Miracle-dz/UniFAR-FTUs huggingface dataset:
Co-Designing Socio-Technical Interventions with Skilled Trade Workers

This paper lays out an approach to co-designing Artificial Intelligence (AI)-enhanced smart hand tools with skilled trade workers employed at a local municipality. Skilled trade workers contribute to society by building the infrastructure upon which the public depends. In addition, these technical interventions offer an opportunity for workers to benefit from the data they generate via smart hand tools, potentially creating a new empowerment dynamic with employers. Therefore, we consider technologies that support skilled trade workers in performing their work effectively, safely, and with increased levels of autonomy to be considered Public Interest Technologies (PIT). Interdisciplinary research is underway that aligns these approaches with Public Interest Design (PID) principles which informs researchers' desire to explore how emerging technologies - data cooperatives, blockchain, smart contracts, and data dividends - can further smart hand tools' empowerment dynamic. Future participatory design efforts may deliver additional insights and further impact technology deployment.
NoFunctionalMagneticResonanceImagingEvidencefor BrightnessandColorFilling-InInEarlyHuman VisualCortex

Thebrightnessandcolorofasurfacedependsonitscontrastwithnearbysurfaces.Forexample,agraysurfacecanappearverylightwhen surroundedbyablacksurfaceordarkwhensurroundedbyawhitesurface.Sometheoriessuggestthatperceivedsurfacebrightnessand colorisrepresentedexplicitlybyneuralsignalsincorticalvisualfieldmaps;theseneuralsignalsarenotinitiatedbythestimulusitselfbut rather by the contrast signals at the borders. Here, we use functional magnetic resonance imaging (fMRI) to search for such neural “filling-in”signals.AlthoughwefindtheusualstrongrelationshipbetweenlocalcontrastandfMRIresponse,whenperceivedbrightness or color changes are induced by modulating a surrounding field, rather than the surface itself, we find there is no corresponding local modulationinprimaryvisualcortexorothernearbyretinotopicmaps.Moreover,whenwemodeltheobtainedfMRIresponses,wefind strong evidence for contributions of both local and long-range edge responses. We argue that such extended edge responses may be caused by neurons previously identified in neurophysiological studies as being brightness responsive, a characterization that may therefore need to be revised. We conclude that the visual field maps of human V1 and V2 do not contain filled-in, topographical representationsofsurfacebrightnessandcolor.
A Cross-Cultural Exploration of Smart Home Energy Devices and Community Well-Being

This study explores the role of smart home energy devices in Home Energy Management Systems (HEMS) and their impact on culture and community well-being. Through a comprehensive seven-step methodology, we analyze the features of HEMS that enhance individual and community well-being across various cultural dimensions. Our findings highlight the significant influence of cultural values on the adoption and usage of smart home energy devices. In individualistic cultures, HEMS prioritize control, convenience, and energy-cost savings for individual well-being, while in collectivist cultures, HEMS focus on social contribution, status enhancement, and energy resilience for community well-being. This study represents an initial step in building a knowledge base to inform the design and promotion of culturally-appropriate and effective devices. By considering diverse cultural needs and values, we aim to contribute to the development of HEMS that better align with different cultural contexts, ultimately enhancing overall well-being.
Verification of Approximate Hardware Designs with ChiselVerify

Many popular applications show resilience to computational errors. Approximate Computing (AxC) exploits this to reduce their execution time and energy consumption by introducing approximations in software and hardware. Using AxC raises new challenges to ensure that hardware designs satisfy their demands before deployment, which hardware designers address by spending significant efforts on verification flows for their designs. However, there exist no tools for verifying approximate hardware designs, meaning that designers must replicate code to keep track of circuit outputs and subsequently compute relevant error metrics. We aim to solve this issue with a library that abstracts away port sampling and error computations behind a simple interface. With the library, designs can retrieve error metric values and constraint satisfaction results with only a few extra lines of code. We demonstrate these features with code examples and by characterizing a collection of inexact adders and multipliers and an approximate matrix-vector multiplier.
Fire Classification Using Attention-Based Deep-CNN in Digital Images

Forests play a crucial role in the environment, but they are facing a serious threat from forest fires caused by both natural and human-made factors. To prevent these fires from causing significant damage, an AI-based forest fire detection system is being proposed for use in smart cities to detect and address fires at an early stage. In this paper, we propose a method that uses transfer machine learning techniques to locate fires in digital material. Our approach is predicated on a deep convolutional neural network (CNN) based on MobileV2 network, that was trained using three dataset of images that featured both fire and non-fire related events. The proposed model is capable to recognize visual cues that indicate the presence of fire, and it makes use of the transfer learning techniques to enhance the fire detection accuracy and effectively assesses whether an input image contains fire or no-fire. The experiments are performed on three fire datasets, a combination of different fire datasets to check the performance of our proposed methods and the results are compared with the classical methods as well as state of the art methods using transfer learning techniques. The results show that the proposed approach achieves 98.42% accuracy, 1.58% error rate, 99.47% recall, and 97.42% precision in classifying the fire and no-fire images. Our approach has the potential to be useful in a range of settings, such as the prevention and extinguishment of fires, the conduct of security and surveillance operations, and the maintenance of public.
Personal OKRs: A Tool for Academic Goal Setting

