Miracle-dz/UniFAR-FTUs huggingface dataset:
VITAL: a new low-cost vibro-tactile display system

Tactile displays are deformable surfaces that simulate skin deformations that occur when interacting with real surfaces (relieves) by transmitting small-scale shape information and vibration to the fingertip. We present a new vibrotactile display system composed of a matrix of 8/spl times/8 tactile display, a control/power system (electrical hardware) and a software interface dedicated to translate and display a black and white picture on the 8/spl times/8 tactile display with different close-ups. The 8/spl times/8 tactile display is based on a new multi-layer concept. This concept allows developing a new generation of tactile matrices with a high density of micro actuators, a simple assembling process and at a quite competitive price.
The Impact of Interest Flooding Attack (IFA) and Collusive IFA on Named Data Networking (NDN)

Named Data Networking (NDN) is a proposed future Internet architecture that has the advantage of bringing content data closer to users. NDN focuses on securing content data rather than securing transmission channels. The NDN network is also not free from threats and attacks that can disrupt network performance. One of the attacks on the NDN network is a denial of service (DoS) attack. The attacker carries out this attack by sending a large number of request packets. This attack is known as an interest flooding attack (IF A). The IF A attack itself has developed into a collusive IFA to avoid existing detection systems. In this paper, we examine the comparative impact of IF A and CIF A attacks on clients or consumers. Knowing the impact and characteristics of IF A and CIF A attacks can be used as a reference in the attack detection system. Attacks are carried out locally and remotely by looking at 3 types of attacks, namely attacks by sending requests for packets that are on the network, attacks by sending requests for containers that are not available on the net, hybrid attacks, and 1 collusive IF A attack. Based on the exploration carried out, it was found that local attacks had a very significant impact compared to remote attacks. IFA attacks that have a major impact on the network are attacks that send content that is not available on the net. The effect of this attack meant that requests sent by consumers were only served by 40% of the total bids.
Comparison of Quantitative Techniques including Xpert MTB/RIF to Evaluate Mycobacterial Burden

Introduction Accurate quantification of mycobacterial load is important for the evaluation of patient infectiousness, disease severity and monitoring treatment response in human and in-vitro laboratory models of disease. We hypothesized that newer techniques would perform as well as solid media culture to quantify mycobacterial burden in laboratory specimens. Methods We compared the turn-around-time, detection-threshold, dynamic range, reproducibility, relative discriminative ability, of 4 mycobacterial load determination techniques: automated liquid culture (BACTEC-MGIT-960), [3H]-uracil incorporation assays, luciferase-reporter construct bioluminescence, and quantitative PCR(Xpert -MTB/RIF) using serial dilutions of Mycobacterium bovis and Mycobacterium tuberculosis H37RV. Mycobacterial colony-forming-units(CFU) using 7H10-Middlebrook solid media served as the reference standard. Results All 4 assays correlated well with the reference standard, however, bioluminescence and uracil assays had a detection threshold ≥1×103 organisms. By contrast, BACTEC-MGIT-960 liquid culture, although only providing results in days, was user-friendly, had the lowest detection threshold (<10 organisms), the greatest discriminative ability (1 vs. 10 organisms; p = 0.02), and the best reproducibility (coefficient of variance of 2% vs. 38% compared to uracil incorporation; p = 0.02). Xpert-MTB/RIF correlated well with mycobacterial load, had a rapid turn-around-time (<2 hours), was user friendly, but had a detection limit of ∼100 organisms. Conclusions Choosing a technique to quantify mycobacterial burden for laboratory or clinical research depends on availability of resources and the question being addressed. Automated liquid culture has good discriminative ability and low detection threshold but results are only obtained in days. Xpert MTB/RIF provides rapid quantification of mycobacterial burden, but has a poorer discrimination and detection threshold.
Detecting Malware Based on Statistics and Machine Learning Using Opcode N-Grams

Malwares have been being a major security threats to enterprises, government organizations and end-users. Beside traditional malwares, such as viruses, worms and trojans, new types of malwares, such as botnets, ransomwares, IoT malwares and crypto-jackings are released daily. To cope with malware threats, several measures for monitoring, detecting and preventing malwares have been developed and deployed in practice, such as signature-based detection, static and dynamic file analysis. This paper proposes 2 malware detection models based on statistics and machine learning using opcode n-grams. The proposed models aim at achieving high detection accuracy as well as reducing the amount of time for training and detection. Experimental results show that our proposed models give better performance measures than previous proposals. Specifically, the proposed statistics-based model is very fast and it achieves a high detection accuracy of 92.75% and the random forest-based model produces the highest detection accuracy of 96.29%.
Mapping Natural Language Intents to User Interfaces through Vision-Language Models

