Reviewerly/exHarmony huggingface dataset:
Signcryption from NTRU Lattices Without Random Oracles

A signcryption scheme provides both the function of digital signature and encryption scheme at the same time. Additionally, it has lower cost than traditional sign-then-encrypt or encrypt-then-sign approaches. Many useful applications have been found where confidentiality and non-repudiation are both required. On the other hand, with the current development of rapid quantum computing, currently schemes based on traditional numerical assumptions (e.g., decisional Diffie-Hellman assumption) are more likely to be attacked by quantum computers. Therefore, lattice-based signcryption schemes have been extensively studied in recent years. However, most of them are either inefficient or secure-proved under the assumptions of random oracles. Although some of them are proved in the standard model, they are not practical in low-cost devices. Since the security is based on "Learning with Errors" (LWE) or "Short Integer Solution" problems, it will result in extremely large size of keys. In this paper, we propose an efficient NTRU-based signcryption scheme, which is inspired from Pion et al.'s key encapsulation mechanism. We provide rigorous security proofs for the confidentiality and unforgeability under NTRU-based assumptions in the standard model. Compared with other schemes, we conclude that our scheme is more efficient and secure.
An Improved Method of Applying a Machine Translation Model to a Chinese Word Segmentation Task

In recent years, a new approach of processing Chinese word segmentation (CWS) as a machine translation (MT) problem has emerged in CWS task research. However, directly applying the MT model to CWS task would introduce translation errors and result in poor word segmentation. In this paper, we propose a novel method named Translation Correcting to solve this problem. Based on the differences between CWS and MT, Translation Correcting eliminates translation errors by utilizing the information of a sentence that needs to be segmented during the translation process. Consequently, the performance of word segmentation is considerably improved. Additionally, We get a new model called CWSTransformer, which is obtained by improving the MT model Transformer using Translation Correcting. The experiment compares the performances of CWSTransformer, Transformer and the previous translation-based CWS model on the benchmark datasets, PKU and MSR. The experimental results show that CWSTransformer outperforms Transformer and the previous translation-based CWS model.
Modified Score Function and Linear Weak Classifiers in LogitBoost Algorithm

This paper presents a new extension of LogitBoost algorithm based on the distance of the object to the decision boundary, which is defined by the weak classifier used in boosting. In the proposed approach this distance is transformed by Gaussian function and defines the value of a score function. The assumed form of transforming functions means that the objects closest or farthest located from the decision boundary of the basic classifier have the lowest value of the scoring function. The described algorithm was tested on four data sets from UCI repository and compared with LogitBoost algorithm.
Discriminant analysis algorithm to classify Hickson’s compact groups of galaxies

In the present paper, we introduce an algorithm to classify Hickson’s compact groups of galaxies. The algorithm uses the discrimination analysis technique to determine the best set of indicators that can be used to construct the models in which the classification validity of the Hickson’s compact groups could be predicted, and thus distinguish between groups that have classification problems. We have concluded that the earlier classifications of the 92 Hickson’s compact groups of galaxies are correct.
Design and Implementation of Self-balancing and Navigation Robot Based on ROS System

Based on the SLAM (instant location and map construction) technology of monocular vision and multi-sensor information fusion, the following robot self-balancing path planning system is proposed. The system can handle strenuous moving images well, and for scenes with simple description and feature points being scarce, no loss of positioning occurs. In the selection of key frames and the connection of local maps, a variety of algorithm optimization and multi-sensor fusion methods are selected, which makes the drawing faster and more accurate. Path planning uses a more accurate and time-saving bionic algorithm-ant colony algorithm, combined with gyroscope and PID processing feedback information to achieve self-balancing and navigation path planning.
A Preliminary Study of Charismatic Speech on YouTube: Correlating Prosodic Variation with Counts of Subscribers, Views and Likes

This paper is a first investigation into the influence of the pitch range and the intensity variation on the number of subscribers, views and likes of YouTube Creators.A total of ten minutes of speech material from five English and five North-American YouTubers was analyzed.The results for pitch range and intensity variation suggest that an increase in both parameters results in higher subscriber counts.For views, there was no influence of pitch range, but an increase in intensity variation results in a lower number of views.Pitch range and intensity variation had no influence on the like count.Furthermore, both origin and gender had an influence on the results.Ultimately, this study will provide further information for the phonetic research of charisma (i.e., the perceived charm, competence, power, and persuasiveness of a speaker), as it is suspected that the acoustic features that have so far been connected to charisma also play an important role in the success of a YouTuber and their channel.
Learning keyphrases from corpora and knowledge models

