Reviewerly/exHarmony huggingface dataset:
Frequent-words analysis for forensic speaker comparison

While many authorship analysis techniques could be applied to any type of linguistic data, there is very limited research on their application on transcriptions of spontaneous speech to help identify the speaker. In authorship analyses, frequency analysis of function words has been quite a successful feature since, when writing a text, people tend to use different function words in different frequencies. Thus, this feature is sensitive to author style while it remains relatively insensitive to the topic of the text. In this paper, we make the cross-over to speech samples and test whether frequency analysis of the most common words in speech, which are mostly function words, has potential as a speaker discriminant. We propose a method within the likelihood ratio framework for court applicability. Our approach takes into account both the similarity and typicality of word frequencies by employing a method using percentile-rank for feature extraction. We apply our method on the forensically relevant dataset FRIDA, achieving good results even when a limited amount of data is available.
Training Needs of Information Specialists at Saudi Universities Libraries to Achieve Cybersecurity Requirements

This study aimed to determine the cybersecurity degree of awareness among information specialists in Saudi university libraries, their required competencies, the vital training needs, and statistically significant differences in cybersecurity awareness among information specialists according to gender, years of experience, and the number of training courses. The researcher used the descriptive analytical method on a sample of male and female university librarians in Saudi libraries. The sample consisted of 200 information specialists from Saudi universities. The questionnaire included six axes: awareness of cybersecurity among information specialists, cybersecurity systems and methods for information professionals in Saudi university libraries, the necessary cybersecurity competencies for information specialists in Saudi universities, the requirements for achieving cybersecurity, and the training needs for information specialists to achieve cybersecurity. A high degree of relative cybersecurity awareness among information specialists at Saudi universities’ libraries was found. Statistically, there were significant differences in gender, years of experience, and training courses.
Islamophobic Discourses in India During COVID-19 Pandemic: A Case of <i>Tablighi Jamaat</i>

ABSTRACTIslamophobia is a hot issue in the world nowadays. This study aimed to analyse the discourses which were produced against Muslims and Islam during the novel coronavirus outbreak in India, where the Muslim minority group Tablighi Jamaat was targeted and held responsible for spreading the virus. The study employed Critical Discourse Analysis to analyse selected tweets from Indian politicians, government dignitaries, and common Indian users to find out Islamophobic themes and ideological structures in their discourses. The researcher hypothesizes that Islamophobia is an integral feature of Indian political communication which is also obvious in their discursive and rhetorical devices. The researcher purposively selected 50 of the most controversial tweets by Indian politicians, government officials, and common Indian Twitter users against Muslims and Islam and carried out a critical discourse analysis through political discourse strategies by Wodak’s (2014). The findings of the study reveal that Indians use discriminatory language against Muslims and Islam to create a difference of “us versus them” and by using anti-Islamic strategies and Islamophobic remarks against the Muslims of India and propagating a common belief in Indians that Muslims and Islam are the main culprit of the COVID-19 outbreak in India.KEYWORDS: IslamophobiaTwitterIndiaTablighi JamaatCritical discourse studies“Us“ versus “them” Disclosure statementNo potential conflict of interest was reported by the authors.
Ocher deposit prospecting using object-based image analysis of WorldView-3 VNIR data: A case study in Hormuz Island, southern Iran

