Reviewerly/exHarmony huggingface dataset:
SCHOOL LIBRARY SERVICES AT UPT SMA NEGERI 5 ENREKANG DURING THE PANDEMIC PERIOD

The purpose of this study was to determine library services in this case circulation services, reference services, on-site reading services and ICT-based services as well as supporting and inhibiting factors in school library services. This research approach is qualitative with a type of description. Data collection techniques were carried out by interview, observation and documentations. Data analysis techniques using data reduction, data presentation and drawing conclusions. Checking the validity of the data is done by trianggulation technique. The reserch results showed that : 1) the circulation service has a collection of 19,839 books, but does not yet have a non-book collection such as tape recorders, sound slides, video cassettes, sound films and many more. The system used in this library is an open service system. 2) The reference service has 587 collections consisting of dictionaries, encyclopedias, maps and others. In this service, users can obtain information and assistance regarding the existence of the collection needed. 3) on-site reading services, in this service the library provides 6 reading tebles placed in several corners of the room so that students get good lighting and air circulation. Howefer, this service is currently unvailable because students rarely use it. 4) ICT-based services, in the SMA Negeri 5 Enrekang library this service is not yet available because officers are still in the stage of entering library materials into the library application. While the supporting factors for library services are the personnel or the staff themselves, staff commitment and adequate books. While the inhibiting factors are the library collections which are still in hard copy form, lack of student interest and limited staff/librarians.
Characterizing the MasterPrint threat on Android devices with capacitive sensors

Fingerprint authentication is being widely adopted in Android smartphones. AI-generated fingerprints (dubbed MasterPrints) have already been proposed, but their real-world feasibility remains unexplored. Indeed, none of Google’s biometric security metrics consider this threat. To overcome this limitation, this paper characterizes the threat in six widespread Android devices. Our results show that it is possible (yet improbable) to unlock smartphones leveraging MasterPrints. We test their effectiveness against both known fingerprints (i.e., those used to create the MasterPrint) and unknown ones. Overall, 40.68% and 5.62% of tests were successful when considering known and unknown fingerprints, respectively. However, each device exhibits dramatically different resistance against MasterPrints. Motorola Moto G5 Plus was compromised in 22.89% of tests with unknown fingerprints. Xiaomi 9 Pro and 11S, as well as Samsung S10e, have also been compromised in > 70% of tests when protected with known fingerprints. It has also been shown that MasterPrints do not generalize well, so the attacker requires local knowledge to succeed.
User Acceptance Criteria for Privacy Preserving Machine Learning Techniques

Users are confronted with a variety of different machine learning applications in many domains. To make this possible especially for applications relying on sensitive data, companies and developers are implementing Privacy Preserving Machine Learning (PPML) techniques what is already a challenge in itself. This study provides the first step for answering the question how to include the user’s preferences for a PPML technique into the privacy by design process, when developing a new application. The goal is to support developers and AI service providers when choosing a PPML technique that best reflects the users’ preferences. Based on discussions with privacy and PPML experts, we derived a framework that maps the characteristics of PPML to user acceptance criteria.
An Exploratory Study on the Use of Threat Intelligence Sharing Platforms in Germany, Austria and Switzerland

Threat intelligence sharing is a promising solution to enhance knowledge and situational awareness of the rapidly growing number of emerging cyber threats. Accordingly, there are a variety of platforms on the security solutions market that enable the efficient and targeted exchange of threat intelligence across organisations. Unfortunately, very little is known so far about the dissemination and use of these platforms from the end-user perspective. To address this issue, we conducted an exploratory study on the use of threat intelligence sharing platforms in Germany, Austria and Switzerland. For this purpose, we surveyed 69 security and IT experts from large companies, federal authorities and public universities in autumn 2022. Our findings show, among other things, a growing interest in threat intelligence sharing platforms and their value to information security processes.
Experiences with Secure Pipelines in Highly Regulated Environments

In this experiential paper, we present observations from our collaborative efforts with multiple entities operating in highly regulated environments that enabled or disrupted the construction, use, and sustainment of secure CI/CD pipelines as part of a larger DevSecOps strategy. From these observations, we provide insights and recommendations to support enablers and avoid or minimize disruptions. Our insights reveal that along with noted established progress in the area of secure pipelines, there still exists a need to amend multiple cultural and technical barriers to fully realize secure pipelines in a highly regulated environment. Areas of improvement include streamlining security approvals, revising and updating polices to relevance with current technology, increasing automation in multiple pipeline relevant tasking, improving inquiries to better understand pipeline requirements at commencement, and ensuring appropriate sustained training of technical staff. Recommendations presented here address observed gap areas with the purpose of assisting further advancement of achieving formal and refined pipeline incorporation in a highly regulated environment.
Digital Forensics Triage App for Android

