Reviewerly/exHarmony huggingface dataset:
Machine learning-based guilt detection in text

We introduce a novel Natural Language Processing (NLP) task called guilt detection, which focuses on detecting guilt in text. We identify guilt as a complex and vital emotion that has not been previously studied in NLP, and we aim to provide a more fine-grained analysis of it. To address the lack of publicly available corpora for guilt detection, we created VIC, a dataset containing 4622 texts from three existing emotion detection datasets that we binarized into guilt and no-guilt classes. We experimented with traditional machine learning methods using bag-of-words and term frequency-inverse document frequency features, achieving a 72% f1 score with the highest-performing model. Our study provides a first step towards understanding guilt in text and opens the door for future research in this area.
DisguisedNets: Secure Image Outsourcing for Confidential Model Training in Clouds

Large training data and expensive model tweaking are standard features of deep learning with images. As a result, data owners often utilize cloud resources to develop large-scale complex models, which also raises privacy concerns. Existing cryptographic solutions for training deep neural networks (DNNs) are too expensive, cannot effectively utilize cloud GPU resources, and also put a significant burden on client-side pre-processing. This article presents an image disguising approach: DisguisedNets, which allows users to securely outsource images to the cloud and enables confidential, efficient GPU-based model training. DisguisedNets uses a novel combination of image blocktization, block-level random permutation, and block-level secure transformations: random multidimensional projection (RMT) or AES pixel-level encryption (AES) to transform training data. Users can use existing DNN training methods and GPU resources without any modification to training models with disguised images. We have analyzed and evaluated the methods under a multi-level threat model and compared them with another similar method—InstaHide. We also show that the image disguising approach, including both DisguisedNets and InstaHide, can effectively protect models from model-targeted attacks.
Ontology extension with NLP-based concept extraction for domain experts in catalytic sciences

Abstract Ontologies store semantic knowledge in a machine-readable way and represent domain knowledge in controlled vocabulary. In this work, a workflow is set up to derive classes from a text dataset using natural language processing (NLP) methods. Furthermore, ontologies and thesauri are browsed for those classes and corresponding existing textual definitions are extracted. A base ontology is selected to be extended with knowledge from catalysis science, while word similarity is used to introduce new classes to the ontology based on the class candidates. Relations are introduced to automatically reference them to already existing classes in the selected ontology. The workflow is conducted for a text dataset related to catalysis research on methanation of CO $$_2$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mrow /> <mml:mn>2</mml:mn> </mml:msub> </mml:math> and seven semantic artifacts assisting ontology extension by domain experts. Undefined concepts and unstructured relations can be more easily introduced automatically into existing ontologies. Domain experts can then revise the resulting extended ontology by choosing the best fitting definition of a class and specifying suggested relations between concepts of catalyst research. A structured extension of ontologies supported by NLP methods is made possible to facilitate a Findable, Accessible, Interoperable, Reusable (FAIR) data management workflow.
Enhancement of Cloud-native applications with Autonomic Features

Abstract The Autonomic Computing paradigm reduces complexity in installing, configuring, optimizing, and maintaining heterogeneous systems. Despite first discussing it a long ago, it is still a top research challenge, especially in the context of other technologies. It is necessary to provide autonomic features to the Cloud-native execution environment to meet the rapidly changing demands without human support and continuous improvement of their capabilities. The present work attempts to answer how to explore autonomic features in Cloud-native environments. As a solution, we propose using the AMoCNA framework. It is rooted in Autonomic Computing. The success factors for the AMoCNA implementation are its execution controllers. They drive the management actions proceeding in a Cloud-native execution environment. A similar concept already exists in Kubernetes, so we compare both execution mechanisms. This research presents guidelines for including autonomic features in Cloud-native environments. The integration of Cloud-native Applications with AMoCNA leads to facilitating autonomic management. To show the potential of our concept, we evaluated it. The developed executor performs cluster autoscaling and ensures autonomic management in the infrastructure layer. The experiment also proved the importance of observations. The knowledge gained in this process is a good authority of information about past and current state of Cloud-native Applications. Combining this knowledge with defined executors provides an effective means of achieving the autonomic nature of Cloud-native applications.
Intrinsically motivated reinforcement learning based recommendation with counterfactual data augmentation

