Reviewerly/exHarmony huggingface dataset:
A Cost-Effective Unmanned Ground Vehicle (UGV) Using Swarm Robotics Technology for Surveillance and Future Combat

Swarm robotics, which takes its inspiration from nature, is a hybrid of swarm intelligence and robotics that has a lot of promise in various areas. The goal of this study is to build and develop a cost-effective and remote operative multi-directional Unmanned Ground Vehicle (UGV) using Swarm robotics technology. This study presents the development of an open-source, low-cost communication module that can connect small-sized robots to communicate on a mobile phone via Wi-Fi. Robots in close proximity are able to communicate and identify each other’s positions and bearings. The development involved Node MCU in remote-controlled and UGV robot, DC motor, L298N motor controller, Servo motor, ESP32 cam, and mechanical wheels. An android-based application called Blynk is used by smartphones to control the speed and directions of the vehicle to reach a specific destination. We investigate four steps of optimizations that were used to improve overall performance by looking at the potential of changes to the hardware level in conjunction with software improvements. In contemporary warfare, UGVs may be employed as a robotic army in any battle and as a surveillance device for a variety of objectives.
Digital safety alarms – Exploring the understandings of the cybersecurity practice in Norwegian municipalities

In this paper we describe initial results from a qualitative study on municipal approaches to cybersecurity in welfare technology or medical IoT. The paper is based on interviews with stakeholders from municipal IT and health services where digital safety alarms are used as a case to study how stakeholders with differing perspectives communicate and cooperate to secure these solutions. We identify three key issues related to the factors that motivate the municipalities to perform cybersecurity work and how different perspectives between IT and healthcare affect this, and discuss these findings utilizing protection motivation theory. We further discuss the need for shared understanding of cybersecurity and risk between health and IT as well as how municipalities are approaching to bridge this gap
Multi-Theorem Fiat-Shamir Transform from Correlation-Intractable Hash Functions

In STOC 2019 Canetti et al. showed how to soundly instantiate the Fiat-Shamir transform assuming that prover and verifier have access to the key of a correlation intractable hash function for efficiently searchable relations. The transform requires the starting protocol to be a special 3-round public-coin scheme that Canetti et al. call trapdoor sigma-protocol. One downside of the Canetti et al. approach is that the key of the hash function can be used only once (or a pre-determined bounded number of times). That is, each new zero-knowledge proof requires a freshly generated hash key (i.e., a freshly generated setup). This is in contrast to what happens with the standard Fiat-Shamir transform, where the prover, having access to the same hash function(modelled as a random-oracle), can generate an unbounded number of proofs that are guaranteed to be zero-knowledge and sound. As our main contribution we extend the results of Canetti et al., by proposing a multi-theorem protocol that follows the Fiat-Shamir paradigm and relies on correlation intractable hash functions. Moreover, our protocol remains zero-knowledge and sound even against adversaries that choose the statement to be proven (and the witness for the case of zero-knowledge) adaptively on the key of the hash function. Our construction is presented in the form of a compiler, that follows the Fiat-Shamir paradigm, which takes as input any trapdoor sigma-protocol for the NP-language L and turns it into a non-interactive zero-knowledge protocol that satisfies the properties we mentioned. To be best of our knowledge, ours is the first compiler that follows the Fiat-Shamir paradigm to obtain a multi-theorem adaptive NIZK relying on correlation intractable hash functions.
Cultural-Ethical Evaluation in the Launch of AI Education Technologies

The unique role of artificial intelligence-based solution in education (AIED) during COVID-19 as a key contributor to saving global education from collapse remains more than significant in a post-pandemic period. However, this positive dynamics for AIED solutions is meeting new challenges connected, among other things, with a certain ignorance to cultural and ethical factors among the audiences (primarily developers/owners of AI technologies) within the AIED environment. This, in turn, results in a question whether it is possible to measure the potential impact of the cultural and ethical factor prior to the introduction of the AIED algorithms to mitigate or avoid possible misunderstandings. To answer it, the author attempts to describe the basics of a cultural-ethical evaluation model for AIED, which can be used by developers for preliminary assessments of cultural-ethical risks for certain audiences (administration and faculty) during the implementation of appropriate AI solutions in educational institutions.
Psychometric validation for a brand-new tool for the assessment of executive functions using 360° technology

