Reviewerly/exHarmony huggingface dataset:
Radar Resource Allocation: Higher Rate or Better Measurements?

When tracking maneuvering targets and the need to improve the tracking arises or designing a sensor tracking system, one is often faced with the choice of increasing either the measurement accuracy or rate. The answer to this question is found by assessing the impact on error in the filtered state estimates and the one-step predicted state estimates. In this paper, the tracking of maneuvering targets with a nearly constant velocity (NCV) Kalman filter is considered and the maximum mean squared error (MMSE) in position and velocity are utilized to study the impacts of doubling the measurement accuracy or rate. For each measurement case and the maximum acceleration of a maneuvering target, the process noise variances that minimize the MMSE in the filtered and the one-step predicted track states are used to assess the impacts of doubling either the measurement accuracy or rate. The analysis shows that doubling the measurement accuracy gives a greater reduction in MMSE in filtered position and velocity, while doubling the measurement rate gives a greater reduction in the MMSE in the one-step predicted position and velocity. Process noise variances that minimize the MMSE in the one-step predicted position and velocity estimates are new in this paper.
Preface to the special issue on the 12th International Conference on Graph Transformation

This special issue contains extended versions of four selected papers from the 12th International Conference on Graph Transformation (ICGT 2019). The articles cover a method for graph pattern matching, the use of graph hierarchies for knowledge representation, the construction of constraint preserving transformation rules, and the definition of a formal framework, extending the theory DPO rewriting to categories of structured objects.
SVM Method and Integrating Pinyin and Dictionary Method for Chinese Spelling Errors Detection

Spelling errors are very common in our daily lives. They sometimes cause serious problems. For example, they appear in government work report. Methods to solve the spelling errors problem based on n-gram language model are widely used. SVM is a famous classifier adopting Hyperplane. This paper introduced a method taking advantage of the SVM model to detect wrong characters. The correct words were considered as positive cases, and the others were considered as negative cases in the training data set. We trained the SVM model and detected wrong characters by the model. Besides, another method is proposed, which integrated the pinyin factor and the dictionary factor to spelling errors detection. Experimental results show that the method based on the SVM model and the method including the pinyin and dictionary factors based on n-gram language model outperform the standard n-gram language model method.
Noise Reduction using Multilevel Hybrid Filters for Ultrasound Images: A Comparative Study

Medical imaging has become a very important non-invasive diagnosis tool for various diseases. During image acquisition, they get acquainted with noise because of modality and/or because of body conditions such as patient's position or body fat. Most common noise in ultrasound is speckle, CT and MRI are motion and electrical noises. And other additional noise can also be present such as salt and pepper, Gaussian, Poisson. These noises degrade image quality. The only expert radiologist can make an appropriate diagnosis. Hence it is very important to perform de-noising. Many techniques are present for de-noising which are basically dependent on noise type. Here an attempt is made to remove speckle noise using multilevel hybrid filters. Ultrasound images of the common carotid artery are degraded by speckle noise and then de-noised using the median, wiener, NLM, Homorphic, Bilateral and hybrid filter are applied. Performance of these algorithms is measured using PSNR, MSE, SSIM and ROC curve is drawn. Results show that the Median filter With Bilateral filter has better performance.
Semantic face segmentation on mobile devices

Semantic face segmentation is the needed preprocessing step in several areas of computer vision and image-based biometrics. It can be used for face occlusion detection, person de-identification or face swapping etc. However this often comes with a large computing costs. With increasingly more powerful mobile devices, processing is transitioning to the socalled edge computing. To analyse possible options and to provide good foundations for future research, we train Deeplabv3+ with MobileNetV2 as a backend encoder on the Labelled Faces in the Wild (LFW) dataset, achieving great results. Additionally, we test how the trained model performs on different high-end Android devices to make our research directly applicable for future uses of semantic segmentation on mobile devices.
Multi-Keyword Text Search with Merkle Hash Tree for Privacy Preservation over the Encrypted Data

