Reviewerly/exHarmony huggingface dataset:
An unsupervised lexical normalization for Roman Hindi and Urdu sentiment analysis

Text normalization is the task of transforming lexically variant words to their canonical forms. The importance of text normalization becomes apparent while developing natural language processing applications. This paper proposes a novel technique called Transliteration based Encoding for Roman Hindi/Urdu text Normalization (TERUN). TERUN utilizes the linguistic aspects of Roman Hindi/Urdu to transform lexically variant words to their canonical forms. It consists of three interlinked modules: transliteration based encoder, filter module and hash code ranker. The encoder generates all possible hash-codes for a single Roman Hindi/Urdu word. The next component filters the irrelevant codes, while the third module ranks the filtered hash-codes based on their relevance. The aim of this study is not only to normalize the text but to also examine its impact on text classification. Hence, baseline classification accuracies were computed on a dataset of 11,000 non-standardized Roman Hindi/Urdu sentiment analysis reviews using different machine learning algorithms. The dataset was then standardized using TERUN and other established phonetic algorithms, and the classification accuracies were recomputed. The cross-scheme comparison showed that TERUN outperformed all the phonetic algorithms and significantly reduced the error rate from the baseline. TERUN was then enhanced from a corpus specific to a corpus independent text normalization technique. To this end, a parallel corpus of 50,000 Urdu and Roman Hindi/Urdu words was manually tagged using a set of comprehensive annotation guidelines. Also, different phonetic algorithms and TERUN were intrinsically evaluated using a dataset of 20,000 lexically variant words. The results clearly showed the superiority of TERUN over well-known phonetic algorithms.
BAD: A Blockchain Anomaly Detection Solution

Anomaly detection tools play a role of paramount importance in protecting networks and systems from unforeseen attacks, usually by automatically recognizing and filtering out anomalous activities. Over the years, different approaches have been designed, all focused on lowering the false positive rate. However, no proposal has addressed attacks specifically targeting blockchain-based systems. In this paper, we present BAD: Blockchain Anomaly Detection. This is the first solution, to the best of our knowledge, that is tailored to detect anomalies in blockchain-based systems. BAD is a complete framework, relying on several components leveraging, at its core, blockchain meta-data in order to collect potentially malicious activities. BAD enjoys some unique features: (i) it is distributed (thus avoiding any central point of failure); (ii) it is tamper-proof (making it impossible for a malicious software to remove or to alter its own traces); (iii) it is trusted (any behavioral data is collected and verified by the majority of the network); and, (iv) it is private (avoiding any third party to collect/analyze/store sensitive information). Our proposal is described in detail and validated via both experimental results and analysis, that highlight the quality and viability of our Blockchain Anomaly Detection solution.
Chatbot-based Emotion Management for Distributed Teams

Fueled by the pervasion of tools like Slack or Microsoft Teams, the usage of text-based communication in distributed teams has grown massively in organizations. This brings distributed teams many advantages, however, a critical shortcoming in these setups is the decreased ability of perceiving, understanding and regulating emotions. This is problematic because better team members? abilities of emotion management positively impact team-level outcomes like team cohesion and team performance, while poor abilities diminish communication flow and well-being. Leveraging chatbot technology in distributed teams has been recognized as a promising approach to reintroduce and improve upon these abilities. In this article we present three chatbot designs for emotion management for distributed teams. In order to develop these designs, we conducted three participatory design workshops which resulted in 153 sketches. Subsequently, we evaluated the designs following an exploratory evaluation with 27 participants. Results show general stimulating effects on emotion awareness and communication efficiency. Further, they report emotion regulation and increased compromise facilitation through social and interactive design features, but also perceived threats like loss of control. With some design features adversely impacting emotion management, we highlight design implications and discuss chatbot design recommendations for enhancing emotion management in teams.
An attention Long Short-Term Memory based system for automatic classification of speech intelligibility

Speech intelligibility can be degraded due to multiple factors, such as noisy environments, technical difficulties or biological conditions. This work is focused on the development of an automatic non-intrusive system for predicting the speech intelligibility level in this latter case. The main contribution of our research on this topic is the use of Long Short-Term Memory (LSTM) networks with log-mel spectrograms as input features for this purpose. In addition, this LSTM-based system is further enhanced by the incorporation of a simple attention mechanism that is able to determine the more relevant frames to this task. The proposed models are evaluated with the UA-Speech database that contains dysarthric speech with different degrees of severity. Results show that the attention LSTM architecture outperforms both, a reference Support Vector Machine (SVM)-based system with hand-crafted features and a LSTM-based system with Mean-Pooling.
Towards Image-Based Automatic Meter Reading in Unconstrained Scenarios: A Robust and Efficient Approach