Abstract Deep reinforcement learning (DRL) has shown promising results in modeling dynamic user preferences in RS in recent literature. However, training a DRL agent in the sparse RS environment poses a significant challenge. This is because the agent must balance between exploring informative user-item interaction trajectories and using existing trajectories for policy learning, a known exploration and exploitation trade-off. This trade-off greatly affects the recommendation performance when the environment is sparse. In DRL-based RS, balancing exploration and exploitation is even more challenging as the agent needs to deeply explore informative trajectories and efficiently exploit them in the context of RS. To address this issue, we propose a novel intrinsically motivated reinforcement learning (IMRL) method that enhances the agent’s capability to explore informative interaction trajectories in the sparse environment. We further enrich these trajectories via an adaptive counterfactual augmentation strategy with a customised threshold to improve their efficiency in exploitation. Our approach is evaluated on six offline datasets and three online simulation platforms, demonstrating its superiority over existing state-of-the-art methods. The extensive experiments show that our IMRL method outperforms other methods in terms of recommendation performance in the sparse RS environment.
Bridging Discrete and Continuous Logicin Automated Reasoning Systems

In this paper we will explore the connection between Functional Programming and reasoning.Functional Programmingis often advanced as a means to increase type safety, write correct programs, or simply to pursue brevity and succinctness in program design.Much less emphasized, but just as powerful, is the connection between functional programming and logic-between functional programming and automated reasoning.On our journey we will consider object orientation, relational databases, and a Lisp based system called Powerloom, a Knowledge Representation and Reasoning System that can represent both (A) discrete mathematics as well as; (B) quantitative models such as regression as used instatistics.Powerloom is being developed at the Intelligent Systems Division at the University of Southern Californiaand was funded by Defence Advanced Projects Agency (DARPA).Along the way, we will look at a system called Coq,a theorem prover for OCaml.This is part 2 of a 3 part series.Part 1 is the Anatomy of a Puzzle.
Solving Blockchain Scalability Problem Using ZK-SNARK

Modern users demands for digital information processing and storage systems, which provide reliable, safe and consistent reproduction of recorded facts and issues. We are well acquainted with distributed ledger technology (DLT) that provides the information storage on independent and unaccountable nodes which interact in complete distrust with each other. It yet provides reliable, secure and historically consistent storage. Due to the increase of blockchain network users it is necessary to provide a resource availability service for billions of users in different parts of the world. Moreover, everyone who interacts with the blockchain network in the mode of complete distrust forces to repeatedly check long chain of blocks containing cryptographic integrity and involvement marks of the party that generates them. Thus, the main recent problem of DLTs is scalability. In this paper, we present the main idea of our solution using new cryptographic technology, known as zero-knowledge proof system. We substantiate the general scheme of fast, reliable and secure DLT verification systems and provide time and measurement result of the classic DLT verification and the new one to demonstrate the perspective of the chosen direction.
Integer Linear Programming Formulations for the Fleet Quickest Routing Problem on Grids

In an automated transportation system, a fleet of vehicles moves on a grid, each from its starting point on one side, to its destination on the opposite side. For the sake of efficiency, the only allowed routes are nonstop shortest paths. Among these, one route for each vehicle has to be properly chosen, avoiding that vehicles collide with each others. Therefore, an assignment of origin-to-destination nonstop collision-free shortest path routes is required. The Fleet Quickest Routing Problem on Grids aims at finding the minimum number of grid lanes allowing for such an assignment. We present two Integer Linear Programming models that exploit some combinatorial properties of conflicting shortest paths: the first one has binary variables and refines a multicommodity flow formulation; the second one exploits a compact representation of shortest paths with a reduced number of integer variables. We compare the two formulations through tests on random and on purpose generated instances, showing the better performance of the compact formulation, and we discuss their potential towards more efficient methods.
Tool-Based Attack Graph Estimation and Scenario Analysis for Software Architectures

With the increase of connected systems and the ongoing digitalization of various aspects of our life, the security demands for software increase. Software architects should design a secure and resistant system. One solution can be the identification of attack paths or the usage of an access control policy analysis. However, due to the system complexity identifying an attack path or analyzing access control policies is hard. Current attack path calculation approaches, often only focus on the network topology and do not consider the more fine-grained information a software architecture can provide, such as the components or deployment. In addition, the impact of access control policies for a given scenario is unclear. We developed an open-source attack propagation tool, which can calculate an attack graph based on the software architecture. This tool could help software architects to identify potential critical attack paths. Additionally, we extended the used access control metamodel to support a scenario-based access control analysis.
Multiple temporal scale aggregation graph convolutional network for skeleton-based action recognition