Abstract Extraction keyphrase systems traditionally use classification algorithms and do not consider the fact that part of the keyphrases may not be found in the text, reducing the accuracy of such algorithms a priori. In this work, we propose to improve the accuracy of these systems with inferential mechanisms that use a knowledge representation model, including symbolic models of knowledge bases and distributional semantics, to expand the set of keyphrase candidates to be submitted to the classification algorithm with terms that are not in the text (not-in-text terms). The basic assumption we have is that not-in-text terms have a semantic relationship with terms that are in the text. To represent this relationship, we have defined two new features to be represented as input to the classification algorithms. The first feature refers to the power of discrimination of the inferred not-in-text terms. The intuition behind this is that good candidates for a keyphrase are those that are deduced from various textual terms in a specific document and that are not often deduced in other documents. The other feature represents the descriptive strength of a not-in-text candidate. We argue that not-in-text keyphrases must have a strong semantic relationship with the text and that the power of this semantic relationship can be measured in a similar way as popular metrics like TFxIDF. The method proposed in this work was compared with state-of-the-art systems using five corpora and the results show that it has significantly improved automatic keyphrase extraction, dealing with the limitation of extracting keyphrases absent from the text.
Bijective S-boxes of different sizes obtained from quasi-cyclic codes

The aim of this paper is to construct S-boxes of different sizes with good cryptographic properties. An algebraic construction for bijective S-boxes is described. It uses quasi-cyclic representations of the binary simplex code. Good S-boxes of sizes 4, 6, 8, 9, 10, 11, 12, 14, 15, 16 and 18 are obtained.
Design and Implementation of the Single-Block Hash FunctionBased on S-box

Hash function is a digest algorithm which has the characteristic of irreversibility. The digest algorithm is usually used in integrity verification, error check and quick search of the data transmitted on Internet. However, the problem of studying and designing hash functions that work for short message remains to be solved. For this reason, some people proposed a single-block hash function. Although the single-block hash function had great improvement on efficiency, there was still no enhancement on security. In this article, a single-block hash function with higher security was designed and implemented. Due to the security assurance of hash function is nonlinear function, then modified S-box, which has stronger ability to resist collision, was applied to substitute nonlinear function. Two S-boxes were constructed based on SEED algorithm and DES algorithm respectively. Eventually, from the avalanche effect experiment and performance analysis experiment, we can conclude that new single-block hash functions are improved on algorithm efficiency and security to a large extent.
Selecting the Mammographic-View for the Parenchymal Analysis-Based Breast Cancer Risk Assessment

Mammography is one of the main diagnostic tools for the breast and is increasingly utilized for breast cancer risk assessment. Recently, parenchymal analysis has emerged as a computational tool that works by extracting imaging features from mammograms in order to infer the level of risk of a patient. In standard screening mammography, two views for each breast are obtained: the cranio-caudal (CC) and the medio-lateral oblique (MLO) views. However, to the best of our knowledge, the question of whether the choice of a given view can influence the performance of parenchymal analysis has not been researched in the literature. The aim of this work is to evaluate the utilization of different views for risk estimation based on the computerized analysis of parenchymal patterns in mammography images. We implemented a parenchymal analysis method and tested it on a sample of 228 women in a retrospective case/control study. Based on the obtained results we support the use of the CC view for parenchymal analysis since this reduces computational cost without affecting performance.
MEASURed: Evaluating Sensor-Based Activity Recognition Scenarios by Simulating Accelerometer Measures from Motion Capture

Human Activity Recognition from accelerometer sensors is key to enable applications such as fitness tracking or health status monitoring at home. However, evaluating the performance of activity recognition systems in real-life deployments is challenging to the multiple differences in sensor number, placement and orientation that may arise in real settings. Considering such differences requires a large amount of labeled data. To overcome the challenges and costs associated to the collection of a wide range of heterogeneous data, we propose a simulator, called MEASURed, which uses motion capture to simulate accelerometer data on different settings. Then, using the simulated data to estimate the performance of activity recognition models under different scenarios. In this chapter, we describe MEASURed and evaluate its performance in estimating the accuracy of activity recognition models. Our results show that MEASURed can estimate the average accuracy of an activity recognition model using real accelerometer magnitude data. By using motion capture to simulate accelerometer data, the sensor research community can profit from visual datasets that have been collected by other communities to evaluate performance of activity recognition in a wide range of activities. MEASURed can be used to evaluate activity recognition classifiers in settings with different number, placement, and sampling rate of accelerometer sensors. The evaluation on a broad spectrum of scenarios gives a more general view of models and their limitations.
Human Body Posture Recognition Using Wearable Devices

