Reviewerly/exHarmony huggingface dataset:
Fuzzy Rough Set based Social IoT Recommender System

Internet of Things (IoT) has brought a revolution in the lives of citizens by providing safety and comfort. Users are more concerned with the Quality of Experience (QoE) while using an application. One such application that enriches the users' experience is the assistance in the form of a recommender system. Recommending IoT services which are beneficial to the users based on their preferences is challenging task keeping in view of the diversity of the information required. The existing solutions incorporate the limited knowledge of the user and the context. However, to the best of the authors' knowledge, there is no efficient solution that recommends the users while taking into account the overall social IoT system. In order to address this problem, the recommender proposed in this paper uses a fuzzy rough set theory which is based on a strong mathematical foundation. The solution proposes an algorithm based upon the collaborative filtering which enables the user to discover new content dissimilar to the items viewed by them in the past. The simulation results prove that superiority of the solution against the standard parameters like Mean Reciprocal Rank (MRR), Mean Average Precision (MAP), Normalized Discounted Cumulative Gain (NDCG) and the state-of-art solutions.
Image Segmentation of Thyroid SPECT Using Edge-Based Active Contour Model

Abstract Nuclear medicine is a reliable method to analyze or diagnose some type of diseases using functional imaging, for example SPECT imaging to obtain organs uptake and biodistribution. In order to get information needed, SPECT images have to be segmented first. Region of interest (ROI) method by the expertise is generally performed for image segmentation in SPECT imaging, but the task is daunting and time-consuming due to large number of images to be segmented. Among many methods that can be used for image segmentation, edge-based active contour is one of the most popular and widely used method in medical image segmentation. In addition to Level Set Method that fails to stop gradient magnitude by only using gradient information, here the edge-stop function (ESF) is proposed for edge-based active contour models to segment images with unclear boundaries using Matlab R2019a. In this study the Level Set Method with k-Nearest Neighbours classifier and fuzzy ESF are used to segment the thyroid images from the 99m Tc-sestamibi SPECT imaging. The number of iterations and initialization are assessed to obtain better segmented images which closely related to segmented images using ROI technique from the experts. The results showed that the proposed method could be used for segmentation of thyroid SPECT images.
Ensemble P-spectral Semi-supervised Clustering

This paper proposes an ensemble p-spectral semi-supervised clustering algorithm for very high dimensional data sets. Traditional clustering and semi-supervised clustering approaches have several shortcomings; do not use the prior knowledge of experts and researchers; not good for high dimensional data; and use less constraint pairs. To overcome, we first apply the transitive closure operator to the pairwise constraints. Then the whole feature space is divided into several subspaces to find the ensemble semi-supervised p-spectral clustering of the whole data. Also, we search to find the best subspace by using three operators. Experiments show that the proposed ensemble pspectral clustering method outperforms the existing semi-supervised clustering methods on several high dimensional data sets.
Modified Asymmetric Time-varying Coefficient of Particle Swarm Optimization

In this paper, a new modification for particle swarm optimization (PSO) is developed. It has been found previously that making the learning coefficients of PSO variable enhances the performance in terms of convergence rate and obtaining the global minima solution. This has inspired a lot of researchers that investigated the effect of the coefficient's behavior on the PSO performance. However, the works in this field is still limited. This work presents a novel idea of using asymmetric curve of the modified PSO to represent the coefficient behavior. The method is tested and compared to previously reported techniques. The results are promising compared to most common methods in the field.
Cycle Fusion Network for Multi-Person Pose Estimation

Abstract Multi-person pose estimation has been largely improved with the development of convolutional neural network, which however remains a challenging task in computer vision. The challenge is particularly pronounced in high flexibility of occlusion, camera angles, imaging-caused body limbs. Existing methods are mostly based on networks with deep architectures, which are capable of inferring the semantic labels with high accuracy while suffering from positioning accuracy degeneration caused by the loss of the spatial information. To address these problems, we present a novel network called cycle fusion network to improve the pose estimation accuracy in multi-person environments. The cycle fusion network has a two-stage structure. In the first stage, a cycle-path feature fusion is used to increase the semantic information for low level layers and the spatial information for high level layers respectively. In the second stage, a cross-scale feature fusion is used for refining the positions of keypoints. Equipped together with RFB module and multi-level supervision, the proposed method has shown the state-of-the-art performance on the large-scale MS COCO benchmark with extensive experiments.
Effect of Transforming Offline Purchasing of Tamil Books into Online Mobile

