Miracle-dz/UniFAR-FTUs huggingface dataset:
A NEW TECHNIQUE BASED ON CHAOTIC STEGANOGRAPHY AND ENCRYPTION TEXT IN DCT DOMAIN FOR COLOR IMAGE

Image steganography is the art of hiding information into a cover image. This paper presents a new technique based on chaotic steganography and encryption text in DCT domain for color image, where DCT is used to transform original image (cover image) from spatial domain to frequency domain. This technique used chaotic function in two phases; firstly; for encryption secret message, second; for embedding in DCT cover image. With this new technique, good results are obtained through satisfying the important properties of steganography such as: imperceptibility; improved by having mean square error (MSE), peak signal to noise ratio (PSNR) and normalized correlation (NC), to phase and capacity; improved by encoding the secret message characters with variable length codes and embedding the secret message in one level of color image only.
Improving the security of real world identity management systems

Although identity management systems (notably OAuth 2.0 and OpenID Connect) have been widely adopted by a range of Relying Parties and Identity Providers, it is not yet clear whether practical implementations of these systems are actually secure. In this thesis we investigate this question. In doing so we describe two large-scale empirical studies of the security of real-world identity management systems; the purposes of these studies include identifying areas for improvement in the design and implementation of the systems, as well as addressing issues acting as barriers to adoption. As part of the underlying goal of improving operational security, a new scheme is also proposed to enhance user security for OpenID Connect. In the first of the two studies we examined 60 Relying Parties (RPs) and ten Identity Providers (IdPs) supporting OAuth 2.0 based identity management services in China. In the second study we considered 103 RPs supporting OpenID Connect-based identity management using Google as the IdP. In both cases we recorded and carefully analysed the browser-relayed messages sent between the RP and IdP, identifying a number of major security vulnerabilities, some with very serious potential consequences for end user security. We further designed and implemented proof-of-concept attacks to demonstrate the seriousness of the vulnerabilities we identified. We also reported the vulnerabilities to the most seriously affected parties, helped them to fix the problem, as well as providing detailed recommendations for both IdPs and RPs, designed to reduce the risk of such vulnerabilities occurring in the future. To improve user security when using OpenID Connect, a novel client-based scheme is proposed, designed to mitigate phishing attacks and to provide a consistent user interface. A prototype of the scheme is described, which allows for greater
Influence of Wheelchair Cushions Fabricated by Therapists on Stability, Comfort, and Functionality

“Ease of comfort” and “functionality” are important factors that have been cited to improve wheelchair seating. The ability to maintain balance and posture while seated in a wheelchair improves stability, comfort, and functionality and is a prerequisite for daily activities. In this study, the authors used a sitting position measuring device to scientifically verify differences in sitting posture, comfort, and functionality depending on the presence or absence of cushions and the type of cushion in elderly individuals who used wheelchairs. Results suggested that a cushion fabricated by a therapist was more useful than a commercially available air cushion. While a commercially available air cushion was the most suitable, the therapist’s self-made cushion could be expected to demonstrate the same or better applicability than an air cushion.
Navigation of Mini Unmanned Aerial Vehicle in Unknown Environment

In this paper, two algorithms are proposed to navigate the mini unmanned aerial vehicle (MUAV) in unknown environment. The first algorithm is path planning algorithm, it proposed to force the MUAV to track a specific trajectory. The second algorithm is obstacle avoidance algorithm, it proposed to avoid the collision between the MUAV and borrower when the MUAV is maneuvering in unknown environment. The pure pursuit algorithm (PPA) is used to force the MUAV to follow the desired trajectory depends on the given waypoints. While the vector field histogram algorithm (VFHA) is used to avoid collision of the MUAV with obstacles block in the desired path. To keep the flight of MUAV without collision, the 3D laser sensors are used to collect the information from the surrounding environment. The GPS sensor is used to keep the MUAV on the specific path. The collected information from the two sensors are analysed by the controller of the MUAV to improve its performance. The Matlab program is used to implement the proposed algorithms and plot the trajectories of MUAV in the unknown algorithm. The Matlab program is connected with the gazebo program via robotic operating system (ROS) to simulate the visual movement of the MUAV in any unknown environment. The obtained results show the effectiveness of the proposed algorithm to flight the MUAV in different environments.
Cooperative Power and Resource Management for Heterogeneous Mobile Architectures

