Reviewerly/exHarmony huggingface dataset:
Opportunities and applications of smart contracts: A vision from the business, academic and scientific literature

Objective. In the present work, the bibliographical production referring to smart contracts is reviewed from the business, academic and scientific points of view. The aim is to analyze precisely how the relationship between smart contracts and blockchain occurs to establish a context in both developments.&#x0D; Design/Methodology/Approach. The Scopus database was consulted using the keyword: "smart contract". It was decided to use the SciMAT bibliometric tool for the bibliometric study with the approach based on a joint analysis of words and the h-index to visualize diagrams and thematic areas relatively simply for two periods: 2010-2018 and 2019-2021.&#x0D; Results/Discussion. It was necessary to know the concept of smart contracts for the future of contract law and the digital economy. Some institutions and people will be against considering them as authorized legal alternatives; However, due to the technological advance currently being presented, their development will only be a matter of time.&#x0D; Conclusions. Smart contracts are a technological innovation that, through blockchain technology, carry out transactions safely, quickly and with almost no risk of non-compliance.
Emojis as anchors to detect Arabic offensive language and hate speech

Abstract We introduce a generic, language-independent method to collect a large percentage of offensive and hate tweets regardless of their topics or genres. We harness the extralinguistic information embedded in the emojis to collect a large number of offensive tweets. We apply the proposed method on Arabic tweets and compare it with English tweets—analyzing key cultural differences. We observed a constant usage of these emojis to represent offensiveness throughout different timespans on Twitter. We manually annotate and publicly release the largest Arabic dataset for offensive , fine-grained hate speech , vulgar, and violence content. Furthermore, we benchmark the dataset for detecting offensiveness and hate speech using different transformer architectures and perform in-depth linguistic analysis. We evaluate our models on external datasets—a Twitter dataset collected using a completely different method, and a multi-platform dataset containing comments from Twitter, YouTube, and Facebook, for assessing generalization capability. Competitive results on these datasets suggest that the data collected using our method capture universal characteristics of offensive language. Our findings also highlight the common words used in offensive communications, common targets for hate speech, specific patterns in violence tweets, and pinpoint common classification errors that can be attributed to limitations of NLP models. We observe that even state-of-the-art transformer models may fail to take into account culture, background, and context or understand nuances present in real-world data such as sarcasm.
Looking for a Needle in a Haystack: A Comprehensive Study of Hallucinations in Neural Machine Translation

Although the problem of hallucinations in neural machine translation (NMT) has received some attention, research on this highly pathological phenomenon lacks solid ground. Previous work has been limited in several ways: it often resorts to artificial settings where the problem is amplified, it disregards some (common) types of hallucinations, and it does not validate adequacy of detection heuristics. In this paper, we set foundations for the study of NMT hallucinations. First, we work in a natural setting, i.e., in-domain data without artificial noise neither in training nor in inference. Next, we annotate a dataset of over 3.4k sentences indicating different kinds of critical errors and hallucinations. Then, we turn to detection methods and both revisit methods used previously and propose using glass-box uncertainty-based detectors. Overall, we show that for preventive settings, (i) previously used methods are largely inadequate, (ii) sequence log-probability works best and performs on par with reference-based methods. Finally, we propose DeHallucinator, a simple method for alleviating hallucinations at test time that significantly reduces the hallucinatory rate.
Using Nuances of Emotion to Identify Personality

Past work on personality detection has shown that frequency of lexical categories such as first person pronouns, past tense verbs, and sentiment words have significant correlationswith personality traits. In this paper, for the first time, we show that fine affect(emotion) categories such as that of excitement, guilt, yearning, and admiration aresignificant indicators of personality. Additionally, we perform experiments to show thatthe gains provided by the fine affect categories are not obtained by using coarse affectcategories alone or with specificity features alone. We employ these features in five SVMclassifiers for detecting five personality traits through essays. We find that the use offine emotion features leads to statistically significant improvement over a competitivebaseline, whereas the use of coarse affect and specificity features does not.
A Model of Learning Task-specific Knowledge for a new Task

In this paper I will present a detailed ACT-R model of how the task-specific knowledge for a new, complex task is learned. The model is capable of acquiring its knowledge through experience, using a declarative representation that is gradually compiled into a procedural representation. The model exhibits several characteristics that concur with Fitts’ and Anderson’s theories of skill learning, and can be used to show that individual differences in working-memory capacity initially have a large impact on performance, but that this impact diminished after sufficient experience, which is consistent with Ackermans’s theory of skill learning. Some preliminary experimental data support these findings.
Optimal arrangements of hyperplanes for SVM-based multiclass classification

