Reviewerly/exHarmony huggingface dataset:
New daily global solar irradiation estimation model based on automatic selection of input parameters using evolutionary artificial neural networks

Assessment of solar potential over a location of interest is introduced as an important step for the successful planning of solar energy systems (photovoltaic or thermal). The aim of this paper is to develop an automatic selection of ANN inputs from available parameters set for the estimation of horizontal daily global solar irradiation in the city of Fez (Morocco). This selection is carried out using the evolutionary artificial neural networks (EANN) with different genetic operators including selection, crossover and mutation taking into account the coding of ANN as an individual in the pool of evolution. The study includes also an exploration of the effect of maximum generations, population size, cutting points positions/nature and the mutation percentage on the performance. In order to validate the efficiency of the proposed EANN model, comparative study is established using an implemented heuristic search and forward stepwise regression models. To compare the performances in term of estimation of the proposed model, three temperature-based and K Nearest Neighbors (K-NN) models were implemented and tested in our case study. Results show that the proposed model, not only achieves good solar irradiation estimation performance but also allows an automatic selection of the best input parameters for the ANN with lower population size and maximum generation ensuring good computation time.
Unsupervised Deep Learning for MU-SIMO Joint Transmitter and Noncoherent Receiver Design

This letter aims to handle the joint transmitter and noncoherent receiver optimization for multiuser single-input multiple-output (MU-SIMO) communications through unsupervised deep learning. It is shown that MU-SIMO can be modeled as a deep neural network with three essential layers, which include a partially-connected linear layer for joint multiuser waveform design at the transmitter side, and two nonlinear layers for the noncoherent signal detection. The proposed approach demonstrates remarkable MU-SIMO noncoherent communication performance in Rayleigh fading channels.
StkTokens: enforcing well-bracketed control flow and stack encapsulation using linear capabilities

We propose and study StkTokens: a new calling convention that provably enforces well-bracketed control flow and local state encapsulation on a capability machine. The calling convention is based on linear capabilities: a type of capabilities that are prevented from being duplicated by the hardware. In addition to designing and formalizing this new calling convention, we also contribute a new way to formalize and prove that it effectively enforces well-bracketed control flow and local state encapsulation using what we call a fully abstract overlay semantics.
Solution of structural and mathematical optimization problems using a new hybrid swarm intelligence optimization algorithm

In this investigation a new optimization algorithm named as interactive search algorithm (ISA) is presented. This method is developed through modifying and hybridizing the affirmative features of recently developed integrated particle swarm optimization (iPSO) algorithm with the pairwise knowledge sharing mechanism of the teaching and learning based optimization (TLBO) method. Proposed ISA provides two different navigation schemes as Tracking and Interacting. Each agent based on its tendency factor can pick one of these two schemes for searching the domain. Additionally, ISA utilizes an improved fly-back technique to handle problem constraints. The proposed method is tested on a set of mathematical and structural optimization benchmark problems with discrete and continuous variables. Numerical results indicate that the new algorithm is competitive with other well-stablished metaheuristic algorithms.
A packet-reordering covert channel over VoLTE voice and video traffics

Covert timing channels hide covert message in some timing behavior of an entity. The most common entity is the inter-packet delays (IPDs) which are modulated to represent the covert message. However, since the IPDs of voice over LTE (VoLTE) traffic are limited within a small range have obvious regularity, modifications to IPDs during modulation are easily detected. Therefore, in this paper, we propose a method for building a VoLTE covert channel, which is based on interleaving reordering of voice packets and video packets over VoLTE mixed traffics. Since the number of video packets in the voice packet interval is random, the covert message can be modulated into the numbers of video packets. Moreover, this method helps to increase the capacity of the covert channel. The approach employs Gray code to improve robustness against network jitter. To achieve the undetectability of the covert channel, the method adopts modular operation and the block composed of voice packet intervals to reduce the distribution difference between the covert traffic and the overt traffic. To deal with different adversary models, two different schemes are designed including the one based on video frames and another based on video packets. The difference between the two schemes is whether the rearranged video packets belong to the same video frame. The experimental results show that this solution can achieve high capacity while keeping undetectable and robust.
A Physics-based Smart Persistence model for Intra-hour forecasting of solar radiation (PSPI) using GHI measurements and a cloud retrieval technique