Abstract Tamil books are less accessible among Indian community due to its less demand. People less aware about Tamil books, thus MG Bookstore mobile application is developed to promote the MG Bookstore among people. Mobile application is the appropriate medium for online business in order to reach people and easily. MG Bookstore mobile application is developed for every age of people, but it is only applicable for Android users. In order to attract the buyers, researcher added free reading feature in the MG Bookstore mobile application, which is barely to find in other online business mobile application. Researcher designed and developed this MG Bookstore mobile application by using Buildfire software. Researcher runs the usability test by conducting interview and questionnaire. Researcher also seeks for media experts’ and content expert’s advices and suggestions to enhance the features of this mobile application. In conclusion, researcher concludes that MG Bookstore mobile application is applicable for everyone and helps the MG Bookstore to expand their business by reaching more people through it.
Examining the Efficiency of Magnetometers in Movement Classification Systems

Magnetic sensors are used in a large variety of applications, including pattern recognition systems. In this paper, a novel a feature extraction approach is explored, which can potentially improve the classification efficiency in magnetometer-based movement classification systems. The basic idea of the approach is that the first derivative of the magnetometer data can carry more information than the raw measurements. The proposed method was tested on a dataset composed of real measurements, which were acquired using wrist-worn wireless sensor units. The applied data were collected with the help of multiple subjects for various movement classes. Different training and validation datasets were generated and tested based on the sampling frequency, the size the processing window and the feature extraction mode. Multi-Layer Perceptron (MLP) neural networks were used as classifiers. The achieved results show that this extraction mode can improve classification efficiency in most of the setups if it is used together with features extracted using raw measurements.
Randomized multi-label subproblems concatenation via error correcting output codes

In this paper, we study the error correcting output codes for multi-label classification problems. Imbalance problem is very common in multi-label task and it has not been effectively solved yet. In previous works, base classifiers are learned from the same pool of training data which may lead to similar classifiers, especially when a large amount of labels exist. To tackle these challenges, we propose a novel model called Randomized Multi-Label Subproblems Concatenation via Error Correcting Output Codes (RMSC). In order to reduce the imbalance problem and enhance the diversity between base classifiers, in our model, the original multi-label problem is transformed into an ensemble of many multi-label sub-problems and the one-vs-all encoding strategy is conducted on these sub-problems and finally corresponding coding matrices are concatenated into a whole coding matrix. Moreover, we provide a proof of enhanced diversity in our model, together with some discussions about the parameter determination and time complexity. Extensive experimental results on various kinds of data sets well demonstrate that the proposed model consistently outperforms the state-of-the-art methods.
Absolute Secrecy Asymptotic for Generalized Splitting Method

Generalized integer splitting differs from the plain one in use of a new random number at each step of the process. It assumed that the receiver is informed on these numbers and the splitting level k, and hence he is able to restore an original text using some known procedures from the number theory. The present paper contains a probabilistic analysis of the information secrecy of the generalized integer splitting method with respect to an unauthorized access to transmission channel. A lemma has been proved in the paper stating that the probability of the successful information restoration is exponentially reduced with increase of the splitting level.
Science Informed Major Event Security Planning: From Vulnerability Analysis to Security Design

The post 9/11 security landscape has seen a fundamental shift in security planning, organization and management across the national and global security domain. This shift is particularly evident with regards to special events. Major national regional and global events such as the Olympic Games, FIFA world cup, the US Super Bowl, and the Boston Marathon draw large crowds of spectators and participants thereby creating vulnerable targets. The 1972 Munich Olympics and 1996 Atlanta Olympics are 2 examples of pre-9/11 attacks on sporting events. Since 9/11, we have seen attacks on events such as the Boston Marathon in 2013, Las Vegas active shooter in 2017 and the bombing in Manchester at the Ariana Grande concert in 2017. These examples highlight the inherent vulnerabilities and security challenges that reside in major events (sporting, cultural, religious, musical). With this in mind, security budgets have increased to deal with such risks and threats. For example, the security budget for the 2012 London Olympics topped $1.46 Billion and the 2010 Vancouver Olympics was over $900 million. This chapter examines the security challenges and solutions for managing major event security. Scenario planning and disaster forensic methodologies (Masys AJ (ed), Disaster forensics: understanding root cause and complex causality. Springer, Cham, 2016) are presented along with the role of predictive analytics in supporting vulnerability analysis and security solution navigation. Key recommendations are made pertaining to vulnerability analysis and the development and design of a security architecture planning framework rooted in a High Reliability Security Organizations (Masys AJ, Designing high reliability security organizations for the homeland security enterprise. In: A Siedschlag, A Jerkovic (eds) Homeland security cultures: enhancing values while fostering resilience. Rowman & Littlefield International, London).
Quantitative Restoration of Noisy Colour Texture Segmentation Benchmark Images using State-of-the-Art Algorithm