EXecutive-functions Innovative Tool 360° (EXIT 360°) is an original 360° instrument for an ecologically valid and multicomponent evaluation of executive functioning. This work aimed to test the diagnostic efficacy of EXIT 360° in distinguishing executive functioning between healthy controls (HC) and patients with Parkinson's Disease (PwPD), a neurodegenerative disease in which executive dysfunction is the best-defined cognitive impairment in the early stage. 36 PwPD and 44 HC underwent a one-session evaluation that involved (1) neuropsychological evaluation of executive functionality using traditional paper-and-pencil tests, (2) EXIT 360° session and (3) usability assessment. Our findings revealed that PwPD made significantly more errors in completing EXIT 360° and took longer to conclude the test. A significant correlation appeared between neuropsychological tests and EXIT 360° scores, supporting a good convergent validity. Classification analysis indicated the potential of the EXIT 360° for distinguishing between PwPD and HC in terms of executive functioning. Moreover, indices from EXIT 360° showed higher diagnostic accuracy in predicting PD group membership compared to traditional neuropsychological tests. Interestingly, EXIT 360° performance was not affected by technological usability issues. Overall, this study offers evidence that EXIT 360° can be considered an ecological tool highly sensitive to detect subtle executive deficits in PwPD since the initial phases of the disease.
A Framework for Advanced Persistent Threat Attribution using Zachman Ontology

Advanced Persistent Threat (APT) is a type of cyber attack that infiltrates a targeted organization and exfiltrates sensitive data over an extended period of time or to cause sabotage. Recently, there has been a trend of nation states backing APT groups in order to further their political and financial interests, making the APT attribution process increasingly important. The APT attribution process involves identifying the actors behind an attack and their motivations, using a method of logical inference called abductive reasoning to determine the most likely explanation for a set of observations. While various attribution methods and frameworks have been proposed by the security community, many of them lack granularity and are dependent on the skills of practitioners rather than a standardized process. This can hinder both the understandability and reproducibility of attribution efforts as this process is practiced but not engineered. To address these issues, we propose a new framework for the APT attribution process based on the Zachman ontology, which offers greater granularity by posing specific primitive questions at various levels of the attribution process. This allows for more accurate conclusions about the attackers and their motivations, helping organizations to better protect themselves against future attacks.
Patient Monitoring Through Intelligent Agents: A Preliminary Study

In this work, preliminary research was carried out considering Smart Hospitals as antecedents, with the purpose of developing a proposal for the automation of a hospital-type room. This room will have the purpose of monitoring bedridden patients, through the paradigm of intelligent agents and will be able to contribute to improving patient care and supporting the main caregiver in this arduous task.
A Graph Contrastive Learning Framework with Adaptive Augmentation and Encoding for Unaligned Views

Recently, graph contrastive learning has emerged as a successful method for graph representation learning, but it still faces three challenging problems. First, existing contrastive methods cannot preserve the semantics of the graph well after view augmentation. Second, most models use the same encoding method to encode homophilic and heterophilic graphs, failing to obtain better-quality representations. Finally, most models require that the two augmented views have the same set of nodes, which limits flexible augmentation methods. To address the above problems, we propose a novel graph contrastive learning framework with adaptive augmentation and encoding for unaligned views, called GCAUV in this paper. First, we propose multiple node centrality metrics to compute edge centrality for view augmentation, adaptively removing edges with low centrality to preserve the semantics of the graph well. Second, we use a multi-headed graph attention network to encode homophilic graphs, and use MLP to encode heterophilic graphs. Finally, we propose g-EMD distance instead of cosine similarity to measure the distance between positive and negative samples. We also perform adversarial training by adding perturbation to node features to improve the accuracy of GCAUV. Experimental results show that our method outperforms the state-of-the-art graph contrastive methods on node classification tasks.
Teaching Object-Oriented Requirements Techniques: An Experiment

Scenario-based software requirements specifications, due to limitations of natural language and scenarios, lack precision and abstraction. Formal methods address this problem, but are rarely used. A Unified Object-Oriented (OO) approach complements the simplicity and appeal of scenario techniques with the rigor and clarity of software contracts. In this study we conduct a teaching experiment to evaluate the perception of usefulness and difficulty of the approach. The obtained results demonstrate that the unified OO requirements approach has a potential to be adopted by requirements engineering practitioners.
Data Discretization for Data Stream Mining

Data discretization has become essential task to cope with streaming data. However, this field still requires particular attention. Indeed, various constraints should be taken into consideration to cope with discretization in data stream scenarios. In this paper, we discuss the discretization process for data stream scenarios. In this setting, we examine the proposed algorithms in the literature, their limitations and the new challenges. Also, we propose a novel discretization method so called Hybrid Online Discretization (HOD). Based on the well-known Minimum Description Length Principle (MDLP) discretization combined with the Online ChiMerge Algorithm, the proposed method guarantees an efficient and effective discretization. Empirical trials show that in most cases the proposed algorithm outperforms its competitors in terms of accuracy level and reduction rate. Finally, we experimentally demonstrate that coupled with HOD, Naive Bayes classifier shows high classification accuracy.
Exploratory Study of Eye-Tracking Path: A Case Study from Switzerland

