Reviewerly/exHarmony huggingface dataset:
Generative Adversarial Network-Based Anomaly Detection and Forecasting with Unlabeled Data for 5G Vertical Applications

With the development of 5G vertical applications, a huge amount of unlabeled network data can be collected, which can be employed for evaluating the user experience and network operation status, such as the identifications and predictions of network anomalies. However, it is challenging to achieve highly accurate evaluation results using the conventional statistical methods due to the limitations of data quality. In this paper, generative adversarial network (GAN)-based anomaly detection and forecasting are studied for 5G vertical applications, which can provide considerable detection and prediction results with unlabeled network data samples. First, the paradigm and deployment of the deep-learning-based anomaly detection and forecasting scheme are designed. Second, the network structure and the training strategy are introduced to fully explore the potential of the GAN model. Finally, the experimental results of our proposed GAN model are provided based on the practical unlabeled network operation data in various 5G vertical scenarios, which show that our proposed scheme can achieve significant performance gains for network anomaly detection and forecasting.
Forensic Operations for Recognizing SQLite Content (FORC): An Automated Forensic Tool for Efficient SQLite Evidence Extraction on Android Devices

Mobile forensics is crucial in reconstructing various everyday activities accomplished through mobile applications during an investigation. Manual analysis can be tedious, time-consuming, and error-prone. This study introduces an automated tool called Forensic Operations for Recognizing SQLite Content (FORC), specifically designed for Android, to extract Simple Query Language Table Database Lightweight (SQLite) evidence. SQLite is a library that serves as a container for mobile application data, employing a zero-configuration, serverless, self-contained, and transactional SQL database engine. While some SQLite files possess extensions such as .db, .db3, .sqlite, and .sqlit3, others have none. The lack of file extensions may result in missing evidence that could unveil the truth. The proposed tool utilizes both the file extensions and headers of the SQLite data to recognize and identify SQLite data generated or modified by a mobile application. The FORC tool’s capability was evaluated using the Chrome application as a case study, and a comparison between FORC and other tools was conducted. The results suggest that FORC significantly simplifies mobile forensic analysis.
Research on Cone Bucket Detection Algorithm Based on Improved YOLOv5s

In order to address the problems associated with low detection accuracy, weak detection ability of small targets, insufficiently obvious differentiation of colors, and inability to accurately locate the actual position of the target object in the Formula Student Autonomous China, the YOLOv5s algorithm is improved by adding coordinate attention, modifying the color space transformation module, and adding a normalized Gaussian Wasserstein distance module and a monocular camera distance measurement module. Finally, it is experimentally verified that by adding and modifying the above modules, the YOLOv5s algorithm’s precision is improved by 6.9%, recall by 4.4%, and mean average precision by 4.9%; although the detection frame rate decreases, it still meets the requirement. Monocular camera distance measurement has a maximum error of 5.64% within 20 m in the Z-direction and 5.33% in the X-direction.
Understanding the Developments in the Business Perspective of Cloud Computing

Research on cloud computing (CC) has gained a lot of momentum owing to its massive adoption. It has moved beyond the exploration of inherent capabilities to understand its disruptiveness and transformative value. In this vein, the authors conducted a comparative literature review of 101 articles to better understand the developments from previous reviews. This article serves as a replication study to evaluate the growth of the business perspective of CC. The authors identify 126 factors guiding the characteristics, adoption, governance, and business impact of the cloud. Further, they employ a rigorous analysis that situates our review at the intersection of these factors and applies a multidimensional scaling technique. The developed matrix (a) helps to clarify the current state of research, (b) identifies research gaps, and (c) identifies potential further research avenues. Unlike previous reviews, this developed multidimensional view of each article uncovers numerous perspectives that can guide future research.
Hexacopter-Based Modern Remote Sensing Using the YOLO Algorithm

Remote sensing technology is essential to various industries such as agriculture, meteorology, surveillance, defence, manufacturing and processing industries. Several sectors widely adopt this technology, so much research has been conducted in this domain. In satellite applications, research in remote sensing has been performed for seven decades. Images and videos captured by satellites have less resolution, which undoubtedly reduces object detection and data analysis accuracy. After analysis, the imprecise nature of captured data might cause difficulties in fields such as defence and agriculture. To combat this problem, in this research, we developed a hexacopter-based modern remote sensing device that can fly with manual intervention and also has an emergency autopilot function. The proposed system is equipped with a compact high-resolution camera which captures images with a higher frame rate. The developed system uses the YOLO v4 algorithm, which is fast and accurate to recognise and track an item or a particular individual in real time. Logged data is shared with the ground station to perform the desired task. The hexacopter-based system has more mobility than the satellite-based system, which overcomes the drawback of the limited range of the proposed system. In this proposed system, we have connected a precise flight controller and a Raspberry Pi 3 Model A+ microprocessor board with other electronic components to more accurately control hexacopter flying and real-time object identification and tracking.
Elementary Number Theory Problems. Part VII

