Reviewerly/exHarmony huggingface dataset:
The Pupil Has Become the Master: Teacher-Student Model-Based Word Embedding Distillation with Ensemble Learning

Recent advances in deep learning have facilitated the demand of neural models for real applications. In practice, these applications often need to be deployed with limited resources while keeping high accuracy. This paper touches the core of neural models in NLP, word embeddings, and presents an embedding distillation framework that remarkably reduces the dimension of word embeddings without compromising accuracy. A new distillation ensemble approach is also proposed that trains a high-efficient student model using multiple teacher models. In our approach, the teacher models play roles only during training such that the student model operates on its own without getting supports from the teacher models during decoding, which makes it run as fast and light as any single model. All models are evaluated on seven document classification datasets and show significant advantage over the teacher models for most cases. Our analysis depicts insightful transformation of word embeddings from distillation and suggests a future direction to ensemble approaches using neural models.
Monitoring of a Dynamic System Based on Autoencoders

Monitoring industrial infrastructures are undergoing a critical transformation with industry 4.0. Monitoring solutions must follow the system behavior in real time and must adapt to its continuous change. We propose in this paper an autoencoder model-based approach for tracking abnormalities in industrial application. A set of sensors collects data from turbo-compressors and an original two-level machine learning LSTM autoencoder architecture defines a continuous nominal vibration model. Normalized thresholds (ISO 20816) between the model and the system generates a possible abnormal situation to diagnose. Experimental results, including hyper-parameter optimization on large real data and domain expert analysis, show that our proposed solution gives promising results.
Pose2Body: Pose-Guided Human Parts Segmentation

Reliable human parts segmentation on 2D images plays an important role in many human-centric computer vision tasks. While significant achievements have been made on human pose estimation, the performance on human parts segmentation remains low. In this paper, we present a novel technique that we call Pose2Body that robustly conducts human parts segmentation based on the pose estimation results. We partition an image into superpixels and set out to assign a segment label to each superpixel most consistent with the pose. We design special feature vectors for every superpixel-label assignment as well as superpixel-superpixel pairs and model optimal labeling as to solve for a conditional random field (CRF). Comprehensive experiments show that our technique achieves substantial improvements over the state-of-the-art solutions.
An attention-based neural framework for uncertainty identification on social media texts

Uncertainty identification is an important semantic processing task.It is crucial to the quality of information in terms of factuality in many applications, such as topic detection and question answering.Factuality has become a premier concern especially in social media, in which texts are written informally.However, existing approaches that rely on lexical cues suffer greatly from the casual or word-of-mouth peculiarity of social media, in which the cue phrases are often expressed in substandard form or even omitted from sentences.To tackle these problems, this paper proposes an Attention-based Neural Framework for Uncertainty identification on social media texts, named ANFU.ANFU incorporates attention-based Long Short-Term Memory (LSTM) networks to represent the semantics of words and Convolutional Neural Networks (CNNs) to capture the most important semantics.Experiments were conducted on four datasets, including 2 English benchmark datasets used in the CoNLL-2010 task of uncertainty identification and 2 Chinese datasets of Weibo and Chinese news texts.Experimental results showed that our proposed ANFU approach outperformed the-state-of-the-art on all the datasets in terms of F1 measure.More importantly, 41.37% and 13.10% improvements were achieved over the baselines on English and Chinese social media datasets, respectively, showing the particular effectiveness of ANFU on social media texts.
Assessment of the Solar Resource in Andean Regions by Comparison between Satellite Estimation and Ground Measurements: Study Case of Ecuador

To develop and implement solar technologies, it is necessary to know accurately the solar resource on the place. Consequently, several institutions working in renewable energy have made efforts to both measure solar radiation and develop models to estimate solar radiation. The main drawback with estimations is their lack of accuracy. Recently, the NREL (NSRDB) has update its meteorological database based in satellite estimations. Some validations have been reported, however, no studies about its validity for the Andean region have been done. In this work, measurements of global horizontal irradiation (GHI) from 53 stations placed along the Ecuadorian territory were compared with satellite estimation data from NSRDB. Statistical descriptive indicators of dispersion (RMSE, MBE) and goodness of fit (KS test) were used. The data were grouped in hourly, daily, weekly and monthly basis, as well as in clear and cloudy basis. Results show that monthly grouping may be used with confidence, since close to 95% of comparisons have a good fit. Also, results show that both, clear sky and cloudy models tend to overestimate solar radiation in such a region. Solar resource seems to be high in Ecuador since more than 75% of the territory has values over 3.8 kWh/m2day.
Dynamical Analysis of Severe Rain Events over Iraq