Digital forensics of smartphones is of utmost importance in many criminal cases. As modern smartphones store chats, photos, videos etc. that can be relevant for investigations and as they can have storage capacities of hundreds of gigabytes, they are a primary target for forensic investigators. However, it is exactly this large amount of data that is causing problems: extracting and examining the data from multiple phones seized in the context of a case is taking more and more time. This bears the risk of wasting a lot of time with irrelevant phones while there is not enough time left to analyze a phone which is worth examination. Forensic triage can help in this case: Such a triage is a preselection step based on a subset of data and is performed before fully extracting all the data from the smartphone. Triage can accelerate subsequent investigations and is especially useful in cases where time is essential. The aim of this paper is to determine which and how much data from an Android smartphone can be made directly accessible to the forensic investigator – without tedious investigations. For this purpose, an app has been developed that can be used with extremely limited storage of data in the handset and which outputs the extracted data immediately to the forensic workstation in a human- and machine-readable format.
Adoption of Information Security Practices in Large-Scale Agile Software Development: A Case Study in the Finance Industry

Agile development methods have pervaded software engineering and are increasingly applied in large projects and organizations. At the same time, security threats and restrictive legislation regarding security and privacy are steadily rising. These two trends of agile software development at scale and increasingly important security requirements are often at odds with each other. Academic literature widely acknowledges the challenges therefrom and discusses approaches to integrate these two partly conflicting trends. However, several researchers point out a need for empirical studies and evaluations of these approaches in practice. To fill this research gap, we conducted a case study in the finance industry. We identified 27 agile security approaches in academic literature. Based on these theoretical findings, we carried out observations, document analysis, and unstructured interviews to identify which approaches the case company applies. We then conducted semi-structured interviews with 10 experts and a survey with 62 participants to evaluate 14 approaches. One of the key results is that role and knowledge approaches, such as dedicated security roles and communities, are especially important in scaled agile development environments. In addition, the most beneficial security activities are easy-to-integrate, such as a security tagging system, peer security code reviews, security stories, and threat poker. We also contribute evaluation criteria as well as drivers and obstacles for the adoption of agile security approaches that can be used for further research and practice.
Securing Data Exchange in the Convergence of Metaverse and IoT Applications

The convergence of Metaverse and Internet of Things (IoT) presents new opportunities for exchanging data, but it also introduces unprecedented security challenges. With the proliferation of IoT devices, the risk of unauthorized access and data breaches is on the rise, posing significant threats to data confidentiality and integrity. To address these challenges and protect user privacy, comprehensive security solutions are essential. We propose the SafeMetaNet approach, which combines proximity-based authentication, encryption, and blockchain technology to establish secure data exchange in the IoT-Metaverse convergence. SafeMetaNet ensures data confidentiality and integrity through encryption and establishes a tamper-proof record of data exchange using blockchain technology. We evaluated the approach’s performance using various metrics, including latency, throughput, and two security metrics: data confidentiality and data integrity, and compared it with existing approaches. Our findings show that SafeMetaNet outperforms existing approaches, providing improved security. SafeMetaNet is a promising solution for secure data exchange in the IoT-Metaverse convergence.
Nakula: Coercion Resistant Data Storage against Time-Limited Adversary

Both private citizens and professionals including journalists and whistleblowers can find themselves in a situation where they need to physically carry confidential data on a mobile device, through a situation where they might have their device seized and be subject to interrogation. In that case the user may be required to hand over the data by providing the password to unlock the device, violating confidentiality. Many existing proposals to address this issue involve the user lying to the interrogator to convince them that there is no data present, or that they forgot the password, or provide them with a second password that reveal different information. Although data hiding or alternative passwords can be useful solutions, we want to avoid this and instead focus on a scheme where the user can show that they cannot possibly access the data.
On Efficiency and Usability of Group Signatures on Smartphone and Single-board Platforms

With increasing digitalization and omnipresent data sensing, security and users’ privacy become essential requirements in new digital services. Group Signatures (GS) or also known as Anonymous Digital Signatures (ADS) are often used as a core Privacy-Enhancing Technology (PET) in order to keep users’ privacy during their access and/or authentication phases within ensuring the security of provided services. In this work, we provide a comprehensive assessment of group signatures on various small computing platforms typically used in modern digital services. Based on our analysis of well-established GS schemes and their libraries, we implement and evaluate chosen schemes on both well-known smartphone platforms (i.e., Android, iOS) and on a single-board computer. Our results indicate that current handheld devices can already effectively perform main group signatures’ phases and make these schemes practical for deployment in various privacy-requiring scenarios.
Development of an administrative tool, based on ICTs, as support for the induction, training and development of human capital