Existing approaches for image-based Automatic Meter Reading (AMR) have been evaluated on images captured in well-controlled scenarios. However, real-world meter reading presents unconstrained scenarios that are way more challenging due to dirt, various lighting conditions, scale variations, in-plane and out-of-plane rotations, among other factors. In this work, we present an end-to-end approach for AMR focusing on unconstrained scenarios. Our main contribution is the insertion of a new stage in the AMR pipeline, called corner detection and counter classification, which enables the counter region to be rectified -- as well as the rejection of illegible/faulty meters -- prior to the recognition stage. We also introduce a publicly available dataset, called Copel-AMR, that contains 12,500 meter images acquired in the field by the service company's employees themselves, including 2,500 images of faulty meters or cases where the reading is illegible due to occlusions. Experimental evaluation demonstrates that the proposed system, which has three networks operating in a cascaded mode, outperforms all baselines in terms of recognition rate while still being quite efficient. Moreover, as very few reading errors are tolerated in real-world applications, we show that our AMR system achieves impressive recognition rates (i.e., > 99%) when rejecting readings made with lower confidence values.
Model Compression for IoT Applications in Industry 4.0 via Multiscale Knowledge Transfer

Recently, Industry 4.0 has attracted much attention. It has close relations with the Internet of Things (IoT). On the other hand, convolutional neural networks (CNNs) have shown promising performance in many foundational services of the IoT applications. For the IoT applications with high-speed data streams and the requirement of time-sensitive actions, fast processing is demanded on small-scale platforms or even on IoT devices themselves. Therefore, it is inappropriate to employ cumbersome CNNs in IoT applications, making the study of model compression necessary. In knowledge transfer, it is common to employ a deep, well-trained network, called teacher, to guide a shallow, untrained network, called student, to have better performance. Previous works have made many attempts to transfer single-scale knowledge from teacher to student, leading to degradation of generalization ability. In this article, we introduce multiscale representations to knowledge transfer, which facilitates the generalization ability of student. We divide student and teacher into several stages. Student learns from multiscale knowledge provided by teacher at the end of each stage. Extensive experiments demonstrate the effectiveness of our proposed method both on image classification and on single image super-resolution. The huge performance gap between student and teacher is significantly narrowed down by our proposed method, making student suitable for IoT applications.
Efficient random graph matching via degree profiles

Random graph matching refers to recovering the underlying vertex correspondence between two random graphs with correlated edges; a prominent example is when the two random graphs are given by Erdős-Rényi graphs $$G(n,\frac{d}{n})$$ . This can be viewed as an average-case and noisy version of the graph isomorphism problem. Under this model, the maximum likelihood estimator is equivalent to solving the intractable quadratic assignment problem. This work develops an $${\widetilde{O}}(n d^2+n^2)$$ -time algorithm which perfectly recovers the true vertex correspondence with high probability, provided that the average degree is at least $$d = \varOmega (\log ^2 n)$$ and the two graphs differ by at most $$\delta = O( \log ^{-2}(n) )$$ fraction of edges. For dense graphs and sparse graphs, this can be improved to $$\delta = O( \log ^{-2/3}(n) )$$ and $$\delta = O( \log ^{-2}(d) )$$ respectively, both in polynomial time. The methodology is based on appropriately chosen distance statistics of the degree profiles (empirical distribution of the degrees of neighbors). Before this work, the best known result achieves $$\delta =O(1)$$ and $$n^{o(1)} \le d \le n^c$$ for some constant c with an $$n^{O(\log n)}$$ -time algorithm and $$\delta ={{\widetilde{O}}}((d/n)^4)$$ and $$d = {\widetilde{\varOmega }}(n^{4/5})$$ with a polynomial-time algorithm.
Point and interval forecasting for metal prices based on variational mode decomposition and an optimized outlier-robust extreme learning machine