In cloud computing, the end user stored their files with minimum maintenance cost, where cloud servers provide the file storage facility to analyze their outsourced data. However, security of the data is a major concern of the end user, because unauthorized person may access the data in cloud. The end-user should encrypt the files for providing data usability and privacy, before outsourcing to the cloud. The main aim of the research work is to secure the outsourced file data in cloud and only the end user can able to access those files from cloud storage system. This research aims to apply Merkle Hash Tree (MHT) to store the encrypted data with the hash value to increase the efficiency of the search process. The application of this method in cloud search engine to search the encrypted data with increasing retrieval efficiency. In multiuser system, to prevent the unauthorized access of data, fine-grained access control plays a major role in cloud. But, some authorized user may leak the secret key purposefully for financial benefits. To overcome these challenging issues, this research work implements the privacy-preserving Multi-keyword Text Search (MTS) with a similarity-based ranking method. Also, two secure index schemes namely known background and cipher text model are developed to enhance the search privacy. When compared with the linear search, the search efficiency is further improved by implementing the MHT. The experiments are conducted to validate the efficiency of the proposed method with existing techniques. The proposed MTS-MHT technique achieved 99.95% retrieval accuracy with minimum searching time (i.e. 80sec) for the various number of files, when compared with traditional encryption techniques.
Auxiliary Dictionary of Diversity Learning for Face Recognition with a Single Sample Per Person

Face recognition for a single sample per person is challenging due to the lack of sufficient sample information. However, using generic training set to learn an auxiliary dictionary is an effective way to alleviate this problem. Considering generic training sample of diversity, we proposed an algorithm of auxiliary dictionary of diversity learning (ADDL). We first produced virtual face images by mirror images, square block occlusion and grey transform, and then learned an auxiliary dictionary of diversity using a designed objective function. Considering patch-based method can reduce the influence of variations, we seek extended sparse representation with l 2 -minimization for each probe patch. Experimental results in the CMUPIE, Extended Yale B and LFW datasets demonstrate that ADDL performs better than other related algorithms.
A Novel Flow-level Session Descriptor With Application to OS and Browser Identification

High level traffic characteristics have the potential to be useful for inference of various host characteristics. This work proposes the novel Flow-Discretize Order (FDO) approach for describing session characteristics in an intuitive manner, while also retaining flow ordering information. The FDO approach allows for flexible construction of flow descriptors, by using different flow properties and applying appropriate discretization. The individual flow descriptors are concatenated to form session descriptors. By utilizing string distance metrics, such as the Damerau-Levenshtein distance (DLD), it is possible to perform both unsupervised and supervised learning on the FDO session descriptors. Here, we utilize FDO as a tool for OS and browser identification coupled to a particular user activity, in this case watching YouTube videos. The variable-length nature of FDO session descriptors precludes learning methods expecting fixed dimensionality from being used. However, experiments show that excellent performance are provided by methods operating on distances such as hierarchical Ward for the unsupervised case, and k-NN for the supervised case. The supervised learning evaluation shows that over 99% accuracy can be achieved for both operating system and browser identification based on video session characteristics. The FDO framework also provides multiple promising avenues for further research and improvements such as improved methods for discretization boundary placement, more elaborate feature selection approaches, and more fine-grained DLD weights.
Evolutionary Computing Assisted Heterogenous Ensemble Model for Web-of-Service Software Reusability Prediction

Software being one of the inevitable needs of industries today requires ensuring reliability of computation. On the other hand, to enhance cost of development and productivity software developers use Component based Software Development (CSD) and Free Open Source Software (FOSS) element. Existing functions or software components are used frequently to perform the task. On contrary, the uncontrolled, excessive or improper use of such components lead software faults, aging or smells, which makes overall software vulnerable. To alleviate it, software reusability estimation can be a potential solution. The classical machine learning models can be confined to perform optimal reusability prediction with highly complicate and large size software design such as Web-of-Service (WoS) software. In this paper evolutionary computing assisted ensemble model is percolated that significantly mitigates the key issues of regional minima and convergence. Being OOP software reusability prediction model, six software metrics have been extracted from WoS software, which have been processed with different base learning models. Majority voting scheme in conjunction with Genetic Algorithm based ensemble decision enables the proposed model to exhibit better performance than any other base classifiers.
Remarks on the cryptanalysis of common prime RSA for IoT constrained low power devices

Standard RSA cryptosystem becomes vulnerable, when private key d<N0.292 is used inside CryptoChips of constrained devices, thus an alternate scheme is the Common Prime RSA (CP-RSA) variant, which provides cryptographic (decryption/signing) operations. In this paper, we perform a cryptanalytic attack on CP-RSA using lattice basis reduction method that is used to exploit possible vulnerabilities of RSA small private key attacks. In addition, we performed detail experiments on CP-RSA weak or overestimated bounds and compare results to the past studies. Our implemented cryptanalytic attack implicates more precise and direct method to exploit the CP-RSA existing theoretical and experimental bounds. Also, our results prove that CP-RSA is an effective approach that provides resistance against standard RSA small private key attacks.
Particle Swarm Optimization: A Study on Enhancement of Algorithm

