Reviewerly/exHarmony huggingface dataset:
A Dynamic Linguistic Decision Making Approach for a Cryptocurrency Investment Scenario

Cryptocurrencies have been receiving the sustained attention of investors since 2009.These new investment vehicles are digitally native, meaning that they are traded exclusively on 24/7 digital platforms.Consequently, they offer an excellent scenario to test the Efficient Market Hypothesis, by developing algorithm-based trading strategies.Such strategies aim to beat the market.It has been previously reported that daily returns do not exhibit long range dependence.However, daily volatility in major cryptocurrencies is highly persistent.Therefore, buy/hold/sell decision support systems could be able to capture such market inefficiency.This is especially important for investors interested in periodically trading a set of cryptocurrencies, in order to maximize their wealth.This paper presents a dynamic linguistic decision making approach for building decision models to support cryptocurrency investors in buy/hold/sell decisions.This approach exhibits a good computational performance for obtaining recommendations based on quantitative data.Moreover, this procedure is able to identify some inefficient cryptocurrency behaviors which are not captured by traditional econometric techniques.Our results uncover arbitrage opportunities that outperform buy-and-hold or random strategies.
Waste Classification Using Support Vector Machine with SIFT-PCA Feature Extraction

Population growth and changes in public consumption patterns cause increases in the volume, types and characteristics of the waste. This increase requires waste management effort. One of the efforts that can be performed is by separating waste into several types. Upon waste separation, the waste can be proceeded to the waste recycling process. Current technological advances have supported automatic waste sorting so that the waste sorting process is easier and faster to do. This research proposes waste image classification to support automatic waste sorting using Support Vector Machine (SVM) classification algorithm and SIFT-PCA (Scale Invariant Feature Transform - Principal Component Analysis) feature extraction. SIFT-PCA is a combination of SIFT to extract feature data and PCA to reduce the dimensionality of the resulting feature data. The data used in this research is Trashnet datasets. The performance of the SVM classification using SIFT feature is compared with the similar algorithm with SIFT-PCA combined feature. The experimental results show that classification using SIFT feature extraction achieve accuracy of 62%. This accuracy is higher than experiment with using SIFT-PCA feature extraction.
Efficient and Privacy Preserving Video Transmission in 5G-Enabled IoT Surveillance Networks: Current Challenges and Future Directions

Vision sensors in Internet of Things (IoT)-connected smart cities play a vital role in the exponential growth of video data, thereby making its analysis and storage comparatively tough and challenging. Those sensors continuously generate data for 24 hours, which requires huge storage resources, dedicated networks for sharing with data centers, and most importantly, it makes browsing, retrieval, and event searching a difficult and time-consuming job. Video summarization (VS) is a promising direction toward a solution to these problems by analyzing the visual contents acquired from a vision sensor and prioritizing them based on events, saliency, person's appearance, and so on. However, the current VS literature still lacks focus on resource-constrained devices that can summarize data over the edge and upload it to data repositories efficiently for instant analysis. Therefore, in this article, we carry out a survey of functional VS methods to understand their pros and cons for resource-constrained devices, with the ambition to provide a compact tutorial to the community of researchers in the field. Further, we present a novel saliency-aware VS framework, incorporating 5G-enabled IoT devices, which keeps only important data, thereby saving storage resources and providing representative data for immediate exploration. Keeping privacy of data as a second priority, we intelligently encrypt the salient frames over resource-constrained devices before transmission over the 5G network. The reported experimental results show that our proposed framework has additional benefits of faster transmission (1.8~13.77 percent frames of a lengthy video are considered for transmission), reduced bandwidth, and real-time processing compared to state-of-the-art methods in the field.
A deep learning-based multi-turn conversation modeling for diagnostic Q&amp;A document recommendation

Online healthcare communities (OHCs) have become producers of medical information. Solving the issue of how to effectively reuse such a large amount of medical data and discover its potential value is of the utmost importance for alleviating the shortage of medical resources. Online consultation has received widespread attention and population since its first appearance in 1999, and as a result, many diagnostic multi-turn questions and answers (Q&A) documents have become available. This type of document is formed by multiple rounds of patient questions and doctors' diagnostic answers and contains massive medical knowledge and doctors' diagnostic experience. Few studies concentrate on the modeling and recommendation of this type of document, yet making these documents convenient for reuse reduces the cost of medical consultation for patients and saves time addressing common diseases for doctors. In this paper, we focus on the modeling and understanding of diagnostic multi-turn Q&A records and propose a deep-learning recommendation framework based on patient medical information needs, the contents of Q&A records and doctor background information. With the evaluation based on a real dataset that contains pediatric consultation dialogues fetched from DingXiangYuan, a famous online consultation application in China, we found that the proposed model achieved a good performance on the recommendation of diagnostic multi-turn Q&A records and outperformed baseline models. In addition, we discussed a potential application scenario of the recommendation model, suggesting that the proposed model can promote the reduction of patient costs and doctors' work pressure in countries or regions with insufficient medical resources.
PADENet: An Efficient and Robust Panoramic Monocular Depth Estimation Network for Outdoor Scenes

