Reviewerly/exHarmony huggingface dataset:
Comparative study of forecasting approaches in monthly streamflow series from Brazilian hydroelectric plants using Extreme Learning Machines and Box &amp; Jenkins models

Abstract Several activities regarding water resources management are dependent on accurate monthly streamflow forecasting, such as flood control, reservoir operation, water supply planning, hydropower generation, energy matrix planning, among others. Most of the literature is focused on propose, compare, and evaluate the forecasting models. However, the decision on forecasting approaches plays a significant role in such models’ performance. In this paper, we are focused on investigating and confront the following forecasting approaches: i) use of a single model for the whole series (annual approach) versus using 12 models, each one responsible for predicting each month (monthly approach); ii) for multistep forecasting, the use of direct and recursive methods. The forecasting models addressed are the linear Autoregressive (AR) and Periodic Autoregressive (PAR) models, from the Box &amp; Jenkins family, and the Extreme Learning Machines (ELM), an artificial neural network architecture. The computational analysis involves 20 time series associated with hydroelectric plants indicated that the monthly approach with the direct multistep method achieved the best overall performances, except for the cases in which the coefficient of variation is higher than two. In this case, the recursive approach stood out. Also, the ELM overcame the linear models in most cases.
Domain Ontology for Utility Infrastructure: Coupling the Semantics of CityGML Utility Network ADE and Domain Glossaries

With the utility infrastructure domain becoming more technologically advanced with the modeling of all types of information across varying sectors, it is imperative to develop a domain ontology to enable the interoperability across the heterogeneous landscape of information modeling. This paper develops an ontology for the utility infrastructure domain by coupling the semantics of City Geography Markup Language (CityGML) Utility Network application domain extension (ADE)—a candidate open standard for modeling utilities—and domain glossaries, lists of utility terms with their textual definitions. First, a base ontology is formalized by abstracting the modeling information in the ADE through a series of semantic mappings. Second, a novel integrated natural language processing (NLP) approach is devised to automatically learn the semantics from the glossaries. The learning process includes the extraction of utility product terms using conditional random field (CRF) and the classification of semantic relationships between the terms using long short-term memory (LSTM) networks. Finally, the semantics learned from the glossaries are incorporated into the base ontology to result in a domain ontology for utility infrastructure. The NLP approach was evaluated using human-annotated test sets, and results show an average accuracy of 96% in term extraction and 86% in semantic relationship classification. For the case demonstration, a glossary of water product terms was learned to enrich the base ontology and the resulting ontology was evaluated to be an accurate, sufficient, and shared conceptualization of the domain. The newly developed ontology is expected to function effectively as an interoperability facilitator for the utility infrastructure domain, attributed to the semantic compatibility with existing utility modeling initiatives and the enriched/expandable semantic vocabulary.
Multiscale Adaptive Edge Detector for Images Based on a Novel Standard Deviation Map

Edge detection plays an important role in many applications, such as industrial inspection and automatic driving. However, it is difficult to effectively distinguish between faint edges and noise, which may result in losing effective edges or generating spurious edges. This will reduce the accuracy of edge detection. In addition, some parameters need to be set artificially. In the case of the fixed parameters, the overall performance of edge detection on different images is not high. The adaptivity of edge detection needs to be improved further. To solve these problems, this article proposes a multiscale adaptive edge detector for images. First, multiscale pyramid images are constructed from an input image to provide multiscale features for edge detection. At each scale, a gradient map and a novel standard deviation map are calculated based on the gradients and the statistical characteristics of the local gradient differences, respectively, to accurately distinguish the edges from the background and noise. By using these two feature maps, candidate edges are adaptively identified from the image by using pixel-by-pixel detection. Then, candidate edges at different scales are thinned and fused together based on a novel voting mechanism. Finally, a binarized edge map is obtained by using adaptive hysteresis linking. These steps make the proposed edge detector accurate and adaptive. Experiments demonstrate that the proposed edge detector achieves good performance, which is beneficial to measurement applications.
A Polyvocal and Contextualised Semantic Web

Current AI technologies and data representations often reflect the popular or majority vote. This is an inherent artefact of the frequency bias of many statistical analysis methods that are used to create for example knowledge graphs, resulting in simplified representations of the world in which diverse perspectives are underrepresented. With the use of AI-infused tools ever increasing, as well as the diverse audiences using these tools, this bias needs to be addressed in both the algorithms analysing data, as well as in the resulting representations. In this problems to solve before you die submission, we explain the implications of the lack of polyvocality and contextual knowledge in the semantic web. We identify three challenges for the Semantic Web community on dealing with various voices and perspectives as well as our vision for addressing it.
Concepts in Testing of Autonomous Systems: Academic Literature and Industry Practice