Heterogeneous architectures have been ubiquitous in mobile system-on-chips (SoCs). The demand from different application domains such as games, computer vision and machine learning which requires massive parallelism of computation has driven the integration of more accelerators into mobile SoCs to provide satisfactory performance energy-efficiently. These on-chip computing resources typically have their individual runtime systems including: (1) a software governor: continuously monitors hardware utilization and makes decisions of trade-off between performance and power consumption. (2) software stack: allows application developers to program the hardware for general purpose computation and perform memory management and profiling. As computation of mobile applications may demand all sorts of combinations of computing resources, we identify two problems: (1) individual runtime can often lead to poor performance-power trade-off or inefficient utilization of computing resources. (2) existing approaches fail to schedule subprograms among different computing resources and further lose the opportunity to avoid resource contention to gain better performance.
Learning-Based Resource Management for SWIPT

In this article, we consider the joint optimization of transmit power and power splitting ratio to maximize the energy efficiency in a simultaneous wireless information and power transfer based interference channel, in which receivers use a power splitting policy to harvest energy from a wireless signal. We propose an optimization-based iterative algorithm (O-IA) from well-known optimization techniques as a comparative scheme, and also devise a neural network based learning algorithm (NN-LA) to deal with nonconvexity caused by cochannel interference among multiple nodes. Through simulations, we provide a comparative study of the two approaches in terms of energy efficiency and time complexity. In particular, we find that NN-LA achieves a near-optimal energy efficiency, whereas its time complexity is significantly reduced, in comparison with O-IA.
Die Funktion definierter Ig-Domänen von Neurolin und des L1-ähnlichen E587-Antigens bei axonalen Wachstums- und Regenerationsprozessen im Sehsystem des Goldfischs

Reihenfolge). Zuletzt möchte ich mich bei Annette bedanken, die mich die grösste Zeit der Doktorarbeit ertragen musste, und natürlich bei Valerie, die mich hoffentlich noch einige Zeit erträgt! III Teile der vorliegenden Arbeit sind in folgende Publikationen eingegangen: antigen is upregulated by goldfish oligodendrocytes after optic nerve lesion and supports retinal axon regeneration. Glia 23: 257-270.regulation of Netrin receptors and loss of Netrin-1 expression in the retina of adult and grafted rats. (in Vorbereitung).
Designing LDPC system using prob domain decoder in Xilinx system generator

Low-Density Parity-Check (LDPC) code represents a type of Forward Error Correction (FEC) codes, it is a very perfect code with the ability to correct the errors and it has a performance in term of Bit Error Rate (BER) close to Shannon limits. As this code has many features like reliability, flexibility in implementation and a facility of adaptation. So it takes place in the current area of 4G and even 5G network communications. In this paper, an efficient and optimum design for LDPC system using prob domain decoder is implemented using a Xilinx system generator to evaluate its performance in terms of BER, its complexity and the time needed. FPGA device Kintex7 (XC7K325T-2FFG900C) is used for the implementation of the proposed model. The results show that by increasing the Signal to Noise Ratio (SNR), the values of the BER will be improved significantly.
Mobile Augmented Reality (AR) Marker-based for Indoor Library Navigation

This paper presents the development of Augmented Reality (AR) for smart campus urbanization using library as the environment for the demonstration of the AR prototype. The main goal of the AR development is to help users to get information and direction easily using AR based mobile application when walking inside the library. In normal circumstances, users typically walk around and explore the library area before reaching their targeted destinations. Depending on the library size and number of reading corners, exploration and walking in the library can be time consuming. Therefore, an AR technology is introduced in this paper to improve the user experience inside the library in the right direction and information instantly. This application is developed using Vuforia software to set the image marker-based and process the output into Unity3D software, Android Studio for the Main Menu interface and IBM Watson for voice recognition. The final form of the application is successfully generated from the development of Augmented Reality (AR) application for the smart campus by using a library for the demonstration of the AR prototype. A series of application tests are conducted in each corner of the library to evaluate the effectiveness of developed AR.
A Simulation Study of Tunneled Voice over Internet Protocol System

In this paper, the effects of real time traffic on non real time traffic are explained for conventional VoIP system. Also two types of Quality of Service models are applied; the End to End delay, the throughput, and the number of dropped packet are the main parameters used to evaluate the network performance. A new technique is proposed to solve the above problems. This technique is based on the tunneling process to carry the real time traffic. Some analysis is performed to expect the maximum call number served by a network. To overcome the overhead problem due the tunnel, two parameters are included; the first one is the number of real time packets tunneled in each frame, and the second one is the voice frame size. The analysis and the simulation show that as the number of the tunneled packet becomes larger, the maximum call number approaches to the non tunnel situation. On other hand, as the frame size becomes larger, the maximum call number exceeds the non tunneled situation. Of course, the maximum number of tunneled packets and the frame size affect the End to End delay which is controlled by the type of CODEC being used. A compromised value can be found to achieve the better network response. OPNET MODELER is the simulating software being used together with a dedicated program for performing some calculations related to network performance.
Image Steganography Scheme Using Parent Child Relationship in Wavelet Domain

