Reviewerly/exHarmony huggingface dataset:
Searchable Symmetric Encryption: Optimal Locality in Linear Space via Two-Dimensional Balanced Allocations

Searchable Symmetric Encryption: Optimal Locality in Linear Space via Two-Dimensional Balanced Allocations
Chaos-Based Image Encipherment Techniques: Analysis and Comparative Review

This paper presents an in-depth review of traditional cryptography and chaos-based cryptography schemes. In the last two decades, security services are achieved by advanced methodologies, which are prominent and reliable, and most of them are based on chaos theory. Chaos theory is useful for several applications in various fields. This paper investigates the traditional mechanisms of cryptography and also compares chaotic maps. Chaotic maps and the generated sequences are examined, and their behavior is analyzed on specific parameters to evaluate the performance. The literature review facilitates to find the appropriate system for real-time applications. A general encryption algorithm is applied to obtain PSNR, entropy analysis, SSIM, and time analysis to evaluate the performance of different chaotic maps. Thus, experimental results exhibit maps’ performance. This study shows that traditional methods are the foundation of the advanced schemes; however, new algorithms are more suitable for bulky-sized data. Comparative analysis of different algorithms and evaluation of chaotic maps provides a better understanding of nonlinear systems and cryptography.
Research on reverse engineering strategy of state transfer for open quantum systems

There has long been interest to control the transfer of population between specified quantum states and protect the coherence of the system at the same time. In this paper, from the perspective of engineering implementation, and based on the idea of reverse engineering, we propose a control scheme for state transfer of two-level open quantum system, which is to drive system from arbitrary initial state to desired target state. In order to ensure that the system can process information effectively, we design the control pulse in advance in terms of population number and coherence, and then verify the validity through numerical simulation. The results show that the system can be driven from any initial state to the desired target state by the designed control pulse, and the coherence of the system is well protected during the state transfer. The influences of environmental decoherence on population transfer and coherence control is further discussed. It reveals that the control pulse presented in this paper is robust to the variation of related parameters.
HIAWare: Speculate Handwriting on Mobile Devices with Built-In Sensors

A variety of sensors are built into intelligent mobile devices. However, these sensors can be used as side channels for inferring information. Researchers have shown that some touchscreen information, such as PIN and unlock pattern, can be speculated by background applications with motion sensors. Those attacks mainly focus on the restricted-area input interface (e.g., virtual keyboard). To date, the privacy risk in the unrestricted-area input interface does not receive sufficient attention.In this paper, we investigate such privacy risk and design an unrestricted-area information speculation framework, called Handwritten Information Awareness (HIAWare). HIAWare exploits the sensors’ signals that are affected by handwriting actions to speculate the handwritten characters. To alleviate the impact of different handwriting habits, we utilize the generality patterns of characters. Furthermore, to mitigate the impact of holding posture in handwriting, we propose a user-independent posture-aware approach. As a result, HIAWare can attack any victim without obtaining the victim’s information in advance. The experiments show that the speculation accuracy of HIAWare is close to 90.0%, demonstrating the viability of HIAWare.
A novel whale optimization algorithm and its application in metallurgical process

To solve the limitations existing in the standard whale optimization algorithm (WOA), a convergence factor based on the sine function is designed to balance the exploration and development abilities of the WOA, furthermore, an improved opposition-based learning strategy is proposed to accelerate the convergence rate of the algorithm, in addition, the mutation operation based on the population diversity is employed to reduce the probability that the algorithm gets caught in the local optimum, the simulation result shows that the novel WOA presents a superior convergence rate and accuracy to the basic WOA.
Strengthening Mathematical Skills with M-Learning

The COVID-19 pandemic transformed education from face-to-face to virtual. This accelerated the creation of new learning environments based on the everyday use of mobile devices, presenting new challenges for education. Emerging technologies today drive mobile learning or m-learning. This research focuses on the development of a mobile application that seeks to strengthen the mathematical abilities of whole number empowerment in high school students in a blended mode. The application includes four missions that make up an interactive game, in which the player has three lives and accumulates points. Mate-potencia Olympics faces two players, who must meet the challenges of each mission, the winners go to a second round, until they have the winner. Students learn the process of calculating square powers, identifying each of its elements, allowing dynamic, ubiquitous and flexible learning.
Attention Based Double Layer LSTM for Chinese Image Captioning