This study aimed to implement the Objectives and Key Results (OKRs) framework as a Personal OKRs training program tailored for first-year students facing challenges in setting academic goals. The program involved a workshop and biweekly progress reviews. Sixteen participants, first-year undergraduate students who had previously taken preparatory courses in mathematics or physics, took part in the study. Six students received the Personal OKRs training program for sixteen weeks, while ten students comprised the control group without intervention. The impact of the Personal OKRs program on students' academic achievement and the three goal-setting components (meaningfulness, personal improvement, and data-based) were examined. Results indicated a marginally significant effect of the intervention on the data-based component ($t$ (14) = -1.97, $p= 0.07$) with a large effect size ($d=1.05$). Regularly tracking progress and engaging in self-reflection during the intervention appeared to play a significant role in enhancing academic goal setting.
Preliminary study effect of hydrogen peroxide on properties of mung bean protein isolates for producing plant-based emulsion sausage

This study aimed to use hydrogen peroxide (H<inf>2</inf>O<inf>2</inf>) to decolorize mung bean protein isolate (MPI) for producing plant-based emulsion sausages. Bleached MPI was obtained by dispersing mung bean protein concentrate (MPC) in 0.5% H<inf>2</inf>O<inf>2</inf> at a ratio of 1:10 for 90 min, at 30 °C before alkaline extraction and acid precipitation. It was found that H<inf>2</inf>O<inf>2</inf> had no significant effect on protein content, water holding capacity (WHC), oil absorption capacity (OAC), and emulsion stability (ES) but significantly lightened the color of MPI and sausage. MPI sausages had higher gel strength than commercial soy protein isolate (SPI) and H<inf>2</inf>O<inf>2</inf> bleached MPI sausage gave the highest gel strength.
Beneficial Effects of Transcutaneous Auricular Vagus Nerve Stimulation on the Emotional State and Cognitive Functioning: A Pilot Study

Vagus nerve stimulation as a modern effective neuromodulation method produces therapeutic effects for treating psychiatric disorders, neuralgia, heart failure, and others. Auricular vagus nerve stimulation reduces anxiety, alleviates stress and emotional burnout, and improves cognitive function. The objectives of the current study were to evaluate the effects of transcutaneous auricular vagus nerve stimulation (taVNS) and sham stimulation (SHAM) on emotional state, and emotional burnout and reveal a causal relation between taVNS and a related cognitive function. 6 right-handed male volunteers aged 18–22 years (Mage= 18.00) participated in the taVNS study. 5 right-handed male volunteers aged 18–22 years (Mage= 18.00) participated in SHAM study. We used the combination of pleasant meditative classical music and a slow bi-polar wave (0.1-0.2 Hz) of electrical non-invasive transcutaneous auricular vagus nerve stimulation for 5 minutes. The set of 4 VNS was performed at intervals of 3 days. EEG was registered during the rest state before and after taVNS (3 min, closed eyes condition). To measure the severity of emotional burnout, we used the 22-items Maslach Burnout Inventory (MBI). To determine the development of short-term memory we used 2 computer subtests. taVNS attenuated emotional burnout (the depersonalization and reduction of personal achievements). The results of the test indicate the improving effect of the set of VNS on short-term memory. Changes in the psychoemotional state of the respondents accompanied increases in the ratio of beta /alpha rhythms, the theta-Fz/alpha-Pz ratio that reflects an enhancement of the activation level, and the amount of brain resources involved in processing the perceived information. The observed increase in the power of the alpha rhythm may relate to improving of mental processes and internally oriented attention in creative activities. A higher left frontal activity under the cognitive test points to a positive emotional attitude toward the task. The detected focus of beta, gamma, and theta activities in the vertex (Cz) reflected the effect of taVNS on learning at the level of the sensorimotor cortex. Activation of the right dorsolateral prefrontal cortex reflects the taVNS effects on the attention processes. The increase in the theta activity in the left occipital cortex indicated the involvement of the visual cortex. The obtained data suggest that the taVNS have prolonged beneficial effects on emotional state and stimulating effect on the brain processes, enhancement of the cognitive process.
Application of Balance Scorecard as a Strategic Management and Performance Measurement Tool for Cyberfraud Mitigation