It is known that due to the nature of 360° videos, a user only utilizes about 20% of the transmitted data, so understanding how the viewer actually watches them, is very crucial for the scenario of the video, and for stimulating the viewer for a certain action, like taking a real trip. We conducted the experiment using the 360° videos produced by the tourist bureau of the Canton of Vaud in Switzerland, on 16 viewers. This study was conducted in January 2023, and therefore, this paper is the initial step for the eye-movement path description from this experiment. The paper consists of 4 parts—introduction, theoretical background for eye-tracking path data, eye-tracking data analysis and conclusion.
Bitcoin Fraudulent Transaction Detection Vulnerability

Bitcoin is expected to play a vital role in the web3 and is attracting attention. Still, it is notorious as a hotbed for various fraudulent transactions, which are on the rise. One of the characteristics of Bitcoin is its anonymity. Bitcoin allows tracing transactions, but the owner cannot be identified. In addition, bitcoin is considered more difficult to trace criminals than bank transactions because of the unlimited number of addresses that can be created. This study shows that it is possible to detect criminal transactions with known transaction patterns using supervised learning. Still, it is difficult to detect criminal transactions with unknown transaction patterns using supervised learning. We also show that even with anomaly detection methods, illicit transactions are difficult to detect. From the above, we believe that Bitcoin is vulnerable to illicit transactions.
Acehnese Celebgrams’ Repetitive Language Style as an Endorsement Strategy: A Stylistic Study

The repetitive language style is a language style to emphasize a speech's meaning.This language style can be found in the speech of Acehnese Instagram celebrities (celebgrams) when they endorse a product or service.The repetitive language style of Acehnese celebgrams can conjure up Instagram users, so they are interested in the goods and services offered.By relying on language style, the celebgrams were proven to increase the turnover of online business people who used their services.It was because the language style was unique, engaging, and creative.Therefore, this study aimed to describe the repetitive language style used by Acehnese celebgrams as a strategy in the endorsement.This study was conducted using a stylistic review.The source of this research data was the celebgrams' speech taken from the Acehnese celebgrams' Instagram story, which consisted of four accounts, namely 1) @cutbul-real, 2) @devy_dean, 3) @dinah_mr, 4) @kakaalfarisi, and 5) @khalidamakmoer.Verbal data were collected using recording and note-taking techniques.The data were analyzed using a qualitative descriptive approach.The results showed that the Acehnese celebgrams used eight styles of repetition as an endorsement strategy, including epizeuxis, tautotes, anaphora, epistrophe, mesodiplosis, anadiplosis, and alliteration.The repetitive language style strongly affected the Instagram users' interest in using the products and services offered.
Multi-Augmentation Contrastive Learning as Multi-Objective Optimization for Graph Neural Networks

Abstract Recently self-supervised learning is gaining popularity for Graph Neural Networks (GNN) by leveraging unlabeled data. Augmentation plays a key role in self-supervision. While there is a common set of image augmentation methods that preserve image labels in general, graph augmentation methods do not guarantee consistent graph semantics and are usually domain dependent. Existing self-supervised GNN models often handpick a small set of augmentation techniques that limit the performance of the model. In this paper, we propose a common set of graph augmentation methods to a wide range of GNN tasks, and rely on the Pareto optimality to select and balance among these possibly conflicting augmented versions, called P areto G raph C ontrastive L earning ( PGCL ) framework. We show that while random selection of the same set of augmentation leads to slow convergence or even divergence, PGCL converges much faster with lower error rate. Extensive experiments on multiple datasets of different domains and scales demonstrate superior or comparable performance of PGCL.
Enhancing Federated Learning Robustness Using Data-Agnostic Model Pruning

Federated learning enables multiple data owners with a common objective to participate in a machine learning task without sharing their raw data. At each round, clients train local models with their own data and then upload the model parameters to update the global model. This multi-agent form of machine learning has been shown prone to adversarial manipulation by recent studies. Byzantine attackers impersonated as benign clients can stealthily interrupt or destroy the learning process. In this paper, we propose FLAP, a post-aggregation model pruning technique to enhance the Byzantine robustness of federated learning by effectively disabling the malicious and dormant components in the learned neural network models. Our technique is data-agnostic, without requiring clients to submit their dataset or training output, well aligned with the data locality of federated learning. FLAP is performed by the server right after the aggregation, which renders it compatible with an arbitrary aggregation algorithm and existing defensive techniques. Our empirical study demonstrates the effectiveness of FLAP under various settings. It reduces the error rate by up to 10.2% against the state-of-the-art adversarial models. Moreover, FLAP also manages to increase the average accuracy by up to 22.1% against different adversarial settings, mitigating the adversarial impacts while preserving learning fidelity.
CondTraj-GAN: Conditional Sequential GAN for Generating Synthetic Vehicle Trajectories