Metal prices forecasting has practical implications for a variety of participants, including producers, consumers, governments and investors. However, metal prices forecasting is a complex and challenging issue widely discussed among researchers, due to the complexity and significant fluctuations observed in metal market. Most of present metal price forecasting models only emphasized deterministic prediction, usually ignored the uncertainty analysis and prediction, and eventually provided limited reference information. Under this background, in this research a novel hybrid system, consisting of distribution functions estimation, point and interval prediction, was proposed. More specifically, five distribution functions based on optimization algorithms were estimated to mine and analyze the traits of metal prices. With respect to the point prediction, an innovative hybrid forecasting models using variational mode decomposition and an optimized outlier-robust extreme learning machine by an optimization algorithm was established for metal prices prediction. Finally, based on the results of the distribution functions and point forecasting, interval forecasting was designed to provide predictive range, confidence level and the other uncertain information. Three metal prices series were taken as illustrated example to test the effective of the presented system and numerical results showed that the developed hybrid system can not only obtain higher prediction accuracy than that of the comparison models and also offer more valuable suggestions for enterprise administrators and investors in financial market.
A clustering based ensemble of weighted kernelized extreme learning machine for class imbalance learning

Most of the classification problems in real word suffer from the problem of class skewness. Various methods are required to handle the classification of problems having class skewness. Several methods have been proposed for this purpose which includes some ensemble methods. Most of such ensemble methods targets on creating balanced sub-problems like UnderBagging based kernelized Extreme Learning Machine (UBKELM), UnderBagging based Reduced Kernel Extreme Learning Machine (UBRKELM), Random Undersampling Boost (RUSBoost), EasyEnsemble and BalanceCascade. This work suggests that apart from class skewness, there are many other factors like class overlapping, length of decision boundary and the number of probability distributions present in the problem which are responsible for performance degradation of a classifier. This work proposes an ensemble method which decomposes a complex imbalanced problem into simpler sub-problems, solves these sub-problems using cost sensitive classifiers and then combines the results of each classifier using voting methods. A classification problem can have mixture distribution, the complexity of the problem increases with increase in the number of probability distributions present in it. The proposed problem decomposition method try to create less complex sub-problems by decreasing the number of distributions present in the sub-problems than the original problem. The proposed method uses a clustering evaluation algorithm to find the optimal number of sub-problems. For decomposing an imbalanced classification problem into sub-problems this work uses fuzzy clustering algorithm (FCM) which is a soft clustering algorithm, i.e. the overlapping between the sub-problems depends on the selected value of threshold (TH) parameter in the FCM algorithm. The sub-problems created in this work may or may not be balanced, so Weighted Kernelized Extreme Learning Machine (WKELM) is used to create the classifiers for these sub-problems. The final prediction of the ensemble of these classifiers is determined using soft voting and majority voting. The proposed method is evaluated on 38 benchmark binary class imbalanced datasets downloaded from KEEL dataset repository. The obtained results show that the proposed method outperforms other state of the art methods of imbalance classification. The Wilcoxon signed rank test is performed to show the significant improvement in results.
Deep Learning-Based Autoencoder for m-User Wireless Interference Channel Physical Layer Design

Deep learning (DL) based autoencoder (AE) has been proposed recently as a promising, and potentially disruptive approach to design the physical layer of beyond-5G communication systems. Compared to a traditional communication system with a multiple-block structure, the DL based AE approach provides a new paradigm to physical layer design with a pure data-driven and end-to-end learning based solution. In this article, we address the dynamic interference in a multi-user Gaussian interference channel. We show that standard constellation are not optimal for this context, in particular, for a high interference condition. We propose a novel adaptive DL based AE to overcome this problem. With our approach, dynamic interference can be learned and predicted, which updates the learning processing for the decoder. Compared to other machine learning approaches, our method does not rely on a fixed training function, but is adaptive and applicable to practical systems. In comparison with the conventional system using n-psk or n-QAM modulation schemes with zero force (ZF) and minimum mean square error (MMSE) equalizer, the proposed adaptive deep learning (ADL) based AE demonstrates a significant achievable BER in the presence of interference, especially in strong and very strong interference scenarios. The proposed approach has laid the foundation of enabling adaptable constellation for 5G and beyond communication systems, where dynamic and heterogeneous network conditions are envisaged.
A systematic literature review on general parameter control for evolutionary and swarm-based algorithms

