Reviewerly/exHarmony huggingface dataset:
One-Time, Oblivious, and Unlinkable Query Processing Over Encrypted Data on Cloud

Location-based services (LBSs) are widely deployed in commercial services. These services always depend on a service provider, e.g., a cloud server, to store the enormous amounts of geospatial data and to process various queries. For example, a Yelp user can retrieve a list of recommended cafés by submitting her/his current location to the service provider. While LBSs offer tremendous benefits, it is vital to safeguard users’ privacy against untrusted service providers. However, no prior secure k nearest neighbor query processing schemes satisfy the three security requirements of one-time, oblivious, and unlinkable. In particular, we are concerned with the problem of item exclusion: how to match one data query with each item on the cloud no more than once in an oblivious and unlinkable manner. In this paper, we propose the first secure k nearest neighbor query processing scheme, Obaq, that satisfies the above requirements. Obaq first introduces an item identifier into an existing secure k nearest neighbor query processing scheme. Each data owner inserts an item identifier and her/his location information into a secure index, and each data user transfers the identifier of a previously received data item and location information into a specific range. Then, Obaq excludes corresponding items via privacy-preserving range querying. We define strong index privacy and strong token privacy and formally prove the security of Obaq in the random oracle model. We further evaluate the performance of Obaq using a prototype and a real-world dataset. The experimental results show that Obaq is highly efficient and practical in terms of computational cost, communication overhead, and response delay.
Relating Semantics as Fine-Grained Semantics for Intensional Logics

This text has a programmatic and introductory character. In the paper, we outline a fine-grained semantics for intensional logics. The fundamental idea of the semantics is that the logical value of a given complex proposition is the result of two things: a valuation of propositional variables supplemented with a valuation of relation between the main components of this complex proposition. The latter thing is a formal representation of intensionality that emerges from the connection of several simpler propositions into one more complex proposition. In the first part of the paper, we present some linguistic motivations for the semantics. Later, we propose a very general, multi-valued view on relating semantics, and, in a more detailed way, we consider its two-valued specification, referring also to its historical applications and origin. A further generalization is made when we combine relating semantics with possible world semantics in the subsequent part. The paper concludes with a proposal of defining intensional operators as secondary notions that are based on relating connectives. By dint of the proposal, we can control the behavior of the operators by changing properties of semantic structures for the relating connectives that we use in the definitions.
High-Resolution SAR Image Despeckling Based on Nonlocal Means Filter and Modified AA Model

A new speckle suppression algorithm is proposed for high-resolution synthetic aperture radar (SAR) images. It is based on the nonlocal means (NLM) filter and the modified Aubert and Aujol (AA) model. This method takes the nonlocal Dirichlet function as a linear regularization item, which constructs the weight by measuring the similarity of images. Then, a new despeckling model is introduced by combining the regularization item and the data item of the AA model, and an iterative algorithm is proposed to solve the new model. The experiments show that, compared with the AA model, the proposed model has more effective performance in suppressing speckle; namely, ENL and DCV measures are 21.75% and 4.5% higher, respectively, than for NLM. Moreover, it also has better performance in keeping the edge information.
AR-TO-KID: A speech-enabled augmented reality to engage preschool children in pronunciation learning

Abstract AR-TO-KID is an application produced for preschool children between ages five to six years old with an Augmented Reality (AR) application. The significant purpose of AR-TO-KID is to improve the pronunciation of the children in English. Hence, this paper discusses an AR application with speech input. The detection of the children speech input when they need to pronounce the words correctly, and they need to have critical thinking to identify the environment suit with the 3D objects that they will utter the word. Educational technology should be interactive and attractive for 5 to 6 years old preschool children learning; however, some at preschool teachers still used the conventional methods in teaching and children are not fully engaged with the method. Therefore, this project is to design and develop an interactive AR tool called AR-TO-KID for preschool children in pronunciation learning and teaching. This paper presents the evaluation and testing for preschool children with non-native English speaking. The article ends with results and discussion.
Privacy-Preserving Network Analytics

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Named entity recognition for Chinese judgment documents based on BiLSTM and CRF

Abstract Chinese named entity recognition (CNER) in the judicial domain is an important and fundamental task in the analysis of judgment documents. However, only a few researches have been devoted to this task so far. For Chinese named entity recognition in judgment documents, we propose the use a bidirectional long-short-term memory (BiLSTM) model, which uses character vectors and sentence vectors trained by distributed memory model of paragraph vectors (PV-DM). The output of BiLSTM is used by conditional random field (CRF) to tag the input sequence. We also improved the Viterbi algorithm to increase the efficiency of the model by cutting the path with the lowest score. At last, a novel dataset with manual annotations is constructed. The experimental results on our corpus show that the proposed method is effective not only in reducing the computational time, but also in improving the effectiveness of named entity recognition in the judicial domain.
A Compressive Sensing Based Image Encryption and Compression Algorithm With Identity Authentication and Blind Signcryption

