Reviewerly/exHarmony huggingface dataset:
Efficient Methods for Mapping Neural Machine Translator on FPGAs

Neural machine translation (NMT) is one of the most critical applications in natural language processing (NLP) with the main idea of converting text in one language to another using deep neural networks. In recent year, we have seen continuous development of NMT by integrating more emerging technologies, such as bidirectional gated recurrent units (GRU), attention mechanisms, and beam-search algorithms, for improved translation quality. However, with the increasing problem size, the real-life NMT models have become much more complicated and difficult to implement on hardware for acceleration opportunities. In this article, we aim to exploit the capability of FPGAs to deliver highly efficient implementations for real-life NMT applications. We map the inference of a large-scale NMT model with total computation of 172 GFLOP to a highly optimized high-level synthesis (HLS) IP and integrate the IP into Xilinx VCU118 FPGA platform. The model has widely used key features for NMTs, including the bidirectional GRU layer, attention mechanism, and beam search. We quantize the model to mixed-precision representation in which parameters and portions of calculations are in 16-bit half precision, and others remain as 32-bit floating-point. Compared to the float NMT implementation on FPGA, we achieve 13.1× speedup with an end-to-end performance of 22.0 GFLOPS without any accuracy degradation. Based on our knowledge, this is the first work that successfully implements a real-life end-to-end NMT model to an FPGA on board.
EmbLexChange at SemEval-2020 Task 1: Unsupervised Embedding-based Detection of Lexical Semantic Changes

This paper describes EmbLexChange, a system introduced by the "Life-Language" team for SemEval-2020 Task 1, on unsupervised detection of lexical-semantic changes.EmbLexChange is defined as the divergence between the embedding based profiles of word w (calculated with respect to a set of reference words) in the source and the target domains (source and target domains can be simply two time frames t 1 and t 2 ).The underlying assumption is that the lexical-semantic change of word w would affect its co-occurring words and subsequently alters the neighborhoods in the embedding spaces.We show that using a resampling framework for the selection of reference words (with conserved senses), we can more reliably detect lexical-semantic changes in English, German, Swedish, and Latin.EmbLexChange achieved second place in the binary detection of semantic changes in the SemEval-2020.
LSTM Networks for Overcoming the Challenges Associated with Photovoltaic Module Maintenance in Smart Cities

Predictive maintenance is a field of research that has emerged from the need to improve the systems in place. This research focuses on controlling the degradation of photovoltaic (PV) modules in outdoor solar panels, which are exposed to a variety of climatic loads. Improved reliability, operation, and performance can be achieved through monitoring. In this study, a system capable of predicting the output power of a solar module was implemented. It monitors different parameters and uses automatic learning techniques for prediction. Its use improved reliability, operation, and performance. On the other hand, automatic learning algorithms were evaluated with different metrics in order to optimize and find the best configuration that provides an optimal solution to the problem. With the aim of increasing the share of renewable energy penetration, an architectural proposal based on Edge Computing was included to implement the proposed model into a system. The proposed model is designated for outdoor predictions and offers many advantages, such as monitoring of individual panels, optimization of system response, and speed of communication with the Cloud. The final objective of the work was to contribute to the smart Energy system concept, providing solutions for planning the entire energy system together with the identification of suitable energy infrastructure designs and operational strategies.
Uralic Language Identification (ULI) 2020 shared task dataset and the Wanca 2017 corpora

This article introduces the Wanca 2017 web corpora from which the sentences written in minor Uralic languages were collected for the test set of the Uralic Language Identification (ULI) 2020 shared task. We describe the ULI shared task and how the test set was constructed using the Wanca 2017 corpora and texts in different languages from the Leipzig corpora collection. We also provide the results of a baseline language identification experiment conducted using the ULI 2020 dataset.
Covid-19 pandemic and The Nigerian primary healthcare system: The leadership question

Since the outbreak of the Covid-19 pandemic, the pathetic nature of the Nigeria healthcare system has become more glaring. In First World countries, the ravaging nature of the pandemic, marked by a high death toll, elicits trepidation among Nigerian citizens. What is more, these fears are not necessarily as a result of the lethal nature of COVID-19 but rather, they are consequent of certain conditions amongst which include: an inept and unconcerned leadership, accompanied by dilapidated health institutions characterized by poor working conditions and incentives. It is in lieu of these unhealthy conditions that the dreaded disease found its footing in the Nigerian environment. Against this background, this paper argues that the outbreak of Covid-19 pandemic in Nigeria, its local dispersion occasioned by paucity of medical personnel and supplies due to decades of neglect of health care system is worrisome. This study recommends an overhaul of the healthcare system with the aim of achieving a robust health care system for Nigerian citizens. Thus far, various scholars have focused on corrupt leadership practices of Nigerian leaders without a detailed and in-depth study on the nexus between the health sector and this failure. It is this obvious gap in scholarship that this present study sets out to fill. The paper adopts a qualitative approach which will be anchored on primary and secondary sources of historical methodology. Thus, its significance lies in its ability to bring to the fore the nexus between leadership failure in Nigeria and the neglect of primary healthcare system. It will also expose the vulnerability of the Nigerian rich and poor in the face of the global pandemic.
High-Capacity Expert Binary Networks

