Reviewerly/exHarmony huggingface dataset:
A Novel Exploitation of Errors in RRNS Architecture

Residue Number System (RNS) is an unweighted number system that symbolizes large integers with smaller numbers. It has the ability to perform operations such as addition and multiplication in parallel. Because of this property, RNS is extensively used in communication, Finite Impulse Response (FIR), cryptography, and signal processing devices. The transfer of data in digital channel is very important for some critical applications where accuracy is very important. This work proposes a novel algorithm that is premised on the Hamming Distance (HD) and one of the reverse conversion methods, which is, the Chinese Remainder Theorem (CRT) and as a joint technique for detecting and correcting multiple bit errors in RNS. The algorithm provides more efficient technique that improves on the hardware size and increases the processing speed with less iterations when compared with other state-of-the-art schemes. An area-delay comparison analysis done and compared with other known schemes in this work revealed the effectiveness of the proposed scheme in terms of area and delay requirements.
A Transfer Learning Method for Handwritten Chinese Character Recognition

The deep convolutional neural network has achieved high accuracy in handwritten Chinese character recognition (HCCR). Large scale handwritten data collection as well as labor-intensive labeling work are required to train an effective model suitable for various writing styles. Typically synthetic data are generated with data augmentation to alleviate the scarcity of labeled training data in real applications. However, the domain shift between synthetic data and real data results in unsatisfying recognition accuracy, bringing a significant challenge. A transfer learning method is proposed for synthetic-to-real handwriting recognition to alleviate the issue. In the framework, a proposed convolutional neural network is pre-trained with the synthetic data as the source model. Then, the source model is optimized with real samples of a specific handwriting style in an unsupervised manner. The effectiveness of the proposed transfer learning method is validated through systematic experiments on the public dataset as the target domain data.
Continuous Leakage-Amplified Public-Key Encryption With CCA Security

Abstract Secret key leakage has become a security threat in computer systems, and it is crucial that cryptographic schemes should resist various leakage attacks, including the continuous leakage attacks. In the literature, some research progresses have been made in designing leakage resistant cryptographic primitives, but there are still some remaining issues unsolved, e.g. the upper bound of the permitted leakage is fixed. In actual applications, the leakage requirements may vary; thus, the leakage parameter with fixed size is not sufficient against various leakage attacks. In this paper, we introduce some novel idea of designing a continuous leakage-amplified public-key encryption scheme with security against chosen-ciphertext attacks. In our construction, the leakage parameter can have an arbitrary length, i.e. the length of the permitted leakage can be flexibly adjusted according to the specific leakage requirements. The security of our proposed scheme is formally proved based on the classic decisional Diffie–Hellman assumption.
Comparative Study of Operating System Quality Attributes

Abstract An Operating System is an interface between a user and computer hardware. It’s a piece of software that handles hardware devices and acts as a bridge between an application software and hardware of the computer. Everything the user does is done in the user mode and what the system does is done in the kernel mode. The kernel is a computer program at the heart of the operating system, with absolute control of everything. Operating system concepts have been developed by many manufacturers over the years and a few quality ones to match with user demand and preferences. This paper helps compare operating systems by their quality attributes and using four of the most influential operating systems – Windows, Linux, Mac and Android OS, along with other technical details in assuring user guide when deciding what operating system to choose based on their requirements. The research uses factor analysis to obtain aggregate score based on selection criteria, with first choice at 50%, second choice at 42%, third choice at 6%, fourth choice at 2%. Windows 10 has highest GUI, Hardware Compatibility, Portability and Process Management features. Also it has high Memory Management, Text Mode Interface and file system features. However, Security feature on Windows 10 was least only 2% as fourth choice. Mac OS X has high percentage for Memory Management, GUI, Security and Architecture features. However, Hardware compatibility, Text Mode Interface, File System and Portability features was least only 2% as fourth choice. Linux Ubuntu 17.4 has highest Security, Text Mode Interface, Architecture and File System features. Also having high Portability and Process Management features. However, GUI and Memory Management was least only 2% as fourth choice. It was observed for latest version of operating system all have quality attributes but selecting most preferred choice of operating system would require proper analysis of the user. Windows with a percentage of 37% was first choice, followed by Linux having 33%, Mac with 23% and finally Android with the lowest score of 7%.
Positional estimation of invisible drone using acoustic array with A-shaped neural network