Short-term solar forecasting models based solely on global horizontal irradiance (GHI) measurements are often unable to discriminate the forecasting of the factors affecting GHI from those that can be precisely computed by atmospheric models. This study introduces a Physics-based Smart Persistence model for Intra-hour forecasting of solar radiation (PSPI) that decomposes the forecasting of GHI into the computation of extraterrestrial solar radiation and solar zenith angle and the forecasting of cloud albedo and cloud fraction. The extraterrestrial solar radiation and solar zenith angle are accurately computed by the Solar Position Algorithm (SPA) developed at the National Renewable Energy Laboratory (NREL). A cloud retrieval technique is used to estimate cloud albedo and cloud fraction from surface-based observations of GHI. With the assumption of persistent cloud structures, the cloud albedo and cloud fraction are predicted for future time steps using a two-stream approximation and a 5-min exponential weighted moving average, respectively. Our model evaluation using the long-term observations of GHI at NREL’s Solar Radiation Research Laboratory (SRRL) shows that the PSPI has a better performance than the persistence and smart persistence models in all forecast time horizons between 5 and 60 min, which is more significant in cloudy-sky conditions. Compared to the persistence and smart persistence models, the PSPI does not require additional observations of various atmospheric parameters but is customizable in that additional observations, if available, can be ingested to further improve the GHI forecast. An advanced technology of cloud forecast is also expected to improve the future performance of the PSPI.
Safety and Security Co-Analyses: A Systematic Literature Review

Latest technological trends lead toward systems connected to public networks even in critical domains. Bringing together safety and security work is becoming imperative, as a connected safety-critical system is not safe if it is not secure. The main objective of this study is to investigate the current status of safety and security co-analysis in system engineering by conducting a systematic literature review. The steps of the review are the following: the research questions identification; agreement upon a search string; applying the search string to chosen databases; a selection criterion formulation for the relevant publications filtering; selected papers categorization and analysis. We focused on the early system development stages and identified 33 relevant publications categorized as follows: combined safety and security approaches that consider the mutual influence of safety and security; safety-informed security approaches that consider influence of safety on security; and security-informed safety approaches that consider influence of security on safety. The results showed that a number of identified approaches are driven by needs in fast developing application areas, e.g., automotive, while works focusing on combined analysis are mostly application area independent. Overall, the study shows that safety and security co-analysis is still a developing domain.
Towards Efficient Fine-Grained Access Control and Trustworthy Data Processing for Remote Monitoring Services in IoT

As an important application of the Internet of Things, many remote monitoring systems adopt a device-to-cloud network paradigm. In a remote patient monitoring case, various resource-constrained devices are used to measure the health conditions of a target patient in a distant non-clinical environment and the collected data are sent to the cloud backend of an authorized health care service for processing and decision making. As the measurements involve private patient information, access control and trustworthy processing of the confidential data become very important. Software-based solutions that adopt advanced cryptographic tools, such as attribute-based encryption and fully homomorphic encryption, can address the problem, but they also impose substantial computation overhead on both client and server sides. In this paper, we deviate from the conventional software-based solutions and propose a secure and efficient remote monitoring framework, called SRM, using the latest hardware-based trustworthy computing technology, such as Intel SGX. In addition, we present a robust and lightweight “heartbeat” protocol to handle notoriously difficult key revocation problem. We implemented a prototype of the framework for SRM and show that SRM can protect user data privacy against unauthorized parties, with minimum performance cost compared to existing software-based solutions.
From fine-grained properties to broad principles for gradual argumentation: A principled spectrum

The study of properties of gradual evaluation methods in argumentation has received increasing attention in recent years, with studies devoted to various classes of frameworks/ methods leading to conceptually similar but formally distinct properties in different contexts. In this paper we provide a novel systematic analysis for this research landscape by making three main contributions. First, we identify groups of conceptually related properties in the literature, which can be regarded as based on common patterns and, using these patterns, we evidence that many further novel properties can be considered. Then, we provide a simplifying and unifying perspective for these groups of properties by showing that they are all implied by novel parametric principles of (either strict or non-strict) balance and monotonicity. Finally, we show that (instances of) these principles (and thus the group, literature and novel properties that they imply) are satisfied by several quantitative argumentation formalisms in the literature, thus confirming the principles' general validity and utility to support a compact, yet comprehensive, analysis of properties of gradual argumentation.
Effect of bifurcation on the interaction between Bitcoin and Litecoin

This paper studied the effect of the bifurcation of Bitcoin on its interactions with its substitute, Litecoin. We applied the Granger causality test and a BEKK-MGARCH model to investigate the return and volatility spillovers between Bitcoin and Litecoin during the period 2013–2018. We divided this period by the date of August 1, 2017, on which Bitcoin underwent its initial bifurcation. In general, our empirical results show that return and volatility spillovers run in one direction only, i.e., from Bitcoin to Litecoin, before the bifurcation, with the direction of shock transmission being reversed after the bifurcation. We conclude that bifurcation has markedly weakened the market position and pricing influence of Bitcoin within cryptocurrency markets. Since bifurcation is convenient for nearly any cryptocurrency, it will likely continue to pose a risk to the cryptocurrency market as a whole.
Persuasion: How phishing emails can influence users and bypass security measures

