifying VR Sickness in VR locomotion research. We discuss the most important factors and measures of VR to develop VR Sickness as a meaningful metric for VR Locomotion.
MAPGN: Masked Pointer-Generator Network for Sequence-to-Sequence Pre-Training

This paper presents a self-supervised learning method for pointer-generator networks to improve spoken-text normalization. Spoken-text normalization that converts spoken-style text into style normalized text is becoming an important technology for improving subsequent processing such as machine translation and summarization. The most successful spoken-text normalization method to date is sequence-to-sequence (seq2seq) mapping using pointer-generator networks that possess a copy mechanism from an input sequence. However, these models require a large amount of paired data of spoken-style text and style normalized text, and it is difficult to prepare such a volume of data. In order to construct spoken-text normalization model from the limited paired data, we focus on self-supervised learning which can utilize unpaired text data to improve seq2seq models. Unfortunately, conventional self-supervised learning methods do not assume that pointer-generator networks are utilized. Therefore, we propose a novel self-supervised learning method, MAsked Pointer-Generator Network (MAPGN). The proposed method can effectively pre-train the pointer-generator net-work by learning to fill masked tokens using the copy mechanism. Our experiments demonstrate that MAPGN is more effective for pointer-generator networks than the conventional self-supervised learning methods in two spoken-text normalization tasks.
Testing in ASP: Revisited Language and Programming Environment

Unit testing frameworks are nowadays considered a best practice, foregone in almost all modern software development processes, to achieve rapid development of correct specifications. The first unit testing specification language for Answer Set Programming (ASP) was proposed in 2011 as a feature of the ASPIDE development environment. Later, a more portable unit testing language was included in the LANA annotation language. In this paper we propose a revisited unit testing specification language that allows one to inline tests within ASP program and an ASP-based test execution mechanism. Moreover, we present a programming environment supporting test driven development (TDD) of ASP programs with our language.
Extract Sentiment from Customer Reviews: A Better Approach of TF-IDF and BOW-Based Text Classification Using N-Gram Technique

To enhance customer and product services, customer reviews play a must-have role for any business organization in the digital era. Business organizations can improve customer and product services to their greatest level by further analyzing the customer’s demands. For this purpose, text mining helps to convert unstructured text into structured form for further analysis. Sentiment analysis is a concept of opinion mining which draws significant advantages by classifying the customer reviews as positive or negative. The principal goal of this paper is to perform opinion classification. Bag of Words (BOW) and Term Frequency-Inverse Document Frequency (TF-IDF) are used as feature extraction techniques along with N-Gram and Random Forest (RF), Support Vector Machine (SVM), and Naive Bayes (NB) and are regarded as machine learning classifiers for reviews classification. Again, voting ensemble classifier has also been applied in order to investigate better performance and customer reviews are considered to accomplish the job. Though all the classifiers and feature extraction techniques achieve good performance, voting ensemble performs better among the classifiers while TF-IDF performs better for feature extraction techniques. From the notable result of the experiment, it can be pointed out that the implemented technique of customer reviews analysis might be an effective use to reshape business products.