Mapping lithological units and conducting mineral exploration in a relatively short time and at a reduced cost, requires high-resolution satellite data and state-of-the-art image processing approaches and methods. In this regard, the current paper aims to test an integration of visible-near infrared imagery from WorldView-3 (WV-3) and object-based classification for mapping potential deposits of ocher in Hormuz Island, southern Iran. A combination of field observations, spectroscopy, microscopic mineralogy, and geochemical-based XRD and XRF analyses were conducted on the samples collected throughout the study area. The reflectance and absorption features of the ocher were extracted from spectroscopic measurements of samples from well-known ocher mines at the area, and were then convolved to the WV-3 bands. Results showed that the spectral characteristics of ocher are governed by iron oxides and clay minerals with a presence of distinct strong absorption and high reflectance features in the 510–625 nm and 630–745 nm wavelength regions, respectively. These features are complemented by molecular vibration processes of water O–H intramolecular stretching and H–O–H bending that generate absorption features in the 1440–1940 nm region. Additional absorption features in the 2210–2300 nm are most likely due to the Al-OH and CO3−2 vibrations. The absorptions centered at 480 nm, 540 nm, and 820 nm correspond to bands 2, 3, and 7 of the WV-3, respectively, whereas the high reflectance feature centered near 700 nm corresponds to band 6. These four bands, which were considered as index bands of ocher in this study, were used to assign the segmentation weights and to create the thresholds during image processing. Brightness, density, compactness, and homogeneity features were the primary factors for selecting the training areas in the index bands. The ocher-rich areas were enhanced by using a two-stepapproach of object-based image analysis (OBIA) and image classification, for suitable threshold selection. The information about the feature variables of the ocher within the image and lithological object hierarchy were obtained to evaluate the features of ocher deposits. Information extracted from the index bands provided an important description of the object features, including mean, standard deviation, minimum and maximum pixel values, hue, saturation, and intensity, while the training areas obtained information from the three known ocher mines. Considering the fixed numerical range of ocher mines as a reference, the fixed numerical range of general and specific features of ocher in index bands of WV-3 were achieved. The numerical ranges of pixels were used for creating threshold conditions when applying the “assign class” algorithm in bands 2, 3, and 7 are 815–975 nm, 930–1052 nm, and 1721–1904 nm, respectively. The presented OBIA approach shows a high potential with an overall accuracy of 88 % to discriminate deposits of ocher based on compatibility between ocher mines and identified ocher-bearing pixels.
Random Manifold Sampling and Joint Sparse Regularization for Multi-Label Feature Selection

Multi-label learning is usually used to mine the correlation between features and labels, and feature selection can retain as much information as possible through a small number of features. ℓ2,1 regularization method can get sparse coefficient matrix, but it can not solve multicollinearity problem effectively The model proposed in this paper can obtain the most relevant few features by solving the joint constrained optimization problems of ℓ2,1 and ℓF regularization. In manifold regularization, we implement random walk strategy based on joint information matrix, and get a highly robust neighborhood graph. In addition, we gave the algorithm for solving the model and proved its convergence. Comparative experiments on real-world data sets show that the proposed method outperforms other methods.
An AADL Contract Language Supporting Integrated Model- and Code-Level Verification

Model-based systems engineering approaches support the early adoption of a model - a collection of abstractions - of the system under development. The system model can be augmented with key properties of the system including formal specifications of system behavior that codify portions of system and unit-level requirements. There are obvious gaps between the model with formally specified behavior and the deployed system. Previous work on component contract languages has shown how behavior can be specified in models defined using the Architecture Analysis and Design Language (AADL) - a SAE International standard (AS5506C). That work demonstrated the effectiveness of model-level formal methods specification and verification but did not provide a strong and direct connection to system implementations developed using conventional programming languages. In particular, there was no refinement of model-level contracts to programming language-level contracts nor a framework for formally verifying that program code conforms to model-level behavioral specifications. To address these gaps and to enable the practical application of model-contract languages for verification of deployed high-integrity systems, this paper describes the design of the GUMBO AADL contract language that integrates and extends key concepts from earlier contract languages. The GUMBO contract language (GCL) is closely aligned to a formal semantics of the AADL run-time framework, which provides a platform- and language- independent specification of AADL semantics. We have enhanced the HAMR AADL code generation framework to translate model-level contracts to programming language-level contracts in the Slang high-integrity language. We demonstrate how the Logika verification tool can automatically verify that Slang-based AADL component implementations conform to contracts, both at the code-level and model-level. Slang-based implementations of AADL systems can be executed directly or compiled to C for deployments on Linux or the seL4 verified microkernel.
Better decision-making strategy with target seeking approach of humanoids using hybridized SOARANN-fuzzy technique

