Miracle-dz/UniFAR-FTUs huggingface dataset:
Securing Multi-Environment Networks using Versatile Synthetic Data Augmentation Technique and Machine Learning Algorithms

The emergence of new network architectures, protocols, and tools has made it easier for cybercriminals to launch attacks using AI-based tools, presenting challenges in network security. To protect such systems, a versatile malicious traffic detection system is required that can identify attacks regardless of the type of traffic coming toward the network. In this paper, a system is proposed that can singly analyze multi-environment traffic (IoT and traditional IP-based) to detect malicious activity. The existing techniques for managing Multi-Environment traffic are inefficient due to the absence of AI utilization. To overcome these issues, the proposed approach generates a novel multienvironment traffic dataset by merging existing network datasets containing both traditional IP-based traffic and IoT network traffic. Synthetic Data Augmentation TEchnique (S-DATE) is also proposed to overcome the problem of imbalanced data distribution in the new multi-environment dataset. The results show that the utilization of S-DATE results in faster machine learning model convergence and an improvement in the detection rate of normal and abnormal traffic. The proposed approach achieves an impressive overall detection rate of 0.991 and is statistically significant compared to other state-of-the-art approaches.
ATTRACTIVENESS AND DESIRE

A large, controversial literature has examined the hypothesis that the attractiveness of potential partners predicts romantic desire more strongly for men than for women. Nevertheless, prior studies demonstrating this sex difference in photograph-evaluation contexts have used extremely small samples of stimuli, which is as detrimental to statistical power as the use of small samples of participants. The current registered report used very large samples of both participants ( N =1,204) and stimuli photographs ( N =593) to test the sex difference in the attractiveness-desire association. The sex difference emerged with objective assessments of attractiveness from independent raters (approximately q = .13, a small effect) but not with participants’ own assessments of attractiveness ( q = .00). Various other moderators that have been summoned to explain cross-study variability in prior research received no support (e.g., the sex difference was not larger in serious relationship contexts, the low-to-moderate range of attractiveness, etc.). Surprisingly, in the small sample of participants who were attracted to same-sex individuals, the attractiveness-desire association was stronger for women than men—the opposite of the sex difference anticipated by prior mate preferences research. This study provides effect-size benchmarks for studies of sex differences and highlights the importance of stimulus sampling when documenting replicable effects.
Measuring Faculty Perceptions of Blackboard Using the Measuring Faculty Perceptions of Blackboard Using the Technology Acceptance Model Technology Acceptance Model

Web-based education offers the combination of self-paced learning and interactivity. We are just now beginning to empirically assess the differences between online education and traditional classroom based instruction. The Technology Acceptance Model (TAM) has been widely used in Information Systems research to analyze user perceptions of technology. This paper describes the results of an empirical study of faculty perception of Blackboard usage, utilizing TAM as its theoretical basis.
Optimizing S-box Implementations Using SAT Solvers: Revisited

We propose a new method to encode the problems of optimizing S-box implementations into SAT problems. By considering the inputs and outputs of gates as Boolean functions, the fundamental idea of our method is representing the relationships between these inputs and outputs according to their algebraic normal forms. Based on this method, we present several encoding schemes for optimizing S-box implementations according to various criteria, such as multiplicative complexity, bitslice gate complexity, gate complexity, and circuit depth complexity. The experimental results of these optimization problems show that, compared to the encoding method proposed in FSE 2016, which represents these relationships between Boolean functions by their truth tables, our new encoding method can significantly accelerate the subsequent solving process by 2-100 times for the majority of instances. To further improve the solving efficiency, we propose several strategies to eliminate the redundancy of the derived equation system and break the symmetry of the solution space. We apply our method in the optimizations of the S-boxes used in Ascon, ICEPOLE, PRIMATEs, Keccak/Ketje/Keyak, Joltik/Piccolo, LAC, Minalpher, Prøst, and RECTANGLE. We achieve some new improved implementations and narrow the range of the optimal values for different optimization criteria of these S-boxes.
Silicon Nitride Cantilevers for Muscle Myofibril Force Measurements