Abstract&#x0D; Rain is one of the most important meteorological elements for the various forms of life. Over time, because of the changes in the Earth's climate, patterns of precipitation have changed. Many areas have suffered from lack of water resources. Others have become completely dry and turned into arid deserts. For these reasons, increased interest in climate studies, especially those related to rainfall. In this study, the statistical indicators was showed for the mean annual rainfall is about 225.84 mm and rainfall fluctuates around this mean, and the trend of rainfall was decreasing for period (1983-2017). The mean of monthly rainfall indicate that the highest amount is less than40 mm/month. The histograms of monthly rain shows the highest counts of(40-50) mm, (30-40) mm, (20-30) mm, ( 10-20)mm, (0-10)mm intervals for same period. By using TRMM daily rainfall maps to study the dynamical analysis of severe rainfall cases was conducted in Iraq for four individual study cases. The highest values are ranged between (80- 160) mm. Eleven meteorological elements were selected to study their behavior in the process of severe rainfall in these study cases as the (1000-500) mb thickness, mean sea level pressure, the 850 hPa (relative humidity, temperature and streamlines), the 500 hPa (vorticity and geopotential height), the 200 hPa (streamlines and isotaches), the Convective Available Potential Energy (CAPE) and the Total Cloud Water Vapor (TCWV) founding some of the results that were illustrated in this paper.
How do Practitioners Manage Decision Knowledge during Continuous Software Engineering? (S)

Continuous software engineering (CSE) is an agile process that supports lightweight, flexible, and rapid software development.Decision-making is crucial for CSE, and developers need to know the decisions made and the related rationale to evolve the software.This knowledge is called decision knowledge.The management of decision knowledge in CSE environments remains unexplored.The agile manifesto suggests to value working software over comprehensive documentation as well as individuals and interactions over processes and tools.What does this mean for the documentation, exploitation, and sharing of decision knowledge?We report on results from an interview study with 24 practitioners from 17 companies on how decision knowledge is managed during CSE.The practitioners mainly capture decision knowledge in an informal way, for example, in natural language discussions.Wiki and issue tracking systems represent the preferred medium to preserve decision knowledge.Mentioned benefits are an improved decision-making process, accountability, knowledge sharing, and reuse.However, the exploitation of the captured decision knowledge remains partly unclear.
Tensor Decomposition and Non-linear Manifold Modeling for 3D Head Pose Estimation

Head pose estimation is a challenging computer vision problem with important applications in different scenarios such as human–computer interaction or face recognition. In this paper, we present a 3D head pose estimation algorithm based on non-linear manifold learning. A key feature of the proposed approach is that it allows modeling the underlying 3D manifold that results from the combination of rotation angles. To do so, we use tensor decomposition to generate separate subspaces for each variation factor and show that each of them has a clear structure that can be modeled with cosine functions from a unique shared parameter per angle. Such representation provides a deep understanding of data behavior. We show that the proposed framework can be applied to a wide variety of input features and can be used for different purposes. Firstly, we test our system on a publicly available database, which consists of 2D images and we show that the cosine functions can be used to synthesize rotated versions from an object from which we see only a 2D image at a specific angle. Further, we perform 3D head pose estimation experiments using other two types of features: automatic landmarks and histogram-based 3D descriptors. We evaluate our approach on two publicly available databases, and demonstrate that angle estimations can be performed by optimizing the combination of these cosine functions to achieve state-of-the-art performance.
Extraction of the Level Lines of a Bilinear Image

We detail precisely an algorithm for the extraction of the level lines of a bilinear image, which is a continuous function interpolating bilinearly a discrete image.If we discard the levels of the discrete image, where topological difficulties arise, a level line is a concatenation of branches of hyperbolas.The algorithm tracks these branches and provides a sampling of the level lines in the form of closed polygons.If the level line contains a saddle point, the hyperbola degenerates to orthogonal segments, where an arbitrary but consistent choice is adopted for the tracking at the bifurcation.In any case, the extracted polygons are disjoint and enclose a bounded region.This allows to order the level lines in an enclosure tree hierarchy, which may be used for a variety of filters.Recovering this tree is a simple post-processing of the extraction algorithm.
Online Feature-Generation of Code Fragments for XCS to Guide Feature Construction

Code Fragments (CFs) are a new representation for classifier conditions in Learning Classifier Systems (LCSs). CFs are Genetic Programming-like trees that use functions as internal nodes, and data input or previously learned CFs as leaf nodes for feature construction. The XCSCFC system used CFs in rule conditions of XCS, an accuracy-based Michigan-style LCS, to transfer knowledge and thus solve large-scale problems. However, the trade-off for the richness and flexibility that allows CFs to compactly describe decision boundaries results in an undesired increase in the search space of solutions. Therefore, this paper proposes a novel model extension for Online Feature-generation (OF), which enables evolving features (CFs) through an online observed list of CFs. This extension enables a method of estimating the worth of CFs to identifying the patterns in the problem in order to construct applicable high-level features. The experiments show that the XCS with OF (XOF) can solve the benchmark problems in fewer generations compared with XCSCFC in non-transfer learning scenarios. The novel search of CFs successfully built high-level features, which show the rules produced by XOF to be more generalised than previously possible. Consequently, the final solutions contain fewer rules to solve problems as they encode more compact and comprehensive decision boundaries.
Quantum Communication: Multi‐Party Semi‐Quantum Key Distribution Protocol With Four‐Particle Cluster States (Ann. Phys. 8/2019)