Image-based object detection is a commonly used algorithm for anti-drone surveillance system. However, there is a disadvantage that it cannot be detected if the target is not visible within the image. In this paper, we propose drone position estimation algorithm using acoustic array to detect objects complementing the difficulty of estimating sudden directional shifts in hiding, occurrence situations and quickly out of the vision of the camera. Sound data is converted into an image via mel-spectrogram to facilitate image sensor and sound sensor fusion and the drone position is estimated via the Convolution Neural Network. The proposed neural network is the A-shape neural network, which consists of up-sampling and down-sampling. Through these methods, we achieve RMSE of 13.045 pixels and show that the location of the drone can be estimated efficiently.
An Improved Visual Detection Model for Oversize Vehicle Intrusion in Transmission Corridors

Aiming at the accuracy and real-time requirements of detection task of oversize vehicle intrusion in transmission corridors, an improved detection method for construction vehicles is proposed. Base on the Yolov4 model, we first analyze the best combination of data enhancement methods for construction vehicles detection. Then we use the K-means clustering algorithm to modify the preset boundary box size of Yolov4. Finally, refer to Soft-NMS algorithm, we improve the process of model boundary boxes detection. The experimental results shows that the average accuracy of the detection can reach 94.15%, which is 12% higher than the original Yolov4. This method can be applied to the detection of dangerous intrusions for the protection of transmission corridors.
Noise image segmentation method based on offset field estimation

The model of level set based on the local offset field solves the problem that the traditional local model cannot deal with the uneven grayscale images. But the performance of the model on noisy images is still not excellent. In response to this problem, this paper introduces a convolutional autoencoder to design an unsupervised noise separation mechanism to model the noise field based on the local model, so that the additive noise field can be separated from the image and avoid its interference to the image segmentation process. The results show that the proposed method can separate the additive image noise effectively and improve the segmentation accuracy in a noisy environment, which is superior to traditional noise robust models and offset field models.
Data Augmentation Enhanced Speaker Enrollment for Text-dependent Speaker Verification

Data augmentation is commonly used for generating additional data from the available training data to achieve a robust estimation of the parameters of complex models like the one for speaker verification (SV), especially for under-resourced applications. SV involves training speaker-independent (SI) models and speaker-dependent models where speakers are represented by models derived from an SI model using the training data for the particular speaker during the enrollment phase. While data augmentation for training SI models is well studied, data augmentation for speaker enrollment is rarely explored. In this paper, we propose the use of data augmentation methods for generating extra data to empower speaker enrollment. Each data augmentation method generates a new data set. Two strategies of using the data sets are explored: the first one is to training separate systems and fuses them at the score level and the other is to conduct multi-conditional training. Furthermore, we study the effect of data augmentation under noisy conditions. Experiments are performed on RedDots challenge 2016 database, and the results validate the effectiveness of the proposed methods.
Improving Usability and Intrusion Detection Alerts in a Home Video Surveillance System

The purpose of this work is improving the functionality and usability of a low cost commercial surveillance system. The original system provides simple motion detection and sends alert messages by means of FTP or email. The modified system adds a software layer to the original system for implementing desirable image processing features. Particularly, people detection functionality was implemented by means of Oriented Gradient Histograms. The modified system also adds the use of Telegram messaging service for sending alerts. When the camera detects motion, the modified system improves the alert information with the results of the intruder detection algorithm. System Usability Scale (SUS) was used to compare the usability of both systems and the results showed that the modified system improved the original one in terms of usability.
Design of Intelligent Meridian Magnetotherapy instrument and Monitoring system based on TCM

An intelligent acupoint magnetotherapy instrument, used in physiotherapy as well as simultaneous magnetotherapy acupoint with multiple acupoints, and its monitoring system are developed. Based on TCM meridian theory, it can be designed patterns of health preservation, self-defined magnetotherapy, auxiliary medical treatment for common diseases and stuff. Through the recommendation of accurate acupoints, the instrument meets the needs of different groups and reduces the threshold of magnetotherapy equipment. At the same time, through the five elements music accompanied with the course of treatment, this instrument realizes the comprehensive treatment of magnetic and Wuyin Therapy and finally improve the effect of physiotherapy. At the same time, according to the data uploaded by the instrument, the monitoring system, with the functions of displaying real-time parameter, analyzing magnetotherapy instrument and course of treatment, which increases the user experience. A questionnaire is also used to design the course recommendation based on the user's own situation, improves the intelligence of the instrument. Therefore, the design has the characteristics of safety, convenience, simplification and intelligence. As it can well meet the needs of health preservation, it also has a good application prospect in the field of intelligent medical devices.
A Comparative Study of Ensemble Approaches to Fact-Checking for the FEVER Shared Task