The prediction and decision-making features of the artificial neural network have been applied to several robotic related research works. Humanoid robot navigation is a challenging task for the researchers with best decision making. Simple artificial neural network (ANN) is the connection models which are poor in multistep decision making with long term planning and logical reasoning. So, in this study artificial neural network is improved in long term cognitive planning by SOAR (State Operator and Result) with powerful feature prediction through ANN (SANN) and hybridized with Fuzzy system for generation effective turning angle value, which will guide the humanoids to reach target location with better decision target seeking ability. An intelligent data fusion model is embedded between SOAR and ANN for converting the logical sequence information of SOAR to probabilistic vectors in multilayer ANNs. The obstacle distances are the input parameters to the SANN model and turning angle (TA) is the output from SANN. The obstacle distances along with the TA obtained from SANN is fed to the fuzzy system as input value and effective turning angle (ETA) is obtained to guide the humanoids towards reaching target. The proposed novel technique and ANN is used by both single and multiple humanoids in both simulation and experimental platforms. The novelty of the research focuses on the development of the new SOAR-ANN technique with better decision strategy for effective turning angle value, to obtain global optimal path by avoiding trapping in local optima and dead ends. The simulation and experimental results in terms of path length travelled and time spent to reach target are obtained from both ANN and novel SANN-Fuzzy technique. When the developed novel technique is compared with standalone ANN method during single humanoid navigation then, a significant improvement of 2–5% and 14–15% is observed in related to trajectory path span and trajectory time spent respectively. Similarly, during multiple humanoid navigation percentage improvement of 5–9% and 14–18% is obtained for humanoids in terms of navigational parameters respectively. The results clearly shows the developed technique requires less time and travels less path to reach target as compared to ANN and the percentage of error is also quite low. Statistical analysis is performed from the results of both the techniques and reflects the effectiveness of the proposed technique in comparison to ANN. Further the developed technique is compared against another existing methodologies like Fuzzy-ANN and Boundary Node Approach and, significant enhancements of 5.64% and 9.72% is attained in terms of track span respectively.
Dataset of Karakalpak language stop words

The dataset presented in this paper aims to address the challenge of automatic extraction of stop words in Natural Language Processing (NLP) for the low-resource Karakalpak language spoken by approximately two million people in Uzbekistan. To accomplish this, we have created a corpus of 23 Karakalpak language school textbooks, which we have named the Karakalpak Language School Corpus (KAASC). Using the KAASC corpus, we have constructed lists of stop words using three methods based on Term Frequency-Inverse Document Frequency (TF-IDF): unigram, bigram, and collocation methods, respectively. The resulting lists of stop words, along with a list of URLs used to construct the corpus, make up the described dataset in this paper.
Benford’s law: What does it say on adversarial images?

Convolutional neural networks (CNNs) are fragile to small perturbations in the input images. These networks are thus prone to malicious attacks that perturb the inputs to force a misclassification. Such slightly manipulated images aimed at deceiving the classifier are known as adversarial images. In this work, we investigate statistical differences between natural images and adversarial ones. More precisely, we show that employing a proper image transformation for a class of adversarial attacks, the distribution of the leading digit of the pixels in adversarial images deviates from Benford's law. The stronger the attack, the more distant the resulting distribution is from Benford's law. Our analysis provides a detailed investigation of this new approach that can serve as a basis for alternative adversarial example detection methods that do not need to modify the original CNN classifier neither work on the high-dimensional pixel space for features to defend against attacks.
Towards a Reference Architecture for Cargo Ports

Cyber-Physical Systems (CPS) are physical systems whose operations are coordinated, monitored, and controlled by computing and communication functions. These systems are typically heterogeneous, including Internet of Things and information technology subsystems, and can present a myriad of implementation details, making them very complex systems. An important type of CPS is a maritime container terminal (cargo port), which is a facility where cargo containers are transported between ships and land vehicles for onward transportation and vice versa. A cargo port performs four basic functions: receiving, storing, staging, and loading for both import and export containers. We present here process patterns that describe the functional aspects of cargo ports and a pattern that describes their structural properties (patterns are encapsulated solutions to recurrent problems). These patterns describe semantic aspects found in any cargo port and can be adapted to describe other CPSs. We decompose these process patterns into use cases that describe their interactions with the system. We then integrate the process patterns with structural patterns to assemble a partial reference architecture (RA) that shows the interactions of all the patterns while also indicating the typical stakeholders found in all ports. We validate the proposed reference architecture, highlighting its theoretical and practical value. Software and system designers of cargo ports need to start from a conceptual and abstract view that is subsequently refined to add more details. The use of reference architectures and patterns is an effective way to organize and describe the functional and non-functional aspects of a system, as well as to unify the design of all its aspects. This is, until now, the only published RA for cargo ports, and it can be a useful guideline for the designers of any type of cargo port.
Improved SwinTrack single target tracking algorithm based on spatio‐temporal feature fusion

Abstract Single target tracking based on computer vision helps to collect, analyse and exploit target information. The SwinTrack algorithm has received widespread attention as one of the twin network algorithms with the best trade‐off between tracking accuracy and speed, but it also suffers from the insufficient fusion of deep and shallow features leading to loss of shallow information and insufficient use of temporal information leading to inconsistency between target and template. Semantic information and detailed information are combined and multiple convolutional forms are introduced to propose a multi‐level feature fusion strategy to effectively fuse features in space. Besides, based on the idea of feedback, a dynamic template branching approach is also designed to fuse temporal features and enhance the representation of target features. The effectiveness of this method was verified on the OTB100 and GOT10K datasets.
Blockchain and mobile client privacy protection in e-commerce consumer shopping tendency identification application