Testing of autonomous systems is extremely important as many of them are both safety-critical and security-critical. The architecture and mechanism of such systems are fundamentally different from traditional control software, which appears to operate in more structured environments and are explicitly instructed according to the system design and implementation. To gain a better understanding of autonomous systems practice and facilitate research on testing of such systems, we conducted an exploratory study by synthesizing academic literature with a focus group discussion and interviews with industry practitioners. Based on thematic analysis of the data, we provide a conceptualization of autonomous systems, classifications of challenges and current practices as well as of available techniques and approaches for testing of autonomous systems. Our findings also indicate that more research efforts are required for testing of autonomous systems to improve both the quality and safety aspects of such systems.
A Differential Squirrel Search Algorithm

This paper presents a new hybrid differential squirrel search algorithm optimization algorithm (DSSE) by combining the searching methods of a squirrel search algorithm and differential evolution optimization process for solving the global optimization problem. The main thrust is to include the crossover mechanism in differential evolution algorithm, to improve its exploration capability and modification of the updating rule of squirrel during foraging to improve its exploitation skill. To evaluate its performance, the proposed algorithm is benchmarked on a set of test functions defined in CEC2005. The results show that the proposed algorithm outperforms a well-known optimization algorithm including the squirrel search algorithm.
Ontology for Cross-Site-Scripting (XSS) Attack in Cybersecurity

In this work, we tackle a frequent problem that frequently occurs in the cybersecurity field which is the exploitation of websites by XSS attacks, which are nowadays considered a complicated attack. These types of attacks aim to execute malicious scripts in a web browser of the client by including code in a legitimate web page. A serious matter is when a website accepts the “user-input” option. Attackers can exploit the web application (if vulnerable), and then steal sensitive data (session cookies, passwords, credit cards, etc.) from the server and/or from the client. However, the difficulty of the exploitation varies from website to website. Our focus is on the usage of ontology in cybersecurity against XSS attacks, on the importance of the ontology, and its core meaning for cybersecurity. We explain how a vulnerable website can be exploited, and how different JavaScript payloads can be used to detect vulnerabilities. We also enumerate some tools to use for an efficient analysis. We present detailed reasoning on what can be done to improve the security of a website in order to resist attacks, and we provide supportive examples. Then, we apply an ontology model against XSS attacks to strengthen the protection of a web application. However, we note that the existence of ontology does not improve the security itself, but it has to be properly used and should require a maximum of security layers to be taken into account.
An Improved Approach for Multiple Image Encryption Using Alternate Multidimensional Chaos and Lorenz Attractor

This chapter proposes a multiple image encryption method based on multi-dimensional chaotic equations. Four-dimensional differential chaotic equations of Lorenz attractor have been used to generate the initial security key, and alternate logistic maps have been used for encryption. Initially, three input images are used in a matrix form, where size of each image is M×N, and a composite image is derived by combining the one dimensional matrix of the input images, where size of the composite image matrix is 3×(M×N). Secondly, Lorenz attractor (LA) generates the security key using the composite matrix, and then alternate logistic map is applied with one-dimensional and two-dimensional logistic maps to confuse the matrix. In every iteration of logistic maps, XOR operation is used to encrypt the composite image, and at last, transformation is applied to diffuse the matrix. Finally, the encrypted composite image is obtained in the form of a confused matrix. The proposed algorithm reduces the correlation, increases entropy, and enhances performance of encryption.
Statistical Programming: Small Mistakes, Big Impacts

Abstract Coding mistakes can lead to false results. Statisticians and data scientists should exploit best practices and tools in statistical programming to enhance reproducible analyses. By Simon Schwab and Leonhard Held
SeXAI: A Semantic Explainable Artificial Intelligence Framework

The interest in Explainable Artificial Intelligence (XAI) research is dramatically grown during the last few years. The main reason is the need of having systems that beyond being effective are also able to describe how a certain output has been obtained and to present such a description in a comprehensive manner with respect to the target users. A promising research direction making black boxes more transparent is the exploitation of semantic information. Such information can be exploited from different perspectives in order to provide a more comprehensive and interpretable representation of AI models. In this paper, we present the first version of SeXAI, a semantic-based explainable framework aiming to exploit semantic information for making black boxes more transparent. After a theoretical discussion, we show how this research direction is suitable and worthy of investigation by showing its application to a real-world use case.
Insider attacks: human-factors attacks and mitigation

