Reviewerly/exHarmony huggingface dataset:
Aspect-Based Sentiment Analysis of Arabic Tweets in the Education Sector Using a Hybrid Feature Selection Method

Sentiment analysis can be applied in many domains given the abundance of views in social networks, including the education sector, reflecting how cultures and nations grow and develop. In this context, aspect-based sentiment analysis with its two main tasks; aspect detection and aspect-opinion classification, might provide an accurate picture of many educational institutions ' strengths and weaknesses. In this research, a real-world Twitter dataset was collected, containing approximately 7,934 Arabic tweets related to Qassim University in Saudi Arabia. In the text classification task, the high dimensionality problem is usually faced, and the feature selection methods contribute to tackling this problem. Accordingly, the purpose of the experimental study is to investigate the effectiveness of using a hybrid feature selection method in improving the results of aspect-based sentiment analysis by reducing the number of features. The proposed hybrid feature method consists of a one-way analysis of variance to examine the relationship between each feature and classes, and the ridge regression that calculates the importance of features together during the learning phase. Several experiments were conducted to study the effects of the proposed feature selection method on improving the Support Vector Machine classifier 's performance. The experimental results demonstrate that the hybrid method successfully enhances the classifier's performance in both subtasks.
3D Steganography by Random Shuffling of Image Contents Using Residue Model

A combination of cryptography and steganography techniques are used to protect and conceal the existence of sensitive data. With the rapid developments of 3D imaging technology and their widespread use over the internet, a 3D image is an exemplary candidate for steganography. The proposed technique separates a 3D image into several 2D slices, and then randomly shuffles their order using a key-based mathematical formula for random sequence generation. The employed shuffling scheme is based on the modulus operation coupled with 2 secret keys. The pixel locations within each slice are shuffled using the same scheme and a different secret key to provide an additional level of security. Although it is possible to use different shuffling schemes, the same scheme is used to reduce the complexity but with different key values to increase the data security. Blowfish is the chosen encryption technique due to its proven security and efficiency. The encrypted data is then embedded in the least significant b its (LSB) of the shuffled pixels of the 2D slices. Several steganography performance evaluation metrics are incorporated including the Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM). The developed scheme is compared to some of its counterparts from the literature and the results show its superior performance over the comparison methods.
Probabilistic programming semantics for name generation

We make a formal analogy between random sampling and fresh name generation. We show that quasi-Borel spaces, a model for probabilistic programming, can soundly interpret the ν-calculus, a calculus for name generation. Moreover, we prove that this semantics is fully abstract up to first-order types. This is surprising for an ‘off-the-shelf’ model, and requires a novel analysis of probability distributions on function spaces. Our tools are diverse and include descriptive set theory and normal forms for the ν-calculus.
Challenges in Agile Transformation Journey

Context: The agile transformation of an organization is influenced by different factors that can hinder the initiative's success, such as lack of adaptation of agile practices to the organizational context, selection of agile alternatives without using appropriate criteria, and lack of knowledge of the associated challenges. Not all organizations properly recognize these factors. Objective: We aim to identify challenges that can impact the success of agile transformations initiatives. Method: We executed a qualitative study using thematic analysis. We interviewed key persons in charge of the agile journey in four Brazilian organizations. Results: We identified 26 challenges in five dimensions: People, Organizational Culture, Processes, Scalability, and Technology. Among the main challenges are inadequate training, communication difficulties, a culture conflicting with agile values, incompatible organizational structure, and lack of a process to guide the transition. Conclusion: The results suggest urgency in the treatment of agile adoption as a contextualized project for organizational improvements.
A systematic review of scales for measuring information security culture