Image captioning is a difficult problem which aims to automatically describe the content of an image using proper textual descriptions. Especially for Chinese captioning, due to its complex semantic and various expressions, it is still a challenging task of improving the caption quality. In this paper, we extend research on automated image captioning in the dimension of language and propose a novel Chinese image captioning model which uses the double-layer LSTM with an attention mechanism to generate more natural Chinese sentences. In our model, the Inception-v3 network is employed to extract image features, and the weights of these features are determined by the attention mechanism based on double-layer LSTM to predict each word. Experimental results on AIC-ICC datasets demonstrate that our proposed model can generate better Chinese captions, which are more accurate and fluent. Compared with traditional Chinese image captioning algorithms, our method greatly improves the performance of captioning and achieves BLEU-4 and CIDEr evaluation scores of 40.2 and 119.9, respectively. The actual generation results also show that the model can generate accurate, diverse, and vivid Chinese caption of images.
Extrinsic Calibration between Camera and LiDAR Sensors by Virtual Planar Junctions Matching

We propose an accurate calibration method for extrinsic parameters of LiDAR and camera by virtual planar junctions matching. It is difficult to find reliable feature correspondences between the LiDAR and the camera due to the sparsity and centimeter-level measurement errors of LiDAR point clouds. In our approach, a large size board with sparse corner markers is used as a calibration board to estimate the extrinsic parameters between a stereo pair and a multi-scanline LiDAR. In data acquisition stage, the board is placed in different positions around the LiDAR-cameras and virtual junctions are computed by each set of three planes for the calibration process. Based on matching virtual planar junctions between camera and LiDAR coordinate system, an ICP algorithm is used to solve the extrinsic parameters iteratively. Our experiments demonstrate that our method can get the rotation error is less than 0.15 degrees, and the translation error is less than 1.5mm.
Relation Aware Attention for Penson Re-identification

Abstract Person re-identification (Re-ID) means matching people across different camera views based on different locations. It is challengeing for person images where there are background clutter, pose variations, illumination changes, etc. Attention mechanisms have become attractive for person re-identification algorithms as they aim at strengthening discriminative features, which accord with the purpose of person re-id, i.e., learning discriminative features for diffirent pedestrains. Previous approches mostly learn attention by useing local convolutions which have limited receptive filds. In this paper, an Relation Aware Attention(RAA) module is proposed to address this issue. RAA infers attention maps along two dimensions, channel and spatial, then them are mutiplied to the feature as the output map. For each feature position, RAA harvests the pairwise relationship with others as its response. Furthermore, in order to grasp the structure information of global scope and the local apperance information, we stack the relations and the feature to lean the final attention with a convlutional model. We designe the experiment and compare it with the existing benchmark. The resulits show that our attention model can increas the abillity of feature representation.
A Classification Method for Network Traffic Based on Semi-supervised Approach

Abstract Traffic data encryption has been a trend in most Internet applications, and traditional protocol filtering based on fixed port and traffic classification based on Deep Packet Inspection are unable to Identify encrypted traffic. Recently, the traffic classification method based on the statistical characteristics of network traffic, which can solve the problem of encrypting data or user privacy protection, has been widely discussed. However, the traditional supervised learning method requires manual marking of a large amount of network traffic data, which is tedious and time-consuming. In this paper, a improved semi-supervised traffic classification framework based on BIRCH clustering method is proposed, and through experiments, the proposed algorithm, supervised learning algorithm and classical semi-supervised traffic classification algorithm are analyzed and compared. The results show that the algorithm proposed in this paper has higher overall accuracy and classification accuracy, and the algorithm can increase the accuracy on traffic classification.
Application of laboratory reflectance spectroscopy in the characterization of uranium-bearing minerals associated with fossils remains