The process of cyberfraud mitigation necessitates performance measurement of the mitigation strategies vis-à-vis the organisation’s goal of cyberfraud mitigation. Thus, the purpose of this study is to demonstrate the application of balance scorecard as a strategic management and performance measurement tool for cyberfraud mitigation. First, the performance measures of the four BSC perspectives were identified and and weights were allocated to the perspectives based on their relative importance. Secondly, each score card was designed to include the performance measure, weight, the target, stretch and actual performance as well as the score and the code for each performance measured. Thereafter, the actual results which represents the real performance of the measure were simulated because of inability to obtain direct measurement. The simulated results were compared with the target and the stretch results, thus, the score of the performance measure were be calculated by interpolation modeling and a code will be allocated based on the calculated scores. This study therefore provides a practical guideline on how to allocate weights, and compute the score for the four perspectives of BSC.
An Investigation on RF Propagation in the Middle of Body Area Networks in Virtual Reality

In the context of body area networks, the establishment of efficient wireless communication protocols necessitates a comprehensive comprehension of the propagation medium. The acquisition of sufficient data for various scenarios in the context of implant communication is hindered, and in some cases rendered unattainable, by the challenges associated with physical testing. A comprehensive three-dimensional virtual reality platform has the potential to be highly advantageous in elucidating the properties of radio frequency propagation within and surrounding the human body. A platform of this nature offers the necessary comprehension of radio wave propagation essential for the implementation of BAN. Additionally, it can be utilized to determine the optimal conditions for conducting limited physical experimentation and data collection. This study presents a virtual reality platform that enables the visualization of three-dimensional radio frequency (RF) propagation originating from implanted and wearable sensors. The utilization of this simulated environment has been employed as an educational instrument for investigating diverse radio frequency communication channels that may arise either within or on the exterior of the human body.
The Effect of Nanobubble Ultrasound Contrast Agent Shell Stiffness and Temperature on Stability and Interactions with Red Blood Cells

Lipid-shell C3F8 nanobubble (NB) ultrasound contrast agents have demonstrated an extended in vivo lifespan compared to traditional microbubble contrast agents. One potential explanation behind this extended lifespan is non-covalent interactions between NBs and red blood cells (RBCs). This has been observed in vitro where interactions increased contrast intensity and signal stability over time. However, the mechanism of this interaction and the factors influencing it have not been fully elucidated. In this study, we investigate the role of NB shell stiffness and solution temperature on signal stability in human whole blood and PBS in vitro. We used three NB shell stiffnesses and a fourth formulation with the addition of a targeting ligand and assessed extent of RBC interaction using autocorrelation analysis. Results demonstrate a clear dependence of RBC interaction on shell stiffness. The stiffest NB formulation had a decorrelation time ~2x faster than all other formulations and showed no effect on signal change over time. Less stiff NBs resulted in an increase in intensity over time and increased decorrelation time, consistent with prior studies. No such effects were observed in PBS. Furthermore, the presence of a targeting ligand did not appear to play a role in contrast enhancement over time or decorrelation time. Decorrelation time in whole blood decreased with increasing temperature. This work examined the effect of shell stiffness and solution temperature on NB signal stability and interactions with RBCs in human whole blood in vitro. Understanding why NBs interact with RBCs could improve NB formulation optimization for extended circulation time in vivo.
Fine-Grained In-Context Permission Classification for Android Apps Using Control-Flow Graph Embedding

Android is the most popular operating system for mobile devices nowadays. Permissions are a very important part of Android security architecture. Apps frequently need the users' permission, but many of them only ask for it once—when the user uses the app for the first time—and then they keep and abuse the given permissions. Longing to enhance Android permission security and users' private data protection is the driving factor behind our approach to explore fine-grained context-sensitive permission usage analysis and thereby identify misuses in Android apps. In this work, we propose an approach for classifying the fine-grained permission uses for each functionality of Android apps that a user interacts with. Our approach, named DroidGem, relies on mainly three technical components to provide an in-context classification for permission (mis)uses by Android apps for each functionality triggered by users: (1) static inter-procedural control-flow graphs and call graphs representing each functionality in an app that may be triggered by users' or systems' events through UI-linked event handlers, (2) graph embedding techniques converting graph structures into numerical encoding, and (3) supervised machine learning models classifying (mis)uses of permissions based on the embedding. We have implemented a prototype of DroidGem and evaluated it on 89 diverse apps. The results show that DroidGem can accurately classify whether permission used by the functionality of an app triggered by a UI-linked event handler is a misuse in relation to manually verified decisions, with up to 95% precision and recall. We believe that such a permission classification mechanism can be helpful in providing fine-grained permission notices in a context related to app users' actions, and improving their awareness of (mis)uses of permissions and private data in Android apps.
System-on-Chip Design for Fast Ultrasonic Chirplet Signal Decomposition Algorithm