In this paper, a steganography scheme for digital images by using Integer Wavelet Transform ( IWT ) is proposed. This scheme is based on the qualified significant wavelet tree ( QSWT ). In this scheme, the secret data bits are embedded into largest and smallest wavelet child coefficients of a parent wavelet coefficient of a cover image. Embedding of secret data bits are performed in selected wavelet subbands only so that the embedding capacity and Peak Signal to Noise Ratio ( PSNR ) can be achieved maximum as PSNR value decreases with the increase of embedding capacity. Visual quality of stego images produced by the proposed scheme is acceptable by human visual system as PSNR between cover and stego images is above 40 dB and extracted secret image is exactly the same as original secret image .
Vindicating Service Quality of Education through Structural Equation Modeling (SEM): International Students’ Perspective

The prime objective of this paper is to identify the factors that influence service quality of education mediating by institutional image in higher education perspective. To this aim, the Nordic model was used as a theoretical base of the study. Program quality as a technical quality as well as industrial link and student satisfaction as functional quality were postulated to positively influence image toward the service quality education. Data (n=294) were collected from foreign students studying at University Utara Malaysia located in northern Malaysia through convenient sampling procedure. The structural equation modeling (SEM) was utilized for analyzing the data. Analysis of the data indicates that image has a full mediating role in the relationship between industrial link and service quality education. To uphold service quality in education, the academic authorities need to nurture earnestly industrial link and image of the institution because image occupied full mediation role between industrial link and service quality of education. Moreover, program quality and student satisfaction have shown direct significant impact on image and service quality of education and can be regarded as critical factors for certifying image and service quality in higher education. The conceptual model of this study would give more recognition if any tertiary level institution addresses this in order to increase their image and overall ranking. This study incorporated institutional image as a mediating variable, which is an exceptional endeavor in tertiary level education for enriching existing body of literature in perspective of international students.
Fully convolutional network with sparse feature-maps composition for automatic lung tumor segmentation from PET images

Accurate lung tumor delineation plays an important role in radiotherapy treatment planning. Since the lung tumor has poor boundary in positron emission tomography (PET) images, it is a challenging task to accurately segment lung tumor. In addition, the heart, liver, bones and other tissues generally have the similar gray value as the lung tumor, therefore the segmentation results usually have high false positive. In this paper, we propose a novel and efficient fully convolutional network with a trainable compressed sensing module and deep supervision mechanism with sparse constraints to comprehensively address these challenges; and we call it fully convolutional network with sparse feature-maps composition (SFC-FCN). Our SFC-FCN is able to conduct end-to-end learning and inference, compress redundant features within channels and extract key uncorrelated features. In addition, we use deep a supervision mechanism with sparse constraints to guide the features extraction by a compressed sensing module. The mechanism is developed by driving an objective function that directly guides the training of both lower and upper layers in the network. We have achieved more accurate segmentation results than that of state-of-the-art approaches with a much faster speed and much fewer parameters.
The Prognostic Importance of Prostate-Specific Antigen in Monitoring Patients Undergoing Maximum Androgen Blockade for Metastatic Prostate Cancer

The changes in serum prostate-specific antigen (PSA) concentrations can be used as a prognostic factor in patients undergoing maximum androgen blockade for metastatic prostate cancer. Materials and methods One hundred and forty-nine patients followed-up in our department were classified into four groups based on PSA changes. Group 1, those with a normalization of PSA levels within the 1st 3 months; group 2, those with a normalization of PSA levels between months 3 and 6; group 3, those with a reduction in PSA levels, but not reaching the normal range; group 4, those with no reduction in PSA levels. The gleason scores and the number of bone metastases were also compared between the groups. Results The time to progression was significantly delayed in group 1 (mean: 23.3 months) compared to that of group 2 (mean: 16.9 months) (p<0.02). The time to progression in group 3 (mean: 8.45 months) was significantly shorter compared to groups 1 and 2 (p<0.001). In addition, in patients with gleason scores 5–7 (grade 2) and gleason scores >7 (grade 3) and in group 1, the time to progression (mean: 21.2 months) was significantly delayed compared to those with the same gleason scores but in group 2 (mean: 13.4 months) (p<0.001). Conclusion The reduction in PSA levels is more important than gleason scores in determining the time to progression. Early normalization of PSA levels delays the time to progression, and when combined with gleason scores, PSA is an important prognostic factor in predicting the success of the therapy.
Timing and duration of social assistance receipt during childhood on early adult outcomes

