Reviewerly/exHarmony huggingface dataset:
Digital Quality Management in Higher Education

The study first examines the concept of digital quality management in higher education, and is aimed at featuring the theoretical justification and practical implementation of digital quality management of education at the university level. A fundamental difference between digital quality management and traditional quality management is shown. The authors present and describe the features and advantages of the developed technology of interactive intellectual environment, which is the basis of digital management. The functionality of quality management in higher education is revealed on the basis of a participative synergistic approach. The practical significance of the study lies in the introduction of an interactive intellectual environment into the higher education system, which enables the effective digital management of the quality of education. The main results comprise building an individual learning trajectory, depending on the diagnosed abilities and intelligence of each student, as well as conditioning students’ research competencies as a means of improving the quality of higher education. Further development of this topic is seen in the study of the cultural aspects of interaction in the educational process, implemented in digital management.
Link-Based Clustering Algorithm for Clustering Web Documents

Abstract Clustering web documents involves the use of a large amount of words to be inputted to clustering algorithms such as K-Means, Cosine Similarity, Latent Discelet Allocation, and so on. This causes the clustering process to consume much time as the number of words in each document increases. In many web documents, web links are available along with the contents; these web link texts may contain a tremendous amount of information for clustering. In our work, we show that just using the web link text alone gives better clustering efficiency than considering the whole document text. We implemented our algorithm with two benchmark datasets, and the results show that the clustering efficiency is increased by our algorithm more than the existing methods.
Temporal Salience Based Human Action Recognition

This paper proposes a new approach for human action recognition exploring the temporal salience. We exploit features over the temporal saliency maps for learning the action representation using a local dense descriptor. This approach automatically guides the descriptor towards the most interesting contents, i.e. the salience region, and obtains the action representation using solely the saliency information. Outperforming results on Weizmann, DHA and KTH datasets confirm the efficiency of the proposed approach as compared to the state-of-the-art methods, in terms of accuracy and robustness to the variations inside the action and similarities among actions. The proposed method outperforms by 2.7% with DHA, 1% with KTH and it is comparable in the case of Weizmann.
Performance enhanced ripplet transform based compression method for medical images

Ripplet transforms provide efficient representation of images with all the spatial details. Since more details are extracted, the compression ratio is usually less. In this work, lossless prediction and lossy singular value decomposition of the ripplet coefficients are carried out to improve the performance of conventional ripplet transform based system. Initially, lossless prediction exploits the correlation between the image pixels and eases the process of transformation. Fast and computationally efficient randomized singular value decomposition technique is used to capture the essential information of the high frequency ripplet coefficients. The low frequency and the high frequency components are then encoded using entropy method. Experimental results show promising results for the proposed approached in terms of performance measure.
Identification and correction of microlens array rotation error in plenoptic imaging systems

Plenoptic imaging systems can acquire spatio-angular information of light rays, allowing complex scene measurements with a single exposure. The measurement accuracy is significantly affected by the orientation parameters of internal components in the system, especially the rotation error between the microlens array (MLA) and the image sensor. In this paper, we formulate an MLA rotation error model to derive the transformations between the error point and its ideal point of ray projection. Furthermore, we present a method for identifying and correcting the rotation error, including error angle estimation, raw image rotation, and refocused image reverse rotation. Through simulation experiments, the imaging characteristics of the MLA rotation error are analyzed and the performance of the proposed model and method are verified. The results demonstrate that the integral distribution of raw image distortion caused by the error is centrosymmetric. In the error range of 0.1–1.0°, the mean square error (MSE) and structural similarity index metric (SSIM) of the corrected refocused images are improved to 0.2146–3.8190 and 0.9986–0.9999, respectively.
Quantifying quantum non-Markovianity via max-relative entropy

We investigate the non-Markovian behavior in open quantum systems from an information-theoretic perspective. Our main tool is the max-relative entropy, which quantifies the maximum probability with which a state ρ can appear in a convex decomposition of a state σ. This operational interpretation provides a new view for the non-Markovian process. We also find that max-relative entropy can be the witness and measure of non-Markovian processes. As applications, some examples are also given and compared with other measures in this paper.
Semi-fragile speech watermarking based on singular-spectrum analysis with CNN-based parameter estimation for tampering detection