This paper presents a systematic literature review on general parameter control for evolutionary and swarm-based algorithms. General methods can be applied to any algorithm, parameter or problem, in contrast to methods that are tailored to specific applications. In this literature review, a total of 4449 studies were retrieved by the search engines and only 50 of them were selected to the extraction phase. Finally, only 15 were fully analyzed and discussed. To the best of our knowledge, this is the first literature review on such a field and one of the very few systematic reviews on parameter adjustment for those algorithms.
Regional Seismic Risk Assessment of Infrastructure Systems through Machine Learning: Active Learning Approach

Regional seismic risk assessment involves many infrastructure systems, and it is computationally intensive to conduct an individual simulation of each system. This paper suggests an approach using active learning to select informative samples that help build machine learning models with fewer samples for regional damage assessment. The potential of the approach is demonstrated with (1) failure mode prediction of bridge columns, and (2) regional damage assessment of the California two-span bridge inventory with seat abutments. The active learning approach involves the selection of column attributes or bridge models that are more informative to the creation of machine learning-based decision boundaries. The results reveal that an active learning target model based on 100 bridge samples can achieve a level of accuracy of 80%, which is equivalent to a machine learning model based on 480 bridge samples in the case of damage prediction following an earthquake. With the proposed approach, the computational complexity associated with regional risk assessment of bridge systems with specific attributes can be drastically reduced. The proposed approach also will help plan experimental studies that are more informative for damage assessment.
A Cooperative Coevolution Genetic Programming Hyper-Heuristics Approach for On-Line Resource Allocation in Container-Based Clouds

Containers are lightweight and provide the potential to reduce more energy consumption of data centers than Virtual Machines (VMs) in container-based clouds. On-line resource allocation is the most common operation in clouds. However, the on-line <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Resource Allocation in Container-based clouds (RAC)</i> is new and challenging because of its two-level architecture, i.e., the allocations of containers to VMs and the allocation of VMs to physical machines. These two allocations interact with each other, and hence cannot be made separately. Since on-line container allocation requires a real-time response, most current allocation techniques rely on heuristics (e.g., First Fit and Best Fit), which do not consider the comprehensive information such as workload patterns and VM types. As a result, resources are not used efficiently and the energy consumption is not sufficiently optimized. We first propose a novel model of the on-line <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">RAC</i> problem with the consideration of VM overheads, VM types and an affinity constraint. Then, we design a Cooperative Coevolution Genetic Programming (CCGP) hyper-heuristic approach to solve the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">RAC</i> problem, named <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CCGP-RAC</i> . <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CCGP-RAC</i> can learn the workload patterns and VM types from historical workload traces and generate allocation rules. The experiments show significant improvement in energy consumption compared to the state-of-the-art algorithms.
Low-Illumination Image Enhancement for Night-Time UAV Pedestrian Detection

To accomplish reliable pedestrian detection using unmanned aerial vehicles (UAVs) under night-time conditions, an image enhancement method is developed in this article to improve the low-illumination image quality. First, the image brightness is mapped to a desirable level by a hyperbolic tangent curve. Second, the block-matching and 3-D filtering methods are developed for an unsharp filter in YCbCr color space for image denoising and sharpening. Finally, pedestrian detection is performed using a convolutional neural network model to complete the surveillance task. Experimental results show that the Minkowski distance measurement index of enhanced images is increased to 0.975, and the detection accuracies, in F-measure and confidence coefficient, reach 0.907 and 0.840, respectively, which are the highest as compared with other image enhancement methods. This developed method has potential values for night-time UAV visual monitoring in smart city applications.
Smartphone Sensors for Modeling Human-Computer Interaction: General Outlook and Research Datasets for User Authentication

In this paper we list the sensors commonly available in modern smartphones and provide a general outlook of the different ways these sensors can be used for modeling the interaction between human and smartphones. We then provide a taxonomy of applications that can exploit the signals originated by these sensors in three different dimensions, depending on the main information content embedded in the signals exploited in the application: neuromotor skills, cognitive functions, and behaviors/routines. We then summarize a representative selection of existing research datasets in this area, with special focus on applications related to user authentication, including key features and a selection of the main research results obtained on them so far. Then, we perform the experimental work using the HuMIdb database (Human Mobile Interaction database), a novel multimodal mobile database that includes 14 mobile sensors captured from 600 participants. We evaluate a biometric authentication system based on simple linear touch gestures using a Siamese Neural Network architecture. Very promising results are achieved with accuracies up to 87% for person authentication based on a simple and fast touch gesture.