Network binarization is a promising hardware-aware direction for creating efficient deep models. Despite its memory and computational advantages, reducing the accuracy gap between such models and their real-valued counterparts remains an unsolved challenging research problem. To this end, we make the following 3 contributions: (a) To increase model capacity, we propose Expert Binary Convolution, which, for the first time, tailors conditional computing to binary networks by learning to select one data-specific expert binary filter at a time conditioned on input features. (b) To increase representation capacity, we propose to address the inherent information bottleneck in binary networks by introducing an efficient width expansion mechanism which keeps the binary operations within the same budget. (c) To improve network design, we propose a principled binary network search mechanism that unveils a set of network topologies of favorable properties. Overall, our method improves upon prior work, with no increase in computational cost by ∼6%, reaching a groundbreaking ∼71% on ImageNet classification.
NodeXP: NOde.js server-side JavaScript injection vulnerability DEtection and eXPloitation

Web applications are widely used, and new ways for easier and cost-effective methods to develop them are constantly introduced. A common omission among the new development and implementation techniques when designing them is security; Node.js is no exception, as Server-Side JavaScript Injection (SSJI) attacks are possible due to the use of vulnerable functions and neglecting to sanitize data input provided by untrusted sources. This specific kind of injection attack stands out because it has the potential to compromise servers, where the JavaScript code is executed. In this work, we fill a significant gap in the literature by introducing NodeXP, which, to the best of our knowledge, is the first methodology (presented as a software tool) that detects and automatically exploits SSJI vulnerabilities. Beyond the capabilities of the current state-of-the-art tools, NodeXP uses obfuscation methods, making it more stealth and adaptive to the current needs of red teaming. To this end, we provide a thorough analysis of SSJI attacks and the foundation upon which they rely on, along with concrete examples to facilitate the reader to comprehend the underlying concepts. Finally, we evaluate NodeXP, compare it to its peers, and discuss its efficacy.
Epistemic and deontic authority in the<i>argumentum ad verecundiam</i>

Abstract The aim of this paper is to elaborate tools that would allow us to analyse arguments from authority and guard against fallacious uses of them. To accomplish this aim, we extend the list of existing argumentation schemes representing arguments from authority. For this purpose, we formulate a new argumentation scheme for argument from deontic authority along with a matching set of critical questions used to evaluate it. We argue that clarifying the ambiguity between arguments from epistemic and deontic authority helps building a better explanation of the informal fallacy of appeal to authority ( argumentum ad verecundiam ).
Augmented Reality assisted by GeoGebra 3-D for geometry learning

Abstract Geometry becomes a difficult subject for students. The abstract object was one of the factors. Therefore, more concrete learning media are needed. One of them was augmented reality assisted by GeoGebra. The purpose of this study was to determine the ability to understand the geometrical concepts of high school students through augmented reality learning assisted by GeoGebra. Treatment was given to students through learning using GeoGebra-assisted augmented reality. This was a quasi-experimental study with a pre-test post-test control group design. This research instrument was a test of the ability to understand the concept of geometry. The test was used to measure students’ initial abilities, and this was referred to as a covariate. Also, to measure the ability to understand concepts after students follow augmented reality learning assisted by GeoGebra. Data were analyzed by covariate analysis (ANCOVA). The results of this study were F = Fo (A) = 9,150 with p-value = 0,000 &lt;0,005. That means, there are differences in the ability to understand the concept of geometry between those taught with augmented reality which is assisted by GeoGebra and conventional learning. Other results show that t = 6.723 with p-value = 0.000 &lt;0.05. This shows that the ability to understand the geometry concepts of students taught with augmented reality assisted by GeoGebra was higher than students who were taught conventionally after controlling for covariates. The conclusion was that the ability to understand the concepts of geometry through GeoGebra-assisted augmented reality learning was better than students taught with ordinary learning.
Automatic weaving method for three-dimensional composite preforms using vision system

Three-dimensional composite preform is the main structure of fiber-reinforced composites. During the weaving process of large-sized three-dimensional composite preform, relative rotation or translation between the fiber feeder and guided array occurs before feeding. Besides, the weaving needles can be at different heights after moving out from the guided array. These problems are mostly detected and adjusted manually. To make the weaving process more precise and efficient, we propose machine vision-based methods which could realize accurate estimation and adjustment of the relative position-pose between the fiber feeder and guided array, and make the needles pressing process automatic by recognizing the position of the weaving needles. The results show that the estimation error of relative position-pose is within 5%, and the rate of unrecognized weaving needles is 2%. Our proposed methods improve the automation level of weaving, and are conducive to the development of preform forming toward digital manufacturing.
Unitary block optimization for variational quantum algorithms

