Reviewerly/exHarmony huggingface dataset:
SealFSv2: combining storage-based and ratcheting for tamper-evident logging

Abstract Tamper-evident logging is paramount for forensic audits and accountability subsystems. It is based on a forward integrity model: upon intrusion, the attacker is not able to counterfeit the logging data generated before controlling the system. There are local and distributed solutions to this problem. Distributed solutions are suitable for common scenarios, albeit not appropriate for autonomous and loosely connected systems. Moreover, they can be complex and introduce new security issues. Traditional local tamper-evident logging systems use cryptographic ratchets. In previous works, we presented SealFS (from now on, SealFSv1), a system that follows a radically different approach for local tamper-evident logging based on keystream storage. In this paper, we present a new version, SealFSv2, which combines ratcheting and storage-based log anti-tamper protection. This new approach is flexible and enables the user to decide between complete theoretical security (like in SealFSv1) and partial linear degradation (like in a classical ratchet scheme), exchanging storage for computation with user-defined parameters to balance security and resource usage. We also describe an implementation of this scheme. This implementation, which showcases our approach, is an optimized evolution of the original Linux kernel module. It implements a stackable file system that enables transparent tamper-evident logging to all user space applications and provides instant deployability. Last, we present a complete performance evaluation of our current implementation and a fair performance comparison of the two opposite approaches for local tamper-evident logging (i.e., storage-based vs. ratcheting). This comparison suggests that, on current systems and general-purpose hardware, the storage-based approach and hybrid schemes perform better than the traditional ratchet approach.
An optimal context-aware content-based movie recommender system using genetic algorithm: a case study on MovieLens dataset

Most research on movie recommender systems has been conducted with Collaborative Filtering (CF) methods. The lack of sufficient information about users' interests in bootstrapping is one of the most critical problems of the CF method. Using a content-based filtering method can mitigate some of these problems. On the other hand, recent research has proven that utilising contextual information about the movie, such as genre, actors, and cast, can increase the efficiency of the recommender system. This paper uses a combined Genetic Algorithm (GA) and content-based filtering to find the best combination of genre, cast, and crew weights. We first convert each movie's contextual information into a descriptive vector, including cast, crew, and genre. Then, we calculate the distance between each pair of sentence vectors using two separate approaches, fully connected and metadata-based. Finally, with GA, we tune the weight of each of the contextual information of movies to maximise the recommender system's efficiency. Performance evaluation on the well-known MovieLens dataset shows that GA can improve the Precision@k criterion by producing fewer, more accurate recommendations. Weight adjustment by GA improves the F-Measure metric by approximately 58%. This, in turn, can improve Precision and Recall metrics. Also, the GA offers a higher correct recommendation rate than other methods.
A Privacy-Preserving Blockchain Platform for a Data Marketplace

Recent data leak scandals, together with the under-utilization of collected data (estimated that around 90% of data never leaves a device’s local storage), limits the applicability and potential of novel data driven applications. Thus, novel ways to treat data, in which users are guaranteed control, usability, and privacy over their generated data are needed. In this paper we propose a novel privacy-preserving blockchain framework for a data sharing marketplace. The proposed framework allows users (such as sensors and devices) that generate data to store it in external servers, while the blockchain is utilized to record buy and sell transactions between parties, as well as perform access control by generating access sequences whenever trades are performed. A novel perspective over data ownership is presented, in which whoever generates the data has completed ownership and control over it and the blockchain transactions are only utilized to guarantee temporary access to it. The proposed blockchain framework also supports different types of data and provides, via the distributed and openness of the framework, quality, timeliness and similarity control over the data stored in the marketplace. In this context, different types of applications that can benefit from this framework are presented and open problems are discussed.
How can I help you? Design principles for task-oriented speech dialog systems in customer service

Abstract Organizations are increasingly delegating customer inquiries to speech dialog systems (SDSs) to save personnel resources. However, customers often report frustration when interacting with SDSs due to poorly designed solutions. Despite these issues, design knowledge for SDSs in customer service remains elusive. To address this research gap, we employ the design science approach and devise a design theory for SDSs in customer service. The design theory, including 14 requirements and five design principles, draws on the principles of dialog theory and undergoes validation in three iterations using five hypotheses. A summative evaluation comprising a two-phase experiment with 205 participants yields positive results regarding the user experience of the artifact. This study contributes to design knowledge for SDSs in customer service and supports practitioners striving to implement similar systems in their organizations.
Co-Design in Electrical Medical Beds with Caregivers