While the ever-increasing amount of available data has enabled complex machine learning algorithms in various application areas, maintaining data privacy has become more and more critical. This is especially true for mobility data. In nearly all cases, mobility data is personal and therefore the drivers' privacy needs to be protected. However, mobility data is particularly hard to anonymize, hindering its use in machine learning algorithms to its full potential. In this paper, we address these challenges by generating synthetic vehicle trajectories that are not subject to personal data protection but have the same statistical characteristics as the originals. We present CondTraj-GAN– Conditional Trajectory Generative Adversarial Network. – a novel end-to-end framework to generate entirely synthetic vehicle trajectories. We introduce a specialized training and inference procedure that enables the application of GANs to discrete trajectory data conditioned on their sequence length. We demonstrate the data utility of the synthetic trajectories by comparing their spatial characteristics with the original dataset. Finally, our evaluation shows that CondTraj-GAN reliably outperforms state-of-the-art trajectory generation baselines.
Few-Shot Human Motion Prediction for Heterogeneous Sensors

Human motion prediction is a complex task as it involves forecasting variables over time on a graph of connected sensors. This is especially true in the case of few-shot learning, where we strive to forecast motion sequences for previously unseen actions based on only a few examples. Despite this, almost all related approaches for few-shot motion prediction do not incorporate the underlying graph, while it is a common component in classical motion prediction. Furthermore, state-of-the-art methods for few-shot motion prediction are restricted to motion tasks with a fixed output space meaning these tasks are all limited to the same sensor graph. In this work, we propose to extend recent works on few-shot time-series forecasting with heterogeneous attributes with graph neural networks to introduce the first few-shot motion approach that explicitly incorporates the spatial graph while also generalizing across motion tasks with heterogeneous sensors. In our experiments on motion tasks with heterogeneous sensors, we demonstrate significant performance improvements with lifts from $$10.4\%$$ up to $$39.3\%$$ compared to best state-of-the-art models. Moreover, we show that our model can perform on par with the best approach so far when evaluating on tasks with a fixed output space while maintaining two magnitudes fewer parameters.
Improving Covert Channel Bandwidth by Variable-Length Exponent Re-Coding in Floating-Point Representations

In previous work, covert channels in network payload data were introduced that recode floating-point representations, yet introduce errors by modifying mantissa bits. The present research refines those covert channels for the case of fully reversible covert channels, i.e., where the original data is reconstructable by the covert receiver. By using knowledge about the distribution of the data over the number range, a variable length encoding of the exponent is derived that allows to improve covert channel bandwidth to levels comparable with previous work but without introducing errors to original data.
MixER: MLP-Mixer Knowledge Graph Embedding for Capturing Rich Entity-Relation Interactions in Link Prediction

Knowledge graphs are often incomplete in practice, so link prediction becomes an important problem in developing many downstream applications. Therefore, many knowledge graph embedding models have been proposed to predict the missing links based on known facts. Convolutional neural networks (CNNs) play an essential role due to their excellent performance and parameter efficiency. Previous CNN-based models such as ConvE and KMAE use kernels to capture interactions between embeddings, yet they are limited in quantity. In this paper, we propose a novel neural network-based model named MixER to exploit more additional interactions effectively. Our model incorporates two types of multi-layer perceptions (i.e., channel-mixing and token-mixing), which extract spatial information and channel features. Hence, MixER can seize richer interactions and boost the link prediction performance. Furthermore, we investigate the characteristics of two core components that benefit in capturing additional interactions in diverse regions. Experimental results reveal that MixER outperforms state-of-the-art models in the branch of CNNs on three benchmark datasets.
Implementation Movement Literacy School Through Corner Read in Improve the Ability of Read Participant Educate in SD Muhammadiyah Metro Center

The problem in this research is that there are still SD Muhammadiyah Pusat participant educate class V Which Not yet fluent read.Study it aims For describes (1) Implementation of the School Literacy Movement (GLS) through reading corners in increase ability read in SD Muhammadiyah Center, (2) The ability to read students in Central Muhammadiyah Elementary School after being implemented reading corner, and (3) Impacts and constraints in implementing GLS through reading corner in increase ability read in SD Muhammadiyah Center.Method study in study This is method qualitative descriptive with type case study research.Data collection techniques with interviews, observation, and documentation.The results of the research show that the implementation of GLS is through corners reading in Central Muhammadiyah Elementary School has reached the habituation stage and stage development.Stage habituation done with activity read 15 min before learning begins and the development stage is carried out by visiting to the school library, there is a class reading corner, and there is a literacy movement team school.The ability to read in Central Muhammadiyah Elementary School has increased after the holding of the GLS through reading corner, but there are still obstacles faced in implementing GLS through reading corners, namely the lack of classrooms and the availability of books is still limited so that the reading books are in the reading corner No updated regularly routine.