: To measure muscle forces in the nano-Newton range, silicon nitride cantilever pairs were manufactured and used. Measuring sarcomere length (SL) variation across a myofibril with 20-30 sarcomeres in-series in skeletal muscle myofibrils provides useful information about variation in individual SLs and indirectly, the isoform mass, extensibility, and quantity of the structural protein titin [2]. Myofibril imaging has traditionally been done using phase-contrast (PC) microscopy, but this technique has limited resolution. Promising and reliable fluorescent epitope labelling can be done on sarcomeric structural proteins α-actinin (Z-line) and myomesin (M-line). The purpose of this study was to 1: Test if antibody labelling techniques affect SL non-uniformities and SL repeatability following a stretch-shortening protocol, and 2: Measure passive stress at matched SL to determine if labelling affects passive force development at long SL. Myofibrils from rabbit psoas muscle were used and labelled with anti-α-actinin, anti-myomesin primary antibodies, and polyclonal IgG (H+L) AlexaFluor488 secondary antibodies and observed using both
Journal Pre-proof Using Thermal Imaging to Measure Hand Hygiene Quality

Objectives : Hand hygiene has long been promoted as the most effective way to prevent the transmission of infection. However, due to the low compliance and quality of hand hygiene reported in previous studies, constant monitoring of healthcare workers’ hand hygiene compliance and quality is crucial. In this study, we investigate the feasibility of using a thermal camera together with an RGB camera to detect hand coverage of alcohol-based formulation, thereby monitoring handrub quality. Methods : We recruited 32 participants for this study. Participants were required to perform four types of handrubs to produce different hand coverage of alcohol-based formulation. After each task, participants' hands were photographed under a thermal camera and an RGB camera, while an ultraviolet (UV) test was used to provide the ground truth of hand coverage of alcohol-based formulation. Then, a U-Net was used to segment areas covered with alcohol-based formulations from thermal images, and the system performance was evaluated by comparing coverage differences between thermal images and UV images regarding accuracy and Dice coefficient. Results : The system yields promising results in terms of accuracy (93.5%) and Dice coefficient (87.1%) when observations take place 10 seconds after performing handrub. In addition, we also examine the system performance change over a 60-second observation period, and the accuracy and Dice coefficient still remain at about 92.4% and 85.7% when observation happens at the 60-second time point. Conclusions : Given these encouraging results, thermal imaging shows its potential feasibility in providing accurate, constant, and systematic hand hygiene quality monitoring.
Proposing a

Problematic Internet Use (PIU), with its adverse health outcomes, is a clinically established phenomenon that is not formally diagnosable yet. The latest PIU research reveals differential psychological mechanisms underlying two types of Internet usage: social use (like using social media, chatrooms, and others) and non-social use (like live streaming, short-form video viewing, and others). Social forms of PIU often signal underlying interpersonal issues, including loneliness, attachment problems, and social skill deficits. In comparison, non-social usage might indicate emotion regulation problems, including emotion recognition deficits, poor cognitive empathy, and emotion suppression. A common developmental concomitant of these functions is the ability to mentalize, operationalized as Reflective Functioning (RF). While studies supporting the role of impaired RF in problematic social uses of the Internet abound, only a few studies relating RF deficits to non-social PIU are identifiable. Also, mentalizing is multidimensional, with cognitive and affective poles influencing emotion regulation and interpersonal functioning. Deficits in both poles of mentalizing have been differentially implicated in non-social and social PIU. Based on the latest empirical evidence in Internet-use problems, the multifaceted role of mentalizing could be meaningfully assimilated into a novel social-cognitive model of PIU. To that end, the current paper attempts to theoretically integrate affective and cognitive mentalizing, with its roles in emotion regulation and interpersonal functioning, into Bandura’s Social-Cognitive Theory of PIU. The theoretical model might have implications in developing a mentalization-based treatment modality for PIU that could train individuals in efficiently regulating emotions and navigating real-life social situations without resorting to an over-involvement with the virtual world
The role of tree stem proximity in the spatial variability of soil water repellency in a eucalypt plantation in coastal Portugal