Variational quantum algorithms are a promising hybrid framework for solving chemistry and physics problems with broad applicability to optimization as well. They are particularly well suited for noisy intermediate-scale quantum computers. In this paper we describe the unitary block optimization scheme (UBOS) and apply it to two variational quantum algorithms: the variational quantum eigensolver (VQE) and variational time evolution. The goal of VQE is to optimize a classically intractable parameterized quantum wave function to target a physical state of a Hamiltonian or solve an optimization problem. UBOS is an alternative to other VQE optimization schemes with a number of advantages, including fast convergence, less sensitivity to barren plateaus, the ability to tunnel through some local minima, and no hyperparameters to tune. We additionally describe how UBOS applies to real and imaginary time-evolution (TUBOS).
Multiple Visual Feature Integration Based Automatic Aesthetics Evaluation of Robotic Dance Motions

Imitation of human behaviors is one of the effective ways to develop artificial intelligence. Human dancers, standing in front of a mirror, always achieve autonomous aesthetics evaluation on their own dance motions, which are observed from the mirror. Meanwhile, in the visual aesthetics cognition of human brains, space and shape are two important visual elements perceived from motions. Inspired by the above facts, this paper proposes a novel mechanism of automatic aesthetics evaluation of robotic dance motions based on multiple visual feature integration. In the mechanism, a video of robotic dance motion is firstly converted into several kinds of motion history images, and then a spatial feature (ripple space coding) and shape features (Zernike moment and curvature-based Fourier descriptors) are extracted from the optimized motion history images. Based on feature integration, a homogeneous ensemble classifier, which uses three different random forests, is deployed to build a machine aesthetics model, aiming to make the machine possess human aesthetic ability. The feasibility of the proposed mechanism has been verified by simulation experiments, and the experimental results show that our ensemble classifier can achieve a high correct ratio of aesthetics evaluation of 75%. The performance of our mechanism is superior to those of the existing approaches.
Optical fiber data glove for hand posture capture

Data gloves are widely used for hand posture monitoring in the field of virtual reality interactive technology. In fiber-based data gloves system, fiber Bragg gratings are usually used to monitor the bending of the finger joints, which is characterized by complex and high cost. In recent years, a new type of intensity-modulated optical fiber sensor with a sensitive zone has been developed, which can measure the bending deformation directly. In this paper, a low-cost data glove is developed based on this kind of sensor, which can monitor the posture of the hand. A sensor belt is proposed. The configuration scheme of the sensor and the packaging process of the sensor belt are studied. An untreated fiber which shares the same light source with fiber sensor is introduced as a base signal. Division operation between sensor signal and base signal is done to reduce the measurement error caused by instability of light source. A direction sensor is fixed on the back of the glove to monitor the posture of the palm. The experimental results show that the measurement errors of data glove are π/40 and π/120 respectively when measuring the finger joints and palm pitch, which indicates the effectiveness of proposed data glove.
Linking bank clients using graph neural networks powered by rich transactional data

Abstract Financial institutions obtain enormous amounts of data about client transactions and money transfers, which can be considered as a large graph dynamically changing in time. In this work, we focus on the task of predicting new interactions in the network of bank clients and treat it as a link prediction problem. We propose a new graph neural network model, which uses not only the topological structure of the network but rich time-series data available for the graph nodes and edges. We evaluate the developed method using the data provided by a large European bank for several years. The proposed model outperforms the existing approaches, including other neural network models, with a significant gap in ROC AUC score on link prediction problem and also allows to improve the quality of credit scoring.
The ensemble of density-sensitive SVDD classifier based on maximum soft margin for imbalanced datasets

Imbalanced problems have recently attracted much attention due to their prevalence in numerous domains of great importance to the data mining community. However, conventional bi-class classification approaches, e.g., Support vector machine (SVM), generally perform poorly on imbalanced datasets as they are originally designed to generalize from the training data, and pay little attention to the minority class. In the paper, we extend traditional support vector domain description (SVDD) and propose a novel density-sensitive SVDD classifier based on maximum soft margin (DSMSM-SVDD) for imbalanced datasets. In the proposed approach, the relative density-based penalty weights are incorporated into the optimization objective function to represent the importance of the data samples. Through optimizing the objective function with the relative density-based penalty weights, the training majority samples with high relative densities are more likely to lie inside the hypersphere, thus eliminating noise effects on traditional SVDD. In addition, to make full use of the minority class samples to refine the boundary in training, the maximum soft margin regularization term is also introduced in the proposed technique inspired by the idea of maximizing soft margin of traditional SVM. This method allows the optimal domain description boundary to more skew toward the minority class than traditional SVDD and thus improves the classification accuracy. Eventually, AdaBoost ensemble version of DSMSM-SVDD is developed so as to further improve the generalization performance and stability in dealing with imbalanced datasets. The extensive experimental results on various datasets demonstrate that the proposed approach significantly outperforms other existing algorithms when dealing with the imbalanced classification problems in terms of G-Mean, F-Measure and AUC performance measures.
Incentivizing High-Quality Data Sets in Construction Using Blockchain: A Feasibility Study in the Swiss Industry