Abstract A semi-fragile watermarking scheme is proposed in this paper for detecting tampering in speech signals. The scheme can effectively identify whether or not original signals have been tampered with by embedding hidden information into them. It is based on singular-spectrum analysis, where watermark bits are embedded into speech signals by modifying a part of the singular spectrum of a host signal. Convolutional neural network (CNN)-based parameter estimation is deployed to quickly and properly select the part of the singular spectrum to be modified so that it meets inaudibility and robustness requirements. Evaluation results show that CNN-based parameter estimation reduces the computational time of the scheme and also makes the scheme blind, i.e. we require only a watermarked signal in order to extract a hidden watermark. In addition, a semi-fragility property, which allows us to detect tampering in speech signals, is achieved. Moreover, due to the time efficiency of the CNN-based parameter estimation, the proposed scheme can be practically used in real-time applications.
Bio-Key Based AES for Personalized Image Cryptography

Abstract In the era of multimedia technologies and rapidly growing multimedia contents, secured information transfer needs to be ensured through robust cryptography techniques. This paper presents a bio-key-generation scheme in the local environment to transfer multimedia content over vulnerable networks. To encrypt the images, one user has to ensure his/her authentication and further needs to provide a secret key for bio-key generation. Secret key aids to fetch biometric features for key formation and key expansion. Thus, the user receives an expanded key from the template database to perform encryption of a plain image. Once the cipher image is delivered to the network, the user has to repeat the same steps for decryption. This paper experiments the proposed bio-key-generation scheme with advanced encryption standard algorithm. This encryption strategy can also be used to encrypt confidential or personal images in various digital systems such as mobile phones, personal digital gadgets and so on. Various analyses ensure the robustness and the performance of the bio-key-based AES encryption scheme. Entropy, correlation coefficient, number of pixel change rate and unified average changing intensity are evaluated to ensure the effectiveness of the proposed method over other key-based encryption techniques.
Optimized Color-guided Filter for Depth Image Denoising

Color Guided Depth image denoising often suffers from the texture coping from the color image as well as the blurry effect at the depth discontinuities. Motivated by this, we propose an optimized color-guided filter for depth image denoising from different types of noises. This is a new framework that helps to mitigate the texture coping and enhance the depth discontinuities, especially in heavy noises. This framework consists of two parts namely depth driven color flattening model and patch synthesis-based Markov random field model. The first part which is a prepare step for the second part is used to mitigate the texture coping problem that faces all color guided methods. This first model consists of a modified joint bilateral filter which is used to mitigate the noise from the noisy depth image and an iterative guided bilateral filter that is proposed to flatten the colors in the color image for mitigating the texture coping problem. Based on the first part, Markov random field with an optimization technique is used for mitigating the blurry effect. Experiments indicate that our method outperforms counterpart filters with guided and non-guided manners in terms of a variety of evaluation metrics.
U-Net and Mean-Shift Histogram for Efficient Liver Segmentation from CT Images

Nowadays, 3D computed tomography (CT) data is commonly employed to diagnose liver cancer or to examine liver condition. This work proposes a method that can quickly find a location of the biggest CT slice of the liver. We also extend this method to demonstrate that it can work as an efficient algorithm for liver segmentation. The proposed method is based on a U-Net, fully convolutional neural network, to roughly segment the liver and employs a mean-shift clustering algorithm to enhance liver localization accuracy. This addition of mean-shift clustering on histogram data after rough segmentation by U-Net prevents oversegmentation of the liver and significantly improves the accuracy of a method solely relying on a U-Net. A novel image-enhancement technique based on statistical thresholding is also introduced to further increase accuracy. This hybrid method processed each slice in 0.35 seconds on average (75 seconds per image) and its median accuracy measured in Dice similarity index in 5-fold cross-validation was 95%.
Mobile Traffic Anonymization Through Probabilistic Distribution

Current implementations of mobile apps offer limited security assurances against traffic analysis. Encryption is not effective in hiding particular patterns within packets that can be used as side-channel information to classify specific apps. What we need is an anonymity system that ensures strong security with acceptable computational overhead and latency for interactive app usage. Recently, we undertook this problem by mutating an app traffic that we try to defend so that it resembles the traffic of another app. Our goal in this paper is to develop a simpler and more scalable system to anonymize mobile app packet traffic without the need of another app's model traffic. This could happen using probabilistic distribution of packet sizes. We propose first a scheme that regenerates statistical modeling of app packet lengths. Then, we present a privacy preserving technique that implements defense against traffic analysis and confines the incurred overhead by mutation of packet lengths of the incoming traffic to the regenerated ones. Experiments show that using this confusion technique, we are able to reduce 91.1% classification accuracy to 0.9% with only 12.51% overhead. On the other hand, we demonstrate a second technique for anonymization where we thwart traffic analysis of mobile app traffic by modifying its packet sizes probability distribution to another dissimilar distribution.
Quantum Features of Macroscopic Fields: Entropy and Dynamics