Summary In this paper problems 48, 80, 87, 89, and 124 from [7] are formalized, using the Mizar formalism [1], [2], [4]. The work is natural continuation of [5] and [3] as suggested in [6].
On the Formalization of Gram-Schmidt Process for Orthonormalizing a Set of Vectors

Summary In this article, we formalize the Gram-Schmidt process in the Mizar system [2], [3] (compare another formalization using Isabelle/HOL proof assistant [1]). This process is one of the most famous methods for orthonormalizing a set of vectors. The method is named after Jørgen Pedersen Gram and Erhard Schmidt [4]. There are many applications of the Gram-Schmidt process in the field of computer science, e.g., error correcting codes or cryptology [8]. First, we prove some preliminary theorems about real unitary space. Next, we formalize the definition of the Gram-Schmidt process that finds orthonormal basis. We followed [5] in the formalization, continuing work developed in [7], [6].
On Bag of 1. Part I

Summary The article concerns about formalizing multivariable formal power series and polynomials [3] in one variable in terms of “bag” (as described in detail in [9]), the same notion as multiset over a finite set, in the Mizar system [1], [2]. Polynomial rings and ring of formal power series, both in one variable, have been formalized in [6], [5] respectively, and elements of these rings are represented by infinite sequences of scalars. On the other hand, formalization of a multivariate polynomial requires extra techniques of using “bag” to represent monomials of variables, and polynomials are formalized as a function from bags of variables to the scalar ring. This means the way of construction of the rings are different between single variable and multi variables case (which implies some tedious constructions, e.g. in the case of ten variables in [8], or generally in the problem of prime representing polynomial [7]). Introducing bag-based construction to one variable polynomial ring provides straight way to apply mathematical induction to polynomial rings with respect to the number of variables. Another consequence from the article, a polynomial ring is a subring of an algebra [4] over the same scalar ring, namely a corresponding formal power series. A sketch of actual formalization of the article is consists of the following four steps: 1. translation between Bags 1 (the set of all bags of a singleton) and N; 2. formalization of a bag-based formal power series in multivariable case over a commutative ring denoted by Formal-Series ( n, R ); 3. formalization of a polynomial ring in one variable by restricting one variable case denoted by Polynom-Ring (1, R ). A formal proof of the fact that polynomial rings are a subring of Formal-Series ( n, R ), that is R -Algebra, is included as well; 4. formalization of a ring isomorphism to the existing polynomial ring in one variable given by sequence: Polynom-Ring (1, R ) →˜ Polynom-Ring .
A text guided multi-task learning network for multimodal sentiment analysis

Multimodal Sentiment Analysis (MSA) is an active area of research that leverages multimodal signals for affective understanding of user-generated videos. Existing research tends to develop sophisticated fusion techniques to fuse unimodal representations into multimodal representation and treat MSA as a single prediction task. However, we find that the text modality with the pre-trained model (BERT) learn more semantic information and dominates the training in multimodal models, inhibiting the learning of other modalities. Besides, the classification ability of each modality is also suppressed by single-task learning. In this paper, We propose a text guided multi-task learning network to enhance the semantic information of non-text modalities and improve the learning ability of unimodal networks. We conducted experiments on multimodal sentiment analysis datasets, CMU-MOSI, CMU-MOSEI, and CH-SIMS. The results show that our method outperforms the current SOTA method.
Synergy of on-surface and in-air trajectories: Exploratory analysis of forensic online signatures implementing lessons learned from biometrics

With the increased use of digital devices, forensic document examiners (FDEs) encounter increasing number of dynamic or online signatures during their physical examinations. This shift expands the possibility of examinations and creates new challenges for FDEs. As such, FDEs require new examination skills using data science-based analyses with artificial intelligence and machine-learning techniques. In recent years, automated signature verification has gained significant interest in biometric research and could be useful in forensic investigations. However, the use of complex black-box systems inconveniencing FDEs in explaining the rationale behind their final assessment, especially when dealing with limited signature samples and various types of forged signatures. Therefore, a new forensic method is needed to assist FDEs’ analysis. To tackle these challenges and incorporate lessons learned from biometrics into forensics, this study proposes a novel forensic online signature analysis method. The proposed method uses a single-template strategy based on recent scientific findings in biometrics while updating the strategy for forensic use. This strategy creates a mean-template set from known signature samples that serve as a writer’s signature master pattern. Consequently, FDEs can evaluate intra-writer and inter-item variations using the mean-template set and a questioned signature. Furthermore, to take advantage of recent digital devices, we focused on both on-surface and in-air trajectories of online signatures, which could improve the discriminative power because in-air trajectories are invisible for imposters. Finally, we demonstrate the effectiveness and applicability of the proposed method in a forensic scenario using a public forensic online signature dataset.
Real-time marine target recognition method of underwater inspection robot based on computer vision

