Miracle-dz/UniFAR-FTUs huggingface dataset:
The prevalence of diabetic retinopathy and its effect on social well-being and health related quality of life in children and young adults with type 1 diabetes

The incidence of childhood onset type 1 diabetes in Finland has been the highest in the world for several decades. Optimal management of the disease presents a lifelong challenge to the affected individuals. Related complications are common and include an ocular pathology called diabetic retinopathy. Type 1 diabetes with its ramifications can impact on several facets of a patient’s physical and psychological well-being. This study aimed to assess the ophthalmic findings and to evaluate the characteristics of general well-being of a population-based cohort of paediatric patients with type 1 diabetes and a population-based cohort of young adults with type 1 diabetes since childhood. The prevalence and risk factors of diabetic retinopathy were assessed of the population-based paediatric cohort in the catchment area of the Northern Ostrobothnia Hospital District and these were compared to a similar paediatric cohort studied 18 years previously. There was no significant change in the overall prevalence of diabetic retinopathy (12%) during the study period. Furthermore, glycaemic balance and other risk factors of diabetic retinopathy had remained almost unchanged. A population-based cohort of young adults was evaluated in 2007 for the prevalence and severity of diabetic retinopathy. Most of the cohort subjects (94%) had developed diabetic retinopathy and in every third subject there was evidence of proliferative retinopathy. Health related quality of life was the same as that in the ageand gender-standardised control population. For the most part, the young adults with a long duration of type 1 diabetes fared equally well as the general population in the measured social aspects. However, proliferative diabetic retinopathy was associated with lower educational achievements and poorer health related quality of life as well as with a higher probability of unemployment or being pensioned. Glycaemic balance and prevalence of diabetic retinopathy have remained unchanged in paediatric cohorts for nearly two decades despite concurrent advances in care. Social well-being was mainly restricted in young adults exhibiting signs of proliferative diabetic retinopathy. The negative impact of advanced complications of type 1 diabetes already in these young adults highlights the importance of strict metabolic control to maintain overall well-being.
The Role of Fine-Wire Localization Breast Biopsy in the Management of BI-RADS Category 4-5 for Non-Palpable Breast Lesions: An Experience at King Abdulaziz University Hospital, Saudi Arabia

The aim of this study is to determine a positive predictive value for malignancy in patients initially categorized as having Breast Imaging Reporting and Data System categories 4 and 5 with non-palpable breast lesions that underwent fine wire-localized biopsy. 116 patients underwent this process at the Surgery Department, King Abdulaziz University Hospital, Saudi Arabia: from December 2007 through November 2009. By histopathology, out of 116 patients, 76 (65.52%) were benign and 44 (37.93%) malignant lesions. According to Breast Imaging Reporting and Data System categories: 89 (76.72%) were category 4 and 27 (23.28%) were category 5. In category 4, 70.80% (n=64) of cases were benign and 28.10% (n=25) were malignant given positive predictive value 28.09%. In category 5, 29.60% (n=8) of cases were benign and 70.10% (n=19) were malignant given positive predictive value 70.73% after fine wire-localized biopsy for non-palpable breast lesions. In conclusion, combination of Breast Imaging Reporting and Data System categories and fine wire-localized biopsy for non-palpable breast lesions is safe, but the positive predictable value of malignancy of Breast Imaging Reporting and Data System was low especially in category 4. Preoperative histological confirmation is very important for suspicious non-palpable breast lesions to avoid unnecessary open breast biopsy.
Amnesiac DRAM: A Proactive Defense Mechanism Against Cold Boot Attacks