Purpose The concept of information security culture, which recently gained increased attention, aims to comprehensively grasp socio-cultural mechanisms that have an impact on organizational security. Different measurement instruments have been developed to measure and assess information security culture using survey-based tools. However, the content, breadth and face validity of these scales vary greatly. This study aims to identify and provide an overview of the scales that are used to measure information security culture and to evaluate the rigor of reported scale development and validation procedures. Design/methodology/approach Papers that introduce a new or adapt an existing scale of information security culture were systematically reviewed to evaluate scales of information security culture. A standard search strategy was applied to identify 19 relevant scales, which were evaluated based on the framework of 16 criteria pertaining to the rigor of reported operationalization and the reported validity and reliability of the identified scales. Findings The results show that the rigor with which scales of information security culture are validated varies greatly and that none of the scales meet all the evaluation criteria. Moreover, most of the studies provide somewhat limited evidence of the validation of scales, indicating room for further improvement. Particularly, critical issues seem to be the lack of evidence regarding discriminant and criterion validity and incomplete documentation of the operationalization process. Research limitations/implications Researchers focusing on the human factor in information security need to reach a certain level of agreement on the essential elements of the concept of information security culture. Future studies need to build on existing scales, address their limitations and gain further evidence regarding the validity of scales of information security culture. Further research should also investigate the quality of definitions and make expert assessments of the content fit between concepts and items. Practical implications Organizations that aim to assess the level of information security culture among employees can use the results of this systematic review to support the selection of an adequate measurement scale. However, caution is needed for scales that provide limited evidence of validation. Originality/value This is the first study that offers a critical evaluation of existing scales of information security culture. The results have decision-making value for researchers who intend to conduct survey-based examinations of information security culture.
KTRUSSEXPLORER: Exploring the Design Space of K-truss Decomposition Optimizations on GPUs

K-truss decomposition is an important method in graph analytics for finding cohesive subgraphs in a graph. Various works have accelerated k-truss decomposition on GPUs and have proposed different optimizations while doing so. The combinations of these optimizations form a large design space. However, most GPU implementations focus on a specific combination or set of combinations in this space. This paper surveys the optimizations applied to k-truss decomposition on GPUs, and presents KTRussExPLORER, a framework for exploring the design space formed by the combinations of these optimizations. Our evaluation shows that the best combination highly depends on the graph of choice, and analyses the conditions that make each optimization attractive. Some of the best combinations we find outperform previous Graph Challenge champions on many large graphs.
Semi-Supervised Classification of Noisy, Gigapixel Histology Images

One of the greatest obstacles in the adoption of deep neural networks for new medical applications is that training these models typically require a large amount of manually labeled training samples. In this body of work, we investigate the semi-supervised scenario where one has access to large amounts of unlabeled data and only a few labeled samples. We study the performance of MixMatch and FixMatch-two popular semi-supervised learning methods-on a histology dataset. More specifically, we study these models' impact under a highly noisy and imbalanced setting. The findings here motivate the development of semi-supervised methods to ameliorate problems commonly encountered in medical data applications.
Radio Frequency Fingerprint Based Wireless Transmitter Identification Against Malicious Attacker: An Adversarial Learning Approach

This paper investigates the problem of wireless transmitter identification in the presence of malicious attacker based on radio frequency (RF) fingerprint. A robust wireless transmitter identification scheme is proposed using Generative Adversarial Networks (GAN). First, to build unique RF fingerprint of different wireless transmitter, in-phase (I) and quadrature (Q) imbalance model is introduced. Then, a classifier which consists of multiple discriminators is proposed to detect attacker and classify trusted transmitters based on RF fingerprint. Every discriminator is trained in adversarial learning framework and serves as one binary classifier to verify RF fingerprint. Wasserstein GAN with gradient penalty (WGAN-GP) is modified to train the discriminator. Finally, a simulation dataset is generated to validate the proposed approach. Experimental results reveal that the approach achieves 99.98% accuracy for detecting attacker and an average accuracy of 97.36% for classifying trusted transmitters.
An efficient lattice‐based threshold signature scheme using multi‐stage secret sharing

Secret sharing is a cryptographic technique used in many different applications such as cloud computing, multi-party computation and electronic voting. Security concerns in these applications are data privacy, availability, integrity and verifiability, where secret sharing provides proper solutions. The authors address some important features like verifiability and being multi-stage to make it usable in various field of application. Here, the authors propose an anonymous threshold signature scheme based on the trapdoor function introduced by Micciancio and Peikert by sharing the private key using a lattice-based threshold multi-stage secret sharing (TMSSS) scheme. Then, the authors improve the previously proposed TMSSS scheme, in such a way that less public values are required to publish on the bulletin board which makes the scheme more efficient while preserving the security of the scheme based on the lattice hard problems. The proposed scheme inherits the desired features such as anonymity and verifiability from the improved TMSSS scheme. Furthermore, both schemes use simple matrix operations, which additionally increase the efficiency.
Agile Global Software Development