In recent years, China’s e-commerce has made great progress, and the growth trend of online shopping is obvious. However, China's online sales are far below half of the United States. One of the important reasons is the privacy risks of e-commerce and insufficient consumer trust, but it still hinders the development of e-commerce online shopping applications. According to the trust survey, interaction is an important condition for generating trust and an important element. However, in the existing e-commerce trust surveys, the impact of interaction is analyzed, but few surveys only have an impact on consumers' shopping tendency. Therefore, this article considers the importance of reliability of e-commerce online shopping applications, as well as issues related to consumer privacy protection between blockchain and mobile clients. On this basis, this article explores the impact of consumer platform based shopping websites on reliability and the factors and mechanisms of consumer shopping behavior through theoretical analysis and empirical research, and designs a survey validation table for blockchain and mobile client privacy protection. The shopping direction identification system of e-commerce consumers has to some extent made up for the shortcomings of previous empirical research on e-commerce trust behavior. In the field of consumer research, e-commerce has the importance of becoming a specific practical indicator.
MORPHEMIC - Optimization of the Deployment and Life-cycle Management of Data-intensive Applications in the Cloud Computing Continuum

In their Cloud strategy companies are choosing more and more multi-cloud computing giving them the opportunity to distribute its assets, redundancies, software, applications, and anything it deems worthy not only on one Cloud-hosting environment, but rather across several. The model of using multiple Cloud services to host the business's functions and features has a list of advantages that can provide security, flexibility, cost-effectiveness and more to increase business's efficiency and ensure it stays up and running 24 hours a day. The paper presents the MORPHEMIC H2020 project and its unique way of adapting and optimising Cloud computing applications. The Morphemic project covers several features from modelling cross-cloud applications, continuous and autonomous optimization and deployment and providing access to several cloud capabilities for data intensive applications. The outcome of the project will be implemented in the form of an open-source platform covering all the data intensive applications deployment phases starting from modelling, through profiling, optimization, runtime reconfiguration and monitoring.
A test vector selection method based on machine learning for efficient presilicon verification

Presilicon verification is a critical inspection process that detects errors in the circuit design of semiconductor chips early in the design process. Presilicon verification is performed by entering into the circuit simulation test vectors that can induce current to flow in the circuit to verify whether current flows at critical points (CPs). CPs are the major management points of the circuit. The current verification approach of randomly choosing a test vector and feeding it into the simulation is inefficient because verified CPs are often reverified. Moreover, certain CPs can be verified with a small number of test vectors. Finding a verifiable vector takes considerable time, leading to an increase in the time required for CP verification. In this study, we propose a test vector selection method that can verify as many CPs as possible with the minimum number of test vectors. Moreover, we propose contrast PrefixSpan, a sequential pattern mining (SPM) algorithm that extracts the sequential pattern used for CP verification. CPs can be verified with many fewer test vectors when test vectors input into the simulation are extracted using the proposed method than when test vectors are randomly selected, thereby shortening the presilicon verification time.
CROSS: A framework for cyber risk optimisation in smart homes

This work introduces a decision support framework, called Cyber Risk Optimiser for Smart homeS (CROSS), which advises both smart home users and smart home service providers on how to select an optimal portfolio of cyber security controls to counteract cyber attacks in a smart home including traditional cyber attacks and adversarial machine learning attacks. CROSS is based on a multi-objective bi-level two-stage optimisation. In stage-one optimisation, the problem is modelled as a multi-leader-follower game that considers both security and economic objectives, where the provider selects a security portfolio to protect both itself and its users, while rational attackers target the weakest path. Stage-two optimisation is a Stackelberg security game that focuses on additional user security controls under the remit of smart home users. While CROSS can potentially be applied to other similar use cases, in this paper, our aim is to address threats against artificial intelligence (AI) applications as the use of AI in smart Internet of Things (IoT) devices introduces new cyber threats to home environments. Specifically, we have implemented and assessed CROSS in a smart heating use case in a prototypical AI-enabled IoT environment that combines characteristics and vulnerabilities currently present on existing commercial off-the-shelf (COTS) devices, demonstrating the selection of optimal decisions.