Efficiently navigating through mobile applications to accomplish specific tasks can be time-consuming and challenging, particularly for users who are unfamiliar with the app or faced with intricate menu structures. Simplifying access to a particular screen is a shared user priority, especially for individuals with diverse needs, including those with specific accessibility requirements. This underscores the exploration of innovative solutions to streamline the navigation process. This work addresses the challenge of mapping natural language intents to user interfaces, with a specific focus on the context of mobile applications. The primary objective of this work is to provide users with a more intuitive and efficient method for accessing desired screens in mobile applications by expressing their intentions in natural language. Existing approaches to this task have relied heavily on qualitative human studies for evaluating the performance. Moreover, widely used pre-trained vision-language models, such as Contrastive Language-Image Pretraining (CLIP), struggle to generalize effectively to the unique visual characteristics of user interfaces. Acknowledging the limitations, we introduce a novel approach that harnesses the power of the pre-trained vision-language models. Specifically, we investigate whether fine-tuning pre-trained vision-language models on mobile screens can address the challenges posed by the intricate nature of mobile application interfaces. Our approach involves the utilization of state-of-the-art pre-trained text and image encoders and employing a supervised fine-tuning process, where pre-trained models are adapted to the specific needs of mobile screen interactions. Moreover, a shared embedding space facilitates the alignment of embeddings of both text and image modalities, fostering a cohesive understanding between the natural language intents and visual features of user interface elements. We conduct extensive experimental evaluations using the Screen2Word dataset. Through systematic analysis and established metrics, we examine the models’ ability to accurately map diverse linguistic intents to specific user interfaces. Our analysis demonstrates that fine-tuning yields substantial improvements over the zero-shot performance of the pre-trained vision-language models.
Visual Environment Preservation of Old Chinese Shop Houses in Jamblang with h-BIM

Jamblang, a small town near Cirebon, West Java, has many indigenous shophouses with Chinese Peranakan architectural style and potential for heritage buildings, which was announced as an old city tourist destination by local authorities in 2019. Many buildings are already hundreds of years old but left uninhabited, abandoned, and damaged. From a few still genuine, operating for small shops and inhabited, although not adequately maintained, building documentation was taken with photogrammetry as an initial approach to visual preservation. Photogrammetry opens possibilities to enrich heritage building documentation in a larger context: hBIM (historic Building Information Modeling). This article is to record the photogrammetry process with 360 videos in the interior of three shophouses. Results show 360 videos helped to depict spatial configuration, yet its 3D modeling output quality depends most on the video capture trail.
Constructing Conditional Random Fields for Automated Land Cover Mapping from Hyper Spectral Images

This paper proposes an automatic land cover mapping technique using Conditional Random Fields (CRFs) from hyper-spectral snapshots. It introduces a hierarchical feature vector method to encode the labels' spatial relationships. The paper opinion associated paintings on land cover mappings and introduces the idea of CRFs inside the context of the land cowl category. A description of the facts, pre-processing techniques, and the parameters used for schooling and checking out the model is supplied. The paper develops and evaluates the CRF model, evaluating the results of other land cowl class strategies. Finally, the authors discuss the capacity packages of this technique in the context of other land cowl mapping obligations. This painting is innovative in its method to land cowl mapping and offers a successful implementation of the CRF model for this hassle. It explores using Conditional Random Fields (CRFs) to facilitate automatic land cover mapping from hyperspectral photos (HSI). The CRF method was implemented to estimate the land cover labels in an unsupervised gaining knowledge of style. To this stop, the pixel-level land cowl information for a given area changed into modeled as a graphical version, permitting place-degree marginal probability computations for a dense prediction. Experimental outcomes validated that the proposed technique ought to obtain a sizeable development in the accuracy of land cover mapping for the tested records set compared to its baseline. The proposed CRF-primarily based land cowl mapping method exhibited a higher convergence price and better nearby minima convergence. Moreover, the proposed approach was located to be sturdy in phrases of sensitivity to preliminary land cowl labels.
Overall Well-Focused Catadioptric Image Acquisition With Multifocal Images: A Model-Based Method

When a catadioptric imaging system suffers from limited depth of field, a single image cannot capture all the objects with clear focus. To solve this problem, a set of multifocal images can be used to extend the depth of the field by fusing the best-focused image regions to an overall well-focused composite image. In this paper, we propose a novel model-based method that avoids the computational cost problem of previous extended-depth-of-field algorithms. On the basis of the special optical geometry properties of catadioptric systems, the proposed model describes the shapes of the best-focused image regions in multifocal images by a series of neighboring concentric annuluses. Then we propose a method to estimate the model parameters. On the basis of this model, an overall well-focused image is obtained by combining the best-focused regions with a fast and reliable online operation. Experiments on catadioptric images of a variety of different scenes and camera settings verify the validity of the model and the robust performance of the proposed method.
Accessible Robots for Improving Social Skills of Individuals with Autism