Particle Swarm Optimization is the hypothetical optimizing approach which is encouraged by the collective and friendly nature of group of birds or schools of fish. Recently many advance techniques have been researched on optimization technique and its variations, they derived various new improved versions of optimization technique, developed a new method to achieve the goal state, proposed many variants of the algorithm. Nowadays many types of research are in different areas like data mining, cloud computing, wireless sensor networking, etc with either Standard Particle Swarm Optimization technique or improved version of it. One of the latest research in this area is improving the particle’s movement method towards a goal state. The effectiveness of the algorithm totally depends on the selection of control parameters and velocity update strategies. In this article, various enhancement on the particle’s movement rule towards goal state is discussed.
Improvement of the POLISAS e-Request Transcript System using Query Optimization

Abstract This paper is about the improvement of the E-Request Transcript system used by POLISAS to be a systematic and effective system. The improvement is implemented to solve the current problem, whereby, students need to access the E-Request Transcript system according to the graduation session in individual file. This situation makes it difficult for the administrators to access more than one E-request transcript system if the application is from different sessions. It is because of the data-access issues to generate reports. To achieve the system improvement, an integrated database using the query optimization technique is implemented. Next, the query statement is analysed to produce an optimum performance. Research results show that the technique is able to improve the query time processing, thus resolving the issues discussed. A user acceptance test for this system consists of a black box test and a beta test has been deployed. A black box test was conducted to test the main menu functions in the E-Request transcript and is found to be able to succeed. Furthermore, the beta test is carried out using a survey form distributed to all students who have graduated. Survey questionnaire showed that 57% of respondents were satisfied using this system and 14% were very satisfied with this system. All respondents have provided a good scaling rate for all items assessed. For overall system assessment, the mean score obtained is 3.78 which is interpreted as a good rate based on the scale of interpretation used. In conclusion, this system has met the needs of consumers as well as simplifying their business based on surveys conducted.
Edge Concierge: Democratizing Cost-Effective and Flexible Network Operations using Network Layer AI at Private Network Edges

We observe two major revolutionary trends in net-work operations: democratization of cost-effective and flexible communication means for vertical players, such as public safety, by private mobile networking combined with edge computing, and automatic and autonomic network operations empowered by Artificial Intelligence (AI). Further innovations are required for making private networking readily available for vertical players that are reluctant to acquire expertise in complex network operations. We propose Edge Concierge, of which concept is to democratize cost-effective and flexible network operations using network layer AI at private network edges. Edge Concierge assists smart network operations for private mobile network operators and energy saving by changing working state of AI-empowered anomaly detection applications by network layer AI. We also employ unsupervised machine learning using Hidden Markov Model (HMM) for estimating contexts by solely observing net-work traffic at mobile edge computing (MEC) middle boxes. In detail, we design a system of real-time and self-learning context estimation by a multi-level probabilistic state transition model trained by unsupervised learning, which is implemented in a commodity PC. In order to evaluate our proposed system, we take public safety context of smart cities as an example use case and show the benefits.
A Convolutional Neural Network Approach to Improving Network Visibility

Increasingly researchers are turning to machine learning techniques such as artificial neural networks to address communication network research questions. At the heart of each challenge is the need to classify packets and improve visibility. To date, multi-layer perceptron neural networks have been used to successfully identify individual packets. This work utilizes convolutional neural networks to classify packets after their conversion to an image matrix. To help address network challenges and aid in visualization, packets are combined into larger images to provide greater insight into a particular time span. Applications of this research can use the surrounding temporal area to gain insight into conversations, exchanges, losses and threats. We demonstrate the use of this technique to identify potential latency problems. This approach of using contemporary network traffic and convolutional neural networks has success rate for individual packets exceeding 99%. Larger images providing a broader view achieve the same high level of accuracy.
Montgomery Multiplication for Public Key Cryptography on MSP430X