Among the plethora of instruments present in healthcare environments, the hospital bed is undoubtedly one of the most important for patients and caregivers. However, their design usually follows a top-down approach without considering end-users opinions and desires. Exploiting Human-centered design (HCD) permits these users to have a substantial role in the final product outcome. This study aims to empower caregivers to express their opinion about the hospital bed using a qualitative approach. For a holistic vision, we conducted six focus groups and six semi-structured interviews with nurses, nursing students, social-health operators and physiotherapists belonging to many healthcare situations. We then used thematic analysis to extract the themes that participants faced during the procedures, providing a comprehensive guide to designing the future electrical medical bed. These work results could also help overcome many issues that caregivers face during their everyday working life. Moreover, we identified the User Experience features that could represent the essential elements to consider.
Deep Enhancement-Object Features Fusion for Low-Light Object Detection

With the robust development of deep learning, object detection has gained much attention for practical use cases such as in autonomous driving and surveillance. However, the task is still challenging to the state-of-the-arts in low-light. Consequently, image enhancement has become a common pre-processing step in the pipeline for object detection in low-light environments. Nonetheless, such two-step approach hinges on the reconstruction of the enhanced image which could introduce unseen artifacts and distortion that deteriorates the detection performance instead. Thus, this work proposes a deep enhancement-object features fusion approach to alleviate the problem by infusing deep features extracted from low-light image enhancement with the deep object features of a detection model. It is postulated that features learned by enhancement models emphasizes visual details which were otherwise disregarded by detection models that focus on the abstract appearance of objects. Hence, the fusion of such complementary features would compensate for the details lost due to low-visibility as well as circumvent the reconstruction error for better detection. Specifically, this work performs a study on fusing deep enhancement features from the state-of-the-art Deep Lightening Network (DLN) with the Yolov5 object detection model at various stages. Experiments on the ExDARK dataset showed that such fusion can improve the precision of object detection in various low-light image conditions and outperforms the conventional two-step pre-process-then-detect approach.
Deep Image and Kernel Prior Learning for Blind Super-Resolution

Recently, single image super-resolution (SR) has witnessed significant progress due to the powerful modeling capability of the deep learning networks. However, conventional deep learning-based super-resolution methods predict high-resolution (HR) images under the assumption of ideal degradation model such as the simulated bicubic down-sampling, and then unavoidably deteriorate the SR performance under un-controlled imaging conditions, such as real-world LR images. This study proposes an universal blind SR framework for adaptively and simultaneously predicting the underlying HR image and the counterpart blurring kernel from the observed LR image only. Specifically, we employ an encoder-decoder-based generative network to learn the inherent statistic prior of the HR image from a noise input while adopt a shallow convolution subnet with several stacked layers to estimate the blurring kernel from the observed LR image. Then, a convolution-based degradation module by setting the estimated blurring kernel as its weights is incorporated to obtain the approximated version of the LR image for formulating the loss function. In addition, a pre-trained discriminator is adopted to integrate the perceptual loss for recovering more accurate and natural HR image. We demonstrate the effectiveness of the proposed deep image and kernel prior learning framework using extensive experiments on both synthetic and real images, showing superiority over the state-of-the-art blind SR performance.
Parallel Queries for Human-Object Interaction Detection

Human-Object Interaction (HOI) Detection requires localizing a pair of humans and objects. Recent transformer-based methods leverage the query embeddings to represent the entire HOI instances. The target embeddings after decoding are used to represent the object and human characteristics at the same time. However, it is ambiguous to use the highly integrated embeddings to localize the human and object simultaneously. To address this problem, we split the detection decoding process into subject decoding and object decoding to detect the humans and objects in parallel. Our proposed method, Parallel Query Network (PQNet) uses two transformer decoders to decode the subject embeddings and object embeddings in parallel, and a novel verb decoder is used to fuse the representation from the detection decoding and predict the interaction. The attention mechanisms in the verb decoder consist of the attention between human and object embeddings and the attention between the fused embeddings and global semantic features. As the transformer architecture maintains the permutation of the input query embeddings, the paired boxes of humans and objects are directly predicted by feed-forward networks. With the full usage of the object detection part, our proposed architecture outperforms the state-of-the-art baseline method with half of the training epochs.
THE EFFECT OF THE 5W1H TECHNOLOGY MODEL ON SOME VARIABLES RELATED TO READING SKILLS IN PRIMARY SCHOOL