Depth estimation is a basic problem in computer vision, which provides three-dimensional information by assigning depth values to pixels. With the development of deep learning, researchers have focused on estimating depth based on a single image, which is known as the “monocular depth estimation” problem. Moreover, panoramic images have been introduced to obtain a greater view angle recently, but the corresponding model for monocular depth estimation is scarce in the state of the art. In this paper, we propose PADENet for panoramic monocular depth estimation and re-design the loss function adapted for panoramic images. We also perform model transferring to panoramic scenes after training. A series of experiments show that our PADENet and loss function can effectively improve the accuracy of panoramic depth prediction while maintaining a high level of robustness and reaching the state of the art on the CARLA Dataset.
Integrating EMD with Multivariate LSTM for Time Series QoS Prediction

Quality of Service (QoS) prediction is a hot topic in services computing, which has been extensively investigated in the past decade. Many approaches have been proposed to predict unknown QoS values of Web services according to their historical invocation records. These methods usually analyze each individual QoS data as a basic unit while ignoring the intrinsic characteristics of these time-series QoS data. In an extremely dynamic environment, how to capture the intrinsic and time-varying characteristics of QoS data from a finer-grained perspective becomes an essential issue to achieve accurate prediction. In this paper, we propose a hybrid QoS prediction approach by combining the Empirical Mode Decomposition (EMD) and the multivariate LSTM (Long Short-Term Memory) model. Our approach aims to capture the potential information in the historical sequence and perform accurate QoS forecasting. Experiments conducted on two realworld datasets show that our approach outperforms several state-of-the-art methods in QoS prediction performance.
Generalized Zero-Shot Learning Via Multi-Modal Aggregated Posterior Aligning Neural Network

The visual-semantic gap between the visual space (visual features) and semantic space (semantic attributes) is one of the main problems in the Generalized Zero-Shot Learning (GZSL) task. The essence of this problem is that the structure of manifolds in these two spaces is inconsistent, which makes it difficult to learn embeddings that unify visual features and semantic attributes for similarity measurement. In this work, we tackle this problem by proposing a multi-modal aggregated posterior aligning neural network based on Wasserstein Auto-encoders (WAE) which learns a shared latent space for visual features and semantic attributes. The key to our approach is that the aggregated posterior distribution of the latent representations encoded from visual features of each class is encouraged to be aligned with a Gaussian distribution predicted by the corresponding semantic attribute in the latent space. On one hand, requiring the latent manifolds of visual features and semantic attributes to be consistent preserves the inter-class association between seen and unseen classes. On the other hand, the aggregated posterior of each class is directly defined as a Gaussian in the latent space, which provides a reliable way to synthesize latent features for training classification models. Using the AWA1, AWA2, CUB, aPY, FLO, and SUN benchmark datasets, we extensively conducted comparative evaluations to demonstrate the advantages of our method over state-of-the-art approaches.
Semi-Supervised Text Classification via Self-Pretraining

We present a neural semi-supervised learning model termed Self-Pretraining. Our model is inspired by the classic self-training algorithm. However, as opposed to self-training, Self-Pretraining is threshold-free, it can potentially update its belief about previously labeled documents, and can cope with the semantic drift problem. Self-Pretraining is iterative and consists of two classifiers. In each iteration, one classifier draws a random set of unlabeled documents and labels them. This set is used to initialize the second classifier, to be further trained by the set of labeled documents. The algorithm proceeds to the next iteration and the classifiers' roles are reversed. To improve the flow of information across the iterations and also to cope with the semantic drift problem, Self-Pretraining employs an iterative distillation process, transfers hypotheses across the iterations, utilizes a two-stage training model, uses an efficient learning rate schedule, and employs a pseudo-label transformation heuristic. We have evaluated our model in three publicly available social media datasets. Our experiments show that Self-Pretraining outperforms the existing state-of-the-art semi-supervised classifiers across multiple settings. Our code is available at https://github.com/p-karisani/self-pretraining .
On the Use of Grey Literature