Certain organic compounds derived from living organisms or their decaying parts are generally accepted to induce soil water repellency. Water repellency may therefore be expected to increase with proximity to organisms releasing hydrophobic compounds. This hypothesis is tested here for Eucalyptus globulus trees, since eucalypt species are frequently associated with elevated repellency levels. In a young, first-rotation plantation on coastal dune sands in central Portugal, repeat measurements of water repellency using the 'Molarity of an Ethanol Droplet' (MED) test were carried out in situ between April 2001 and May 2002. On 25 dates, repellency was measured at initially 2 and later 3 distances on 2 sides of 8-11 randomly selected trees. On 15 occasions, additional repellency measurements were performed within small grids aside 3 of the selected trees. The postulated decrease in topsoil water repellency with increased distance from eucalypt tree stems was found to apply on several individual measurement dates, as well as, more unexpectedly since repellency usually is a transient phenomenon, for the study period as a whole. The results confirm the general association of eucalypt trees with water repellency, and indicate that tree stem proximity is an important but not sufficient factor to explain repellency distribution in topsoil.
A Visualization Method of Knowledge Graphs for the Computation and Comprehension of Ultrasound Reports

Knowledge graph visualization in ultrasound reports is essential for enhancing medical decision making and the efficiency and accuracy of computer-aided analysis tools. This study aims to propose an intelligent method for analyzing ultrasound reports through knowledge graph visualization. Firstly, we provide a novel method for extracting key term networks from the narrative text in ultrasound reports with high accuracy, enabling the identification and annotation of clinical concepts within the report. Secondly, a knowledge representation framework based on ultrasound reports is proposed, which enables the structured and intuitive visualization of ultrasound report knowledge. Finally, we propose a knowledge graph completion model to address the lack of entities in physicians’ writing habits and improve the accuracy of visualizing ultrasound knowledge. In comparison to traditional methods, our proposed approach outperforms the extraction of knowledge from complex ultrasound reports, achieving a significantly higher extraction index (η) of 2.69, surpassing the general pattern-matching method (2.12). In comparison to other state-of-the-art methods, our approach achieves the highest P (0.85), R (0.89), and F1 (0.87) across three testing datasets. The proposed method can effectively utilize the knowledge embedded in ultrasound reports to obtain relevant clinical information and improve the accuracy of using ultrasound knowledge.
Heating Methods and Detection Limits for Infrared Thermography Heating Methods and Detection Limits for Infrared Thermography Inspection of Fiber-Reinforced Polymer Composites Inspection of Fiber-Reinforced Polymer Composites

The use of fiber-reinforced polymer (FRP) composites to strengthen existing civil infrastructure is expanding rapidly. Many FRP systems used to strengthen reinforced concrete are applied using a wet lay-up method in which dry fibers are saturated on site and then applied to the surface. This research investigated using infrared thermography (IRT) as a nondestructive evaluation (NDE) tool for detecting air voids and epoxy-filled holes in FRP systems bonded to a concrete substrate. Four small-scale specimens with FRP thicknesses ranging from 1 to 4 mm (0.04 to 0.16 in.) containing fabricated defects were constructed and inspected in a laboratory setting. Three heating methods (flash, scan, and long pulse) were employed and a quantitative analysis of resulting IRT data was used to establish detection limits for each method. Scan heating was shown to be most effective for basic defect detection. Air-filled defects at the FRP/concrete interface as small as 2.9 cm 2 (0.45 in. 2 ) were detected in a 4 mm (0.16 in.) thick FRP system. Defects as small as 0.3 cm 2 (0.05 in. 2 ) were detected in a 1 mm (0.04 in.) thick FRP system.
Case Report