Macroscopic fields such as electromagnetic, magnetohydrodynamic, acoustic or gravitational waves are usually described by classical wave equations with possible additional damping terms and coherent sources. The aim of this paper is to develop a complete macroscopic formalism including random/thermal sources, dissipation and random scattering of waves by environment. The proposed reduced state of the field combines averaged field with the two-point correlation function called single-particle density matrix. The evolution equation for the reduced state of the field is obtained by reduction of the generalized quasi-free dynamical semigroups describing irreversible evolution of bosonic quantum field and the definition of entropy for the reduced state of the field follows from the von Neumann entropy of quantum field states. The presented formalism can be applied, for example, to superradiance phenomena and allows unifying the Mueller and Jones calculi in polarization optics.
RHFCN:: Fully CNN-based Steganalysis of MP3 with Rich High-pass Filtering

Recent studies have shown that convolutional neural networks (CNNs) can boost the performance of audio steganalysis. In this paper, we propose a well-designed fully CNN architecture for MP3 steganalysis based on rich high-pass filtering (HPF). On the one hand, multi-type HPFs are employed for residual extraction to enlarge the traces of the signal in view of the truth that signal introduced by secret messages can be seen as high-pass frequency noise. On the other hand, to utilize the spatial characteristics of feature maps better, fully connected (Fc) layers are replaced with convolutional layers. Moreover, this fully CNN architecture can be applied to the steganalysis of MP3 with size mismatch. The proposed network is evaluated on various MP3 steganographic algorithms, bitrates and relative payloads, and the experimental results demonstrate that our proposed network performs better than state-of-the-art methods.
Optimal Path Planning Satisfying Complex Task Requirement in Uncertain Environment

Summary Robots often need to accomplish some complex tasks such as surveillance, response and obstacle avoidance. In this paper, a dynamic search method is proposed to generate optimal robot trajectories satisfying complex task requirement in uncertain environment. The LTL-A* algorithm is presented to generate a global optimal path and the A* algorithm is provided to modify the global optimal path. The task is specified by a linear temporal logic (LTL) formula, and a weighted transition system according to the known information in uncertain environment is modeled to describe the robot motion. Subsequently, a product automaton is constructed by combining the transition system with the task requirement. Based on the product automaton, the LTL-A* algorithm is proposed to generate a global optimal path. The local path planning based on the A* algorithm is employed to deal with the environment change during the process of tracking the global optimal path for the robot. The results of the simulation and experiments show that the proposed method can not only meet the complex task requirement in uncertain environment but also improve the search efficiency.
Generative Adversarial Networks Based Error Concealment for Low Resolution Video

In this paper, a novel deep generative model-based approach for video error concealment is proposed. Our method is comprised of completion network and two critics. The frame completion network is trained to fool the both the local and global critics, which requires completion network to conceal frame distortions with regard to overall consistency as well as in details. Specifically, mask attention convolution layer is proposed, which utilize not only the temporal information of the previous frame, but also the intact pixels of the current distorted frame to mask and re-normalize convolution features. Then, both qualitative and quantitative experiments validate the effectiveness and generality of our approach in advancing the error concealment on low resolution video.
An FPGA Based Approach For People Counting Using Image Processing Techniques

People detection and counting using image processing technique is a computation extensive task. The algorithm for this purpose need to manipulate various features usually in millions and then assign the object to a specific class i.e. human and non-human. Finally it counts incoming and outgoing people. Therefore a robust hardware having fast computational capability is required. This work suggests an implementation of people counting system based on modified histogram of gradients (HoG), support vector machine (SVM) and a new counting technique. We have suggested a system on chip (SoC) pipelined approach which can operate at the rate of 196.2 MHz. During literature review it was observed that no such FPGA implementation of the whole system is reported in the literature. It was also observed that the proposed SoC is almost five times faster than the typical software core system.
Near-infrared Image Guided Neural Networks for Color Image Denoising

Noisy color image and guided near-infrared (NIR) image can be jointly employed to eliminate noise and enhance details. Existing methods mostly rely on explicit designed filters and hand-crafted objective function optimization. These methods usually introduce erroneous structures from guidance signal. Besides, they are time-consuming and not suitable for real time applications. In this paper, we come up with a learning based method. The noisy color image and NIR image are fused, then fed into a fully convolutional neural network. The network learns a directly map from degraded image to restored sharp image. Our architecture can effectively eliminate image noise and transfer detail structure from guided image. Our trained network accepts any resolution of input image and runs in constant time. We evaluate the presented approach on both synthetic and real images. Results show that our approach outperforms the state-of-art methods.
Methodology for the quantification of the effect of patterns and anti‐patterns association on the software quality