In the complex marine environment, target recognition is difficult, and the real-time detection has a slow speed. In this article, a target recognition method combining underwater generative adversarial network and improved YOLOv4 is proposed, which is named M-YOLOv4. Firstly, the images collected by the underwater inspection robot are enhanced using the underwater generative adversarial network algorithm to obtain the training datasets. Secondly, the YOLOv4 target detection algorithm combines the feature extraction network of MoblieNetv3 for lightweight processing, which reduces the network model size, and reduces the number of algorithm calculations and parameters. Then, change the size of the spatial pyramid pooling module pooling kernel, which can enlarge receptive field and integrate characteristics of different receptive fields. Finally, the processed datasets are transferred to the improved M-YOLOv4 algorithm for training, and the trained model is transplanted to the Jetson Nano hardware device for real-time detection. The results of experiments show that the mean average precision value of the improved M-YOLOv4 recognition is 90.77%, which is 2.02% higher than that of the unimproved one. The frame per second value of the lightweight YOLOv4 algorithm with MobileNetv3 is 27, an increase of 12 compared with YOLOv4. The improved M-YOLOv4 algorithm can perform accurate detection of marine multi-targets on embedded devices.
Peer Review Statement

All papers published in this volume have been reviewed through processes administered by the Editors. Reviews were conducted by expert referees to the professional and scientific standards expected of a proceedings journal published by IOP Publishing. · Type of peer review : Single Anonymous · Conference submission management system : Morressier · Number of submissions received : 63 · Number of submissions sent for review : 58 · Number of submissions accepted : 42 · Acceptance Rate (Submissions Accepted / Submissions Received × 100) : 66.7 · Average number of reviews per paper : 2 · Total number of reviewers involved : 3 · Contact person for queries : Name : Mochammad Tanzil Multazam Email : tanzilmultazam@umsida.ac.id Affiliation : Universitas Muhammadiyah Sidoarjo
Hidden Treasures of Kediri’s Medicinal Plants: A Collaborative Effort to Map and Validate Authentic Information Using Innovative QR Code Security and Cryptography

Abstract This research aims to map the distribution of rare and medicinal plants in Kediri Regency, Indonesia, and develop an interactive database system to provide accurate information on these plants. The study was conducted in collaboration between Balitbangda Kediri and Nusantara University PGRI Kediri. The data collection utilized innovation in creating a web-based information system with QR code security and cryptography validation to ensure the authenticity of the plant data. The results of this research provide a comprehensive database of rare and medicinal plants in Kediri Regency that can support drug information, particularly during the Covid-19 pandemic. The interactive system facilitates the dissemination of accurate information and raises public awareness about the potential of medicinal and rare plants in the region. This research has significant implications for the conservation of rare and medicinal plants and their potential contribution to public health.
HT2ML: An efficient hybrid framework for privacy-preserving Machine Learning using HE and TEE

Outsourcing Machine Learning (ML) tasks to cloud servers is a cost-effective solution when dealing with distributed data. However, outsourcing these tasks to cloud servers could lead to data breaches. Secure computing methods, such as Homomorphic Encryption (HE) and Trusted Execution Environments (TEE), have been used to protect outsourced data. Nevertheless, HE remains inefficient in processing complicated functions (e.g., non-linear functions) and TEE (e.g., Intel SGX) is not ideal for directly processing ML tasks due to side-channel attacks and parallel-unfriendly computation. In this paper, we propose a hybrid framework integrating SGX and HE, called HT2ML, to protect user's data and models. In HT2ML, HE-friendly functions are protected with HE and performed outside the enclave, while the remaining operations are performed inside the enclave obliviously. HT2ML leverages optimised HE matrix multiplications to accelerate HE computations outside the enclave while using oblivious blocks inside the enclave to prevent access-pattern-based attacks. We evaluate HT2ML using Linear Regression (LR) training and Convolutional Neural Network (CNN) inference as two instantiations. The performance results show that HT2ML is up to ∼11× faster than HE only baseline with 6-dimensional data in LR training. For CNN inference, HT2ML is ∼196× faster than the most recent approach (Xiao et al., ICDCS'21).
Online conflict resolution: Algorithm design and analysis