Recently, the activities of elder people are monitored to support them live independently and safely, where the embedded hardware systems such as wearable devices are widely used. It is a research challenge to deploy deep learning algorithms on embedded devices to recognize the human activities, with the hardware constraints of limited computing resources and low power consumption. In this paper, human body posture recognition methods are proposed for the wearable embedded systems, where back propagation neural network (BPNN) and binary neural network (BNN) are employed to classify the human body postures. The BNN quantizes the synaptic weights and activation values to +1 or −1 based on the BPNN, and is able to achieve a good trade-off between the performance and cost for the embedded systems. In the experiments, the proposed methods are deployed on embedded device of Raspberry Pi 3 for real application of body postures recognition. Results show that compared with BPNN, the BNN can achieve a better trade-off between classification accuracy and cost including required computing resource, power consumption and processing time, e.g. it uses 85.29% less memory, 8.86% less power consumption, and has 5.19% faster classification speed. Therefore, the BNN is more suitable for deployment to resource constrained embedded hardware devices, which is of great significance for the application of human body posture recognition using wearable devices.
IBDNet: Lightweight Network for On-orbit Image Blind Denoising

To reduce the data transmission pressure from the satellite to the ground, it is meaningful to process the image directly on the satellite. As the cornerstone of image processing, image denoising exceedingly improves the image quality to contribute to subsequent works. For on-orbit image denoising, we propose an end-to-end trainable image blind denoising network, namely IBDNet. Unlike existing image denoising methods, which either have a large number of parameters or are unable to perform image blind denoising, the proposed network is lightweight due to the residual bottleneck blocks as the main structure. Although our network does not use clean images for training, the experimental results on the public datasets indicate that the blindly denoised image quality of our method can be roughly the same as that of the state-of-the-art denoisers. Furthermore, we deploy the model (513 KB only) on the same equipment as the one on a satellite, which verifies the feasibility of running on the satellite.
Research on Character Vector Generation Method Based on Words and Context

In order to solve the problem of character information limitation and the neglect of polysemy, a character vector generation method based on words and context is proposed. First of all, introduce the concept of context vector. Considering the fact that the same word has different meanings in different contexts, some even have the opposite meanings, therefore, to correctly understand the meaning of Chinese words, we must fully consider the context in which it is located. The sentence in which the character or word is located is expressed in the form of a vector called context vector. Secondly, construct the polysemous storage of words. Considering the importance of context and the polysemy of words, this paper expands the expression of the original word vector, and adds a context linked list for each word vector. The multiple semantics of words are stored in multiple context vectors in the linked list, so as to solve the polysemy problem of words. Finally, a character vector generation method based on words and context is proposed. The target character vector is calculated by using the word vectors of all words containing the target character and its context similarity. Meanwhile, attention mechanism is introduced. When calculating, the current character context and the word context are compared, according to the character and word context similarity to distribute weights, so that the words with more similar context make greater contribution to the generation of the character vector. This method not only add the word information to the character, but also distinguishes different semantics according to the context of the character, and makes all words participate in the calculation of the character vector according to different weights. The experiment uses the character vector evaluation method based on external tasks, taking the news title similarity task as an example, comparing the performance of the traditional CBOW method and the new words and context based character generation method in the task. And the result shows that the improved model performs better than the traditional CBOW model in this title similarity task.
Method for Extraction and Fusion Based on KL Measure

Feature extraction and fusion is important in big data, but the dimension is too big to learn a good representation. To learn a better feature extraction, a method that combines the KL divergence with feature extraction is proposed. Firstly the initial feature was extracted from the primitive data by matrix decomposition. Then the feature was further optimized by using KL divergence, where KL divergence was introduced to the loss function to make the goal function with the shortest KL distance. The experiment is implemented in four datasets such as COIL-20, COIL-100, CBCI 3000 and USPclassifyAL. The result shows that the proposed method outperforms the other four methods in the accuracy when using least number of features.
Single-Qubit Driving Fields and Mathieu Functions

We report a new family of time-dependent single-qubit radiation fields for which the correspondent evolution operator can be disentangled in an exact way via the Wei–Norman formalism. Such fields are characterized in terms of the Mathieu functions. We show that the regions of stability of the Mathieu functions determine the nature of the driving fields: For parameters in the stable region, the fields are oscillating, being able to be periodic under certain conditions. Whereas, for parameters in the instability region, the fields are pulse-like. In addition, in the stability region, this family admits solutions for evolution loops in quantum control. We obtain some prescriptions to reach such a control effect. Geometric phases in the evolution are also analyzed and discussed.
A Hybrid Multi-Frame Super-Resolution Algorithm Using Multi-Channel Memristive Pulse Coupled Neural Network and Sparse Coding

