Reviewerly/exHarmony huggingface dataset:
Image Enhancement with Improved Global and Local Visual Effects

Background: In many applications of image processing, the enhancement of images is often a step necessary for their preprocessing. In general, for an enhanced image, the visual contrast as a whole and its refined local details are both crucial for achieving accurate results for subsequent classification or analysis. Objective: This paper proposes a new approach for image enhancement such that the global and local visual effects of an enhanced image can both be significantly improved. Methods: The approach utilizes the normalized incomplete Beta transform to map pixel intensities from an original image to its enhanced one. An objective function that consists of two parts is optimized to determine the parameters in the transform. One part of the objective function reflects the global visual effects in the enhanced image and the other one evaluates the enhanced visual effects on the most important local details in the original image. The optimization of the objective function is performed with an optimization technique based on the particle swarm optimization method. Results: Experimental results show that the approach is suitable for the automatic enhancement of images. Conclusion: The proposed approach can significantly improve both the global and visual contrasts of the image.
Coupled Bi-Orientation Octet Pattern for Medical Image Retrieval

In this paper a novel feature descriptor is proposed named coupled bi-orientation octet (COBOT) pattern for medical image retrieval. COBOT enhances the image with octet encoding so that minute features of an image can be utilized for classification and retrieval. Loss of features is comparatively very less in COBOT when compared with other feature descriptors. COBOT can be explained in terms of orientation and octet. Bi orientation refers to both clockwise and anti-clockwise movements whereas, octet property divides the whole image in eight equal patches and extracts information from each of them. COBOT discern and augment the lines, corner point and texture information in the precinct octet across diverse directions. To further improve the robustness, multiresolution Gaussian filters with COBOT is incorporated and it is termed as MEGABOT. The proposed method transcends various other extensively used hand-crafted feature descriptors used for classification and retrieval of biomedical images.
Efficient FPGA implementation of high-speed true random number generator

High-speed true random number generator is a building block in the modern information security system. We propose and demonstrate an efficient high-speed true random number generator based on multiple parallel self-timed rings (STRs). To improve the security, we evaluate the randomness of the entropy source by min-entropy and exploit the information-theoretically provable Toeplitz-hashing extractor. To minimize the consumption of hardware resources of a field programmable gate array at a predetermined high throughput and maximize the throughput with the limited hardware resources, we systematically derive and investigate the dependence of the data throughput and the total consumed resources of the random number generator on the system parameters. On this basis, we make a joint optimization for the degree of parallelism of the STRs and the extraction efficiency of the extractor. A 10-Gbps true random number generator is implemented efficiently, so that the output random bits can pass all the National Institute of Standards and Technology (NIST) and Dieharder test suites.
Dynamic vs Static Encryption Tables in DES Key Schedules

The DES is a symmetric cryptosystem which encrypts data in blocks of 64 bits using 48 bit keys in 16 rounds. It comprises a key schedule, encryption and decryption components. The key schedule, in particular, uses three static component units, the PC-1, PC-2 and rotation tables. However, can these three static components of the key schedule be altered? The DES development team never explained most of these component units. Understanding the DES key schedule is, thus, hard. In addition, reproducing the DES model with unknown component units is challenging, making it hard to adapt and bring implementation of the DES model closer to novice developers' context. We propose an alternative approach for re-implementing the DES key schedule using, rather, dynamic instead of static tables. We investigate the design features of the DES key schedule and implement the same. We then propose a re-engineering view towards a more white-box design. Precisely, generation of the PC-1, rotation and PC-2 tables is revisited to random dynamic tables created at run time. In our views, randomly generated component units eliminate the feared concerns regarding perpetrators' possible knowledge of the internal structures of the static component units. Comparison of the performances of the hybrid DES key schedule to that of the original DES key schedule shows closely related outcomes, connoting the hybrid version as a good alternative to the original model. Memory usage and CPU time were measured. The hybrid insignificantly out-performs the original DES key schedule. This outcome may inspire further researches on possible alterations to other DES component units as well, bringing about completely white-box designs to the DES model.
Hand Gesture Recognition with Color Descriptors