The experience of economic disadvantage during childhood is a major predictor of a variety of negative outcomes during early adulthood. This study provides evidence on the significance of timing of social assistance receipt during childhood on children’s later adjustment problems. The study uses data from the 1987 Finnish Birth Cohort (FBC), which covered all children born in Finland in 1987 (N=59476) and followed them until the age of 25. The data were gathered from Finnish registers that cover health and sociodemographic data for cohort members and their parents. Altogether 11,062 female (38.1%) and 11,537 male (37.9%) cohort members had parents who had received social assistance. Social assistance receipt during childhood increased the risk for all measured adjustment problems: early school leaving (OR 2.37), conviction (OR 1.87), teenage pregnancy (OR 1.89) and mental disorders (OR 1.68) even when adjusting for several social background variables. Economic disadvantage during early childhood (0–2 years) was found to associate with highest risk; all measured adjustment problems compared to exposure to poverty later in childhood. The study concludes that early childhood is a period in which children acquire cognitive and social competencies that form the basis for future wellbeing. Our analysis, based on a total nation-wide birth cohort, indicates that economic disadvantage in early childhood poses the most significant risk for later adjustment problems.
Activation of Cardiac Aldosterone Production in Rat Myocardial Infarction

Background—This study analyzed the regulation and the role of the cardiac steroidogenic system in myocardial infarction (MI). Methods and Results—Seven days after MI, rats were randomized to untreated infarcted group or spironolactone- (20 and 80 mg · kg−1 · d−1), losartan- (8 mg · kg−1 · d−1), spironolactone plus losartan–, and L-NAME– (5 mg · kg−1 · d−1) treated infarcted groups for 25 days. Sham-operated rats served as controls. In the noninfarcted myocardium of the left ventricle (LV), MI raised aldosterone synthase mRNA (the terminal enzyme of aldosterone synthesis) by 2.0-fold and the aldosterone level by 3.7-fold. Conversely, MI decreased 11β-hydroxylase mRNA (the terminal enzyme of corticosterone synthesis) by 2.4-fold and the corticosterone level by 1.9-fold. MI also induced a 1.9-fold increase in cardiac angiotensin II level. Such cardiac regulations were completely prevented by treatment of the infarcted heart with losartan. The MI-induced collagen deposition in noninfarcted LV myocardium was p.
Cell Nuclei Classification in Histopathological Images using Hybrid OLConvNet

Computer-aided histopathological image analysis for cancer detection is a major research challenge in the medical domain. Automatic detection and classification of nuclei for cancer diagnosis impose a lot of challenges in developing state-of-the-art algorithms due to the heterogeneity of cell nuclei and dataset variability. Recently, a multitude of classification algorithms have used complex deep learning models for their dataset. However, most of these methods are rigid, and their architectural arrangement suffers from inflexibility and non-interpretability. In this research article, we have proposed a hybrid and flexible deep learning architecture OLConvNet that integrates the interpretability of traditional object-level features and generalization of deep learning features by using a shallower Convolutional Neural Network (CNN) named as CNN3L. CNN3L reduces the training time by training fewer parameters and hence eliminating space constraints imposed by deeper algorithms. We used F1-score and multiclass Area Under the Curve (AUC) performance parameters to compare the results. To further strengthen the viability of our architectural approach, we tested our proposed methodology with state-of-the-art deep learning architectures AlexNet, VGG16, VGG19, ResNet50, InceptionV3, and DenseNet121 as backbone networks. After a comprehensive analysis of classification results from all four architectures, we observed that our proposed model works well and performs better than contemporary complex algorithms.
Distribution and survival of primary sarcoma in Korea: A single center analysis of 2017 cases

Sarcomas were mesenchymal originated tumors, arising in connective tissue. These were a very rare incidence of overall human malignant tumors, accounting for less than 1% of all new cancer diagnoses [1]. This heterogeneous malignancy tumor consists of 70 different histologic subtypes [2]. Histopathologic classification of sarcoma has changed considerably since 1978 [3]. The World Health Organization (WHO) published new sarcoma classification, representing updated consensus, in 2013. According to this, gastrointestinal tumor (GIST) has included in sarcoma and many cases of malignant fibrous histiocytoma (MFH) were classified as specific sarcoma [4]. Many previous reports about the epidemiology of sarcoma were not based on new classification. Only a few studies described the characteristics of sarcoma, and prognosis with new classification. In Korea, one study reported epidemiology and prognosis of sarcoma. It was based on a previous classification with low volume of data [5]. Therefore, the aim of the study is to analyze the distribution, characteristics, prognosis of sarcoma based on new classification with a retrospective review of single center 2,017 cases.
Serum albumin conformation in patients with melancholic depression under antidepressant therapy

Discovery of biomarkers of mental disorders for evaluation of efficacy of psychopharmacotherapy is an important task. The last years it became clear that disturbances in molecular processes in pathological conditions can be connected with conformational changes in protein structure [1,2]. The aim of the study was to investigate the blood albumin conformation in patients with melancholic depression (MD) under pharmacotherapy using fluorescent laser spectroscopy.