In this paper, we present a novel approach to construct multiclass classifiers by means of arrangements of hyperplanes. We propose different mixed integer (linear and non linear) programming formulations for the problem using extensions of widely used measures for misclassifying observations where the \textit{kernel trick} can be adapted to be applicable. Some dimensionality reductions and variable fixing strategies are also developed for these models. An extensive battery of experiments has been run which reveal the powerfulness of our proposal as compared with other previously proposed methodologies.
Platform-Independent Dynamic Taint Analysis for JavaScript

Previous approaches to dynamic taint analysis for JavaScript are implemented directly in a browser or JavaScript engine, limiting their applicability to a single platform and requiring ongoing maintenance as platforms evolve, or they require nontrivial program transformations. We present an approach that relies on instrumentation to encode taint propagation as instructions for an abstract machine. Our approach has two key advantages: it is platform-independent and can be used with any existing JavaScript engine, and it can track taint on primitive values without requiring the introduction of wrapper objects. Furthermore, our technique enables multiple deployment scenarios by varying when and where the generated instructions are executed and it supports indirect taint sources, i.e., situations where taint enters an application via arguments passed to dynamically registered event-listener functions. We implemented the technique for the ECMAScript 5 language in a tool called Ichnaea, and evaluated it on 22 NPM modules containing several types of injection vulnerabilities, including 4 modules containing vulnerabilities that were not previously discovered and reported. On these modules, run-time overheads range from 3.17x to 38.42x, which is significantly better than a previous transformation-based technique. We also report on a case study that shows how Ichnaea can be used to detect privacy leaks in a Tizen web application for the Samsung Gear S2 smart watch.
Weight Standardization

Batch Normalization (BN) has become an out-of-box technique to improve deep network training. However, its effectiveness is limited for micro-batch training, i.e., each GPU typically has only 1-2 images for training, which is inevitable for many computer vision tasks, e.g., object detection and semantic segmentation, constrained by memory consumption. To address this issue, we propose Weight Standardization (WS) and Batch-Channel Normalization (BCN) to bring two success factors of BN into micro-batch training: 1) the smoothing effects on the loss landscape and 2) the ability to avoid harmful elimination singularities along the training trajectory. WS standardizes the weights in convolutional layers to smooth the loss landscape by reducing the Lipschitz constants of the loss and the gradients; BCN combines batch and channel normalizations and leverages estimated statistics of the activations in convolutional layers to keep networks away from elimination singularities. We validate WS and BCN on comprehensive computer vision tasks, including image classification, object detection, instance segmentation, video recognition and semantic segmentation. All experimental results consistently show that WS and BCN improve micro-batch training significantly. Moreover, using WS and BCN with micro-batch training is even able to match or outperform the performances of BN with large-batch training.
An efficient linkable group signature for payer tracing in anonymous cryptocurrencies

Cryptocurrencies, led by bitcoin launched in 2009, have obtained wide attention in recent years. Anonymous cryptocurrencies are highly essential since users need to preserve their privacy when conducting transactions. However, some users might misbehave with the cover of anonymity such as rampant trafficking or extortion. Thus, it is important to balance anonymity and accountability of anonymous cryptocurrencies. In this paper, we address this issue by proposing a linkable group signature for signing cryptocurrency transactions, which can be used to trace a payer’s identity in consortium blockchain based anonymous cryptocurrencies, in case a payer misbehaves in the system. The anonymity can be retained if a user behaves honestly. We prove that the proposed scheme achieves full-anonymity, full-traceability and linkability in the random oracle model. Implementation of the proposed linkable group signature scheme demonstrates its high efficiency and thus, can be adopted in anonymous cryptocurrencies in reality.
PCNN Mechanism and its Parameter Settings

The pulse-coupled neural network (PCNN) model is a third-generation artificial neural network without training that uses the synchronous pulse bursts of neurons to process digital images, but the lack of in-depth theoretical research limits its extensive application. By analyzing the working mechanism of the PCNN, we present an expression for the fire-extinguishing time of neurons that fire in the second iteration and an expression for the firing time of neurons that extinguish in the second iteration. In addition, we find a phenomenon of the PCNN and name it mathematically coupled fire extinguishing. Based on the above analysis, we propose a new working mode for the PCNN, where the refiring of fire-extinguishing neurons is only allowed when all firing neurons are extinguished. We also work out the constraint conditions of the parameter settings under this mode. Furthermore, we analyze the relationship between the network parameters and mathematically coupled fire extinguishing, the coupling of neighboring neurons, and the convergence rate of the PCNN, respectively. In addition, we demonstrate the essential regularity of extinguished neuron in the PCNN and then propose an optimal parameter setting to achieve the best comprehensive performance of the PCNN.
Data-Driven Requirements Engineering - An Update