The recognition of hand gesture based on computer vision is still a challenging problem, due to the high degrees of freedom or variability in the characteristics of hand gesture with respect to individuals. In this paper, combined with the color descriptors based on scale invariant feature transform features, a vision-based recognition with class specific DPL algorithm, or dictionary pairs learning, is proposed for the applications of hand gesture recognition, which may be considered as the extension of over complete dictionaries learning based on sparse representation. And it is also unnecessary to obtain fixed-length feature vectors for each hand image in the DPL related classification. The proposed approach has been evaluated on a publicly available hand gesture dataset of American sign languages with full digits and alphabet. The obtained experimental results indicate that the provided approach successfully recognizes 36 classes of hand gesture. In another way, the related experiments state that the proposed recognition scheme can offer high recognition accuracy with 97.18% for isolated hand signs and robust to noisy data. Then, it has been verified that DPL algorithm based on color descriptors can be applied to hand gesture recognition.
Online Bayesian Sparse Learning with Spike and Slab Priors

In many applications, a parsimonious model is often preferred for better interpretability and predictive performance. Online algorithms have been studied extensively for building such models in big data and fast evolving environments, with a prominent example, FTRL-proximal [1]. However, existing methods typically do not provide confidence levels, and with the usage of L1 regularization, the model estimation can be undermined by the uniform shrinkage on both relevant and irrelevant features. To address these issues, we developed OLSS, a Bayesian online sparse learning algorithm based on the spike-and-slab prior. OLSS achieves the same scalability as FTRL-proximal, but realizes appealing selective shrinkage and produces rich uncertainty information, such as posterior inclusion probabilities and feature weight variances. On the tasks of text classification and click-through-rate (CTR) prediction for Yahoo!'s display and search advertisement platforms, OLSS often demonstrates superior predictive performance to the state-of-the-art methods in industry, including Vowpal Wabbit [2] and FTRL-proximal.
A Compact Shape Descriptor Using Empirical Mode Decomposition to Detect Malignancy in Breast Tumour

Breast cancer is the most common cancer in India and the world. Mammogram helps the radiologists to detect abnormalities in breast. Analysis of the lesions on breast helps doctors in the detection of cancer in early stages. Lesion contours of breast are characterized by their shape. Malignant lesion contours have speculated and ill-defined shapes and benign have circular and lobulated shape. In the present work, we proposed a method to classify breast contours into benign/malignant using empirical mode decomposition (EMD) technique. Initially, the two-dimension contours of breast lesions are compacted into 1D signature. Further, 1D signatures of lesions are decomposed into intrinsic mode functions (IMFs) by the EMD algorithm and statistical based features are calculated from these IMFs. This parameters form a input feature vector which are further fed to classifier.
OR-Benchmark: An Open and Reconfigurable Digital Watermarking Benchmarking Framework

Benchmarking digital watermarking algorithms is not an easy task because different applications of digital watermarking often have very different sets of requirements and trade-offs between conflicting requirements. While there have been some general-purpose digital watermarking benchmarking systems available, they normally do not support complicated benchmarking tasks and cannot be easily reconfigured to work with different watermarking algorithms and testing conditions. In this paper, we propose OR-Benchmark, an open and highly reconfigurable general-purpose digital watermarking benchmarking framework, which has the following two key features: 1) all the interfaces are public and general enough to support all watermarking applications and benchmarking tasks we can think of; 2) end users can easily extend the functionalities and freely configure what watermarking algorithms are tested, what system components are used, how the benchmarking process runs, and what results should be produced. We implemented a prototype of this framework as a MATLAB software package and demonstrate how it can be used in three typical use cases. The first two use cases show how easily we can define benchmarking profiles for some robust image watermarking algorithms. The third use case shows how OR-Benchmark can be configured to benchmark some image watermarking algorithms for content authentication and self-restoration, which cannot be easily supported by other digital watermarking benchmarking systems.
SISTEM PAKAR MENDIAGNOSA PENYAKIT PERUT DENGAN METODE CERTAINTY FACTOR BERBASIS WEB

Stomacth disease is a disease that can be suffered by all people from children to adults. This diseases cause due to luck of healthy lyfestyle, such as over consuming fast food, unhygienic food or something unhealthy food, then lack of exercise also causes the stomach pain. One of the common methods in diagnosing stomach pain still have to visit and make consultation with the doctor. It tokes time to conduct face to face consultation and pay expensive cost for it. Many stomach disease sufferers do not know the type of stomach disease they experience because of the lack of information about stomach ailments, symptoms and the lack of abdominal specialist doctors. The method used is the method of Certainty Factor (CF) or the certainty value of an illness. Therefore, the purpose of this study is to make expert system software that is expected to help the community or patients in diagnosing the type of Stomacth disease. The development of expert system software includes, software requirements analysis which consists of user needs analysis, analysis of admin requirements. The results of the study were expert system application programs that were able to diagnose 24 stomach diseases.Keywords: Expert system, Certainty Factor, Diagnoses, Stomacth Diases.
Robust 3D Human Pose Estimation Model Based on Temporal Convolution