Skeleton-based human action recognition is attracting increasing attention and is widely applied in virtual reality, human–computer interaction system and other cases. Nevertheless, the performance of recent methods on actions with similar appearance features is still barely satisfactory due to their inherent weakness in modeling the discriminative temporal dynamics. Besides, previous methods have limitation in mining the global information of action. To this end, a multiple temporal scale aggregation graph convolutional network is proposed. Firstly, taking advantage of the varying temporal resolutions offered by different layers in the graph convolutional network, we develop a multiple temporal scale aggregation module to extract discriminative temporal feature. Secondly, a new skeleton feature representation method termed as relative joint across frames is proposed, which provides more global structure clue than the absolute coordinates. Furthermore, we propose a five-stream structure that comprehensively models complementary features and eventually achieves a significant performance boost. In the empirical experiments, our method shows an improvement of 2.38% and 4.08% compared to the baseline method on the cross-subject evaluation benchmark of NTU-RGB+D 60 and NTU-RGB+D 120, respectively.
Multichannel image encryption using dynamic substitution and JSMP map

The confidentiality of images is crucial in medical, military, satellite, and IoT fields. Encipherment ensures confidentiality, relying on strong Substitution boxes (S-boxes) and Permutation boxes (P-boxes). Dynamic S-boxes prevent fixed weak points, while randomness weakens pixel correlations. Numerous techniques are proposed in the field but are highly complex or complicated. This paper presents a simplified approach for constructing a robust dynamic S-box that utilizes the properties of modular arithmetic and JoanS-MuraliP’s (JSMP) chaotic map. Several tests were performed that proved that the suggested S-box is highly non-linear and has strict avalanche criterion (SAC) value above 0.5. Additionally, a powerful encipherment system is proposed, utilizing dynamic substitution boxes and JSMP chaotic map. Dynamic substitutions and permutations are applied separately to the image’s red, green, and blue channels. Experimental results demonstrate resistance to statistical and differential attacks, outperforming advanced algorithms with a simpler design. The system achieves 100 % NPCR across all channels, a benchmark typically only reached by complex systems. Comparative analysis validates the proposed algorithm’s efficacy over established methods.
Chatbot Widget as Online Virtual Assistant for Dormitory Room Information and Reservation Service at Klabat University

Klabat University provides residential facilities for students, better known as a dormitory. Information services regarding dormitories and reservation rooms at Klabat University are still managed manually between the dormitory and students. Some problems occur when students want to find information and/or reserve a room at the same time. From the problems that have been observed, researchers have designed a Chatbot to facilitate the students to find for information as well as reserve dormitory rooms. The development method used in this research is the prototyping model. To design a chatbot by implementing two-way communication, researchers implemented Natural Language Processing technique with a Machine Learning approach. The result of this research is a new Chatbot widget as a virtual assistant for information services and reservation rooms in the dormitory at Klabat University
Towards a systematic approach to manual annotation of code smells

Code smells are structures in code that may indicate maintainability issues. They are challenging to define, and software engineers detect them differently. Mitigation of this problem could be an AI code smell detector. However, to develop it, we need a standardized benchmark dataset. Existing datasets suffer from (1) annotation subjectivity, (2) lack of ground-truth consensus among annotators, and (3) reproducibility issues. This paper aims to develop a systematic manual code smell annotation procedure that addresses these issues. We tailored the prescriptive natural language processing annotation methodology to code smell detection: (1) we cross-validate annotations to mitigate subjectivity, (2) we develop clear annotation guidelines to reach the ground-truth consensus, and (3) we follow literature recommendations for reproducibility and open-source our tools and dataset. We extracted the annotation guidelines from existing empirical code smell research. The annotators refined the guidelines and their understanding of the task through proof-of-concept annotation encompassing retrospective discussion and disagreement resolution and then performed full annotation. We confirmed that the ground-truth consensus was reached by measuring annotation consistency. Our contributions are the proposed annotation procedure, a novel code smell dataset of open-source C# projects, the annotators' experience report, and the open-sourced supporting tool.
Reliable machine learning models for estimating effective software development efforts: A comparative analysis