DRAMs in modern computers or hand-held devices store private or often security-sensitive data. Unfortunately, one known attack vector, called a cold boot attack, remains threatening and easy-to-exploit, especially when attackers have physical access to the device. It exploits the fundamental property of current DRAMs: remanence effects that retain the stored contents for a certain period of time even after powering off. To magnify the remanence effect, cold boot attacks typically freeze the victim DRAM, thereby providing a chance to detach, move, and reattach it to an attacker’s computer. Once power is on, attackers can steal all the security-critical information from the victim’s DRAM, such as a master decryption key for an encrypted disk storage. Two types of defenses were proposed in the past: 1) CPU-bound cryptography, where keys are stored in CPU registers and caches instead of in DRAMs, and 2) full or partial memory encryption, where sensitive data are stored encrypted. However, both methods impose non-negligible performance or energy overheads to the running systems, and worse, significantly increase the hardware and software manufacturing costs. We found that these proposed solutions attempted to address the cold boot attacks passively: either by avoiding or by indirectly addressing the root cause of the problem, the remanence effect. In this article, we propose and evaluate a proactive defense mechanism, Amnesiac DRAM, that comprehensively prevents the cold boot attacks. The key idea is to discard the contents in the DRAM when attackers attempt to retrieve (i.e., power on) them from the stolen DRAM. When Amnesiac DRAM senses a physical separation, it locks itself and deletes all the remaining contents, making it amnesiac. The Amnesiac DRAM causes neither performance nor energy overhead in ordinary operations (e.g., load and store) and can be easily implemented with negligible area overhead in commodity DRAM architectures.
Undergraduates' News Consumption and Perceptions of Fake News in Science

abstract:This study investigates undergraduates' news consumption and their perceptions of fake news in science while they took an online introductory geography course at a large Midwestern university. The authors collaborated with university science instructors to design and integrate a news literacy curriculum into a set of learning activities to promote critical thinking and research skills. A qualitative analysis of 108 students' written essays presents empirical evidence of their perceptions of fake news in terms of content (what), purpose (why), and source (who). The results show that students primarily sought news from traditional sources, such as CNN, the British Broadcasting Corporation (BBC), and the New York Times, while working on their research assignments. They spent, on average, about 7 hours per week reading, watching, or listening to news while taking the online course. More than half the students indicated that they used mobile devices to browse news most of the time. The strategies and tools the students used to manage multiple news sources are discussed.
Suspicious URL Detection using Dynamic Learning Model with Machine Learning

: Malicious websites that bargain defenseless PCs are an ever-present risk on the web. They are the foundation of Internet criminal exercises and a generally perceived risk to the security of the web and a developing security worry on the Internet because of their popularity and their potential genuine effects. They can 'shroud' the content of the web pages, i.e., serving distinctive content to various customers. The perils of these sites have made an interest for safeguards that shield end-users from visiting them. At the point when a powerless client utilizes a web program to surf a URL, a vindictive server can send to the web program a web page with malignant code to exploit and bargain the customer side framework.
Convolution Neural Networks and Support Vector Machines for Automatic Segmentation of Intracoronary Optical Coherence Tomography

Cardiovascular diseases are closely associated with deteriorating atherosclerotic plaques. Optical coherence tomography (OCT) is a recently developed intravascular imaging technique with high resolution approximately 10 microns and could provide accurate quantification of coronary plaque morphology. However, tissue segmentation of OCT images in clinic is still mainly performed manually by physicians which is time consuming and subjective. To overcome these limitations, two automatic segmentation methods for intracoronary OCT image based on support vector machine (SVM) and convolutional neural network (CNN) were performed to identify the plaque region and characterize plaque components. In vivo IVUS and OCT coronary plaque data from 5 patients were acquired at Emory University with patient’s consent obtained. Seventy-seven matched IVUS and OCT slices with good image quality and lipid cores were selected for this study. Manual OCT segmentation was performed by experts using virtual histology IVUS as guidance, and used as gold standard in the automatic segmentations. The overall classification accuracy based on CNN method achieved 95.8%, and the accuracy based on SVM was 71.9%. The CNN-based segmentation method can better characterize plaque compositions on OCT images and greatly reduce the time spent by doctors in segmenting and identifying plaques.
Efficient Hybrid Data Dissemination for Edge-Assisted Automated Driving