For many organisations, the human element is generally the weakest link and often the reason why many security incidents occur. As an organisation grows, the chance of a human-caused security incident increases exponentially. Even given the most security-oriented individual, it only takes one slip-up, a minor mistake, or someone having a bad day to create a security incident.
Severity: a QoS-aware approach to cloud application elasticity

Abstract While a multitude of cloud vendors exist today offering flexible application hosting services, the application adaptation capabilities provided in terms of autoscaling are rather limited. In most cases, a static adaptation action is used having a fixed scaling response. In the cases that a dynamic adaptation action is provided, this is based on a single scaling variable. We propose Severity, a novel algorithmic approach aiding the adaptation of cloud applications. Based on the input of the DevOps, our approach detects situations, calculates their Severity and proposes adaptations which can lead to better application performance. Severity can be calculated for any number of application QoS attributes and any type of such attributes, whether bounded or unbounded. Evaluation with four distinct workload types and a variety of monitoring attributes shows that QoS for particular application categories is improved. The feasibility of our approach is demonstrated with a prototype implementation of an application adaptation manager, for which the source code is provided.
S3: An AI-Enabled User Continuous Authentication for Smartphones Based on Sensors, Statistics and Speaker Information

Continuous authentication systems have been proposed as a promising solution to authenticate users in smartphones in a non-intrusive way. However, current systems have important weaknesses related to the amount of data or time needed to build precise user profiles, together with high rates of false alerts. Voice is a powerful dimension for identifying subjects but its suitability and importance have not been deeply analyzed regarding its inclusion in continuous authentication systems. This work presents the S3 platform, an artificial intelligence-enabled continuous authentication system that combines data from sensors, applications statistics and voice to authenticate users in smartphones. Experiments have tested the relevance of each kind of data, explored different strategies to combine them, and determined how many days of training are needed to obtain good enough profiles. Results showed that voice is much more relevant than sensors and applications statistics when building a precise authenticating system, and the combination of individual models was the best strategy. Finally, the S3 platform reached a good performance with only five days of use available for training the users’ profiles. As an additional contribution, a dataset with 21 volunteers interacting freely with their smartphones for more than sixty days has been created and made available to the community.
Dual attention residual group networks for single image deraining

Single image deraining is one of challenges in image processing. An efficient algorithm for single image deraining can significantly improve the image quality in severe weather conditions. Existing deraining algorithms only pay attention to spatial characteristics or channel information, which leads to poor performance of the network. In this paper, we propose a novel dual attention residual group network (DARGNet) to get better deraining performance. Specifically, the framework of dual attention includes spatial attention and channel attention. The spatial attention can extract the multi-scale feature to adapt to different shapes and size of the rain streaks. Meanwhile, channel attention has established the dependence relationship among different channels. In addition, in order to simplify the structure, we integrate the dual attention module and convolution layers into the residual groups, which also improves information transmission. Extensive experiments on synthesized and real-world datasets demonstrate that the proposed network achieves a good effect of deraining tasks. The source code is available at https://github.com/zhanghai404.
Multi-exposure X-ray image fusion quality evaluation based on CSF and gradient amplitude similarity

Due to the limitation of dynamic range of the imaging device, the fixed-voltage X-ray images often produce overexposed or underexposed regions. Some structure information of the composite steel component is lost. This problem can be solved by fusing the multi-exposure X-ray images taken by using different voltages in order to produce images with more detailed structures or information. Due to the lack of research on multi-exposure X-ray image fusion technology, there is no evaluation method specially for multi-exposure X-ray image fusion. For the multi-exposure X-ray fusion images obtained by different fusion algorithms may have problems such as the detail loss and structure disorder. To address these problems, this study proposes a new multi-exposure X-ray image fusion quality evaluation method based on contrast sensitivity function (CSF) and gradient amplitude similarity. First, with the idea of information fusion, multiple reference images are fused into a new reference image. Next, the gradient amplitude similarity between the new reference image and the test image is calculated. Then, the whole evaluation value can be obtained by weighting CSF. In the experiments of MEF Database, the SROCC of the proposed algorithm is about 0.8914, and the PLCC is about 0.9287, which shows that the proposed algorithm is more consistent with subjective perception in MEF Database. Thus, this study demonstrates a new objective evaluation method, which generates the results that are consistent with the subjective feelings of human eyes.
Constructing vectorial bent functions via second-order derivatives