Effort estimation in the software industry is recognised as one of the most critical activities for the success of the overall solution delivery in the software development process. Software Development Effort Estimation (SDEE) predicts the efforts to develop any software project in terms of persons-month or person-hours. Effort prediction in the early stages of the software development life cycle (SDLC) is always been a challenge. The primary objective of this paper is to analyse different machine learning models and to identify a stable and accurate estimation model for efficient effort estimation. The paper performs a comparative analysis, to analyze the performance of eight different machine learning models, with commonly used datasets in the domain of Software Development Effort Estimation, in terms of eight metrics. On performing experiments and comparing metrics, the analysis showed that the various machine learning estimation models outperformed some particular datasets, hence it can be concluded that machine learning boost-up the performance in the domain of SDEE. Moreover, to conclude, the paper also compared the models based upon the first three choices of models and identified Random Forest has comprehensive superiority and stability in terms of accuracy in estimation, followed by other models.
Cyberbullying detection framework for short and imbalanced Arabic datasets

Cyberbullying detection has attracted many researchers to detect negative comments deployed on communication platforms as cyberbullying can take many forms: verbal, implicit, explicit, or even nonverbal. The successful growth of social media in recent years has opened new perspectives on the detection of cyberbullying, although related research still encounters several challenges, such as data imbalance and expression implicitness. In this paper, we propose an automated cyberbullying detection framework designed to produce satisfactory results, especially when imbalanced short text and different dialects exist in the Arabic text data. In the proposed framework a new method to solve the imbalance problem is suggested, where the modified simulated annealing optimization algorithm is used to find the optimal set of samples from the majority class to balance the training set. This method has been evaluated using traditional machine learning algorithms including support vector machine, and deep learning algorithms including Long Short-Term Memory (LSTM) and Bidirectional LSTM (Bi-LSTM). To generate a framework that can detect Arabic written cyberbullying on communication platforms, the accuracy, recall, specificity, sensitivity and mean squared error are used as the main performance indicators. The results indicate that the proposed framework can improve the performance of the tested algorithms, and Bi-LSTM outperforms other methods for cyberbullying classification.
Asymmetric image encryption algorithm using a new chaotic map and an improved radial diffusion

In order to address the security risks associated with the theft of private images over open Internet of Things (IoT), this paper introduces a new Logistic-Sine-Coupling Map (LoSCM) in three dimensions with a better chaotic behavior, and proposes an image encryption algorithm using an improved Radial Diffusion (ImRD) to achieve a better diffusion effect in image encryption process. Firstly, a preprocessed image is obtained through an XOR operation between the original plain image and a random generated matrix, followed by Secure Hash Algorithm 512 (SHA-512) performed on it, producing a plaintext information with length of 512 bits. Then, a parameter transformation model (PaTM) is constructed, and the plaintext key associated with the plain image is computed by the plaintext property. Secondly, by employing public-key Rivest-Shamir-Adleman cryptogram, i.e., RSA, the ciphertext key is obtained through the plaintext key and opened to public. Then, a new initial value acquisition model (InVAM) is designed to calculate the initial values of LoSCM, thereby generating the corresponding keystream. Finally, XOR, row scrambling, ImRD diffusion, and column scrambling are performed on secret plain image, followed by modular addition with a random matrix to get the final cipher image. Furthermore, the simulation results and performance analysis also prove that the new suggested image encryption algorithm in this paper can effectively oppose chosen or known-plaintext attack. The highlights are: (1) Construct chaotic map LoSCM with better chaotic behavior. (2) Design diffusion ImRD with better diffusion effect. (3) Build models PaTM and InVAM with high security to produce keystream.
Implementing Handheld Burst Super-Resolution

Nowadays, smartphone cameras capture bursts of raw photographs whenever the trigger is pressed.These photos are then fused to produce a single picture with higher quality.This paper details the implementation of the method "Handheld Multi-Frame Super-Resolution algorithm" by Wronski et al. (used in the Google Pixel 3 camera), which performs simultaneously multiimage super-resolution demosaicking and denoising from a burst of images.Hand tremors during exposure cause subpixel motions, which combined with the Bayer color filter array of the sensor results in a collection of aliased and shifted raw photographs of the same scene.The algorithm efficiently aligns and fuses these signals into a single high-resolution one by leveraging the aliasing to reconstruct the high-frequencies of the signal up to the Nyquist rate of the sensor.This approach yields digitally zoomed images up to a factor of 2, which is the limit naturally set by the sensor pixel integration.We present an in-depth description of this algorithm, along with numerous implementation details we have found to reproduce the results of the original paper, whose code is not publicly available.