Automatic driving services have large volume, location-aware, and time-changing contents, which are suitable to be cached by the edge. However, the traffic on the edge will be extremely high especially in the area with high vehicle density, if the vehicles directly access the contents from the edge as they demand. A hybrid data dissemination model with both vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) disseminations has been proposed to reduce the traffic on the edge, in which the edge (infrastructure) selectively injects data to the vehicles and leverages the vehicle network to disseminate the data. In this article, we study the hybrid data dissemination problem, i.e., to optimally determine when and which vehicle the data are injected into, and whether the vehicle acquires the demanded data directly from the edge or from nearby neighbors, with the aim of minimizing the traffic cost on the edge and meeting the deadlines of acquiring the data. Existing approach prioritizes the selection of V2I disseminations at first and then explores the V2V disseminations which have no conflict with the V2I disseminations. This approach cannot fully take advantage of V2V to reduce the traffic cost on the edge. We propose a new data dissemination algorithm, named the offline algorithm for hybrid data dissemination (OFDD), which seeks the most beneficial V2V broadcasts with priority, and then choose feasible V2I disseminations. Based on OFDD, we develop both the snapshot and prediction-based online algorithms. We follow with extensive simulations to validate the proposed algorithms. The results show that our algorithms significantly outperform the state-of-the-art approaches in terms of data acquisition rate and traffic cost.
Sonar-Based Monte Carlo Localization for Autonomous Underwater Vehicles

The overall objective of this thesis is to investigate the use of a mechanically scanned imaging sonar in localization for underwater vehicles. The imaging sonar stands out due to its low cost and weight and is therefore desirable in the field of underwater robotics. After an introduction about mobile robot localization, and its specific issues in the underwater domain, the thesis will focus on developing a sonar-based Monte Carlo localization algorithm for an underwater vehicle. In order to incorporate the sonar readings in the localization algorithm, a thorough review of the imaging sonar and the peculiarities of the sensor has been conducted. The main contribution of this thesis is the implementation and verification of the sonarbased particle filter. The algorithm is tested and verified using two different datasets. The first dataset was a pre-existing dataset from the University of Girona, and the second dataset was gathered in the Marine Cybernetics Lab (MC Lab) at NTNU for the purpose of this thesis. The dataset in the MCLab was collected using the vehicle BlueRov2. The results show that the localization algorithm is able to locate the vehicle given a a priori map, without any knowledge of the initial position of the vehicle. By adding a number of new particles when the estimate from the filter is considered as a poor estimate, the algorithm is also able to recover from localization failures and converge to the correct solution. Two different methods for incorporating the sonar measurements in the particle filter have been compared with the last dataset. Only one of the methods showed satisfactory results.
DEPRESSION AMONG 100 DIABETICS REFERRING TO UNIVERSITY HOSPITALS

هديكچ فده و هقباس : يم لااب ً اتبسن عويش اب نمزم تيهام اب ياهيراميب زا يكي تبايد دشاب . صوصخب يكشزپناور تلاكشم هب هجوت نايلاتبمرد يگدرسفا هب ش يم اهنآ يگدنز تيفيك شيازفا و زكولگ لرتنك دوبهب ثعاب تبايد دو . يگدرسفا هكنيا هب هجوت اب يلصا فده دوش يم هتفرگ هديدان اهيراميبرد رد هعلاطم نيا كيتبايد ناراميب رد يگدرسفا نازيم يبايزرا و شور يريگراك هب يگدرسفا صيخشت تهج هداس و تسا نآ عقوم هب نامرد و يراميب نيا هب رتشيب هجوت ترورض يروآداي . و داوم اهشور : اح هعلاطم دشاب يم يفيصوت عون زا رض . ۱۰۰ نيسح ماما ياهناتسراميب هب هدننك هعجارم يتبايد راميب ) ع ( و لاس رد نامقل ۱۳۸۱ همانشسرپ طسوت يگدرسفا هب لاتبا رظن زا Beck دنتفرگ رارق يبايزرا دروم . يتاـعلاطا نآ ليمكت زا سپ ،تبايد عون ،يراميب تدم لوط ، سنج ، لهات تيعضو ،نس هلمج زا تـبايد ضراوـع و يگدرسفا دض ياهوراد فرصم هقباس تفرگ رارق زيلانآ دروم يرامآ ياهشور هليسوب . هتفاي اه : هك دش صخشم شهوژپ رد % ۷۸ تست ساسارب كيتبايد ناراميب Beck دندوب يگدرسفا هب لاتبم . رب دارفا هسياقم رد راميب هورگ ود رد نس و يراميب تدم لوط ،تبايد هضراع ،سنج ساسا زا راد ينعم توافت هدرسفاريغ و هدرسفا كيتبايد نا تشاد دوجو يرامآ رظن ) ترت هب بي ۰۰۵ / ۰ p< ، ۰۰۱ / ۰ p< ، ۰۵ / ۰ p< و ۰۰۵ / ۰ p< . ( هجيتن هيصوت و يريگ اه : دوـب كيتبايد ناراميب رد يگدرسفا مئلاع نازيم ندوب لااب ةدنهدناشن هلصاح جياتن شهوژپ نيا رد طسوت هك زرا دروم كب تست نتفرگ رارق يباي د . هـب هـجوت نآ ناـمرد دـيفم تارـثا و يگدرـسفا ندوـب نامرد لباق هب هجوت اب تسا دنمدوس و مهم اهنآ رد يگدرسفا عقوم هب صيخشت و يرگلابرغ ،كيتبايد ناراميب نامرد تملاس . يديلك ناگژاو : تست ،يگدرسفا ،تبايد Beck .
Machine Learning for Signal Processing