In this paper, hybrid multi-frame image super-resolution (SR) algorithm is proposed. This method combines the concepts of both multi-frame and single-frame SR to generate a high-resolution (HR) image. Specifically, the multi-frame SR stage is executed through the hierarchical iterative sub-pixel registration method and multi-channel memristive pulse coupled neural network (MPCNN) based image fusion method. And the single-frame SR stage using the sparse coding based method is performed for improving the image quality of the HR image. Finally, a series of contrast experiments and the relevant subjective/objective analysis jointly illustrate the effectiveness and superiority of the entire scheme.
Global Dynamic Path Planning Based on Fusion of Improved A* Algorithm and Morphin Algorithm

To meet the requirements of global optimal and real-time obstacle avoidance in mobile robot path planning, A novel method based on the fusion of improved A* algorithm and Morphin search tree algorithm is proposed. Firstly, A more appropriate heuristic function is designed for A* algorithm; Then a key node culling scheme is introduced into the traditional A* algorithm to remove the redundant nodes; Finally, the Morphin algorithm is integrated to perform real-time dynamic path planning on the global path based on the local information collected by the mobile robot sensor. On the basis of ensuring the better global path, the smoothness and local obstacle avoidance ability of path planning are improved. MATLAB simulation tests show that the mixed algorithm with A* and Morphin search tree algorithm can effectively shorten the path length and improve the efficiency to reach the target in the autonomous mobile robot path planning, efficiently complete path planning.
Two dimensional ElGamal public key cryptosystem

In this work, we propose a secure version of ElGamal public key cryptosystem, and prove that it is semantically secure assuming the hardness of what we call the two-dimensional decisional Diffie-Hellman (2DDDH) problem, this cryptosystem is distinguished by the speed of encryption and decryption processes and by its resistance to active adversaries. Since the 2DDDH problem is harder than the decisional Diffie-Hellman (DDH) problem (as it will be seen), one may conclude that our model reinforces the exchange security compared to the existed cryptosystems falling within the same context, also we discuss the difficult problems that guarantee its security.
Equivalent definitions for (degree one) Cameron–Liebler classes of generators in finite classical polar spaces

In this article, we study degree one Cameron–Liebler sets of generators in all finite classical polar spaces, which is a particular type of a Cameron–Liebler set of generators in this polar space, De Boeck et al. (2019). . These degree one Cameron–Liebler sets are defined similar to the Boolean degree one functions, Filmus and Ihringer (2019). We summarize the equivalent definitions for these sets and give a classification result for the degree one Cameron–Liebler sets in the polar spaces W(5,q) and Q(6,q).
A Gesture approach of Interaction with Mechatronic Systems

This paper presents an approach of interaction with mechatronics systems through gestures. With the help of a half-closed palm, a wheeled robotic vehicle controls its movement. Depending on the slope of the gesture, the vehicle moves in the corresponding direction by varying its velocity according to the magnitude of the gesture gradient. The gesture is processed using classical computer vision methodologies and the results are transferred to the mechatronics system for testing. Tests made using the methodology have been found to be easy to use and effective to satisfy end-users.
Learnable Audio Encryption for Untrusted Outsourcing Machine Learning Services

Applying machine learning to problems has become an avoidable trend. With the help of machine learning techniques, people can make predictions more accurately and can get more benefits. However, the machine learning technique relies on training from lots of data. That is, data should be open to the machine learning service provider. Considering the user privacy issue, the release of user data is not acceptable. In this paper, we propose an approach to take care both the machine learning feature and the data privacy. We focus on audio data and propose an audio encryption technique to keep audio data credential. In the meantime, we make the encrypted audio be able to be trained though machine learning service providers.
AES Based Online Voting System

International Journal of Computer Sciences and Engineering (A UGC Approved and indexed with DOI, ICI and Approved, DPI Digital Library) is one of the leading and growing open access, peer-reviewed, monthly, and scientific research journal for scientists, engineers, research scholars, and academicians, which gains a foothold in Asia and opens to the world, aims to publish original, theoretical and practical advances in Computer Science,Information Technology, Engineering (Software, Mechanical, Civil, Electronics & Electrical), and all interdisciplinary streams of Computing Sciences. It intends to disseminate original, scientific, theoretical or applied research in the field of Computer Sciences and allied fields. It provides a platform for publishing results and research with a strong empirical component. It aims to bridge the significant gap between research and practice by promoting the publication of original, novel, industry-relevant research.