Background: The use of Grey Literature (GL) has been investigated in diverse research areas. In Software Engineering (SE), this topic has an increasing interest over the last years. Problem: Even with the increase of GL published in diverse sources, the understanding of their use on the SE research community is still controversial. Objective: To understand how Brazilian SE researchers use GL, we aimed to become aware of the criteria to assess the credibility of their use, as well as the benefits and challenges. Method: We surveyed 76 active SE researchers participants of a flagship SE conference in Brazil, using a questionnaire with 11 questions to share their views on the use of GL in the context of SE research. We followed a qualitative approach to analyze open questions. Results: We found that most surveyed researchers use GL mainly to understand new topics. Our work identified new findings, including: 1) GL sources used by SE researchers (e.g., blogs, community website); 2) motivations to use (e.g., to understand problems and to complement research findings) or reasons to avoid GL (e.g., lack of reliability, lack of scientific value); 3) the benefit that is easy to access and read GL and the challenge of GL to have its scientific value recognized; and 4) criteria to assess GL credibility, showing the importance of the content owner to be renowned (e.g., renowned author and institutions). Conclusions: Our findings contribute to form a body of knowledge on the use of GL by SE researchers, by discussing novel (some contradictory) results and providing a set of lessons learned to both SE researchers and practitioners.
Linear sets from projection of Desarguesian spreads

Every linear set in a projective space is the projection of a subgeometry, and most known characterizations of linear sets are given under this point of view. For instance, scattered linear sets of pseudoregulus type are obtained by considering a Desarguesian spread of a subgeometry and projecting from a vertex which is spanned by all but two director spaces. In this paper we introduce the concept of linear sets of h-pseudoregulus type, which turns out to be projected from the span of an arbitrary number of director spaces of a Desarguesian spread of a subgeometry. Among these linear sets, we characterize those which are h-scattered and solve the equivalence problem between them; a key role is played by an algebraic tool recently introduced in the literature and known as Moore exponent set. As a byproduct, we classify asymptotically h-scattered linear sets of h-pseudoregulus type.
Machine Learning and Feature Selection for Authorship Attribution: The Case of Mill, Taylor Mill and Taylor, in the Nineteenth Century

In this article we revisit a dividing issue as regards the corpus of one of the most famous nineteenth-
Training Neural Networks by Lifted Proximal Operator Machines

We present the lifted proximal operator machine (LPOM) to train fully-connected feed-forward neural networks. LPOM represents the activation function as an equivalent proximal operator and adds the proximal operators to the objective function of a network as penalties. LPOM is block multi-convex in all layer-wise weights and activations. This allows us to develop a new block coordinate descent (BCD) method with convergence guarantee to solve it. Due to the novel formulation and solving method, LPOM only uses the activation function itself and does not require any gradient steps. Thus it avoids the gradient vanishing or exploding issues, which are often blamed in gradient-based methods. Also, it can handle various non-decreasing Lipschitz continuous activation functions. Additionally, LPOM is almost as memory-efficient as stochastic gradient descent and its parameter tuning is relatively easy. We further implement and analyze the parallel solution of LPOM. We first propose a general asynchronous-parallel BCD method with convergence guarantee. Then we use it to solve LPOM, resulting in asynchronous-parallel LPOM. For faster speed, we develop the synchronous-parallel LPOM. We validate the advantages of LPOM on various network architectures and datasets. We also apply synchronous-parallel LPOM to autoencoder training and demonstrate its fast convergence and superior performance.
Hierarchical Representation Via Message Propagation for Robust Model Fitting

In this paper, we propose a novel hierarchical representation via message propagation (HRMP) method for robust model fitting, which simultaneously takes advantages of both the consensus analysis and the preference analysis to estimate the parameters of multiple model instances from data corrupted by outliers, for robust model fitting. Instead of analyzing the information of each data point or each model hypothesis independently, we formulate the consensus information and the preference information as a hierarchical representation to alleviate the sensitivity to gross outliers. Specifically, we firstly construct a hierarchical representation, which consists of a model hypothesis layer and a data point layer. The model hypothesis layer is used to remove insignificant model hypotheses and the data point layer is used to remove gross outliers. Then, based on the hierarchical representation, we propose an effective hierarchical message propagation (HMP) algorithm and an improved affinity propagation (IAP) algorithm to prune insignificant vertices and cluster the remaining data points, respectively. The proposed HRMP can not only accurately estimate the number and parameters of multiple model instances, but also handle multi-structural data contaminated with a large number of outliers. Experimental results on both synthetic data and real images show that the proposed HRMP significantly outperforms several state-of-the-art model fitting methods in terms of fitting accuracy and speed.
Efficient algorithms for task mapping on heterogeneous CPU/GPU platforms for fast completion time