Nowadays, users can easily submit feedback about software products in app stores, social media, or user groups. Moreover, software vendors are collecting massive amounts of implicit feedback in the form of usage data, error logs, and sensor data. These trends suggest a shift toward data-driven user-centered identification, prioritization, and management of software requirements. Developers should be able to adopt the requirements of masses of users when deciding what to develop and when to release. They could systematically use explicit and implicit user data in an aggregated form to support requirements decisions. In this talk we will present and discuss most recent achievements in this direction since the paper's original publication. We will also show to mine data sets mobile apps, give a few success/failure stories and a few practical advises.
Convolutional Models for the Detection of Firearms in Surveillance Videos

Closed-circuit television monitoring systems used for surveillance do not provide an immediate response in situations of danger such as armed robbery. In addition, they have multiple limitations when human operators perform the monitoring. For these reasons, a firearms detection system was developed using a new large database that was created from images extracted from surveillance videos of situations in which there are people with firearms. The system is made up of two parts—the “Front End” and “Back End”. The Front End is comprised of the YOLO object detection and localization system, and the Back End is made up of the firearms detection model that is developed in this work. These two systems are used to focus the detection system only in areas of the image where there are people, disregarding all other irrelevant areas. The performance of the firearm detection system was analyzed using multiple convolutional neural network (CNN) architectures, finding values up to 86% in metrics like recall and precision in a network configuration based on VGG Net using grayscale images.
Random Sum-Product Networks: A Simple and Effective Approach to Probabilistic Deep Learning

Sum-product networks (SPNs) are expressive probabilistic models with a rich set of exact and efficient inference routines. However, in order to guarantee exact inference, they require specific structural constraints, which complicate learning SPNs from data. Thereby, most SPN structure learners proposed so far are tedious to tune, do not scale easily, and are not easily integrated with deep learning frameworks. In this paper, we follow a simple “deep learning” approach, by generating unspecialized random structures, scalable to millions of parameters, and subsequently applying GPU-based optimization. Somewhat surprisingly, our models often perform on par with state-of-the-art SPN structure learners and deep neural networks on a diverse range of generative and discriminative scenarios. At the same time, our models yield well-calibrated uncertainties, and stand out among most deep generative and discriminative models in being robust to missing features and being able to detect anomalies.
Photo-realistic face age progression/regression using a single generative adversarial network

Face age progression/regression is enjoying renewed interest due to the remarkable improvements in image synthesis achieved by the deep generative models (e.g. the Generative Adversarial Networks (GANs)) and its tremendous impact on a wide-range of practical applications like finding back missing individuals with photos of childhood, entertainment, etc. Most existing approaches are focusing on face age progression and have proven to be successful and effective in learning the transformation between age groups with the aid of paired samples, i.e., face images of the same person at different ages. Although some signs of aging are synthesized by these approaches, they heavily rely on the availability of paired samples which are difficult and costly to collect. Inspired by the significant success achieved by using GANs in unsupervised image transduction, in this paper, we formulate this task as an unsupervised multi-domain image-to-image translation problem, and devise a novel generative framework using only a single generative adversarial network, dubbed FaceGAN which is capable of synthesizing photo-realistic face images with aging effects without paired samples and achieves face age progression and regression in a holistic framework. Experimental results show the superiority of our proposed method in terms of visual fidelity. We further empirically demonstrate the broad application capability of our approach on a facial attribute transfer and a facial expression synthesis tasks.
Real-Time Information-Theoretic Exploration with Gaussian Mixture Model Maps

This paper develops an exploration framework that leverages Gaussian mixture models (GMMs) for high-fidelity perceptual modeling and exploits the compactness of the distributions for information sharing in communications-constrained applications.State-of-the-art, high-resolution perceptual modeling techniques do not always consider the implications of transferring the model across limited bandwidth communications channels, which is critical for real-time information sharing.To bridge this gap in the state of the art, this paper presents a system that compactly represents sensor observations as GMMs and maintains a local occupancy grid map for a sampling-based motion planner that maximizes an information-theoretic objective function.The method is extensively evaluated in long duration simulations on an embedded PC and deployed to an aerial robot equipped with a 3D LiDAR.The result is significant memory efficiency as compared to state-of-the-art techniques.
Fuzzy Control of Uncertain Positive Markov Jump Fuzzy Systems With Input Constraint

