Miracle-dz/UniFAR-FTUs huggingface dataset:
The efficacy of ultrasonography in monitoring the healing of jaw lesions

Purpose This study aimed to assess the reliability of ultrasonography (US) in comparison with cone-beam computed tomography (CBCT) as a tool for monitoring the healing of jaw lesions. Materials and Methods Twenty-one radiolucent lesions in jaws referred to the Oral Surgery Department at our institution were selected for this study. All lesions underwent CBCT and US examinations. The anteroposterior, superoinferior, and mesiodistal dimensions of the lesions were measured on CBCT and US images before surgery and at 6 months after surgery. The dimensions were compared between the US and CBCT images. Blood-flow velocity around the lesions was measured by color Doppler before surgery and at 1 week and 6 months after surgery to assess the capability of US to show changes in blood-flow velocity around the lesion. Results Before surgery, there were no significant differences between US and CBCT in the mesiodistal and anteroposterior dimensions, although a significant difference was found in the superoinferior dimension (P<.05). However, at 6 months after surgery, significant differences were found between US and CBCT in all dimensions, and it is likely that the US measurements more accurately reflected the extent of healing. The average blood-flow velocity increased at 1 week after surgery (5.84 cm/s) compared with the velocity before surgery (4 cm/s) (P<.05). Then, at 6 months after surgery, the blood-flow velocity significantly decreased (3.53 cm/s) compared to the velocity measured at 1 week after surgery (P<.05). Conclusion US with color Doppler was confirmed to be a more efficient tool than CBCT for monitoring bone healing.
Fluorescent Fiber Implementation of an Angle Sensor

Fluorescent optical fibers employ the photoluminescence property of doping dyes in order to produce fluorescence in response to incident illumination. Fluorescent fibers are conveniently employed for the implementation of sensing applications, since fluorescence is strongly dependent on a series of parameters of interest. In this work we propose the fluorescent fiber implementation of an angle sensor. The incident light is applied laterally to a blue fluorescent fiber through a macrobending and the produced fluorescence is propagated along the fiber following exponential attenuation. Thus, monitoring the radiated fluorescence intensity gives an accurate estimate of the macrobending angle. The proposed angle sensor is employed for the measurement of the elbow bending angle. The laboratory proof of principle validates the proposed solution for the angle sensor.
Towards Cognitive IoT: Autonomous Prediction Model Selection for Solar-Powered Nodes

The future devices connected to the IoT must have the ability to solve their tasks intelligently, optimize their operations and adapt to changes autonomously. Machine learning, executed as part of device management, is the key to such intelligence. We discuss an evolution of device management systems, that also takes meta-knowledge into account, i.e., has the ability to manage the learning in the system. This is a step towards the vision of Cognitive IoT. As one important function for device management, we identified the selection among various prediction models. As an example, we use autonomous energy management of solar-powered sensor devices in an environment with volatile weather and seasonal changes. We investigate an algorithm that selects among a set of prediction models based on historic performance. Our results show that even though a classical machine learning model is able to learn quickly in some periods, it has trouble generalizing well over the data in other periods, compared to a simple physical model. We show that providing a system with the ability to select among a set of predictors can mitigate the bootstrapping problem for constrained devices and also help them stay in operation in periods when training data is missing.
The Interplay among PINK1/PARKIN/Dj-1 Network during Mitochondrial Quality Control in Cancer Biology: Protein Interaction Analysis

PARKIN (E3 ubiquitin ligase PARK2), PINK1 (PTEN induced kinase 1) and DJ-1 (PARK7) are proteins involved in autosomal recessive parkinsonism, and carcinogenic processes. In damaged mitochondria, PINK1’s importing into the inner mitochondrial membrane is prevented, PARKIN presents a partial mitochondrial localization at the outer mitochondrial membrane and DJ-1 relocates to mitochondria when oxidative stress increases. Depletion of these proteins result in abnormal mitochondrial morphology. PINK1, PARKIN, and DJ-1 participate in mitochondrial remodeling and actively regulate mitochondrial quality control. In this review, we highlight that PARKIN, PINK1, and DJ-1 should be regarded as having an important role in Cancer Biology. The STRING database and Gene Ontology (GO) enrichment analysis were performed to consolidate knowledge of well-known protein interactions for PINK1, PARKIN, and DJ-1 and envisage new ones. The enrichment analysis of KEGG pathways showed that the PINK1/PARKIN/DJ-1 network resulted in Parkinson disease as the main feature, while the protein DJ-1 showed enrichment in prostate cancer and p53 signaling pathway. Some predicted transcription factors regulating PINK1, PARK2 (PARKIN) and PARK7 (DJ-1) gene expression are related to cell cycle control. We can therefore suggest that the interplay among PINK1/PARKIN/DJ-1 network during mitochondrial quality control in cancer biology may occur at the transcriptional level. Further analysis, like a systems biology approach, will be helpful in the understanding of PINK1/PARKIN/DJ-1 network.
Techniques to Secure Data on Cloud: Docker Swarm or Kubernetes?