Digital signal processing (DSP) is one of the ‘foundational’ engineering topics of the modern world, without which technologies such the mobile phone, television, CD and MP3 players, WiFi and radar, would not be possible. A relative newcomer by comparison, statistical machine learning is the theoretical backbone of exciting technologies such as automatic techniques for car registration plate recognition, speech recognition, stock market prediction, defect detection on assembly lines, robot guidance and autonomous car navigation. Statistical machine learning exploits the analogy between intelligent information processing in biological brains and sophisticated statistical modelling and inference. DSP and statistical machine learning are of such wide importance to the knowledge economy that both have undergone rapid changes and seen radical improvements in scope and applicability. Both make use of key topics in applied mathematics such as probability and statistics, algebra, calculus, graphs and networks. Intimate formal links between the two subjects exist and because of this many overlaps exist between the two subjects that can be exploited to produce new DSP tools of surprising utility, highly suited to the contemporary world of pervasive digital sensors and high-powered and yet cheap, computing hardware. This book gives a solid mathematical foundation to, and details the key concepts and algorithms in, this important topic.
Through-Wall Pose Imaging in Real-Time with a Many-to-Many Encoder/Decoder Paradigm

Overcoming the visual barrier and developing "see-through vision" has been one of mankind's long-standing dreams. Unlike visible light, Radio Frequency (RF) signals penetrate opaque obstructions and reflect highly off humans. This paper establishes a deep-learning model that can be trained to reconstruct continuous video of a 15-point human skeleton even through visual occlusion. The training process adopts a student/teacher learning procedure inspired by the Feynman learning technique, in which video frames and RF data are first collected simultaneously using a co-located setup containing an optical camera and an RF antenna array transceiver. Next, the video frames are processed with a computer-vision-based gait analysis "teacher" module to generate ground-truth human skeletons for each frame. Then, the same type of skeleton is predicted from corresponding RF data using a "student" deep-learning model consisting of a Residual Convolutional Neural Network (CNN), Region Proposal Network (RPN), and Recurrent Neural Network with Long-Short Term Memory (LSTM) that 1) extracts spatial features from RF images, 2) detects all people present in a scene, and 3) aggregates information over many time-steps, respectively. The model is shown to both accurately and completely predict the pose of humans behind visual obstruction solely using RF signals. Primary academic contributions include the novel many-to-many imaging methodology, unique integration of RPN and LSTM networks, and original training pipeline.
Simultaneous use of apple pomace and molasses as a source of energy for broiler

Fourteen days old commercial broiler chicks were fed diets in which maize of the control diet (T1) was replaced by 15% apple pomace (T2), 15% apple pomace and 5% molasses (T3); 15% apple pomace and 10% molasses (T4), 5% molasses (T5) and 10% molasses (T6) up to 42 days of age to study the effect on growth, feed intake, FCR and survivability. Diets of treatments T2, T3 and T4. were supplemented with multi-enzyme mixture *@ 0.1%.
A2-piece six-axis force/torque sensor capable of measuring loads applied to tools of complex shapes