In GPU-based embedded systems, the problem of computation and data mapping for multiple applications while minimizing the completion time is quite challenging due to large size of the policy space. To achieve fast competition time, a fine-grain mapping framework that explores a set of critical factors is needed for heterogeneous embedded systems. In this paper, we present a theoretical framework that yields a sub-optimal solution via three practical mapping algorithms with low time complexity. We evaluate such algorithms upon StarPU with a large set of popular benchmarks. Experimental results demonstrate that algorithms proposed by the original EMSOFT paper can achieve up to 30% faster completion time compared to state-of-the-art mapping techniques, and can perform consistently well across different workloads. We further extend such algorithms to minimize the completion time and enhance the runtime performance of complex heterogeneous applications under resource-limited infrastructure. We also extend the evaluation by deploying StarPU under multiple setups with an additional benchmark testing suite for simulating real-world runtime neural networks. Experimental results demonstrate that our extended algorithm can achieve much faster completion time (averagely 30% to 37% under multiple resource-constraint scenarios) compared to the state-of-the-art mapping techniques.
eNaBLe, an On-Line Tool to Evaluate Natural Background Levels in Groundwater Bodies

Inorganic compounds in groundwater may derive from both natural processes and anthropogenic activities. The assessment of natural background levels (NBLs) is often useful to distinguish these sources. The approaches for the NBLs assessment can be classified as geochemical (e.g., the well-known pre-selection method) or statistical, the latter involving the application of statistical procedures to separate natural and anthropogenic populations. National Guidelines for the NBLs assessment in groundwater have been published in Italy (ISPRA 155/2017), based mainly on the pre-selection method. The Guidelines propose different assessment paths according to the sample size in spatial/temporal dimension and the type of the distribution of the pre-selected dataset, taking also into account the redox conditions of the groundwater body. The obtained NBLs are labelled with a different confidence level in function of number of total observations/monitoring sites, extension of groundwater body and aquifer type (confined or unconfined). To support the implementation of the Guidelines, the on-line tool evaluation of natural background levels (eNaBLe), written in PHP and using MySQL as DBMS (DataBase Management System), has been developed. The main goal of this paper is to describe the functioning of eNaBLe and test the tool on a case study in central Italy. We calculated the NBLs of As, F, Fe and Mn in the southern portion of the Mounts Vulsini groundwater body, within the volcanic province of Latium (Central Italy), also separating the reducing and oxidizing facies. Specific results aside, this study allowed to verify the functioning and possible improvements of the online tool and to identify some criticalities in the procedure NBLs assessment at the groundwater body scale
Evaluating the Performance of a State-of-the-Art Group-oriented Encryption Scheme for Dynamic Groups in an IoT Scenario

New emerging technologies, such as autonomous driving, intelligent buildings, and smart cities, are promising to revolutionize user experience and offer new services. The world has to undergo large scale deployment of billions of things - cost-efficient intelligent sensors that will be interconnected into extensive networks and will collect and supply data to intelligent algorithms - to make it happen. To date, however, it is challenging to secure such an infrastructure for many-fold reasons, such as resource constraints of things, large scale deployment, many-to-many communication patterns, and dynamically changing communication groups. All these factors rule out most of the state-of-the-art encryption and key-management techniques. Group encryption algorithms are well-suitable for many-to-many communication patterns typical for IoT networks, and many of them can deal with dynamic groups. There are, however, very few constructions that could potentially fulfill the computational and storage constraints of IoT devices while providing sufficient scalability for large networks. The promising candidates, such as construction by Nishat et al. [1], were not evaluated using IoT platforms and under constraints typical for IoT networks. In this paper, we aim to fill this gap and present the evaluation of a state-of-the-art group-oriented encryption scheme by Nishat et al. to identify its applicability to IoT systems. In detail, we provide a measurement workflow, a revised version of the approach, and describe a reproducible hardware testbed. Using this evaluation environment, we analyze the performance of the encryption scheme in a typical IoT scenario from a group member perspective. The results show that all calculation times can be assumed to be constant and are always below 2 seconds. The memory requirement for permanent parameters can also be considered to be constant and are below 8.5 kbit in each case. However, the information that has to be stored temporarily for group updates has turned out to be the bottleneck of the scheme, since their memory requirements increase linearly with the group size.
VGGreNet: A Light-Weight VGGNet with Reused Convolutional Set

This article introduces a light-weight VGGNet for deeper neural networks. In our model, we present a reusable convolution set that is designed to capture as much information as possible until the feature size is reduced to 1. The use of reusable layers for convolution can ensure the convergence without using a pre-trained model, and can greatly reduce the number of training parameters. Since these can be about 22.0% of the VGGNet, this leads to a reduction in memory consumption and faster convergence. As a result, the proposed model can improve the accuracy of testing. Moreover, the design and implementation can be easily deployed in the CNN approach related to the VGGNet model.