For traditional public key cryptography and post-quantum cryptography, such as elliptic curve cryptography and supersingular isogeny key encapsulation, modular multiplication is the most performance-critical operation among basic arithmetic of these cryptographic schemes. For this reason, the execution timing of such cryptographic schemes, which may highly determine that the service availability for low-end microprocessors (e.g., 8-bit AVR, 16-bit MSP430X, and 32-bit ARM Cortex-M), mainly relies on the efficiency of modular multiplication on target embedded processors. In this article, we present new optimal modular multiplication techniques based on the interleaved Montgomery multiplication on 16-bit MSP430X microprocessors, where the multiplication part is performed in a hardware multiplier and the reduction part is performed in a basic arithmetic logic unit (ALU) with the optimal modular multiplication routine, respectively. This two-step approach is effective for the special modulus of NIST curves, SM2 curves, and supersingular isogeny key encapsulation. We further optimized the Montgomery reduction by using techniques for “Montgomery-friendly” prime. This technique significantly reduces the number of partial products. To demonstrate the superiority of the proposed implementation of Montgomery multiplication, we applied the proposed method to the NIST P-256 curve, of which the implementation improves the previous modular multiplication operation by 23.6% on 16-bit MSP430X microprocessors and to the SM2 curve as well (first implementation on 16-bit MSP430X microcontrollers). Moreover, secure countermeasures against timing attack and simple power analysis are also applied to the scalar multiplication of NIST P-256 and SM2 curves, which achieve the 8,582,338 clock cycles (0.53 seconds@16 MHz) and 10,027,086 clock cycles (0.62 seconds@16 MHz), respectively. The proposed Montgomery multiplication is a generic method that can be applied to other cryptographic schemes and microprocessors with minor modifications.
Technologies for Tuning Neural Networks in Problems of Inertial Data Correction in Navigation Systems for Low-Speed Autonomous Objects

At present, when satellite navigation aids are widely available, inertial navigation systems still remain relevant where it is required to determine not only the coordinates of the object, but also its orientation in space; where the reception of satellite navigation signals is impossible, as well as in cases where the navigation solution should not depend on the operability of navigation satellites. However, due to the low accuracy of inertial sensors, provided by the presence of systematic and random errors, the characteristics of navigation systems based on them are clearly insufficient for continuous autonomous movement. An inertial navigation algorithm is beingdeveloped for low-speed autonomous objects. As an estimate of the coordinates of the objects, a Kalman recursive filter is used, in this case – an extended one. The computational cycle of the algorithm consists of two steps. At the first step, an estimate of the current errors of the navigation system in determining the coordinates, speeds and orientation angles is calculated. At the second step, the correction of the basic navigation solution is carried out on the basis of the amendments received. A neural network is used to filter primary inertial information. At the input of the neural network, acceleration and angular velocity vectors are supplied, and their filtered values are formed at the output. The neural network is configured for filtering by training it for a sequence of inertial data, corresponding to the passage along certain trajectories.
Steady-State Filter Design for Radar Tracking with Up-Chirp-Down-Chirp Waveforms

The up-chirp (UC) LFM waveform is known to give better radar tracking than a monotone waveform or a down-chirp LFM waveform. However, the question of the value of other LFM waveforms to tracking is raised. In this paper, the value of an up-chirp-down-chirp (UCDC) waveform to radar tracking is considered. The optimal steady-state filter for tracking a nearly constant velocity target with an UCDC waveform is investigated and compared to that for a standard UC LFM waveform. The filter gains and state covariance are shown to be defined by Discrete White Noise Acceleration (DWNA) tracking index, the sample period, normalized pulse length, and the normalized range-doppler coupling coefficient. After developing the steady-state gain and covariance relationships for tracking with an UCDC waveform, tracking performances with UCDC and UC waveforms are compared. The UCDC waveform is shown to give a decrease in track filter covariance.
The estimated number of irreducible binomials

Let Fq be the finite field with q elements, and T a positive integer. In this article, we find an asymptotic formula for the total number of monic irreducible binomials in Fq[x] of degree less or equal to T, when T is large enough. We also show explicit lower and upper bounds for the number of binomials in the case when T is small.
City space type recognition by leveraging deeply-learned topological features

Accurately recognizing the rich variety of geometric spaces with different functions is an indispensable but difficult task in modern artificial intelligence systems. In this work, we propose a novel city geometric space classification system, which is inspired by human visual perception toward different geometries within each city. First, we extract multiple subgraphs from different sub-regions inside each city’s aerial photo. We call these subgraphs graphlets and they can optimally capture the geometric feature inside each city. Subsequently, it is observable that there are an exponential number of graphlets within each region. And thereby we designed a ranking algorithm to select a few highly discriminative graphlets for city space recognition. Such selection process follows human visual attention, where only a small proportion of visually/semantically salient regions are selected for human visual cognition. Based on the selected graphlets from each city subspace, we integrate them into an image kernel for city subspace recognition. Comprehensive experimental results as well as several visualization results have demonstrated the efficiency and effectiveness of our method.