Global Software Development (GSD) continues to grow substantially and it is fast becoming the norm and fundamentally different from local Software Engineering development. Withal, agile software development (ASD) has become an appealing choice for companies attempting to improve their performance although its methods were originally designed for small and individual teams. The current literature does not provide a cohesive picture of how the agile practices are taken into account in the distributed nature of software development: how to do it, who, and what works in practice. This study aims to highlight how ASD practices are applied in the context of GSD in order to develop a set of techniques that can be relevant in both research and practice. To answer the research question, "how are agile practices adopted in agile global software development teams?" We conducted a systematic literature review of the ASD and GSD literature. A synthesis of solutions found in seventy-six studies provided 48 distinct practices that organizations can implement, including "collaboration among teams", "agile architecture", "coaching", "system demo" and "test automation". These implementable practices go some way towards providing solutions to manage GSD teams, and thus to embrace agility.
Constrained Energy-Cost-Aware Workflow Scheduling for Cloud Environment

Nowadays, cloud computing providers offer to their users the computing resources that are capable of operating on the CPU frequency in between minimum and maximum values. This gives to the users a big number of Virtual Machine(VM) configurations to choose from when planning for the execution of their applications. On the user side, higher CPU frequency incurs a high monetary cost, on the providers side higher CPU frequency incurs high energy consumption. The reduction rate of cost and the reduction rate of energy differ from each other. A big challenge that arises is how to select the proper CPU frequency that can lead to energy-cost-efficient configuration and strike a good balance between energy-cost and deadline. In this paper, an algorithm that achieves energy-cost-aware VM provisioning by selecting different CPU frequencies for each VM in order to execute workflow within a deadline is presented.
Integration of PV into the Sarajevo Canton Energy System-Air Quality and Heating Challenges

The use of renewable energy sources increases the energy self-sustainability of cities, enabling citizens to reduce energy costs, which results in an increase in their standard of living. However, solar energy penetration in Bosnia and Herzegovina, and its capital Sarajevo, is not in line with the possibilities. Furthermore, the Sarajevo Canton is extremely polluted during the winter months because of the use of unacceptable heating fuel. The aim of this paper is to introduce photovoltaic power systems use in heating electrification system. In this paper AQI is calculated based on historical data and the hybrid model EMD-SARIMA for air pollution and a solar production forecast is presented. The methodology was tested in the Sarajevo Canton, taking into account 35,000 households. In order to ensure clean air, renewable electric energy use for household heating should be implemented. The widespread use of inefficient individual heating systems characterized by inefficient and expensive use of firewood and the use of coal in individual furnaces in populated areas are the main problems of internal and urban air pollution in Sarajevo Canton. In order to reduce energy poverty in Sarajevo Canton, the use of a floating photovoltaic power plant located on Lake Jablanica with a capacity of 30 MW and the solar prosumers with capacity of 115 MW to provide the 196 GWh necessary for heating electrification of 35,000 households is implemented in this paper. Finally, based on correlation between AQI forecast and solar production it was calculated that the values of the AQI, considering the application of solar energy during 150 days (five months) in one heating season, have significantly decreased. Also renewable energy sources have a very important role in reducing carbon dioxide (CO2) emissions into the atmosphere and reducing urban pollution. With this approach, households would be heated by renewable electricity, which would make Sarajevo a cleaner, smarter city.
Secure Messaging Authentication Ceremonies Are Broken

People who use secure messaging apps are vulnerable to a hacked or malicious server unless they manually complete an authentication ceremony. In this article, we describe the usability challenges of the authentication ceremony and research to improve it. We conclude with recommendations for service providers and directions for research.
An Insecurity Study of Ethereum Smart Contracts

Ethereum is the second most valuable cryptocurrency, right after Bitcoin. The most distinguishing feature of Ethereum was the introduction of smart contracts which are essentially small computer programs that sit on top of the blockchain. They are written in programming languages like Solidity and are executed by the Ethereum Virtual Machine (EVM). Since these contracts are present on the blockchain itself, they become immutable as long as the blockchains integrity is not compromised. This makes it a nightmare for security researchers as the vulnerabilities found cannot be patched. Also, since Ethereum is a public blockchain, all the contract bytecodes are available publicly. The DAO and the Parity attack are two prominent attacks that have caused great monetary losses. There are many tools that have been developed to cope with these challenges. However, the lack of a benchmark to compare these tools, non-standard vulnerability naming conventions, etc. make the job of a security analyst very difficult. This paper provides the first ever comprehensive comparison of smart contract vulnerability discovery tools which are available in the public domain based on a comprehensive benchmark developed here. The benchmark development is based on a novel taxonomy of smart contract vulnerabilities which has been created after a thorough study of security vulnerabilities present in smart contracts.
Fragility and Robustness in Multiagent Systems