Software for Induction and Training (SOFIC)", its main purpose is to take advantage of new technologies and develop an innovative tool to facilitate the induction and training processes, to be implemented in any type of organization regardless of its size, its purposes and characteristics, as an aid to the fulfillment of institutional objectives.
Exploring how to use virtual tours to create an interactive customer remote experience

This paper investigates the use of Virtual Reality (VR) to develop virtual tour applications for marketing purposes. The aim is to explore how virtual technologies can support the creation of knowledge about a specific food product and the achievement of user engagement by a multi-sensory virtual tour of the real production site. The study provides design guidelines to create a valuable, multisensory experience by VR tours and demonstrate how the adoption of a user-driven approach, instead of a technology-driven approach, allows to achieve a positive intention to buy. The case study was represented by one of the excellences among Italian food products, the Parmigiano Reggiano (PR) cheese. The PR virtual tour was validated by a user testing campaign, involving more than 70 users: users’ reactions and feedback were collected by human physiological data monitoring and questionnaires’ administration. The research results demonstrated how virtual technologies could effectively help people to create a solid knowledge about a food product to support the marketing process and to form an intention to buy thanks to a better understanding of the quality of the local and traditional productions.
Neural 3D face rendering conditioned on 2D appearance via GAN disentanglement method

Previewing the shaded output of 3D models has been a long-standing requirement in the field. Typically, this is achieved by applying common materials; however, this approach is often labor-intensive and can yield only rough results in the trial stage. Conventional 2D style transfer methods are unsuitable for 3D-to-2D cross-domain conversion, and they cannot accurately reflect the mesh's geometry. Inspired by StyleGAN2's related research, we propose a method for rendering 2D images of 3D face meshes directly controlled by a single 2D reference image, using GAN disentanglement. Our approach involves an input of a 3D mesh and a reference image, where encoders extract geometric features from the mesh and appearance features from the reference image. These features control the StyleGAN2 generator to obtain a generated image that preserves the 3D mesh's geometry and the reference image's appearance. Our experiments demonstrate that this method performs well in generating images while maintaining geometric consistency.
Semantic Textual Similarity in Requirement Specification and Use Case Description based on Sentence Transformer Model

The compatibility between the Use Case Description (UCD) and the Functional Requirements (FR) is essential for the successful development of software. Nevertheless, discrepancies may occur if the UCD does not precisely reflect the intended functionalities specified in the FR. This paper uses a Sentence Transformer Model to evaluate the alignment between the UCD and FR, both written in natural language. The study aims to identify potential discrepancies and ambiguities in the UCD and suggest modifications to better their correspondence with the FR. The Sentence Transformer Model quantifies the degree of alignment between the UCD and FR by analyzing semantic similarity. According to the findings, modifications to the UCD, such as refining terminology, elucidating definitions, and correcting writing errors, can substantially increase semantic similarity with the FR. The Pearson correlation coefficient of 0.70 indicates the correlation between the predicted and the ground truth of semantic similarity is linearly positive. The Spearman rank correlation coefficient value of 0.715 suggests a positive monotonic relationship, with the two text types maintaining their rank of semantic similarity. The low mean squared error (MSE) value of 0.024 demonstrates the model’s predictive accuracy for semantic similarity.
Implementation of Random Forest Classifier for Real-time Earthquake Detection System

An earthquake is one disaster that happened unpredictably and in some cases, it harms humanity. There are lots of research that studies earthquake vibrations using machine learning algorithms. However, implementing it in real-time application systems such as early warning systems is quite challenging due to the similarity of earthquake vibrations and non-earthquake vibrations (human activities and noises). Therefore, this study proposed an earthquake detection with Random Forest Classifier to distinguish earthquake and non-earthquake vibrations in a real-time application earthquake detection system. This study shows that Random Forest Classifier in a detection device is capable of classifying non-earthquake vibrations very well while it can classify earthquake vibrations with a success rate of 78.89%.
Monocular 3D Face Reconstruction Using 3D Morphable Model and ElasticFace