Decomposition and characterization of backscattered ultrasonic multiple interfering echoes is a critical step for analyzing the ultrasonic propagation path, propagation modes, coupling condition of the transducers and detecting defects within the propagation path. Chirplet Signal Decomposition (CSD) is an efficient way of analyzing ultrasonic echoes. However, CSD is computationally expensive and time-consuming for real-time signal processing applications. To address this problem, we present a System-on-Chip (SoC) implementation of the CSD algorithm, with the goal of speed optimization and real-time execution without sacrificing the accuracy of the signal decomposition and reconstruction. The implementation was tested on a Zynq Ultrascale+ series FPGA and achieved an echo estimation within two milliseconds.
UAMP-based delay-Doppler channel estimation for OTFS systems

Orthogonal time frequency space (OTFS) technique, which modulates data symbols in the delay-Doppler (DD) domain, presents a potential solution for supporting reliable information transmission in high-mobility vehicular networks. In this paper, we study the issues of DD channel estimation for OTFS in the presence of fractional Doppler. We first propose a channel estimation algorithm with both low complexity and high accuracy based on the unitary approximate message passing (UAMP), which exploits the structured sparsity of the effective DD domain channel using hidden Markov model (HMM). The empirical state evolution (SE) analysis is then leveraged to predict the performance of our proposed algorithm. To refine the hyperparameters in the proposed algorithm, we derive the update criterion for the hyperparameters through the expectation-maximization (EM) algorithm. Finally, Our simulation results demonstrate that our proposed algorithm can achieve a significant gain over various baseline schemes.
The Impact of Large Language Modeling on Natural Language Processing in Legal Texts: A Comprehensive Survey

Natural Language Processing (NLP) has witnessed significant advancements in recent years, particularly with the emergence of large language models. These models, such as GPT-3.5 and its variants, have revolutionized various domains, including legal text processing (LTP). This survey explores the impact of large language modeling on NLP in the context of legal texts. By analyzing the latest research and developments, we seek to understand the benefits, challenges, and potential applications of large language models in the field of legal language processing.
The Evolution of Administrative Information Systems: Assessing the Revolutionary Impact of Artificial Intelligence

In the rapidly evolving landscape of organizational technologies, the integration of Artificial Intelligence (AI) into Administrative Information Systems (AIS) stands out as a pivotal and transformative development. This research explores the historical trajectory of AI and its dynamic interplay with AIS, employing a combination of historical analysis, case studies, and in-depth examinations. Our findings reveal the substantial impact of AI on administrative efficiency and decision-making processes, showcasing successful implementations in diverse organizational settings. Concurrently, the study addresses the challenges and ethical considerations accompanying this integration, contributing to a nuanced understanding of the promises and pitfalls of AI in administrative contexts. Beyond retrospective analysis, the research speculates on future trends and innovations, providing insights that inform practitioners, policymakers, and scholars about the multifaceted implications of AI for modern organizations.
Machine Learning-Based Android Malware Detection

The use of mobile phones, particularly smartphones, has been growing exponentially in recent times. From 2016 to 2021, smartphone users increased by more than 70%. With the increase in the popularity of smartphones, smartphones have become the prime target for criminal hackers. As a result, Android malware samples are coming to the market at an alarming rate. A study shows that there are more than 4 million malicious Android apps in the market, and each day around 11,000 new malwares add to this number. To combat this mass number of malware, we need a malware detection system that is efficient in detecting malicious Android apps. There are numerous existing malware detection systems, but most of them require countless features from both dynamic and static analysis. Thus, they are not scalable, lightweight, and efficient in detecting malware. Additionally, most studies that used limited features like only permission data, had done their research on much older dataset. Hence, there is a need for new research on this topic. In this paper, we build a permission-based malware detector for Android application with a new dataset and significantly less permissions. Initially, we used support vector machine (SVM) and all the extracted permission data as features to build our classification model. The model accuracy, precision, recall and F1 score were 97.41 percent, which is higher than the other state of the art similar approaches done on an older dataset. Next, we replicated this similar study with a few different machine learning algorithms: random forest, decision tree and logistic regression, and observed they all give similar results. However, tree-based algorithm performs a little better than the other algorithms. Finally, to achieve a lightweight malware detection system, we reduced the number of permissions or the features on a two-step process, and found only a slight difference in results. In the first step, even after reducing the number of permissions by about 94%, the accuracy dropped by only 2.7%. In the second step, we further reduced the number of features or permissions and observed the difference in results. We managed to prune to 9 permissions while maintaining accuracy of 93%, which is lower than technique mentioned in other literature to reduce features.