was operated on using one-stage bilateral subtalar arthrodesis with percutaneous screw fixation and bone grafting performed through a mini-open sinus tarsi approach. The early postoperative period went unremarkably. The patient did not walk for the first seven postoperative weeks, after which rehabilitation was continued with partial weight bearing with a gradual increase of weight-bearing to full through the next five weeks, after which walking aids were completely phased out. Three years after surgery patient has no symptoms and has a close-to-normal gait. Conclusion Although this type of injury has traditionally been treated with open reduction and internal fixation, we believe that primary subtalar arthrodesis with bone grafting through a mini-open sinus tarsi approach can benefit patients with severe comminution of calcaneus, allowing good functional results and patient satisfaction, with fewer postoperative complications and faster definitive recovery.
FOREST LAND AREA ESTIMATES FROM VEGETATION CONTINUOUS FIELDS

The USDA Forest Service’s Forest Inventory and Analysis (FIA) program provides data, information, and knowledge about our Nation’s forest resources. FIA regional units collect data from field plots and remotely sensed imagery to produce statistical estimates of forest extent (area); volume, growth, and removals; and health and condition. There is increasing need to analyze and display FIA estimates within a geospatial context. A nationwide map of forest land would provide the basis for mapping additional FIA attributes. Forest land maps derived from existing satellite image-based land cover products are inconsistent with FIA estimates of forest land area, especially within smaller geographic areas. We describe an image stratification approach for calibrating MODIS Vegetation Continuous Fields (VCF) data to produce maps of forest land that provide unbiased estimates of forest land area compared to FIA estimates across multiple spatial scales.
DDPQN: An Efﬁcient DNN Ofﬂoading Strategy in Local-Edge-Cloud Collaborative Environments

— WiththerapiddevelopmentoftheInternetofThings(IoT)andcommunicationtechnology,DeepNeuralNetwork(DNN) applicationslikecomputer vision,cannowbewidelyusedinIoTdevices.However,duetotheinsufﬁcientmemory,lowcomputingcapacity,and lowbatterycapacityofIoTdevices,itisdifﬁculttosupportthehigh-efﬁciencyDNNinferenceandmeetusers’ requirementsforQualityof Service(QoS). Worsestill,ofﬂoadingfailuresmayoccurduringthemassiveDNNdata transmissionduetotheintermittentwireless connectivitybetweenIoTdevicesandthecloud.Inorder toﬁllthisgap,weconsider thepartitioningandofﬂoadingoftheDNNmodel,and designanoveloptimizationmethodfor parallelofﬂoadingoflarge-scaleDNNmodelsinalocal-edge-cloudcollaborativeenvironmentwith limitedresources.Combinedwiththecouplingcoordinationdegreeandnodebalancedegree,animprovedDoubleDuelingPrioritizeddeepQ-Network(DDPQN)algorithmisproposedtoobtaintheDNNofﬂoadingstrategy. Comparedwithexistingalgorithms,theDDPQNalgorithmcan obtainanefﬁcientDNNofﬂoadingstrategywithlowdelay,lowenergyconsumption,andlowcostunder thepremiseofensuring“delay-energy-cost” coordinationandreasonableallocationofcomputingresourcesinalocal-edge-cloudcollaborativeenvironment.
Improved YOLOv5-based Infrared Dim-small Target Detection under Complex Background