Measuring forces and torques applied to tools of unconventional shapes can be challenging due to difficulties in connecting commercially available force/torque sensors. We propose an innovative, customizable six-axis force/torque sensor which can be clamped around a tool or end-effector to minimize the proximity between tool tip and sensor structure. The sensor is fabricated using 3d printing technology and consists of two pieces which together form an 8-legged Stewart platform. Each leg is designed as a cantilever beam to allow for a measurable displacement under an external load. The displacements of the legs are measured with 8 light intensity-based optoelectronic sensors, which exhibit high sensitivities and low noise levels without the need for external amplification circuitry. A customized printed circuit board and peripheral hardware are proposed to allow for efficient analog-to-digital conversion of the force/torque measurement. A calibration process is proposed which makes use ofa commercial ATI Mini40 sensor and custom hardware to allow for fast calibration routines. Finally, the calibrated sensor design is compared to the ATI Mini40 sensor by measuring sequences of forces and torques, and the maximum errors of force/torque components ($F_{x},\ F_{y},\ F_{\mathrm{z}},\ M_{x},\ M_{y},\ M_{\mathrm{z}}$) are 16.3%, 20.0%, 27.5%, 20.5%, 21.6%, 14.9% respectively.
A consistent QoS routing strategy for video streaming services in SDN networks

The software‐defined networking (SDN) paradigm proposes to decouple the control plane (decision‐making process) and the data plane (packet forwarding) to overcome the limitations of traditional network infrastructures, which are known to be difficult to manage, especially at scale. Although there are previous works focusing on the problem of quality of service (QoS) routing in SDN networks, only few solutions have taken into consideration the network consistency, which reflects the adequacy between the decisions made and the decisions that should be taken. Therefore, we propose a network architecture that guarantees the consistency of the decisions to be taken in an SDN network. A consistent QoS routing strategy is then introduced in a way that avoids any quality degradation of prioritized traffic while optimizing resources usage. Thus, we proposed a traffic dispersion heuristic in order to achieve this goal. We compared our approach with several existing framework in terms of best‐effort flows average throughput, average video bitrate, and video quality of experience (QoE). The emulation results, which are performed using the Mininet environment, clearly demonstrate the effectiveness of the proposed approach that outperforms existing frameworks.
Reliable Energy-Efficient Routing Algorithm for Vehicle-Assisted Wireless Ad-Hoc Network

: All the devices can be connected that provide utility from internet connection by the development of fifth generation (5G) wireless network. A fuzzy engine is typified by the inference system that includes the system rule base, input membership functions that fuzzify the input variables and the output variable de-fuzzification process. The Dynamic cluster head selection algorithm (D-LEACH) is used. The average energy consumption is obtained from the proposed dynamic cluster head using fuzzy interference system. Also a comparison is made for energy consumption with and without the D-LEACH algorithm
Transparent authentication utilising gait recognition

Securing smartphones has increasingly become inevitable due to their massive popularity and significant storage and access to sensitive information. The gatekeeper of securing the device is authenticating the user. Amongst the many solutions proposed, gait recognition has been suggested to provide a reliable yet nonintrusive authentication approach – enabling both security and usability. Whilst several studies exploring mobilebased gait recognition have taken place, studies have largely been preliminary, with various methodological restrictions that have limited the number of participants, samples and type of features. Furthermore, prior studies have relied upon evaluating the approach on a limited number of activities namely walking and running, and there is some concern over the capacity of the approach to correctly verify individuals when the nature of the signals across a wider range of activities is likely to be more variable. This paper has sought to overcome these weaknesses and provide a comprehensive evaluation, including an analysis of motion sensors (accelerometer and gyroscope), an investigation and analysis of features, understanding the variability of feature vectors during differing activities across a multi-day collection involving 60 participants. This is framed into two experiments involving five types of activities: normal, fast, with a bag, downstairs, and upstairs walking. The first experiment explores the classification performance of individual activities in order to understand whether a single classifier or multi-algorithmic approach would provide a better level of performance. The second experiment explored the features vector (comprising of a possible 304 unique features) to understand how its composition affects performance and for a comparison a more selective set of the minimal features are involved. Overall, results from the experimentation have shown an EER of 4.40-12.2% for a single classifier (using