In the current world with immense technological advancement, the Information Technology(IT) world is switching from physical storage to cloud storage since the “cloud” providers supply resources on demand over the Internet. Cloud computing is a successful and speedy evolving model with new features and capabilities being announced regularly. The concept of this is known as “pay as you use” which enables the firms to shift to cloud. Due to this, security of such data has become an issue. Security of cloud-based applications is one of the key concerns of cloud customers. These three principles of cloud security are Availability, Confidentiality and Integrity. One of the most efficient ways is with the help of Container Clustering. There are various ways in which container clustering can be achieved. This paper presents the study and the comparison between two such technologies, i.e. Docker Swarm and Kubernetes.
Application of Data Fusion Methods for Nondestructive Assessment of Hardness of D2 Tool Steels

Data fusion methods are widely used in different application areas especially in decision making. In nondestructive detection of hardness which is one of the most important features of cold work tool steels, these methods can effectively be used to increase accuracy of measurements. In this paper, two data fusion methods have been designed to apply on magnetic Barkhausen noise (MBN) method for determining hardness of D2 tool steel nondestructively and compared with other method of decision making. In the proposed fusion methods, different learners are trained to estimate hardness of the objective materials based on the MBN outputs (height, position and width of the peaks). These learners are neural networks with different structures. The decision of the learners for estimating hardness is fused in a fusion process to make final decision about hardness of the material. The experimental results show that data fusion methods applied on MBN outputs have more effectiveness in comparison with other one for assessment of hardness variations of AISI D2 tempered steel.
A Systematic Literature Review of Automated Feedback Generation for Programming Exercises

Formative feedback, aimed at helping students to improve their work, is an important factor in learning. Many tools that offer programming exercises provide automated feedback on student solutions. We have performed a systematic literature review to find out what kind of feedback is provided, which techniques are used to generate the feedback, how adaptable the feedback is, and how these tools are evaluated. We have designed a labelling to classify the tools, and use Narciss’ feedback content categories to classify feedback messages. We report on the results of coding a total of 101 tools. We have found that feedback mostly focuses on identifying mistakes and less on fixing problems and taking a next step. Furthermore, teachers cannot easily adapt tools to their own needs. However, the diversity of feedback types has increased over the past decades and new techniques are being applied to generate feedback that is increasingly helpful for students.
A High-Speed 2-bit/Cycle SAR ADC With Time-Domain Quantization

This brief presents a 2-bit/cycle successive approximation register (SAR) analog-to-digital converter (ADC) with time-domain quantization, which only needs one capacitive digital-to-analog converter (DAC) array. A duplicated dynamic comparator is adopted to generate the time references. To quantize the time value, a dynamic latch-based high precision time-domain comparator is proposed. Moreover, a redundancy technique is utilized to overcome the effect of nonideal factors, such as incomplete DAC settling, reference scale mismatch, and offset of comparators. A design example of 9-bit 700 MS/s SAR ADC in 65-nm CMOS technology is presented. Simulation results show that with a differential 600- ${\text {mV}}_{p-p}$ input, the spurious free dynamic range at Nyquist input is above 65 dB. The simulated effective number of bit is up to 8.3 bits at 10-MHz input with the presence of noise and mismatches calibration.
All-dielectric Metasurfaces as an Efficient Tool for Electromagnetic Waves Manipulation

In this paper, all-dielectric metasurfaces for different scenario of electromagnetic waves manipulation in microwave and visible frequency domains are presented. The water-based metasurface to dynamically control the transmission coefficient of an incident wave is proposed and experimentally studied in microwave frequency range. The possibility to control the magnitude of the transmission coefficient for linearly and circularly polarized incident waves is demonstrated. The bianisotropic metasurface for focusing of reflected waves is discussed. A novel type of all-dielectric metasurfaces which support broadband dominant toroidal dipole response in the visible is proposed.
Hammer Slide: Work- and CPU-efficient Streaming Window Aggregation