The surge of information globally motivates for automated rumour detection. We use Fact-checking to detect rumours of the type misinformation. The FEVER-shared task is the Fact-checking task used for this comparative study. The task is divided into Document Retrieval, Sentence Selection, and Claim Verification components. We standardise TF-IDF for Document Retrieval, before creating the pipelines of Sentence Selection and Claim Verification algorithms. We evaluate each unique combination on the FEVER score, then compare the four pipelines to the baseline and state of the art. Our results show that the 2-way classification task using the Siamese BiLSTM achieves better Evidence Retrieval F1 scores than the state of the art models, and that the pipeline combinations, rival the state of the art for the Shared Task. The novelty of this research lies in the standardised text processing on novel pipeline combinations, allowing for comparable results, as well as the evaluation of the Siamese BiLSTM.
Clustering and Classification

A summary is not available for this content so a preview has been provided. Please use the Get access link above for information on how to access this content.
A Vigorous Chaotic Function Based Image Authentication Structure

Abstract In this work framework request picture square size and tumultuous capacity control as a piece of information. Here this builds the adaptability and mix of a secret phrase. As the quality of a secret key relies upon number bits or characters. So proposed work store entire square as the mystery key of the client. For creating an alternate arrangement of pictures proposed work utilize tumultuous capacity where an alternate arrangement of muddling is feasible for a similar picture. The proposed plan will be usable anyplace and whenever with a low mistake rate just as a quicker outcome confirmation. This exposition work primarily centered around the snap-based methodology and mix with the proposed algorithmic disorderly approach. A test was done on a genuine arrangement of pictures and results that that proposed work has high join rate, low execution time when contrasted with past secret word creation calculation.
Research on trajectory optimization of handling robot based on ABB

In this paper, computer-aided design is used to program industrial robots with language programming. Taking ABB IRB1200 robot as the research object, the robot kinematics model is established according to the D-H method, and the robot space model, D-H parameters, and the forward and backward solution of the robot kinematics equation are verified by MATLAB. Then, according to the working requirements of industrial robots, trajectory planning and design of robot execution programs are carried out in Cartesian coordinate system and joint space coordinate system.
Personally significant topic and a new typology of lessons in contemporary education

The prerequisites for the present article have been abstracting and moving away from the knowledge-based learning paradigm, creating innovative forms of lessons that carry elements of metadisciplinarity, and the transformation of a set of lessons combined into personally-significant topics into a complete technological complex. A personally significant topic, in its productivity, carries a change in the pupil’s attitude to the world. It is understood as a structural unit of this change. The article offers an invariant structure that includes image, analysis, meaning, action, and reflection, which corresponds to new types of lessons, namely, image lesson, thinking lesson, mood lesson, independent action lesson, as well as actualization and reflection lesson. In general, the proposed set of lessons represents a new technology, whose purpose is to address the subject content to the emotional sphere of pupils, to perceive the value-semantic basis of the topic as conditions for future digital analysis. The methods used included observing the work of the experimental and control classes, test tasks, using the valuable words of the text, and color analysis of emotional expressions. The results of this work have been aimed at improving the quality of pupils’ knowledge, developing ways of thinking, appealing to the value-semantic context of the humanities-based subjects, and embodying the acquired competencies in action situations as the basis for adaptation and socialization of the education subject.
GOING FURTHER INTO MODELING PROBABILISTIC COMBINATORIAL OPTIMIZATION USING COMMUNICATION CHANNEL

This work builds upon a previous novel approach developed by N. Ngambou. Djakam and Murat. M. Tanik to tackle combinatorial optimization problems in which uncertainty is present as a significant concept. It is an approach akin to Herbert Simon's notion of “satisficing” in the context of bounded rationality. This paper demonstrates that a probabilistic maximum independent set(PIS) as a bipartite graph can be represented as a communication channel and explore a technique to find satisficing solutions of probabilistic combinatorial problems. Our approach differs from the a priori optimization method proposed by D. J. Bertsimas, P. Jaillet, and A. R. Odoni in the sense that after determining the solution of the whole instance (a priori solution), instead of applying a modification strategy, we can derive a solution of any sub-instance from the communication channel model.
Catalog of U.S. Government Publications

Many librarians and staff find government documents confusing, especially those distributed by the Government Printing Office (GPO) through the Federal Depository Library Program (FDLP). The FDLP p...
Secure Sharing of Data for Dynamic Group in Cloud Storage Application