Abstract This paper reports on an ongoing project between members of the computer science and special education departments of Bradley University and Murray State University, detailing the robotic platforms developed and investigated as a potential tool to improve social interactions among individuals with Autism Spectrum Disorders (ASD). Development of a fourth generation robotic agent is described, which uses economically available robotic platforms (Lego NXT) as Socially Assistive Robotics (SAR), combined with direct instruction pedagogy and social scripts to support an alternative educational approach to teaching social behavior. Specifically, in this fourth generation, changes to the physical design of the robots were made to improve the maintainability, reliability, maneuverability, and aesthetics of the robots. The software architecture was designed for modularity, configurability, and reusability of the software.
Factors distinguishing suicide attempters from suicide ideators in a community sample: social issues and physical health problems

Background. Few studies have focused on factors that uniquely distinguish suicide attempters from suicide ideators. This study assesses prevalence of suicide attempts among suicide ideators within a community sample; explores demographics, employment status, mental and physical health conditions, personality, life stresses and social environment as factors that may distinguish these groups; examines effects of age and gender upon suicide attempts and associated factors; and investigates the increase in suicide attempts when multiple factors related to this behaviour are present. Method. Data were drawn from the PATH Through Life Project, a community survey of 7485 people in Canberra, Australia. A subsample of 522 suicide ideators were used for this study. Results. Logistic regression was used to identify factors associated with suicide attempts. Physical medical conditions (OR 1·95) and negative interactions with friends (OR 1·20) were associated with an increased likelihood of suicide attempts among suicide ideators. Age and gender interaction effects for suicide attempts were found involving physical medical condition and mastery among men (OR 3·78 and 0·83 respectively) and not being employed for those aged 40–44 years (OR 8·94). A cumulative effect was found when multiple factors associated with suicide attempts were present, and the probability of an attempt was significantly elevated. Conclusions. Factors distinguishing those who attempt suicide from suicide ideators involve being unemployed, physical ill health and relationship difficulties. Contrary to expectation, this study found that ideators and attempters experience comparable levels of depression and anxiety.
Smart Soft Sensor for Froth flotation quality monitoring using pre-trained feature extractor and Vision Transformer ViT

Smart sensor technologies have gained prominence in the field of machine vision-based applications. Within this context, the utilization of machine vision systems for monitoring and controlling flotation plants has become increasingly prevalent. This paper addresses the critical need for accurate froth image classification in the development of an online machine vision-based control system. This paper explores the recent advancements in machine learning, with a focus on deep learning techniques. Specifically, it introduces the application of a Vision Transformer (VIT) model as an alternative to the traditional Convolutional Neural Network (CNN) for classifying froth images collected from an industrial lead flotation circuit. The results of this research demonstrate the remarkable performance of the ViT model, achieving an impressive overall accuracy rate of 96% when combined with pre-trained feature extractor models. This achievement surpasses the capabilities of conventional artificial neural network (CNN) classifiers of the state of art in terms of both accuracy and computational efficiency. These findings underscore the promising potential of deep learning neural networks in froth image analysis, highlighting their significance in the advancement of smart sensor-based machine vision systems within the field of flotation plant control.
Deeply Exploit Depth Information for Object Detection

This paper addresses the issue on how to more effectively coordinate the depth with RGB aiming at boosting the performance of RGB-D object detection. Particularly, we investigate two primary ideas under the CNN model: property derivation and property fusion. Firstly, we propose that the depth can be utilized not only as a type of extra information besides RGB but also to derive more visual properties for comprehensively describing the objects of interest. So a two-stage learning framework consisting of property derivation and fusion is constructed. Here the properties can be derived either from the provided color/depth or their pairs (e.g. the geometry contour adopted in this paper). Secondly, we explore the fusion method of different properties in feature learning, which is boiled down to, under the CNN model, from which layer the properties should be fused together. The analysis shows that different semantic properties should be learned separately and combined before passing into the final classifier. Actually, such a detection way is in accordance with the mechanism of the primary neural cortex (V1) in brain. We experimentally evaluate the proposed method on the challenging dataset, and have achieved state-of-the-art performance.
An Efficient Lung Cancer Detection Model using Convnets and Residual Neural Networks