The computation of sliding window aggregates is one of the core functionalities of stream processing systems. Presently, there are two classes of approaches to evaluating them. The ﬁrst is non-incremental, i.e., every window is evaluated in isolation even if overlapping windows provide opportunities for work-sharing. While not algorithmically efﬁcient, this class of algorithm is usually very CPU efﬁcient. The other approach is incremental: to the amount possible, the result of one window evaluation is used to help with the evaluation of the next window. While algorithmically efﬁcient, the inherent control-dependencies in the CPU instruction stream make this highly CPU inefﬁcient. In this paper, we analyse the state of the art in efﬁcient incremental window processing and extend the fastest known algorithm, the Two-Stacks approach with known as well as novel optimisations. These include SIMD-parallel processing, internal data structure decomposition and data minimalism. We ﬁnd that, thus optimised, our incremental window aggregation algorithm outperforms the state-of-the-art incremental algorithm by up to 11 × . In addition, it is at least competitive and often signiﬁcantly (up to 80% ) faster than a non-incremental algorithm. Consequently, stream processing systems can use our proposed algorithm to cover incremental as well as non-incremental aggregation resulting in systems that are simpler as well as faster.
BLDC Motor Speed Control Based on MPC Sliding Mode Multi-Loop Control Strategy – Implementation on Matlab and Arduino Software

In this paper, a sliding mode – model predictive controller for a BLDC motor has been designed and implemented. This cascade controller consists of two loops, the inner loop is a sliding mode controller to control and adjust the motor current and the outer loop is a model predictive controller that is used for controlling and tracking of the reference speed. The goal for adopting sliding mode and model predictive controller is that the former is robust against model uncertainties and the model predictive controller has the capability to place some constraints on the input and state variables. In the end, the suggested algorithm has been simulated using Simulink software and the experimental results are implemented and then validated using an Arduino.
Use of Technology and SME Managers' Financial Literacy in Developing Economies

Our study aims at analyzing the perceived impact of technology utilization by Micro, small and medium enterprises (SMEs) on managers' financial literacy in developing economy setting. Employing a survey sample of 311 SMEs from Tanzania we use the partial least square structural equation modelling (PLS-SEM) to model the use of technology and financial literacy variables relationship. The financial literacy variables are categorized into financial knowledge, attitude and behavior. The results present significant positive relationship on use of technology and firm managers' financial knowledge and attitude. But we found insignificant results of use of technology on firm managers' financial behavior. The analysis further offer insight on how financial knowledge and attitude are significant in changing financial behavior of the business manager. The result offer a foretaste of how the array of technology application and improved financial literacy could enhance financial management decisions in business operations.
Automated Design and Optimization Of A CMOS OTA Using Geometric Programming

In this paper, a new design approach for low-power analog circuits is presented which uses an optimization algorithm to find the optimized values for transistor dimensions. Geometric Programming (GP) is used to find the globally optimal results using an optimization core. In this method, low power consumption is considered as the main objective along with considering other subjects such as gain and bandwidth. Using a feedback from HSPICE simulations, the best values for design variables can be achieved by continuously comparing the simulated results with the desired values and the acceptable error value. Finally, a folded-cascode structure is designed in 130nm CMOS technology to justify the performance of the proposed algorithm.
The effect of aerobic exercises on lipid profile of patients with human immunodeficiency virus infection undergoing highly active antiretroviral therapy: a protocol for a systematic review

Patients living with human immunodeficiency virus (HIV) infection who are treated with highly active antiretroviral therapy (HAART) often experience metabolic changes that have an adverse effect on their over-all health status. This review will assess the effect of aerobic exercises on the lipid profile of individuals with HIV infection undergoing HAART. Cochrane Library, MEDLINE, CINAHL (Cumulative index to Nursing and Allied Health Literature), OVID, ProQuest, AMED (Allied and Complementary Medicine Database), PsycINFO, Web of Science Core Collection and Pedro databases will be searched until June 2018. Studies that investigated the effects of aerobic exercises on the lipid profile of HAART treated patients with HIV will be included. Two reviewers will screen all articles for eligibility and independently evaluate the risk of bias, complete quality assessment and extract data for all included articles. Homogenous quantitative outcome data will be analyzed using a random effect model of meta-analysis with results presented as relative risk for dichotomous variable and standardized mean for continuous variable. Heterogeneous qualitative data will be analyzed using narrative synthesis. This systematic review will provide evidence about the effectiveness of aerobic exercises in managing HAART related dyslipidemia in patients with HIV infection and thus provide the impetus for more structured exercise interventions.
The information-theoretic meaning of Gagliardo-Nirenberg type inequalities

Gagliardo--Nirenberg inequalities are interpolation inequalities which were proved independently by Gagliardo and Nirenberg in the late fifties. In recent years, their connections with theoretic aspects of information theory and nonlinear diffusion equations allowed to obtain some of them in optimal form, by recovering both the sharp constants and the explicit form of the optimizers. In this note, at the light of these recent researches, we review the main connections between Shannon-type entropies, diffusion equations and a class of these inequalities.
iDocChip: A Configurable Hardware Architecture for Historical Document Image Processing: Percentile Based Binarization