The introduction of the semi-quantum concept makes secure communication easier to realize, but semi-quantum key distribution (SQKD) is only half as efficient as quantum key distribution (QKD), or even lower. In particular, the efficiency is more worrying when the quantum sender distributes the key to several classical receivers. In article number 1800520, Nan-Run Zhou and co-workers propose a semi-quantum key distribution protocol with four-particle cluster states that can achieve key distribution among one quantum party and two classical parties simultaneously, which can be expanded to more than two classical communication parties. Compared with the existing multi-party semi-quantum key distribution protocol, the proposed protocol owns more excellent time efficiency and qubit efficiency. The protocol's security under ideal circumstances and key rate under non-ideal conditions is analyzed.
A Complex Transformation of Monoalphabetic Cipher to Polyalphabetic Cipher: (Vigenère-Affine Cipher)

Fast progression of digital data exchange in electronic way, information security is becoming much more important in data storage and transmission on public communication networks.Cryptography has come up as a solution which plays a vital role in information security system against malicious attacks.In cryptography, there are various cipher techniques such as monoalphabetic cipher, polyalphabetic cipher, etc. to support data confidentiality as security mechanisms.They are methods of encrypting plain text message into cipher text protecting it from adversaries.The process of encryption of alphabets is the converting original message into non readable form.One of the most popular cipher techniques is the Vigenè re cipher.It is a polyalphabetic cipher technique which uses the Vigenè re table for the process of encryption of alphabets.As the Vigenè re cipher does not have the properties of diffusion and confusion, it is longer vulnerable to Kasiski and Friedman attacks based on letter frequency analysis.Thus, in this paper we propose a polyalphabetic cipher that is a new encryption and decryption technique with diffusion and confusion properties based on the concept of the complex cipher used by combining of Vigenè re cipher with Affine cipher for the increase of data security in data storage and transmission on public communication networks.Our proposed technique can also be considered as a complex transformation technique from Affine cipher known as a monoalphabetic cipher technique to a new polyalphabetic cipher technique that is called Vigenè re-Affine cipher.
Trends in the Regulation of Hate Speech and Fake News: A Threat to Free Speech?

The Information and Communication Technology (ICT) revolution heralding the emergence and dominance of social media has always been viewed as a turning point in free speech and communication. Indeed, the social media ordinarily represents the freedom of all people to speech and information. But then, there is also the side of the social media that has been often ignored; that it serves as platform for all and sundry to express themselves with little, if any regulation or legal consequences. This as a result has led to global explosion of hate speech and fake news. Hate speech normally lead to tension and holds in it, the potential for national or even international crisis of untold proportions. It also has the likelihood to scare people away from expressing themselves for fear of hate-filled responses and becoming a source of fake news. Using doctrinal as well as comparative methodologies, this paper appraises the trend between states of passing laws or proposing laws to regulate hate speech and fake news; it also appraises the contents of such laws from different countries with the aim of identifying how they may be used to suppress free speech under the guise of regulating hate speech and fake news. It argues that the alarming trend of hate speech and fake news presented an opportunity for leaders across the globe to curb free speech. The paper concludes that the advancement in ICT helped in a great deal to advance free speech; it may as well, because of the spread of hate speech and fake news, lead to a reverse of that success story.
Morphology-aware Word-Segmentation in Dialectal Arabic Adaptation of Neural Machine Translation

Parallel corpora available for building machine translation (MT) models for dialectal Arabic (DA) are rather limited. The scarcity of resources has prompted the use of Modern Standard Arabic (MSA) abundant resources to complement the limited dialectal resource. However, dialectal clitics often differ between MSA and DA. This paper compares morphology-aware DA word segmentation to other word segmentation approaches like Byte Pair Encoding (BPE) and Sub-word Regularization (SR). A set of experiments conducted on Egyptian Arabic (EA), Levantine Arabic (LA), and Gulf Arabic (GA) show that a sufficiently accurate morphology-aware segmentation used in conjunction with BPE outperforms the other word segmentation approaches.
Lexicon Guided Attentive Neural Network Model for Argument Mining