Robustness is an important property of software systems, and the availability of proper feedback is seen as crucial to obtain it, especially in the case of systems of distributed and interconnected components. Multiagent Systems (MAS) are valuable for conceptualizing and implementing distributed systems, but the current design methodologies for MAS fall short in addressing robustness in a systematic way at design time. In this paper we outline our vision of how robustness in MAS can be granted as a design property. To this end, we exploit the notion of accountability as a mechanism to build reporting frameworks and, then, we describe how robustness is gained. We exemplify our vision on the JaCaMo agent platform.
Two-Level Adversarial Visual-Semantic Coupling for Generalized Zero-shot Learning

The performance of generative zero-shot methods mainly depends on the quality of generated features and how well the model facilitates knowledge transfer between visual and semantic domains. The quality of generated features is a direct consequence of the ability of the model to capture the several modes of the underlying data distribution. To address these issues, we propose a new two-level joint maximization idea to augment the generative network with an inference network during training which helps our model capture the several modes of the data and generate features that better represent the underlying data distribution. This provides strong cross-modal interaction for effective transfer of knowledge between visual and semantic domains. Furthermore, existing methods train the zero-shot classifier either on generated synthetic image features or latent embeddings produced by leveraging representation learning. In this work, we unify these paradigms into a single model which, in addition to synthesizing image features, also utilizes the representation learning capabilities of the inference network to provide discriminative features for the final zero-shot recognition task. We evaluate our approach on four benchmark datasets i.e. CUB, FLO, AWA1 and AWA2 against several state-of-the-art methods, and show its performance. We also perform ablation studies to analyze and understand our method more carefully for the Generalized Zero-shot Learning task.
Federated Intelligence for Active Queue Management in Inter-Domain Congestion

Active Queue Management (AQM) has been considered as a paradigm for the complicated network management task of mitigating congestion by controlling buffer of network link queues. However, finding the right parameters for an AQM scheme is very challenging due to the dynamics of the IP networks. In addition, this problem becomes even more complex in inter-domain scenarios where several organizations interconnect each other with the limitation of not sharing raw and private data. As a result, existing AQM schemes have not been widely employed despite their advantages. Therefore, we present a solution that tackles the challenges of tuning the AQM parameters for inter-domain congestion control scenarios where the network management goes beyond an organization's domain. We then introduce the Federated Intelligence for AQM (FIAQM) architecture, which enhances the existing AQM schemes by leveraging the Federated Learning approach. The proposed FIAQM framework is capable of dynamically adjusting the AQM parameters in a multi-domain setting, which is hard to achieve with the conventional AQM solutions working alone. To this end, FIAQM uses an artificial neural network, trained in a federated manner, to predict beyond-own-domain congestion and an intelligent AQM parameter tuner. The evaluation results show that FIAQM can effectively improve the performance of the inter-domain connections by reducing the congestion on their links while preserving the network data private within each participating domain.
Interval Type-2 Beta Fuzzy Near Sets Approach to Content-Based Image Retrieval

In computer-based search systems, similarity plays a key role in replicating the human search process which underlies many natural abilities, such as image recovery, language comprehension, decision-making, or pattern recognition. The search for images consists of establishing a correspondence between the available images and those sought by the user, by measuring the similarity between the images. In fact, image search per content is generally based on the similarity between the visual characteristics of the images. The distance function used to evaluate the similarity between images depends not only on the criteria of the search but also on the representation of the characteristics of the image. This is the main idea of a content-based image retrieval system. In this article, we first constructed type-2 beta fuzzy membership of descriptor vectors to help manage inaccuracy and uncertainty of the characteristics extracted from the feature of images. Subsequently, the retrieved images are ranked according to the novel similarity measure, which is noted type-2 fuzzy nearness measure (IT2FNM). By analogy to type-2 fuzzy logic, and motivated by a near sets theory, we advanced a new fuzzy similarity measure (FSM) noted as IT2FNM. Then, we propose three new IT2FSMs and provide mathematical justification to demonstrate that the proposed FSMs satisfy proximity properties (i.e., reflexivity, transitivity, symmetry, and overlapping). The experimental results generated using three image databases show consistent and significant results.