This article considers the stochastic stability of uncertain positive Markov jump fuzzy systems (PMJFSs). The uncertainties consist of partially unknown membership functions and generally uncertain transition rates (GUTRs). In the GUTR model, each transition rate (TR) is completely unknown or only its estimate value is known. The coexistence of the partially unknown membership functions and GUTRs makes the control problem difficult to handle. Based on the T-S fuzzy scheme, a switching-type controller with input constraint which parallels as the structure of the uncertain fuzzy model is applied to ensure the positivity and stochastic stability of such systems. A linear decoupling strategy is proposed to tackle the coupling problems when designing the controller and further derives the desired controller parameters. Finally, two simulation examples are given to illustrate the effectiveness of the proposed method.
Fully Parameterized Quantile Function for Distributional Reinforcement Learning

Distributional Reinforcement Learning (RL) differs from traditional RL in that, rather than the expectation of total returns, it estimates distributions and has achieved state-of-the-art performance on Atari Games. The key challenge in practical distributional RL algorithms lies in how to parameterize estimated distributions so as to better approximate the true continuous distribution. Existing distributional RL algorithms parameterize either the probability side or the return value side of the distribution function, leaving the other side uniformly fixed as in C51, QR-DQN or randomly sampled as in IQN. In this paper, we propose fully parameterized quantile function that parameterizes both the quantile fraction axis (i.e., the x-axis) and the value axis (i.e., y-axis) for distributional RL. Our algorithm contains a fraction proposal network that generates a discrete set of quantile fractions and a quantile value network that gives corresponding quantile values. The two networks are jointly trained to find the best approximation of the true distribution. Experiments on 55 Atari Games show that our algorithm significantly outperforms existing distributional RL algorithms and creates a new record for the Atari Learning Environment for non-distributed agents.
Implementation of Blockchain technology for Energy Trading with Smart Meters

Proliferation of Distributed Energy Resources (DER) changed power system operation drastically. They introduced generation at distribution level, which was unprecedented. Also, bidirectional power flow created more optimization opportunities such as Demand Side Management (DSM) and Vehicle-to-Grid (V2G) support. These schemes allow for efficient use of grid infrastructure, storage devices and DERs. In order to plan the operation and calculate bills, it is imperative to keep a record of these transactions. When some sort of trading occurs between multiple parties, trust is a major concern. Traditionally, a third part trusted by all keeps the record of transactions. Using blockchain technology for energy trading eliminates the role of trusted third party. This paper discusses the feasibility and benefits of implementing blockchain for smartgrids. It also develops an Ethereum-based implementation of blockchain technology for energy trading. Results are shown for a case where energy transactions are undertaken between Distribution System Operator (DSO) and smart meters of individual houses.
A Two-Level Transfer Learning Algorithm for Evolutionary Multitasking

Different from conventional single-task optimization, the recently proposed multitasking optimization (MTO) simultaneously deals with multiple optimization tasks with different types of decision variables. MTO explores the underlying similarity and complementarity among the component tasks to improve the optimization process. The well-known multifactorial evolutionary algorithm (MFEA) has been successfully introduced to solve MTO problems based on transfer learning. However, it uses a simple and random inter-task transfer learning strategy, thereby resulting in slow convergence. To deal with this issue, this paper presents a two-level transfer learning (TLTL) algorithm, in which the upper-level implements inter-task transfer learning via chromosome crossover and elite individual learning, and the lower-level introduces intra-task transfer learning based on information transfer of decision variables for an across-dimension optimization. The proposed algorithm fully uses the correlation and similarity among the component tasks to improve the efficiency and effectiveness of MTO. Experimental studies demonstrate the proposed algorithm has outstanding ability of global search and fast convergence rate.
The evolution of the cryptocurrencies market: A complex networks approach

In this paper, the price synchronization in the cryptocurrencies market is investigated, using descriptive metrics and a novel methodology both from the area of Complex Networks. The study is conducted for three consecutive years, 2016–2018. In doing so, the cross-correlation-based networks of the cryptocurrencies under examination are created and subsequently their properties for each year are analyzed and compared. The novel Threshold Weighted-Minimum Dominating Set (TW-MDS) methodology is employed in order to identify the so-called dominant cryptocurrencies for each year. In this framework, a node is identified as dominant when it can adequately describe the collective behavior of its relevant neighborhood. The empirical results provide strong evidence of an increased price synchronization. One interesting finding is that, contrary to what may be expected, the cryptocurrencies that are identified as dominant are not always the most popular that are usually the ones with the highest capitalization (such as Bitcoin or Ethereum).