The existing temporal convolution model does not take full advantage of the characteristics of the low level network, and there is noise in the input 2D joint sequence. Therefore, a temporal convolution model based on intermediate supervision is proposed to estimate 3D human posture. Firstly, a 2D joint filter is designed to locate and correct obvious noise points, so that the input data is closer to the real data. Secondly, an intermediate supervision structure is added to the sequential convolution model to make full use of the characteristics of the lower level. Finally, the slicing function of the original model is replaced by the pooling layer to improve the abandonment of some features of the residual layer. The experimental results in the Human3.6M show that the model with 2D joint filter and network optimization is superior to the existing temporal convolution model, and the prediction error is reduced by 2.56% compared with the original model.
Research on Interpretability of SAR Image Component Recognition Based on Residual Attention Mechanism

The interpretability of SAR target images has always been an important and challenging subject in the field of SAR image research. In recent years, deep learning technology has been successfully applied to the recognition of SAR target images, and significantly surpasses the performance of traditional methods. However, its internal working mechanism is opaque and lack of interpretability, which restricts the reliable and credible application of SAR target images recognition technology. Therefore, we make a research on the interpretability of SAR target images recognition from the perspective of component recognition. First, based on the VGG16 model structure, we construct a new convolutional neural network as the backbone model. Then, to obtain highly discriminating component features in the SAR target images and improve the interpretability of the network, the residual attention module is embedded between the convolution blocks of the backbone network. Finally, we use class activation map (CAM) to visualize the features, thus enabling a visually intuitive understanding of the model's decision logic and rationale for input samples. We have verified the performance of the proposed model on the MSTAR datasets. Experiments show that the proposed model can effectively identify the components of the SAR target images under both the Standard Operating Conditions (SOC) and the Extended Operating Conditions (EOC).
Adversarial Deception in Deep Learning: Analysis and Mitigation

The burgeoning success of deep learning has raised the security and privacy concerns as more and more tasks are accompanied with sensitive data. Adversarial attacks in deep learning have emerged as one of the dominating security threats to a range of mission-critical deep learning systems and applications. This paper takes a holistic view to characterize the adversarial examples in deep learning by studying their adverse effect and presents an attack-independent countermeasure with three original contributions. First, we provide a general formulation of adversarial examples and elaborate on the basic principle for adversarial attack algorithm design. Then, we evaluate 15 adversarial attacks with a variety of evaluation metrics to study their adverse effects and costs. We further conduct three case studies to analyze the effectiveness of adversarial examples and to demonstrate their divergence across attack instances. We take advantage of the instance-level divergence of adversarial examples and propose strategic input transformation teaming defense. The proposed defense methodology is attack-independent and capable of auto-repairing and auto-verifying the prediction decision made on the adversarial input. We show that the strategic input transformation teaming defense can achieve high defense success rates and are more robust with high attack prevention success rates and low benign false-positive rates, compared to existing representative defense methods.
Research on neural network in consolidation effect of soft foundation

Abstract The method and application situation of neural network is universal application. Datum of project and monitoring from a project of soft foundation by vacuum preloading was filtrated and summarized. After analysis and comparison, establishment of optimization neural network model was used to evaluate the consolidation effect. By the sensitivity of impact factors, it was analyzed that the main parameter of soft foundation consolidation process.
Robust wavelet-based estimation for varying coefficient dynamic models under long-dependent structures

This paper considers a class of robust estimation problems for varying coefficient dynamic models via wavelet techniques, which can adapt to local features of the underlying functions and has less restriction to the smoothness of the functions. The convergence rates and asymptotic distributions of the robust wavelet-based estimator are established when the design variables are stationary short-range dependent (SRD) and the errors are long-range dependent (LRD). Particularly, a rate of convergence [Formula: see text] in terms of estimation consistency can be achievable when the true components satisfy certain smoothness for a LRD process. Furthermore, an asymptotic property of the proposed estimator is given to indicate the confidence level of our proposed method for varying coefficient models with LRD.
An Enhanced Design of E-learning Courses based on Instructional Model of SCO in MANET

E-learning plays vital role in the current scenario. Most of the learner given more interest towards the e-learning in the cyber world. This e-learning mainly focused on Sharable Content Object with an instructional model based on the Mobile Adhoc Network. This article deals with mainly four phases such as instructional based mode, sharable content object, e-learning course, and Mobile Adhoc Network. The instructional based modeact like providing instruction to the student or the mentor. The SCO Design is used to sharable content object based on the various resources dynamically. E-learning course is developed and implemented for the mentor and the learner. The learner can dynamically learn the course using Mobile Adhoc Network in the cyber world. The design of E-learning Courses based on Instructional Model of SCO is enchanced using MANET.
Blockchain for the Security and Privacy of IoT-Based Smart Homes