Data sharing is increasingly important for many users and sometimes an essential requirement, especially for industries and society’s used to gain proceeds. Sharing group resource among cloud users is a major problem, still the data privacy leak. The existing system Group Key Management method used for sharing Key Generation and distribution in the group member or users. Sometimes change to user one group to another group, the group key to enable authenticated users to access the files securely and efficiently is still a challenging problem. This problem to avoid that sharing data in group using the Time Based Group Key Management (TGKM) techniques for cryptographic cloud storage application , which Conditional Identity Based Broadcast Proxy Re-Encryption (CIBPRE) used to transforming the data’s(files) major process in cloud server. To Key Generation method for user a time based dynamic group Key which capably to make stronger in cloud security. Generally, security and performance evaluated that the proposed scheme is highly efficient and satisfies the security requirements for cloud based application.
On the Round Complexity of Fully Secure Solitary MPC with Honest Majority

We study the problem of secure multiparty computation for functionalities where only one party receives the output, to which we refer as solitary MPC. Recently, Halevi et al. (TCC 2019) studied fully secure (i.e., with guaranteed output delivery) solitary MPC and showed impossibility of such protocols for certain functionalities when there is no honest majority among the parties. In this work, we study the round complexity of fully secure solitary MPC in the honest majority setting and with computational security. We note that a broadcast channel or public key infrastructure (PKI) setup is necessary for an n-party protocol against malicious adversaries corrupting up to t parties where $$n/3 \le t < n/2$$ . Therefore, we study the following settings and ask the question: Can fully secure solitary MPC be achieved in fewer rounds than fully secure standard MPC in which all parties receive the output? All our results also assume the existence of a common reference string (CRS) and pairwise-private channels. Our upper bounds use a decentralized threshold fully homomorphic encryption (dTFHE) scheme (which can be built from the learning with errors (LWE) assumption) as the main building block.
The Perspectives of Supply Chain Management Using Natural Knowledge From 3D Blockchain Technologies and Avatar-Based and Geographic Information Systems (GIS)

The blockchain restores control and ownership of information back to its rightful owner, thus eliminating dependencies on central authorities and third parties. These material chains are immensely complex; they can be subject to the laws and regulations of more than 200 countries and territories, and they are heavily influenced by many different factors ranging from freight consolidation to the timing of hundreds of concurrent shipments. This chapter is about algorithmic modeling of supply chain management using natural knowledge from a 3D-hybrid blockchain as a dragon chain.
An Adaptive Model for Face Distortion Correction

The age of social media insists on developing devices that are able to capture and share ones' moments with high fidelity. Handheld devices such as smartphones with wide-angle cameras have shown the current trend in mobile photography. Although one can take great delight in a wide field of view through modern cameras, nearby objects or faces may be distorted significantly. Recent works have obtained impressive results in this research area, but there is still a tradeoff between image quality and processing time to consider. This work introduces an adaptive polynomial model that automatically selects faces and performs image distortion correction. Since the photos are processed locally, faces are undistorted, and the background is close to the original state. Unlike other content-aware based methods which rely on heavy computing components and high image resolution, our model is suitable for mobile devices to tackle face distortion issue in selfie photos.
FINANCIAL DEVELOPMENT AND INCLUSIVE RURAL FINANCIAL SYSTEM IN NIGERIA

Currently, as a result of increase in population which has put high demand on rural financial institutions for credit and savings accessibility, the market for rural finance is facing a challenge of inadequate flow of funds. The large resources required to meet this demand call for the establishment of new rural financial institutions while the existing institutions require more funds to scale-up the activities and outreach. The enormous potentials of the rural sector make sound financial development to be of priority. The priority for sound financial development hinges on effective inclusive rural financial system because it is evident that large majority of the people live in rural areas in Nigeria and more so, do not have access to financial services. This informed the need to examine the relationship between financial development and inclusive rural financial system in Nigeria. The data collected from Central Bank of Nigeria and World Bank data base from 1981 to 2017 were subjected to Vector Error Correction Model (VECM), Johansen co-integration and impulse response function methods of analysis. The results of the Johansen co-integration test show three co-integration equations which indicate evidence of long run among the variables of the study. Also, the result of the VEC indicates that FID, RLDR and RUTP equations constitute the true cointegrating equations while the others are statistically flawed. In addition, post-estimation diagnostic test shows that there is no presence of first order serial autocorrelation in the model, absence of heteroscedasticity, and the model is not normally distributed. The study concluded that access to financial markets is still limited in the rural areas as a result of lack of inclusive rural financial system in Nigeria. Therefore, the study recommended among others that it is necessary to increase the efficiency of rural financial institutions which may promote inclusive rural financial system for effective financial development.