3D Morphable Model, one of the models used to reconstruct 3D face from 2D monocular image of face, has achieved satisfactory results along with computer vision and graphics development. However, reconstructing 3D face using a 3D Morphable Model in a weakly-supervised manner has its challenges because it does not require labels as ground truth and only relies on the similarity of features between 2D monocular image and 3D face. This research uses weakly-supervised 3D face reconstruction by comparing identity feature extraction. In this case, deep face recognition techniques used for identity feature extraction are ArcFace, CosFace, and ElasticFace. The 3D face reconstruction process is divided into 1) rigid fitting to fit the 3D face landmarks into face landmarks of 2D monocular image and 2) non-rigid fitting feature similarity with hybrid-level weak supervision applying diverse deep face recognition models. The results of the reconstruction are subsequently evaluated using the NoW challenge. Experimental results on the NoW protocol show that ElasticFace-Arc is the best deep face recognition for monocular 3d face reconstruction.
Snacks Detection Under Overlapped Conditions Using Computer Vision

This research aims to detect and classify snacks. The detection and classification process uses the Mask R-CNN algorithm. The training process is carried out in the training stage with 250 epochs and 150 steps per epoch. The dataset used in this study consists of 687 snack images with a resolution of 640 x640 pixels divided into 549 training data and 137 validation data. In addition, System testing results were conducted using scenarios 1-7 in an overlapping or partially covered state within the 10-70% range. It can be interpreted that snack overlap detection has optimal performance in the 10-50% range, as evidenced by the high mAP value of 0.99. However, the system cannot detect well in the 60% and 70% overlap range, as seen from the low mAP values of only 0.2 and 0. The evaluation results show that the system has an excellent performance in performing object detection and classification tasks with high accuracy and consistency.
General Chaos Implementation as a Construction Element of Primitive Cryptography

In recent years, the development of Industry 4.0 has accelerated. In their implementation, various primitive cryptography methods such as block cipher, stream cipher, hash function, and pseudo-random number generator are used in this development. These primitive cryptography algorithms, for example, are used in Internet of Things technology to improve security, as well as in cloud computing technology to improve security. Furthermore, hash functions are heavily used as primitive cryptography in blockchain technology to provide the necessary security services. To provide good results in their implementation, the primitive cryptography algorithms used must have good properties. The confusion and diffusion properties of an encryption algorithm can help determine whether or not it is good. If the confusion and diffusion properties are good, the results of their implementation will be good as well. Chaos functions, which have deterministic but chaotic properties, are suitable for designing primitive cryptography algorithms with good confusion and diffusion properties. In this paper, a general implementation of chaos functions is proposed as a building block of primitive cryptography algorithms. The proposed general implementation of chaos will be evaluated based on its confusion and diffusion properties using various chaos functions to ensure that if this proposal is used in the construction of cryptographic algorithms, it will produce algorithms with good confusion and diffusion properties. The evaluation results indicate that the proposed algorithm exhibits good confusion and diffusion characteristics and has a linear time complexity.
Temporal-Spatial Time Series Self-Attention 2D &amp; 3D Human Motion Forecasting

The ability to forecast human motion is crucial in increasing awareness of moving objects in the environment. To address this challenge, this study focuses on human motion forecasting based on annotated 2D and 3D data and the model’s usability on data obtained from pose estimation. This research presents the Temporal-Spatial Time Series Self-Attention method for human motion forecasting. The approach is evaluated using the Human 3.6M, 3DPW, and AMASS datasets based on standard evaluation protocols. Our method performed well in the 2D ground truth and pose estimation data compared to the other time series method. Our method did not yet outperform previous research in 3D input data. However, based on the quantitative and qualitative assessments, our approach demonstrated excellent performance in predicting human motion for short- and long-term objectives.
An End-to-end Anchorless Approach to Recognize Hand Gestures using CenterNet

Hand gesture recognition is one of the interesting problems of Computer Vision. It has a wide range of applications in the fields of Human-Computer Interaction, Robotics, Sign language interpretation, Augmented Reality, etc. Most of the existing deep learning methods detect hand gestures in two stages. The hand is located in the first stage, and classification is performed on the hand portion in the second stage to estimate the hand pose. Although these methods are accurate, they are slow and cant be used for real-time applications. Few existing literature even explored one-stage approaches, like YOLO, SSD, etc., for hand gesture recognition as they have less inference time. But they place many anchor boxes over an image of which only a small percentage are positive. This leads to a huge imbalance between positive and negative anchor boxes and slows the training process. In this paper, we have used an end-to-end, one-stage hand detection-based approach, namely, CenterNet, for hand gesture recognition. It detects the object as a point, i.e., the center point of the bounding box encompassing the object, and regresses to the object size. This eliminates the need for anchor boxes in CenterNet. We have added Dual Attention Network to the CenterNet architecture to improve the performance. Our model achieves a mean F1-score of 84.40% and 98.83% on Ouhands and NUS hand pose datasets, respectively. Results show that our model can perform well even under complex backgrounds and varying illumination conditions, and the F1-scores obtained are close to benchmark values.