Lung cancer is a formidable global health challenge, representing a major cause of Mortalities. One of the main causes of cancer mortality globally, lung cancer demands innovative approaches to improve early detection, diagnosis, and subsequent treatment outcomes. Machine learning, a subset of artificial intelligence, provides a stating avenue to enhance the reliability and efficiency of lung cancer detection. The purpose of this work is to make effective use of the capabilities of deep learning to develop a robust and effective tool for the early detection of lung cancer through the analysis of CT scan images. We used a lung cancer dataset from Kaggle for experimentation. We applied two techniques namely CNN, Resnet50 for lung cancer detection and acquired reasonable detection rate. The results shown that proposed model acquired good accuracy when compared to conventional methods for lung cancer detection.
Effect of Fructose on Blood Pressure: A Systematic Review and Meta-Analysis of Controlled Feeding Trials

Concerns have been raised about the adverse effect of fructose on blood pressure. International dietary guidelines, however, have not addressed fructose intake directly. A systematic review and meta-analysis was conducted to assess the effect of fructose in isocaloric exchange for other carbohydrates on systolic, diastolic, and mean arterial blood pressures. Studies were identified using Medline, Embase, and Cochrane databases (through January 9, 2012). Human clinical trials of isocaloric oral fructose exchange for other carbohydrate sources for ≥7 days were included in the analysis. Data were pooled by the generic inverse variance method using random-effects models and expressed as mean differences with 95% CI. Heterogeneity was assessed by the Q-statistic and quantified by I2. Study quality was assessed using the Heyland Methodological Quality Score. Thirteen isocaloric (n=352) and 2 hypercaloric (n=24) trials met the eligibility criteria. Overall, fructose intake in isocaloric exchange for other carbohydrates significantly decreased diastolic (mean difference: −1.54 [95% CI: −2.77 to −0.32]) and mean arterial pressure (mean difference: −1.16 [95% CI: −2.15 to −0.18]). There was no significant effect of fructose on systolic blood pressure (mean difference: −1.10 [95% CI: −2.46 to 0.44]). The hypercaloric fructose feeding trials found no significant overall mean arterial blood pressure effect of fructose in comparison with other carbohydrates. To confirm these results, longer and larger trials are needed. Contrary to previous concerns, we found that isocaloric substitution of fructose for other carbohydrates did not adversely affect blood pressure in humans.
Automatic Detection of Hypernasality in Cleft Palate Speech Using Phase Feature

The detection of hypernasality, a speech disorder common in children with cleft palate, typically involves analyzing vowels within speech stimuli. An essential acoustic cue for hypernasality detection is the presence of a nasal peak near the first formant $(F_{1})$. Traditionally, a two-step process is employed, where vowel selection from stimuli is initially performed through manual annotation, followed by automatic feature extraction and classification. This work introduces a novel method for hypernasality detection, automating the entire process, including vowel region selection, feature extraction, and classification. The method leverages vowel onset points for the automatic identification of the vowel region. Subsequently, a modified group delay cepstral coefficient feature is extracted from this automatically selected vowel region. The feature is extracted from modified group delay spectrum, offering enhanced resolution to distinguish closely spaced nasal peaks and the $F_{1}$, thus improving the capture of nasality evidence. The proposed method demonstrates promising results when applied to hypernasality detection using a support vector machine classifier with the accuracy of 83.50% for /a/ vowel and 93.21% for /i/ vowel.
A Software Architecture for Adaptive Modular Sensing Systems

In this paper, a software architecture and knowledge representation scheme that enables the combination and reconfiguration of modular sensor and actuator components is described. The proposed software architecture utilizes a realtime operating system with a pre-emptive kernel, which simplifies the implementation of the architecture itself through the modularization and concurrent execution of its various software components. A virtual machine-based middleware layer runs on top of the operating system, enabling platform-independent logical algorithms to be written once, and run on any module irrespective of its underlying hardware architecture. Logical algorithms govern the behaviour of a given set of heterogeneous modules, providing them with intelligence and enabling them to behave as a single entity known as a logical module.
A Defect Monitoring Method for Power Cables Based on EWT in Frequency Domain

Power cables have been widely used in urban distribution network systems, and the detection of cable defects has attracted much attention of the researchers. The detection signal of the cable has the problem of mode mixing caused by the discontinuous time-frequency scale, which leads to the spurious peak of the traditional frequency domain reflectometry (FDR), and makes the detection of defects cause interference. In this paper, a cable defect state detection method based on empirical wavelet transform (EWT) is proposed. First, EWT is used to adaptively decompose FDR signals, because it has good identification characteristics for modal signals. Then analyze the correlation between the decomposed signal and the original signal, and use the threshold to reconstruct the original signal, so as to weaken the influence of spurious peaks on the detection. Finally, the effectiveness of the method proposed in this paper is verified by simulation and experiment under multi-reflection signals.