Let n be an even positive integer, and m<n be one of its positive divisors. In this paper, inspired by the work of Tang et al. (2017) [23] on constructing large classes of bent functions from known ones, we consider the construction of vectorial bent and vectorial plateaued (n,m)-functions of the form H(x)=G(x)+g(x), where G is a vectorial bent (n,m)-function, and g is a Boolean function. We find an efficient generic method to construct such kind of vectorial bent (plateaued) functions by establishing a link between the bentness (plateaudness) of H and the second-order derivatives of g. This allows us to provide (at least) three new infinite families of vectorial bent functions with high algebraic degrees. New vectorial plateaued (n,m+t)-functions are also obtained (t≥0 depending on n can be large numbers), two classes of which have the maximal number of bent components.
On the (In)Security of the Diffie-Hellman Oblivious PRF with Multiplicative Blinding

Oblivious Pseudorandom Function (OPRF) is a protocol between a client holding input x and a server holding key k for a PRF F. At the end, the client learns \(F_k(x)\) and nothing else while the server learns nothing. OPRF’s have found diverse applications as components of larger protocols, and the currently most efficient instantiation, with security proven in the UC model, is \(F_k(x)=H_2(x,(H_1(x))^k)\) computed using so-called exponential blinding, i.e. the client sends \(a=(H_1(x))^r\) for random r, the server responds \(b=a^k\), which the client unblinds as \(v=b^{1/r}\) to compute \(F_k(x)=H_2(x,v)\).
Robust Optical Based Hand Interaction for Virtual Reality

Using optical sensors to track hand gestures in virtual reality (VR) simulations requires issues such as occlusion, field-of-view, accuracy and stability of sensors to be addressed or mitigated. We introduce an optical hand-based interaction system that comprises two Leap Motion sensors mounted onto a VR headset at different orientations. Our system collects sensor data from the leap motions, combines and processes it to produce optimal hand tracking data, that minimises the effect of sensor occlusion and noise. This contrasts with previous systems that do not use multiple head-mounted sensors or incorporate hand-data aggregation. We also present a study that compares the proposed system with glove-based and traditional motion controller-based interaction. We investigate hand interactions effect on the feeling of naturalness and immersion. The results show that the use of two head-mounted sensors and the data aggregation system increased the number of valid hands presented to the user and can be successfully applied to VR. The user study shows that there is a strong preference for the proposed system due to the natural feeling and freeing interaction. The absence of an indirect interface such as gloves or controllers was found to aid in creating a more natural and immersive experience.
A forecasting the consumer price index using time series model

This article examines the behavior of the consumer price index in Ukraine for the period from January 2010 to September 2020. The characteristics of the initial time series, the analysis of autocorrelation functions made it possible to reveal the tendency of their development and the presence of annual seasonality. To model the behavior of the consumer price index and forecast for the next months, two types of models were used: the additive ARIMA*ARIMAS model, better known as the model of Box-Jenkins and the exponential smoothing model with the seasonality estimate of Holt-Winters. As a result of using the STATISTICA package, the most adequate models were built, reflecting the monthly dynamics of the consumer price index in Ukraine. The inflation forecast was carried out on the basis of the Holt-Winters model, which has a minimum error.
BSLA: Blockchain-assisted Secure and Lightweight Authentication for SGIN

Space–Ground Integrated Network (SGIN) can dramatically reduce the coverage restriction of terrestrial networks, and it therefore can meet the high service requirements to achieve low-latency and high-speed global interconnection at anytime and anywhere. Due to the vulnerable communication channels and the limited resources of spatial nodes such as satellite, SGIN is facing many potential threats. As the first gateway of the SGIN, access authentication is an essential step that guarantees the internal security of the network. However, there are some problems in traditional cryptosystem-based authentication mechanisms, such as certificate management problem and centralized key escrow problem. Consequently, designing a lightweight and secure identity authentication protocol can effectively solve the problems faced by SGIN. In this paper, we focus on the private key escrow problem caused by the Private Key Generator (PKG) in Identity-Based Cryptosystems (IBC) mechanism, and propose a Blockchain-assisted Secure and Lightweight Authentication (BSLA) scheme which introduces the blockchain to improve the robustness and reliability of authentication. In BSLA, user’s private key is determined by itself through combining the partial private keys from different PKGs, which prevents the risk of private key exposure caused by single PKG. Meanwhile, the blockchain also serves as the information source for synchronizing user revocation lists to the spatial nodes, which realizes the fast detection of user revocation. We further analyse the security of BSLA and the analytical results show that BSLA can meet various security requirements. In addition, according to the performance evaluation results, BSLA has good scalability in terms of key security strength. Therefore, users can choose different key security strength and make a trade-off between efficiency and security based on the specific requirements.