: Using the traditional algorithm to meet the detection requirements of interference factors, such as complex background and noise, relying on the precise separation and information extraction of infrared targets and environmental background, is difficult. This paper presents a dim–small target detection method for an infrared imaging algorithm based on the improved YOLOv5 for complex backgrounds. Based on YOLOv5, an attention mechanism is introduced in the algorithm to improve the feature extraction ability and detection efficiency. In addition, the loss function and prediction box screening method of the original YOLOv5 target detection network are used to improve the accuracy of the algorithm for infrared dim–small target detection. In the experiment, seven sets of infrared dim–small target image datasets with different complex backgrounds are selected, the data are labeled and trained, and an infrared dim–small target detection model is established. Finally, the accuracy of the algorithm and model is evaluated in terms of the model training and target detection results. The experimental results show that the model trained by employing the improved YOLOv5 algorithm in this study has a significant improvement in detection accuracy and speed compared with several target detection algorithms used in the experiment, and the average accuracy can reach more than 99.6%. The model can effectively detect infrared dim–small targets in different complex backgrounds, and the leakage and false alarm rates are low.
Assessing the Inﬂuence of Customer Relationship Management (CRM) Dimensions on Bank Sector in Sudan

: The objective of this study is to explore the relationship between CRM dimensions (customer orientation, CRM organization, knowledge management, and technology based CRM) and the organization performance (customer perspective) in Sudanese banks. This study conducted a quantitative study, the response came from the managers of 127 banks and regression analysis in pursuance of the study’s stated objectives. Findings – The results of this study suggest that all dimensions of CRM (i.e. customer orientation, CRM organization, knowledge management and technology based CRM) have a positive and signiﬁcant impact on different perspectives of banks performance. However, CRM technology failed to show a signiﬁcant relationship with learning and growth perspective of banks performance. Practical implications – Meaningful implications are made that building an extensive and effective CRM dimensions in banks ﬁrms is crucial to face a high competition and improve performance in Sudanese banks sector. Originality/value – The paper addresses CRM dimensions issues speciﬁcally for banks in Sudan.
Analytical & Neural approaches to Physically Based Rendering

Path tracing is ubiquitous for photorealistic rendering of various light transport phenomenon. At it’s core, path tracing involves the stochastic evaluation of complex & recursive integrals leading to high computational complexity. Research efforts have thus focused on accelerating path tracing either by improving the stochastic sampling process to achieve better convergence or by using approximate analytical evaluations for a restricted set of these integrals. Another interesting set of research efforts focus on the integration of neural networks within the rendering pipeline, where these networks partially replace stochastic sampling and approximate it’s converged result. The analytic and neural approaches are attractive from an acceleration point of view. Formulated properly & coupled with advances in hardware, these approaches can achieve much better convergence and eventually lead to real-time performance. Motivated by this, we make contributions to both avenues to accelerate path tracing. The first set of efforts aim to reduce the computational effort spent in stochastic direct lighting calculations from area light sources by instead evaluating it analytically. To this end, we introduce the analytic evaluation of visibility in a previously proposed analytic area light shading method. Second, we add support for anisotropic GGX to this method. This relaxes an important assumption enabling the analytic rendering of a wider set of light transport effects. Our final contribution is a neural approach that attempts to reduce yet another source of high computational load - the recursive evaluations. We demonstrate the versatility of our approach with an application to hair rendering, which exhibits one of the most challenging recursive evaluation cases. All our contributions improve on the state-of-the art and demonstrate photo-realism on par with reference path tracing.
Comparative Performance Analysis of AODV, DSDV and OLSR Routing Protocols in MANET Using OPNET

: Manet (Mobile Ad-Hoc Network) refers to a network that is designed for a specific purpose only and employ where it is difficult to set-up a centralized network. It is a collection of mobile devices forming a communication network without using any pre-established infrastructure. Because of the dynamic nature like node mobility, resource constrained environment and routing using wireless media of Manet networks routing protocols play a significant role to measure the efficiency and performance of the network. Some typical application areas of Manet consider some efficiency parameters. Generally it includes packet delivery ratio, throughput and shortest available path. This paper evaluates and analyzes the performance of three Manet routing protocols namely, AODV, DSDV and OLSR using simulation. On the basis of detailed simulation results and analysis, a suitable routing protocol can be indentified to meet the specified network conditions and the targeted goals.