End-to-end Optical Character Recognition (OCR) systems are heavily used to convert document images into machine-readable text. Commercial and open-source OCR systems (like Abbyy, OCRopus, Tesseract etc.) have traditionally been optimized for contemporary documents like books, letters, memos, and other end-user documents. However, these systems are difficult to use equally well for digitizing historical document images, which contain degradations like non-uniform shading, bleed-through, and irregular layout; such degradations usually do not exist in contemporary document images. The open-source anyOCR is an end-to-end OCR pipeline, which contains state-of-the-art techniques that are required for digitizing degraded historical archives with high accuracy. However, high accuracy comes at a cost of high computational complexity that results in 1) long runtime that limits digitization of big collection of historical archives and 2) high energy consumption that is the most critical limiting factor for portable devices with constrained energy budget. Therefore, we are targeting energy efficient and high throughput acceleration of the anyOCR pipeline. Generalpurpose computing platforms fail to meet these requirements that makes custom hardware design mandatory. In this paper, we are presenting a new concept named iDocChip. It is a portable hybrid hardware-software FPGA-based accelerator that is characterized by low footprint meaning small size, high power efficiency that will allow using it in portable devices, and high throughput that will make it possible to process big collection of historical archives in real time without effecting the accuracy. In this paper, we focus on binarization, which is the second most critical step in the anyOCR pipeline after text-line recognizer that we have already presented in our previous publication [21]. The anyOCR system makes use of a Percentile Based Binarization method that is suitable for overcoming degradations like non-uniform shading and bleed-through. To the best of our knowledge, we propose the first hardware architecture of the PBB technique. Based on the new architecture, we present a hybrid hardware-software FPGA-based accelerator that outperforms the existing anyOCR software implementation running on i7-4790T in terms of runtime by factor of 21, while achieving energy efficiency of 10 Images/J that is higher than that achieved by low power embedded processors with negligible loss of recognition accuracy.
Explainable Black-Box Attacks Against Model-based Authentication

Establishing unique identities for both humans and end systems has been an active research problem in the security community, giving rise to innovative machine learning-based authentication techniques. Although such techniques offer an automated method to establish identity, they have not been vetted against sophisticated attacks that target their core machine learning technique. This paper demonstrates that mimicking the unique signatures generated by host fingerprinting and biometric authentication systems is possible. We expose the ineffectiveness of underlying machine learning classification models by constructing a blind attack based around the query synthesis framework and utilizing Explainable-AI (XAI) techniques. We launch an attack in under 130 queries on a state-of-the-art face authentication system, and under 100 queries on a host authentication system. We examine how these attacks can be defended against and explore their limitations. XAI provides an effective means for adversaries to infer decision boundaries and provides a new way forward in constructing attacks against systems using machine learning models for authentication.
Microscopic Road Traffic Scene Analysis Using Computer Vision and Traffic Flow Modelling

This paper presents the development of a vision-based system for microscopic road traffic scene analysis and understanding using computer vision and computational intelligence techniques. The traffic flow model is calibrated using the information obtained from the road-side cameras. It aims to demonstrate an understanding of different levels of traffic scene analysis from simple detection, tracking, and classification of traffic agents to a higher level of vehicular and pedestrian dynamics, traffic congestion build-up, and multi-agent interactions. The study used a video dataset suitable for analysis of a T-intersection. Vehicle detection and tracking have 88.84% accuracy and 88.20% precision. The system can classify private cars, public utility vehicles, buses, and motorcycles. Vehicular flow of every detected vehicles from origin to destination are also monitored for traffic volume estimation, and volume distribution analysis. Lastly, a microscopic traffic model for a T-intersection was developed to simulate a traffic response based on actual road scenarios.
A Dynamic Window Approach with Collision Suppression Cone for Avoidance of Moving Obstacles

In this paper, we present a new approach, named as dynamic window approach with collision suppression cone (DWA-CSC), to handle the issue of a robot avoiding moving obstacles in a partially known dynamic environment. The concept of collision suppression cone is firstly introduced to define a probable collision area. When moving obstacles approach this area, the proposed DWA-CSC will be activated to allow the robot avoiding the obstacles smoothly and thus preventing collision with them by suppressing its motion direction and velocity, just like the way of obstacle avoidance done by human beings. Various simulation results show that the proposed DWA-CSC approach indeed achieve the avoidance of moving obstacles safely and smoothly.
Automatic System for Diseased Artery Transverse Section Detection

This article deals with an artery transversal section detection from ultrasound medical images. Exact localization of artery is needed for measuring artery properties, which can be used for diagnosing vascular diseases such as atherosclerosis, hypoplasia. Image processing methods are non-invasive, fast and cheap. In this work, the Viola-Jones object detector was used and further adopted for artery detection problem. In comparison with the other approaches created method improves speed and accuracy of detection mainly for unhealthy arteries. Created methods achieves 96% accuracy for healthy arteries and 95% accuracy of detection for unhealthy arteries. Average detection time per image was 2.5 milliseconds.