In today's digital age, the widespread dissemination of information through social media platforms has a profound impact on people's thoughts and actions. It fosters lively discussions and debates among individuals, but also sparks online conflicts that exacerbate social tensions and divisions. Therefore, it brings a challenge: how to eliminate these online conflicts. In this paper, we analyze and solve it from an algorithmic perspective. We introduce individuals who are willing to persuade their close friends to avoid conflicts with others. Our objective is to strategically select a group of influencers who can encourage others to act as mediators, thus mitigating online conflicts within the realm of social networks. Hence, an optimization problem is formulated, which is proved to be NP-hard and #P-hard. Subsequently, to solve the problem, an algorithm grounded in a reverse sampling technique is proposed, which achieves a performance bound of (1−1/e). Finally, the result of experiments on real-world networks datasets to evaluate our algorithm and shows that our algorithm outperforms all comparisons.
Seeking a safe and efficient similarity-based unfolding rule

The unfolding transformation has been widely used in many declarative frameworks for improving the efficiency of programs. Inspired by our previous experiences in fuzzy logic languages that do not deal with similarity relations, in this work we try to adapt such operation to the framework of the fuzzy logic programming language FASILL (acronym of “Fuzzy Aggregators and Similarity Into a Logic Language”) which has been developed in our research group for coping with implicit/explicit truth degree annotations, a great variety of connectives and unification by similarity. The traditional unfolding transformation is based on the application of unifiers on the heads and computational steps on the bodies of program rules. However, when considering similarity relations, the premature generation and application of weak (similarity-based) unifiers at unfolding time could destroy the correctness (i.e., the soundness and completeness) of the transformation. In this paper we study how to avoid the risk, by imposing some conditions on programs in order to be safely unfolded. We show that these conditions are sufficient to guarantee the correctness of the unfolding transformation, and we present some experiments performed with the implemented online tool for illustrating the improved efficiency of unfolded programs.
EXTRAPOLATION USAGE FOR PREDICTING CRYPTOCURRENCY PRICING

In this article, we delve into the challenging problem of forecasting cryptocurrency prices using mathematical extrapolation techniques. We highlight the scarcity of research in this domain, underlining the necessity for in-depth investigation. The article outlines the unresolved issues related to extrapolation-based cryptocurrency price prediction, such as market volatility and non-linearity. It primarily aims to showcase the potential of extrapolation for predicting bitcoin prices. The analysis involves a year-long bitcoin price trend, with the application of linear and polynomial extrapolation methods. While some correlation exists, notable discrepancies, especially during abrupt price changes, are evident. The conclusion emphasizes the limitations of extrapolation and advises a diversified approach to cryptocurrency investment decisions, considering various factors beyond mathematical data. In this article, we delve into the challenging problem of forecasting cryptocurrency prices using mathematical extrapolation techniques. We highlight the scarcity of research in this domain, underlining the necessity for in-depth investigation. The article outlines the unresolved issues related to extrapolation-based cryptocurrency price prediction, such as market volatility and non-linearity. It primarily aims to showcase the potential of extrapolation for predicting bitcoin prices. The analysis involves a year-long bitcoin price trend, with the application of linear and polynomial extrapolation methods. While some correlation exists, notable discrepancies, especially during abrupt price changes, are evident. The conclusion emphasizes the limitations of extrapolation and advises a diversified approach to cryptocurrency investment decisions, considering various factors beyond mathematical data.
Rhythm-based authorship recognition in syllabic and accentual-syllabic verse

This contribution explores the extent to which rhythm-based features of poetic texts can contribute meaningfully to authorship recognition. We show that, although a binary categorization of languages as syllabic vs. accentual-syllabic fails to fully explain the differences. However, once we formalize accentual regularity as a continuum, our analysis shows that authorship attribution results improve as we move from the most to the least accentually regular languages. This result supports our hypothesis that accentual regularity is an inhibiting factor in authorship attribution as long as accentual regularity is understood as a continuous property.
Developing Informatics Modules for Teachers of All Subjects Based on Professional Activities

In recent years it has become clear that teachers need digital competency to master their everyday work. However, this digital competency is based on informatics concepts and phenomena that cannot be addressed in an appropriate way without informatics education. This article describes an approach to basic informatics education for all teachers, regardless of their subjects or stage of their professional life. The results of a funded project in the federal country of North Rhine-Westphalia in Germany are presented in which, among other things, a community of practice (CoP) with members from all three phases of teacher education (pre-service, induction and in-service) developed and tested a modular concept of basic interactive informatics teaching units.