The main purpose of this research is to examine the effect of the technology integration model-based application example on some reading variables of primary school freshmen. For this purpose, the research was designed in a single-group post-test weak experimental design and a single-group pre-test-post-test weak experimental design. The study group of the research consists of 30 freshmen studying in a branch of a primary school at the middle socioeconomic level determined by criterion sampling method. Dependent t-test and descriptive statistics analysis were used in the analysis of the research data. The application example based on the technology integration model has been effective in the development of phonological awareness skills of primary school freshmen, their transition time to reading, and their reading speed and levels on paper and screen. It was determined that primary school freshmen made fewer reading errors in reading on screen than reading on paper, and the students' reading levels from the screen were better. It was determined that students' attitudes towards reading were quite high.
Blockchain for the electronic voting system: case study: student representative vote in Tunisian institute

Votingis a basic element of running a country. Voting will continue to take place by physically entering the voting booth. No security is guaranteed for this operation, and several cases of tampering have been noted. In order to eliminate this type of problem, the paper proposes an online voting process with blockchain technology. With encryption and hashing, the security of each vote is ensured. The votes will be stored as transactions. A peer-to-peer network is leveraged to share this distributed ledger with voting transactions. The application is designed to hide the complexities of the architecture from the user. With the QR code, each student is uniquely identified. This ensures that each voter has only one chance to vote. With the public and private key, each node will have the ability to securely encrypt, hash, and add transactions to the blockchain. Votes cannot be traced back to the voters. This paper creates a peer-to-peer network with at least three peers. This paper plans to increase voter turnout through online voting. The scalability of blockchain applications depends on the secondary storage limits of peers.
Efficient dynamic key‐aggregate cryptosystem for secure and flexible data sharing

Summary Secure and efficient enforcement of dynamic access control policies on shared data is a central problem in dynamic and scalable application scenarios, including the Internet of Things and smart cities. Key‐aggregate cryptosystem (KAC) is an efficient mechanism to delegate decryption rights of a subset of a large collection of data items to authorized users. While KAC provides an efficient solution to the problem of secure data sharing, we identify that using it to enforce continuously changing access policies is highly inefficient. In this article, we propose a novel dynamic KAC that, in addition to satisfying all key‐aggregate efficiency requirements, enforces dynamic updates to the aggregate sets with constant computation and constant communication cost. We propose a concrete construction for our dynamic KAC, prove its soundness and formal security, and analyze its theoretical and practical performance. We implement the procedures of a dynamic hierarchical key assignment scheme using the proposed as well as the existing dynamic KACs. We compare the costs of enforcing dynamic updates in large hierarchies. We conclude that the performance enhancement achieved by the proposed dynamic KAC compared to the existing KACs is even more significant in practical access control scenarios.
The usability of a novel mobile augmented reality application for excavation process considering safety and productivity in construction

Purpose This study aims to assess the usability of augmented reality (AR) based mobile app for excavation and earthmoving processes using a novel tool entitled Excavator Augmented Reality (EAR). Design/methodology/approach A mixed-methods research approach was used through conducting experimentation to collect qualitative and quantitative data collected from the Sri Lankan construction sector. EAR app was used for experimentation in outdoor areas examining how a 360° tracked hydraulic excavator can be navigated in different physical environments similar to the real prospected job. Findings The findings reveal that EAR could make a considerable impact on enhancing productivity, safety and training processes. However, the developed EAR App subjected to assessment demonstrated the highest satisfaction gap for the auditory aspects. Among the remaining criterion, the satisfaction met user expectations for comfortability and no-risk practice. An analysis of strengths, weaknesses, opportunities and threats (SWOT analysis) conducted revealed that visualising the excavator activities and the requirements of improved features were the highest agreed strengths and weaknesses of the EAR. Among the opportunities for improvement, the necessity of improving emergency and safety reached the highest agreement. Moreover, the study presented the challenges in introducing mobile augmented reality (MAR) to the construction sector under the political, economic, sociocultural, technological, environmental and legal (PESTEL) model along with solutions to be taken. Originality/value This study provides a novel approach to addressing the safety, productivity and training concerns in heavy mobile plants and machinery on construction sites which remains to be unexplored to this end.
Justified belief, knowledge, and the topology of evidence