Phishing is a very dangerous form of social engineering with the aim to deceive people into disclosing private/confidential information. Despite widespread warnings and means to educate users to identify phishing messages, these are still a prevalent practice and a lucrative business. The authors believe that persuasion, as a style of human communication designed to influence others, has a central role in successful digital scams. Research on persuasion applied to phishing emails is scarce and tends to build on Cialdini's work alone. Only a single study has proposed a list of merged principles from three different perspectives but it has methodological limitations regarding the analysis' performance by a single researcher and the testing of principles in a small, not validated sample of phishing emails. This paper aims to fill those gaps by building on Cialdini's, Gragg's and Stajano & Wilson's works to derive a unique list of Principles of Persuasion in Social Engineering (PPSE), resulting from the application of the relational method by two independent researchers. The PPSE are identified, by two independent researchers (Kappa > 0.789) on a sample of phishing email subject lines (N = 194), dated from 2008 to 2017 and randomly selected from a reliable phishing archive (millersmiles.co.uk). A thematic content analysis, together with the sample characterization in terms of visual elements and targeted content, revealed that the most prominent principles of persuasion in phishing emails were 'Authority', 'Strong Affect', 'Integrity' and 'Reciprocation'. The larger percentage of references with the presence of visual elements was found for the 'Strong Affect' principle. The use of the pronouns 'you' and 'your' was more evident for the categories 'Strong Affect' and 'Authority', while the employment of the pronouns 'we, us, our' was more frequent in the 'Reciprocation' principle. This paper constitutes a step further in understanding the use of principles of persuasion in phishing emails with future applications on how their recognition can be automated.
Fuzzy cognitive mapping to support multi-agent decisions in development of urban policymaking

The awareness about environmental complexity involves real-time knowledge and demands urban planning initiatives. Knowledge is multiform, multi-agent and mirrors environmental complexity. Problems characterizing urban sustainability particularly claim non-expert knowledge, being informal, puzzling, uncertain, incomplete, hard to be handled, formalized, modelled. This study utilizes Fuzzy cognitive maps to explore such complexity and support multiagent decisions. It concerns the scenario-building process of the new plan of Taranto (Italy), a paradigmatic example of decaying industrial area, heavily characterized by social fragmentation and environment degradation. This approach aims at structuring environmental problems, modelling future strategies and contributing to build a multi-agent decision support system for complex urban planning contexts.
People re-identification using skeleton standard posture and color descriptors from RGB-D data

This paper tackles the problem of people re-identification by using soft biometrics features. The method works on RGB-D data (color point clouds) to determine the best matching among a database of possible users. For each subject under testing, skeletal information in three-dimensions is used to regularize the pose and to create a skeleton standard posture (SSP). A partition grid, whose sizes depend on the SSP, groups the samples of the point cloud accordingly to their position. Every group is then studied to build the person signature. The same grid is then used for the other subjects of the database to preserve information about possible shape differences among users. The effectiveness of this novel method has been tested on three public datasets. Numerical experiments demonstrate an improvement of results with reference to the current state-of-the-art, with recognition rates of 97.84% (on a partition of BIWI RGBD-ID), 61.97% (KinectREID) and 89.71% (RGBD-ID), respectively.
Coupling privileged kernel method for multi-view learning

Multi-view learning concentrates on fully using the data collected from diverse domains or obtained from various feature extractors to learn effectively. The consensus and complementarity principles provide significant guidance in multi-view modeling. Many support vector machine (SVM)-based multi-view learning models have been proposed, which mainly follow the consensus principle through exploiting the label correlation with regularization terms. In this paper, we propose a simple yet effective coupling privileged kernel method for multi-view learning, termed as MCPK. The coupling term included in the primal objective allows the combination of the errors from all views to be minimized, which guarantees the consensus principle. Similar to our previous work PSVM-2V, MCPK realizes the complementarity principle by applying the learning using privileged information (LUPI) paradigm. The proposed model not only fully integrates the information from all views in the learning process, but also maintains the characteristic of different views to some extent. We employ the standard quadratic programming solver to solve MCPK. Further more, we theoretically analyze the performance of MCPK from the viewpoints of the generalization capability and the PSVM-2V and SVM-2K models. Experimental results demonstrate that MCPK compares more favorably than other state-of-the-art multi-view algorithms in terms of classification accuracy and efficiency.
Towards privacy preservation for “check-in” services in location-based social networks