The employment of design patterns is considered as a benchmark of software quality in terms of reducing the number of software faults. However, the quantification of the information about the hinder design issues such as the number of roles, type of design pattern, and their association with anti-pattern classes is still required. The authors propose a new methodology to evaluate the impact of certain design issues on the software quality in terms of quantification of fault density. Firstly, they mine the required information about the classes of each system under study. Secondly, they describe taxonomy to group the classes. Subsequently, they used statistical techniques to formulate and benchmark the results. They include the analysis of four open source projects with six design patterns and six anti-patterns in the case study. The main consequences are (i) the pattern participant classes are less dense in faults, (ii) the classes involved in the structural association between design patterns and anti-patterns are denser in faults, (iii) the pattern participant classes with multi-role and anti-pattern smell association is denser in faults as compared to others. The significant difference between fault density distributions of groups of classes is still unclear and required further empirical investigation.
The usage of e-book as learning media through the sigil application in history

This paper aims to: (1) obtain learning media products using the Sigil application as a learning media for history in the first year high school with a validated 4D model, and (2) learning the application of digital books using the Sigil application as a medium for learning history. The problem presented in this paper is the learning motivation of students in learning history. This is because students are not practical enough to carry and also read thick history books. Another problem is that history learning is known as a lesson issued because of the attractiveness of interactive learning media used in historical learning. Observations made by the authors make learning learning interesting, namely: developing digital books (e-books) using the Sigil application, while students do not need to carry historical reading books. Students can open digital books (e-books) via cellphones (smartphones) or their respective laptops related to historical learning. So that students are expected to be able to improve learning outcomes and help learn history and students are expected to reach a high level by using literacy after using E-book learning media through the Sigil application in learning history. The type of research used is development research with a 4D model.
Sharing Cyber Threat Intelligence Under the General Data Protection Regulation

Sharing Cyber Threat Intelligence (CTI) is a key strategy for improving cyber defense, but there are risks of breaching regulations and laws regarding privacy. With regulations such as the General Data Protection Regulation (GDPR) that are designed to protect citizens’ data privacy, the managers of CTI datasets need clear guidance on how and when it is legal to share such information. This paper defines the impact that GDPR legal aspects may have on the sharing of CTI. In addition, we define adequate protection levels for sharing CTI to ensure compliance with the GDPR. We also present a model for evaluating the legal requirements for supporting decision making when sharing CTI, which also includes advice on the required protection level. Finally, we evaluate our model using use cases of sharing CTI datasets between entities.
Appropriate CNN Architecture and Optimizer for Vehicle Type Classification System on the Toll Road

The growth in the number of vehicles in Indonesia causes traffic jam problems, including on the toll roads. Traffic jam problem on the toll roads occurs because the users must stop and make payments in the toll gate. Government built an Automatic Toll Gate Shelter (or Gardu Tol Otomatis/GTO) as an effort to reduce this problem. However, GTO can only be used by certain type of vehicles only. In this study, we developed a system that can classify type of vehicles so that GTO can be used for various types of vehicles that cross the toll road. The developed system will receive vehicle input to be classified. The learning process to do the classification is using the Convolutional Neural Network (CNN). The CNN algorithm is trained first with 2,930 vehicle images divided into 1,794 vehicles type 1 (van, jeep, and pick-up) image, 507 vehicle type 2 images (truck with 2 axle), and 631 vehicle type 3 images (truck with 3 axle). From the experimental results of CNN architecture and various parameters of the architecture, the best accuracy is found on MiniVGGNet architecture which applies Adadelta optimization function and input image parameter 64x64 and epoch 40. The result obtained from the network has accurate evaluation or out-sample accuracy of 73%.
Relevant Question Answering in Community Based Networks Using Deep LSTM Neural Networks

Community based Question Answering (CQA) websites enable users to post their questions and their questions will be answered by other users. These group of social networking websites are one of the most popular websites on the Internet. The responses on these CQA websites can be for specific questions related to a specific field of interest to the users or to all kind of questions. Creating automated CQA websites is of great interest for the natural language processing research. One of task in development of automated CQA websites is finding similar questions to the question asked by the user. In this paper, a novel method for finding questions relevant questions to the question of a user using deep LSTM neural networks is proposed. Experimental results show that the proposed algorithm has high accuracy for finding questions in CQA social networks.