Abstract We propose a new topological semantics for evidence, evidence-based justifications, belief, and knowledge. Resting on the assumption that an agent’s rational belief is based on the available evidence, we try to unveil the concrete relationship between an agent’s evidence, belief, and knowledge via a rich formal framework afforded by topologically interpreted modal logics. We prove soundness, completeness, decidability, and the finite model property for the associated logics, and apply this setting to analyze key epistemological issues such as “no false lemma” Gettier examples, misleading defeaters, undefeated justification versus undefeated belief, as well as the defeasibility theories of knowledge.
ROSE: A RObust and SEcure DNN Watermarking

Protecting the Intellectual Property rights of DNN models is of primary importance prior to their deployment. So far, the proposed methods either necessitate changes to internal model parameters or the machine learning pipeline, or they fail to meet both the security and robustness requirements. This paper proposes a lightweight, robust, and secure black-box DNN watermarking protocol that takes advantage of cryptographic one-way functions as well as the injection of in-task key image-label pairs during the training process. These pairs are later used to prove DNN model ownership during testing. The main feature is that the value of the proof and its security are measurable. The extensive experiments watermarking image classification models for various datasets as well as exposing them to a variety of attacks, show that it provides protection while maintaining an adequate level of security and robustness.
Assessing the Robustness in Predictive Process Monitoring through Adversarial Attacks

As machine and deep learning models are increasingly leveraged in predictive process monitoring, the focus has shifted towards making these models explainable. The successful adoption of a model is dependent on whether decision-makers can trust the predictions and explanations made. However, recent studies have shown that deep learning models are vulnerable to adversarial attacks -small perturbations to the inputs-which trick deep learning algorithms into making incorrect predictions. An additional crucial property is that the explanations are robust against these adversarial attacks when the model decision was not affected. Therefore, this paper introduces a robustness assessment framework by investigating the impact of adversarial attacks on the robustness of predictive accuracy and explanations used in the field of predictive process monitoring. First, adversarial examples of cases in the independent test set are generated to examine the robustness of the predictive model against intentionally manipulated data. Next, the predictive models are compared with similar models trained on data imputed with adversarial attacks. We monitor the impact on predictive performance in terms of AUC at different stages of the case execution. Finally, the robustness of the explanations is calculated as the distance between the original explanations and the explanations extracted from the model trained on attacked data. We test multiple machine and deep learning techniques, namely the transparent logistic regression, random forests with Shapley values, and LSTM neural networks with attention. Results show that especially neural networks suffer from adversarial attacks, and the former two are mostly robust in terms of both predictive accuracy and explanations.
Challenges of Face Image Authentication and Suggested Solutions

The rapid development of face image manipulation algorithms and the distribution of their easy-to-use applications bring an urgent need to manipulation detection techniques that can reveal the modification in face image and prove its authenticity. Recently, the term “DeepFakes” and their detection techniques have attracted the attention of the research community. To current date, there is no universal detection technique and each one has its own limitations. In this paper, a brief review of face image manipulation techniques and their detection is presented followed by a summary of the limitations and challenges that can face the manipulation detection techniques. Then, the paper presents some suggestions which can be considered for future researches in this attracted and interesting research topic. The review in this paper can be considered as a starting point for the researchers who are concerned about this research field.
Exploring the Synthetic Speech Attribution Problem Through Data-Driven Detectors

In recent years, numerous techniques to manipulate multimedia data and generate hyper-realistic synthetic content have been presented. These inauthentic data are hazardous as they can lead to numerous threats and dangers when misused. This has led the forensic community to propose multiple approaches to tackle both detection and attribution problems. Solving the detection problem consists in determining whether some given data is genuine or false. Solving the attribution problem consists in determining which specific technique has been used to manipulate or generate the observed data. In this paper we address the attribution problem on synthetic speech. We consider a set of methods initially proposed for synthetic speech detection, and adapt them to identify which speech generation algorithm has been used to synthesize a speech track. Our goal is to sample the versatility of these systems and verify how far the detection and attribution tasks are from each other. We test the models in a closed-set scenario and compare their performance with that of a well-established baseline. Moreover, we propose different solutions to address the task in an open-set situation. The encouraging results show that the considered methods can provide a representation of the input signal that is meaningful for both detection and attribution.