The characterization of uranium-bearing minerals by different remote sensing technologies is a challenging task due to the implications for the exploration or the protection of uranium deposits. In nature, uranium-rich mineral ores are often associated with phosphates. Fossil bones may contain almost 1% of uranium as a result of interaction with ground water or other uranyl-bearing mineralizing fluids during fossil diagenesis. Sediments in the Lower Miocene fossil site of Córcoles (Guadalajara, Spain), in the Tajo Basin, host uranyl-vanadate metatyuyamunite associated with mammal fossils, constituting a remarkable example of the relationship between uranium ores and phosphates. The mineralogical composition and the spectral response of different phosphates (geological fluorapatite, fossil fluorapatite from Paracuellos III fossil site, and Recent hydroxyapatite) and uranyl-bearing minerals (metatorbernite, metautunite, metauranocircite and fossils from Córcoles Fm with metatyuyamunite) were studied through laboratory reflectance spectroscopy. Spectral features of the different samples were compared with XRD, XRF and FTIR-ATR to determine the relationships between reflectance spectral curves, mineralogy and chemical composition. It should be highlighted that reflectance signatures of uranyl micas and Ca-phosphates allow for the mineral identification. Fluorapatite samples show absorption bands at 586, 737, 750, 805 nm related to the phosphate anion, not always present in uranyl-phosphate micas and a characteristic chemical heterogeneity probably caused by REE content. Uranyl-bearing minerals show absorption features at 1100, 1330 and 1672 nm attributed to uranyl anions (UO<sub>2</sub> <sup>2+</sup>). Although further research is needed this study highlights the use of laboratory reflectance spectroscopy in uranium detection.
Emerging Trends in Image De-noising Methods

Many researches are carried out to remove noise in an image. Various approaches have been proposed each has its own advantages and limitations. Some methods used and some concepts which may be used in future to remove noise is discussed in this paper.
Filtration and canonical completeness for continuous modal mu-calculi

The continuous modal mu-calculus is a fragment of the modal mu-calculus, where the application of fixpoint operators is restricted to formulas whose functional interpretation is Scott-continuous, rather than merely monotone. By game-theoretic means, we show that this relatively expressive fragment still allows two important techniques of basic modal logic, which notoriously fail for the full modal mu-calculus: filtration and canonical models. In particular, we show that the Filtration Theorem holds for formulas in the language of the continuous modal mu-calculus. As a consequence we obtain the finite model property over a wide range of model classes. Moreover, we show that if a basic modal logic L is canonical and the class of L-frames admits filtration, then the logic obtained by adding continuous fixpoint operators to L is sound and complete with respect to the class of L-frames. This generalises recent results on a strictly weaker fragment of the modal mu-calculus, viz. PDL.
A Hybrid Post Hoc Interpretability Approach for Deep Neural Networks

Every day researchers publish works with state-of-the-art results using deep learning models, however as these models become common even in production, ensuring fairness is a main concern of the deep learning models. One way to analyze the model fairness is based on the model interpretability, obtaining the essential features to the model decision. There are many interpretability methods to produce the deep learning model interpretation, such as Saliency, GradCam, Integrated Gradients, Layer-wise relevance propagation, and others. Although those methods make the feature importance map, different methods have different interpretations, and their evaluation relies on qualitative analysis. In this work, we propose the Iterative post hoc attribution approach, which consists of seeing the interpretability problem as an optimization view guided by two objective definitions of what our solution considers important. We solve the optimization problem with a hybrid approach considering the optimization algorithm and the deep neural network model. The obtained results show that our approach can select the features essential to the model prediction more accurately than the traditional interpretability methods.
HEAT as an example for efficient environmental perception for autonomous shuttle systems

IAV developed a novel data fusion approach to ensure a comprehensive environmental perception for the autonomous driving in urban environments. The approach is highly scalable, easily adaptable, and sensor independent while processing heterogeneous data. It is based on a dynamic occupancy grid in conjunction with a sophisticated combination of inverse sensor models which are taking the sensor properties into account. The approach has been successfully used within the HEAT project and will be discussed in this context.
An English Video Teaching Scheme Based on Content Recognition

To meet the requirement of improving students' learning ability of English online course, a set of video teaching scheme is constructed. The system is designed based on IP video processing technology and improved k-means clustering algorithm. Considering the relationship of video frame sequence and maintaining the acceptable retrieval speed, the system selects the campus network teaching content related to the college mining strategy for the target student group and perform the corresponding mining management. In this paper, the innovative ideas are further studied and confirmed. The database of the system is developed and designed, and the system is run and tested. The results show that this method is based on permutation and combination K-means clustering algorithm to deeply mine the inherent rules of the strategy, and it has a good implementation effect for better recognition of video content. The improvement scheme greatly improves the quality of English multimedia teaching and students' satisfaction is high.
Application of Latent Dirichlet Allocation (LDA) for clustering financial tweets