Identification of argumentative components is an important stage of argument mining.Lexicon information is reported as one of the most frequently used features in the argument mining research.In this paper, we propose a methodology to integrate lexicon information into a neural network model by attention mechanism.We conduct experiments on the UKP dataset, which is collected from heterogeneous sources and contains several text types, e.g., microblog, Wikipedia, and news.We explore lexicons from various application scenarios such as sentiment analysis and emotion detection.We also compare the experimental results of leveraging different lexicons.
IoT–smart contracts in data trusted exchange supplied chain based on block chain

Internet of Things (IoT) assumes a critical part in the advancement of different fields. The IoT data trusted exchange in recent year extend of uses influence an awesome request and increasing scale. In such a platform, exchange the data sets that they require and specialist organization can search. However, the enough trust as the third-party mediators for data exchange in centralized infrastructure cannot provide. This paper proposes a blockchain for IoT data trusted exchange based on decentralized solution. In particular, the fundamental standards of blockchain in verify manner, individuals can communicate with each other without a confided in mediator intermediary. Blockchain enable us to have a distributed, digital ledger. IoT (Internet of Things) sensor devices (zigbee) utilizing blockchain technology to assert public availability of temperature records, tracking location shipment, humidity, preventing damage, data immutability. The sensor devices looking the temperature, location, damage of each parcel during the shipment to completely guarantee directions. In blockchain all data is got moved from one position to another, where a smart contract assesses against the product attributes. Ethereum blockchain and smart contracts atlast it gets through knowledge a design to be copied and presents its decentralized distributed digital ledger, auditable, transparent, features visually.
An Evaluation of Language-Agnostic Inner-Attention-Based Representations in Machine Translation

In this paper, we explore a multilingual translation model with a cross-lingually shared layer that can be used as fixed-size sentence representation in different downstream tasks.We systematically study the impact of the size of the shared layer and the effect of including additional languages in the model.In contrast to related previous work, we demonstrate that the performance in translation does correlate with trainable downstream tasks.In particular, we show that larger intermediate layers not only improve translation quality, especially for long sentences, but also push the accuracy of trainable classification tasks.On the other hand, shorter representations lead to increased compression that is beneficial in non-trainable similarity tasks.We hypothesize that the training procedure on the downstream task enables the model to identify the encoded information that is useful for the specific task whereas nontrainable benchmarks can be confused by other types of information also encoded in the representation of a sentence.
Hypothesis Set Stability and Generalization

We present a study of generalization for data-dependent hypothesis sets. We give a general learning guarantee for data-dependent hypothesis sets based on a notion of transductive Rademacher complexity. Our main result is a generalization bound for data-dependent hypothesis sets expressed in terms of a notion of hypothesis set stability and a notion of Rademacher complexity for data-dependent hypothesis sets that we introduce. This bound admits as special cases both standard Rademacher complexity bounds and algorithm-dependent uniform stability bounds. We also illustrate the use of these learning bounds in the analysis of several scenarios.
Foundations of Comparison-Based Hierarchical Clustering

We address the classical problem of hierarchical clustering, but in a framework where one does not have access to a representation of the objects or their pairwise similarities. Instead, we assume that only a set of comparisons between objects is available, that is, statements of the form ``objects i and j are more similar than objects k and l.'' Such a scenario is commonly encountered in crowdsourcing applications. The focus of this work is to develop comparison-based hierarchical clustering algorithms that do not rely on the principles of ordinal embedding. We show that single and complete linkage are inherently comparison-based and we develop variants of average linkage. We provide statistical guarantees for the different methods under a planted hierarchical partition model. We also empirically demonstrate the performance of the proposed approaches on several datasets.
k-Root-n: An Efficient Algorithm for Avoiding Short Term Double-Spending Alongside Distributed Ledger Technologies such as Blockchain

Blockchains such as the bitcoin blockchain depend on reaching a global consensus on the distributed ledger; therefore, they suffer from well-known scalability problems. This paper proposes an algorithm that avoids double-spending in the short term with just O(√n) messages instead of O(n); each node receiving money off-chain performs the due diligence of consulting k√n random nodes to check if any of them is aware of double-spending. Two nodes receiving double-spent money will in this way consult at least one common node with very high probability, because of the ‘birthday paradox’, and any common honest node consulted will detect the fraud. Since the velocity of money in the real world has coins circulating through at most a few wallets per day, the size of the due diligence communication is small in the short term. This ‘k-root-n’ algorithm is suitable for an environment with synchronous or asynchronous (but with fairly low latency) communication and with Byzantine faults. The presented k-root-n algorithm should be practical to avoid double-spending with arbitrarily high probability, while feasibly coping with the throughput of all world commerce. It is resistant to Sybil attacks even beyond 50% of nodes. In the long term, the k-root-n algorithm is less efficient. Therefore, it should preferably be used as a complement, and not a replacement, to a global distributed ledger technology.