Incentivizing High-Quality Data Sets in Construction Using Blockchain: A Feasibility Study in the Swiss Industry Jens J. Hunhevicz, Tobias Schraner and Daniel M. Hall Pages 1291-1298 (2020 Proceedings of the 37th ISARC, Kitakyushu, Japan, ISBN 978-952-94-3634-7, ISSN 2413-5844) Abstract: Data sets are often incomplete and low-quality at the end of a construction project. This creates rework or hinders opportunities to use data during future activities of the facility lifecycle (e.g. facility management, renovation, demolition). This research prototypes and evaluates a novel process to use blockchain and smart contracts in construction projects to incentivize high quality data sets. The proposed solution is defined in collaboration with construction professionals. The process traces and saves project data while incentivizing participants to create high-quality data sets through reward tokens. To validate the process, an Ethereum-based blockchain prototype is developed. A simple case study is conducted in collaboration with local industry professionals to simulate how the prototype can function in a typical design bid build process used in Switzerland. The early findings and possible subsequent research steps are presented. Overall, it was found that such a blockchain based incentive systems has the potential to not only incentivize high-quality data sets, but also change the way of tendering and related construction processes. Keywords: Blockchain; Incentives; Data Management; Construction Projects DOI: https://doi.org/10.22260/ISARC2020/0177 Download fulltext Download BibTex Download Endnote (RIS) TeX Import to Mendeley
BlockRoam: Blockchain-based Roaming Management System for Future Mobile Networks

Mobile service providers (MSPs) are particularly vulnerable to roaming frauds, especially ones that exploit the long delay in the data exchange process of the contemporary roaming management systems, causing multi-billion dollars loss each year.In this paper, we introduce BlockRoam, a novel blockchain-based roaming management system that provides an efficient data exchange platform among MSPs and mobile subscribers.Utilizing the Proof-of-Stake (PoS) consensus mechanism and smart contracts, BlockRoam can significantly shorten the information exchanging delay, thereby addressing the roaming fraud problems.Through intensive analysis, we show that the security and performance of such PoS-based blockchain network can be further enhanced by incentivizing more users (e.g., subscribers) to participate in the network.Moreover, users in such networks often join stake pools (e.g., formed by MSPs) to increase their profits.Therefore, we develop an economic model based on Stackelberg game to jointly maximize the profits of the network users and the stake pool, thereby encouraging user participation.We also propose an effective method to guarantee the uniqueness of this game's equilibrium.The performance evaluations show that the proposed economic model helps the MSPs to earn additional profits, attracts more investment to the blockchain network, and enhances the network's security and performance.
A Classification of Arab Ethnicity Based on Face Image Using Deep Learning Approach

Human face and facial features gain a lot of attention from researchers and are considered as one of the most popular topics recently. Features and information extracted from a person are known as soft biometric, they have been used to improve the recognition performance and enhance the search engine for face images, which can be further applied in various fields such as law enforcement, surveillance videos, advertisement, and social media profiling. By observing relevant studies in the field, we noted a lack of mention of the Arab world and an absence of Arab dataset as well. Therefore, our aim in this paper is to create an Arab dataset with proper labeling of Arab sub-ethnic groups, then classify these labels using deep learning approaches. Arab image dataset that was created consists of three labels: Gulf Cooperation Council countries (GCC), the Levant, and Egyptian. Two types of learning were used to solve the problem. The first type is supervised deep learning (classification); a Convolutional Neural Network (CNN) pre-trained model has been used as CNN models achieved state of art results in computer vision classification problems. The second type is unsupervised deep learning (deep clustering). The aim of using unsupervised learning is to explore the ability of such models in classifying ethnicities. To our knowledge, this is the first time deep clustering is used for ethnicity classification problems. For this, three methods were chosen. The best result of training a pre-trained CNN on the full Arab dataset then evaluating on a different dataset was 56.97%, and 52.12% when Arab dataset labels were balanced. The methods of deep clustering were applied on different datasets, showed an ACC from 32% to 59%, and NMI and ARI result from zero to 0.2714 and 0.2543 respectively.