Texture segmentation is a well-known research domain and a large number of researchers are working on it across the globe due to its wide varieties of applications in various domains such as medical imaging, shape extraction, product inspection, remote sensing, and segmentation of natural images. Many times, images are corrupted by various types of noises such as Gaussian, speckle, and salt-pepper noise. Most often Gaussian noise is a source of corruption in many applications. Prague texture dataset is extensively used by researchers due to wide varieties of multi-class textures in it. The present study offers the restoration of Prague texture database benchmark images. These images are corrupted with Gaussian noise with different variance values. The state-of-the-art algorithm viz. Colour Block Matching 3D (C-BM3D) filter, which achieves the first rank among 15 algorithms reported in the most recent literature, is used for the restoration of noisy texture benchmark images. Image restoration performance metrics used are peak signal to noise ratio (PSNR) and structural similarity index (SSIM).
The Enhancement of Graphic QR Code Recognition using Convolutional Neural Networks

The use of QR Code has been flourishing on mobile and tablet platforms. By scanning the code, we can obtain targeted information synchronously. The regular QR Code, which consists of black and white modules, is neither visually pleasing nor recognizable by human vision. The application of graphic QR Code to product packaging and promotion campaign in the market has skyrocketed nowadays. However, printed graphic QR Code accompanies noise phenomenon that interferes the recognition itself and causes failure when user scanning. Therefore, we produce graphic QR Codes by data hiding with error diffusion techniques that first become training data. Then, we apply convolution neural networks to improve the data point recognition of graphic QR Codes. The experimental results show the superiority of the performance in both accuracy and recognition ability in comparison with normal QR Code readers.
A Right Ventricle Segmentation Method based on U-Net with Weighted Convolution and Dense Connection

To solve the problem that the traditional convolutional neural network uses the pooling layers to reduce the image feature dimensions, which leads to information loss and affects the accuracy of right ventricular segmentation, a right ventricular segmentation method based on U-Net improved network is proposed. The dense blocks are used to combine the bottom features of the contracting path, and shortcut connections are used to connect the low-level features and high-level features on the expanding path, which increase the reusability of features. Depthwise separable weighted convolutions are used to enhance the edge detail information and improve the possibility of information reconstruction. An improved shinkage loss function is proposed to solve the problem of unbalanced positive and negative samples. Finally, RVSC-MACCAI 2012 datasets are used in the comparison experiments of different models, and the results show the effectiveness of the improved algorithm with the Dice coefficient of 0.90 and Hausdorff distance of 6.42.
Current Indian Judicial System: Issues and Blockchain Solutions

Indian Judicial System is the strongest institution of the world’s largest democracy. Its purpose is to give justice to innocent and punish the guilty. Yet pendency of thousands of cases is the matter of serious concern. Shortage of judges and infrastructure has been highlighted as one of the major reasons for the same. Undeniably corruption and counsel-side delays are other prominent reasons for prolonged delay. To mitigate such delays, there is a need for more accountable and transparent judicial system. This paper aims to overcome the current loopholes in Indian Judicial System with an application of blockchain system. The paper discusses sample issues faced by common man in court along with the alternatives available and provides blockchain solution as the substitute. This approach implicitly limits the advocates, agents, and court officials to exploit judicial system for the benefit of individual and culprits. The proposed system will help regain the trust of common man in the Indian Judicial System.
Branch-and-Bound Search for Training Cascades of Classifiers

We propose a general algorithm that treats cascade training as a tree search process working according to the branch-and-bound technique. The algorithm allows to reduce the expected number of features used by an operating cascade—a key quantity we focus on in the paper. While searching, we observe suitable lower bounds on partial expectations and prune tree branches that cannot improve the best-so-far result. Both exact and approximate variants of the approach are formulated. Experiments pertain to cascades trained to be face or letter detectors with Haar-like features or Zernike moments being the input information, respectively. Results confirm shorter operating times of cascades obtained owing to the reduction in the number of extracted features.
A Clustering Algorithm for Varied Density Clusters based on Similarity of Local Density of Objects

Cluster analysis aims to discover the hidden relationship between objects in the dataset so that similar objects are placed in the same cluster and non-similar ones are placed in different clusters. This research proposes a clustering algorithm for datasets that contain diverse clusters in density; the cluster is a connected graph where the similarity between any two adjacent neighbors is greater than or equal to a threshold. The similarity is based on the local density of objects, where the local density of an object is the sum of the distances between it and its k-nearest neighbors. The strategy starts from any object to collect its similar objects from its neighbors and continues collecting similar objects for the collected neighbors until there is no similar object can be added to the current cluster. The suggested strategy is tried on to two synthetic datasets and many reference datasets used in this field, which are available on http://cs.joensuu.fi/sipu/datasets/. All of them have two dimensions to easily visualize the result. The results uncover the effectiveness of the proposed strategy in determining groups with varied forms, sizes, and densities from the given datasets.
Designing video profile fashion program Malang State University using adobe premiere