Location-based social networks (LBSNs) are convenient, but users must reveal their location information to network servers to enjoy an LBSN's services. However, user location information is sometimes sensitive; its disclosure can potentially cause severe privacy issues, for example, by revealing identity or health information. In this paper, we propose a k-anonymity-based algorithm that preserves user location information. To solve the location-privacy problem, the proposed algorithm, called the dummy-location selection algorithm, generates dummy locations that can be used to hide users' actual locations. At the same time, because users may want to search through the check-in records of their friends, we design a novel algorithm that enables a quick search either by location or by friends' social networks. We evaluate the performance of our proposed algorithm via extensive simulations.
Ship detection based on squeeze excitation skip-connection path networks for optical remote sensing images

Ship detection plays a crucial role in remote sensing image processing, which has drawn great attention in recent years. A novel neural network architecture named squeeze excitation skip-connection path networks (SESPNets) is proposed. A bottom-up path is added to feature pyramid network to improve feature extraction capability, and path-level skip-connection structure is firstly proposed to enhance information flow and reduce parameter redundancy. Also, squeeze excitation module is adopted, which can adaptively recalibrate channel-wise feature responses by adding an extra branch after each shortcut path connection block. The multi-scale fused region of interest (ROI) align is then proposed to obtain more accurate and multi-scale proposals. Finally, soft-non-maximum suppression is utilized to overcome the problem of non-maximum suppression (NMS) in ship detection. As demonstrated in the experiments, it can be seen that the SESPNets model has achieved the state-of-the-art performance, which shows the effectiveness of proposed method.
Convolutional long short term memory deep neural networks for image sequence prediction

Although done nearly effortlessly by humans, digital systems cannot easily recognize images or predictions from recent observations. Tackling these limitations by proposing novel algorithms to improve the performance of image processing would have widespread implications in a variety of fields, including robotics, manufacturing, biomedicine, and automation. To provide a computer with this combined ability and transform it into an intelligent system, an algorithm must combine memory with an image decomposition procedure. Artificial neural networks (ANNs) are algorithms that aim to solve tasks such as classification, clustering, pattern recognition, and prediction by resembling brain connections. Specifically, three ANNs have excelled in specific areas: deep neural networks (DNNs), which use intrinsic connections to create prediction maps; long short-term memory neural networks (LSTMs), which use recurrent connections to emulate a type of memory; and convolutional neural networks (CNNs), which can decompose complex data through layers for simpler analysis. Although these algorithms can solve certain tasks of image sequence prediction, they cannot easily solve entire problems on their own. Nevertheless, combining these networks may enable solving such problems with ease. Thus, this article evaluates the combination of ANNs into two novel algorithms developed with the aim of improving image sequence prediction: (i) a combination of CNNs and LSTMs to form a CLNN and (ii) a combination of CNNs, LSTMs, and DNNs to form a CLDNN. Although the developed algorithms require a longer training time, they require less training epochs to have better accuracy than their predecessors. Furthermore, both developed methods were capable of accurately performing the image sequence prediction task, outperforming each individual method, as well as predicting longer and greater numbers of sequences correctly. Overall, the developed algorithms were able to better decompose inputs, remember previous inputs, and more accurately predict sequences of images. This allows the prediction of the next step in the sequence, which can be used as part of an intelligent system to make an analysis and an informed decision on the next course of action.
Image Captioning with Bidirectional Semantic Attention-Based Guiding of Long Short-Term Memory

Automatically describing contents of an image using natural language has drawn much attention because it not only integrates computer vision and natural language processing but also has practical applications. Using an end-to-end approach, we propose a bidirectional semantic attention-based guiding of long short-term memory (Bag-LSTM) model for image captioning. The proposed model consciously refines image features from previously generated text. By fine-tuning the parameters of convolution neural networks, Bag-LSTM obtains more text-related image features via feedback propagation than other models. As opposed to existing guidance-LSTM methods which directly add image features into each unit of an LSTM block, our fine-tuned model dynamically leverages more text-conditional image features, acquired by the semantic attention mechanism, as guidance information. Moreover, we exploit bidirectional gLSTM as the caption generator, which is capable of learning long term relations between visual features and semantic information by making use of both historical and future contextual information. In addition, variations of the Bag-LSTM model are proposed in an effort to sufficiently describe high-level visual-language interactions. Experiments on the Flickr8k and MSCOCO benchmark datasets demonstrate the effectiveness of the model, as compared with the baseline algorithms, such as it is 51.2% higher than BRNN on CIDEr metric.