Sentiment classification is one of the hottest research areas among the Natural Language Processing (NLP) topics. While it aims to detect sentiment polarity and classification of the given opinion, requires a large number of aspect extractions. However, extracting aspect takes human effort and long time. To reduce this, Latent Dirichlet Allocation (LDA) method have come out recently to deal with this issue.In this paper, an efficient preprocessing method for sentiment classification is presented and will be used for analyzing user’s comments on Twitter social network. For this purpose, different text preprocessing techniques have been used on the dataset to achieve an acceptable standard text. Latent Dirichlet Allocation has been applied on the obtained data after this fast and accurate preprocessing phase. The implementation of different sentiment analysis methods and the results of these implementations have been compared and evaluated. The experimental results show that the combined uses of the preprocessing method of this paper and Latent Dirichlet Allocation have an acceptable results compared to other basic methods.
Multi-dimensional data analysis technology of business application system based on Spark framework

Abstract The current multidimensional data analysis technology uses the data approximation to obtain deep data information in the data cube, which requires repeated traversal of the full link data, resulting in low processing efficiency and unable to analyze a large amount of data in parallel. Therefore, a multi-dimensional data analysis technology based on Spark framework for business application system is proposed to optimize the defects of traditional technology. Spark technology is used to build a full-link data analysis framework, and the data warehouse logic is designed to build a full-link data warehouse. Genetic algorithm is used to mine the full-link data of the business system, and the data are stored in the data warehouse, and the data warehouse and the sequential structure neural network are established. In the sequential structure neural network, multi-dimensional data analysis is realized from multiple angles and aspects. The technical feasibility verification results show that the research technology shortens about 14.7% of the time on average, significantly improves the analysis efficiency and can conduct concurrent analysis on a large number of data, which is better than the application effect of traditional analysis technology.
Unsupervised Learning of Explainable Parse Trees for Improved Generalisation

Recursive neural networks (RvNN) have been shown useful for learning sentence representations and helped achieve competitive performance on several natural language inference tasks. However, recent RvNN-based models fail to learn simple grammar and meaningful semantics in their intermediate tree representation. In this work, we propose an attention mechanism over Tree-LSTMs to learn more meaningful and explainable parse tree structures. We also demonstrate the superior performance of our proposed model on natural language inference, semantic relatedness, and sentiment analysis tasks and compare them with other state-of-the-art RvNN based methods. Further, we present a detailed qualitative and quantitative analysis of the learned parse trees and show that the discovered linguistic structures are more explainable, semantically meaningful, and grammatically correct than recent approaches. The source code of the paper is available here.
Towards Machine Learning-Assisted Output Checking for Statistical Disclosure Control

There is an increasing demand by researchers to access the microdata (data on individual persons or enterprises) collected by national statistical institutes or other data controllers. If microdata are personally identifiable information, the most usual way for data controllers to share them in a way compliant with the privacy legislation (notably the EU General Data Protection Regulation) is to release anonymized microdata. Yet, data analysts often need access to the original microdata in order to avoid the information loss caused by anonymization. To answer that need, safe access centers (on physical premises or on-line) have been set up by several national statistical institutes. In these centers, users can run their analyses on original data using the controller's software, and the controller checks the outputs of the users' analyses before returning those outputs to them, in order to make sure users do not take home any result that might leak the confidential microdata on which it has been computed. Output checking is currently implemented with human checkers, which is expensive and slow, especially because checkers need to have specific statistical expertise. In this work, we explore the use of machine learning to partially automate output checking. We follow the rule-based approach and our empirical results show that our system can generalize the rules it is trained on. In conclusion, output checking assisted by machine learning seems to work well and should be trialed in safe access centers and decentralized data marketplaces.
An Approach to Extractive Bangla Question Answering Based On BERT-Bangla And BQuAD

A language model trained on a well-organized Bangla dataset can play a significant role towards perfecting informative question answering systems in Bangla by addressing several issues faced in the development of such systems in resource limited languages. Bidirectional Encoder Representations from Transformers (BERT) is a truly bidirectional language model introduced by Google which achieves state-of-the-art performance in natural language understanding tasks. In this paper, we introduce BERT-Bangla, a language model pre-trained on a large amount of Bangla unlabeled text. We evaluate BERT-Bangla on several Bangla NLP classification tasks and achieve better performances than other existing Bangla language models. In response to the need for a well-suited Bangla question-answering dataset, we develop BQuAD (Bangla Question Answering Dataset) comprising question-answer pairs and contexts featuring various topics from multiple domains. We fine-tune BERT-Bangla on BQuAD, resulting in an extractive question-answering model that can process user-provided Bangla contexts to produce specific answer to questions.