The Internet of Things (IoT) has given people a gift, in the “smart home,” which is a coordinated living arrangement that offers increased comfort and convenience. Smart homes can expand personal satisfaction. Inside a smart home, a system interconnects various devices, such as cell phones, sensors, and actuators. While smart homes have great advantages, they remain defenseless against the possible dangers of vindictive digital assaults, because of the expanding assortment and sharing of the live data of property holders. Blockchain is suited to meet each of the three CIA parameters: confidentiality, integrity, and availability. This chapter discusses blockchain technology, its framework, and its characteristics, to provide security to smart homes. A new model is proposed to ensure smart home security, using gateways and blockchain. The model is implemented and evaluated using response time and accuracy as metrics. Performance is compared to that of a traditional centralized network. The proposed architecture outperforms the competing model and provides security and privacy to smart homes based on the IoT.
3D Image Stitching of Train Bottom based on SURF and Cross Gauss Laplace Descriptor

With the modern development of the city, highspeed train traffic plays a significant role in public transportation. However, high-speed trains run frequently through long routes. Once a train fault occurs, the personal safety of passengers will be in danger. Therefore, fault diagnosis of trains becomes particularly important. In order to speed up fault detection, the train bottom is scanned by 3D camera before the train enters the station so that train bottom fault can be detected before train enter the station. It will buy more time for staff to carry the corresponding tools to repair if faults are detected. But the obtained data needs to be stitching to obtain the train bottom. In this paper, the 3D data obtained from the scanning of the train bottom is stitching to prepare for fault detection. This paper presents a new method for image stitching. First, select potential feature points based on SURF, then calculate the Cross Gaussian Laplace Descriptor (CGLD) of these feature points, finally determine whether the two feature points are the same feature points by matching the approximation of the CGLD of the candidate feature points. Experimental tests confirm that the matching accuracy of the feature points after the secondary matching has been improved to a certain extent.
Socio-psychological problems of teachers with social isolation

Ihe coronavirus pandemic has made significant changes in all areas of human life, It has affectted teaching activities among other things, This article is devoted to the issue of identifying socio-psychological problems which has been arised for teachers when it has been organizing distance education of subject “physical culture”. The relevance of the topic to which the article is devoted is due to the peculiarity of the current economic and social situation in the world community in general, and in Russia in particular, as a result of the global COVID-19 pandemic and the announced quarantine measures, the paradigm of education is changing, The above mentioned features of the current state of Affairs cause an urgent need to modernize the system of higher professional education.&#x0D; Due to the fact that at this stage the human community is mainly digital and systemic, during the COVID-19 pandemic, the transition of education to the e-learning format was accelerated.The purpose of the study, the results of which are presented in this article is to identify the socio-psychological problems of teaching disciplines related to physical activity that arise in teachers in the conditions of distance learning during quarantine social isolation. The accelerated process of transferring universities to distance learning inevitably caused and still causes a critical attitude, determined by a number of problems of a psychological, pedagogical, legal, economic and technological nature. educational institutions in modern conditions should set fundamentally new tasks in the direction of training specialists. Higher education institutions should strive to develop students ' critical thinking, the ability to self-regulate and, most importantly, self-organized learning.
Measuring Software Size and Effort Estimation on Islamic Banking Application

To maintain customer trust, Islamic banking continues to improve its services which rely on the use of information technology (software and its supporting peripherals). In the meantime, research on software in Islamic banking are still finite. Thus, the authors are interested in calculating the size and effort of working on the core application of an Islamic bank, which is called the Islamic Banking Application (IBA). Two methods are used in the study to see the size of the IBA. The size can be seen from the point of view of functional-based processes and non-functional aspects. The functional one, we propose the Use Case Points (UCP) method. In the UCP method, there are at least4 steps that must be passed for measuring software size. On the other hand, for non-functional measurement, we used the original Function Points Analysis (FPA) concepts. As a result, this research produces size measurement software using the FPA technique of 2,109.40 function points, while the UCP method results in 3,024.54 use case points. To convert size measurement into effort estimation, the author uses previous research with a productivity rate of 8.2 person/hour. Thus, the predicted effort consumed to develop the IBA is 17,297.08 person/hour (based on FPA technique) and 24, S01.23 person/hour (by UCP method). We found the deviation of 7,504 person/hour between both methods. This is due to the existence of different measurement parameters.