Abstract This research aims to make a video profile of Fashion Program in Malang state university. Whereas for a Program it must have a video profile, especially in digital era, it is necessary to make a video profile of the Fashion Program that serves as a very effective means of promotion to the public or others. In this research use Adobe Premiere CC 2019 and Adobe Photoshop CS6 software, so as to create an attractive video profile display. The research method divided into 3 stages, namely pre-production, production, and post-production. The results of the expert validation test, the video profile of Fashion Program was declared very feasible. Some suggestions given by experts, improvements were made, so as to create a video profile that was ready to be used as a promotional tool for Fashion Programs in Malang State University.
Reducing Latency in Interactive Live Video Chat Using Dynamic Reduction Factor

In best-effort packet networks, a buffer is commonly used to eliminate network jitter and enable smooth video playback at the expense of additional delay (i.e. jitterdelay). In this work, we examine how jitter buffer performs in the Web Real-Time Communications (WebRTC), which is the de-facto standard used in interactive multimedia applications. We collect a dataset from a live video streaming service provider, which adopts WebRTC. After an in-depth analysis of the dataset, we find that jitter buffer can dynamically adjust the jitterdelay but is too conservative, resulting in a very slow decline of jitterdelay. To address the issue, we analyze the control logic of the jitter buffer and find that the reason lies in the use of a fixed reduction factor, known as psi (ψ). We propose an enhanced jitter buffer adaptation mechanism called JTB-ψ, which dynamically adjusts ψ according to frame size and frame duration, to reasonably speed up the decline of jitterdelay. Practical testbed experiments show that JTB-ψ achieves a 41.5% lower jitterdelay and improves receiving frame rate, quantization parameter (QP) and sending bit-rate under different network conditions, compared to the fixed-ψ approach.
SNR-Centric Power Trace Extractors for Side-Channel Attacks

Existing power trace extractors consider the case where the number of power traces available to the attacker is sufficient to guarantee successful attacks, and the goal of power trace extraction is to extract a small part of traces with high signal-to-noise ratio (SNR) to reduce the complexity of attacks rather than to increase the success rates. Although strict theoretical proofs are given, the existing power trace extractors are too simple and leakage characteristics of Points-of-Interest (POIs) have not been thoroughly analyzed. They only maximize the variance of the data-dependent power consumption component and ignore the noise component, which results in very limited SNR that hampers the performance of extractors. In this article, we provide a rigorous theoretical analysis of SNR of power traces, and propose a simple yet efficient SNR-centric extractor, named shortest distance first (SDF), to extract power traces with the smallest estimated noise by taking advantage of known plaintexts. In addition, to maximize the variance of the exploitable component while minimizing the noise, we refer to the SNR estimation model and propose another novel extractor named maximizing estimated SNR first (MESF). Finally, we further propose an advanced extractor called mean-optimized MESF (MMESF) that exploits the mean power consumption of each plaintext byte value to more accurately and reasonably estimate the data-dependent power consumption of the corresponding samples. Experiments on both simulated power traces and measurements from an ATmega328p micro-controller demonstrate the superiority of our new extractors.
Fast and precise spatial-carrier phase-shifting algorithm with the matrix VU factorization

A fast and precise spatial-carrier phase-shifting algorithm based on the matrix VU factorization strategy that can realize dynamic real-time phase detection is proposed. First, the proposed algorithm divides the spatial-carrier interferogram into four phase-shifting subinterferograms. Second, the matrix VU factorization strategy, an excellent fast iterative algorithm, is used to accurately obtain the measured phase from these subinterferograms. Numerical simulation and experimental comparison verify that this method is an efficient and accurate single-frame phase demodulation algorithm. Meanwhile, the performance of the proposed method is analyzed and discussed for the influencing factors, such as random noise level, carrier-frequency value, and carrier-frequency direction. The results show that the method proposed is a fast and precise phase detection method that provides another effective solution for dynamic real-time phase measurement.
Tackling the Challenge of Hands-on Learning from Cognitive Perspective

Hands-on learning has been advocated by many educators to promote students’ motivation and provide concrete learning experience. The end-product of hands-on work is also treated as the main learning goal and adopted to gauge students’ performance. However, the industrial automation of robotics and advance of artificial intelligence cast doubt on such education rationale. This study, from the perspective of cognition, addressed the challenge of hands-on learning and summarized contemporary literatures to provide supports for teachers. Students’ high-level cognition ability and capability in grasping core concepts and the concepts themselves are claimed to be the undeniable value of learning, especially in higher education. This study hopes to contribute to teachers’ better understanding of hands-on learning and adopt such pedagogy accordingly.