Recently, a robust and secure image sharing scheme with personal identity information embedded was proposed based on Compressive Sensing, Secret Image Sharing and Diffie-Hellman Agreement.However, there exists a security flaw in this scheme.It cannot resist the man-in-the-middle attack in the authentication stage.Anyone can disguise himself as a legal person and get the information when exchanging the secret keys, which provides the possibility for information leakage, tampering, and other attacks.In this paper, we propose an image encryption and compression algorithm with identity authentication and blind signcryption based on Parallel Compressive Sensing (PCS), Secret Sharing(SS) and Elliptic Curve Cryptography (ECC).Firstly, Logistic-Tent system and PCS are employed to complete compression and lightweight encryption in the compression stage.Secondly, random sequences are generated based on Chebyshev map to construct four encryption matrices to perform the encryption process.Meanwhile, the participants' identity authentication and blind signcryption can be achieved by using ECC.Finally, we prove the efficiency and security of the blind signcryption, which can authenticate the participants' identity before restoring the original image.Experiments and security analysis demonstrate that the proposed scheme not only reduce the storage space and computational complexity effectively, but also has resistance against the man-in-the-middle attack, forgery attack and chosen-text attack.
Biplots for compositional data derived from generalized joint diagonalization methods

Biplots constructed from principal components of a compositional data set are an established means to explore its features. Principal Component Analysis (PCA) is also used to transform a set of spatial variables into spatially decorrelated factors. However, because no spatial structures are accounted for in the transformation the application of PCA is limited. In geostatistics and blind source separation a variety of different matrix diagonalization methods have been developed with the aim to provide spatially or temporally decorrelated factors. Just as PCA, many of these transformations are linear and so lend themselves to the construction of biplots. In this contribution we consider such biplots for a number of methods (MAF, UWEDGE and RJD transformations) and discuss how and if they can contribute to our understanding of relationships between the components of regionalized compositions. A comparison of the biplots with the PCA biplot commonly used in compositional data analysis for the case of data from the Northern Irish geochemical survey shows that the biplots from MAF and UWEDGE are comparable as are those from PCA and RJD. The biplots emphasize different aspects of the regionalized composition: for MAF and UWEDGE the focus is the spatial continuity, while for PCA and RJD it is variance explained. The results indicate that PCA and MAF combined provide adequate and complementary means for exploratory statistical analysis.
A Transfer Learning LSTM Network-Based Severity Evaluation for Intermittent Faults of an Electrical Connector

Traditional machine learning methods work well under a general assumption that the training data and the testing data should be drawn from the same distribution. However, this assumption does not hold in many real-world applications. For two structure-similar but specification-different connectors with intermittent fault, one connector has sufficient intermittent fault data with severity labeled but another connector has no labeled intermittent fault data. In this situation, the severity evaluation model trained by the samples from the former connector will have large evaluation errors for the samples from the latter connector. For realizing the transfer of the intermittent fault severity evaluation model between two similar but different connectors, this article proposes a transfer learning long short term memory (LSTM) network-based severity evaluation method for intermittent faults of an electrical connector. Conventional transfer learning methods with maximum mean discrepancy (MMD) have two shortcomings: 1) MMD only considers the mean discrepancy between the source and target domains and 2) MMD only measures the marginal distribution discrepancy of the source and target domains but ignored the conditional distribution discrepancy of different labels. Therefore, this article proposes a modified MMD metric which not only considers the higher order statistics but also the label discrepancy information to enhance the severity evaluation accuracy. Finally, this transfer learning model is applied in the intermittent fault experiment of three electrical connectors, and the effectiveness of the proposed method is verified using comparisons with four state-of-art transfer learning methods.
WITHDRAWN: Breast cancer classification and identification using machine learning approaches

This article has been withdrawn: please see Elsevier Policy on Article Withdrawal (https://www.elsevier.com/about/our-business/policies/article-withdrawal). This article has been withdrawn as part of the withdrawal of the Proceedings of the International Conference on Emerging Trends in Materials Science, Technology and Engineering (ICMSTE2K21). Subsequent to acceptance of these Proceedings papers by the responsible Guest Editors, Dr S. Sakthivel, Dr S. Karthikeyan and Dr I. A. Palani, several serious concerns arose regarding the integrity and veracity of the conference organisation and peer-review process. After a thorough investigation, the peer-review process was confirmed to fall beneath the high standards expected by Materials Today: Proceedings. The veracity of the conference also remains subject to serious doubt and therefore the entire Proceedings has been withdrawn in order to correct the scholarly record.
Finding Collisions in a Quantum World: Quantum Black-Box Separation of Collision-Resistance and One-Wayness

Since the celebrated work of Impagliazzo and Rudich (STOC 1989), a number of black-box impossibility results have been established. However, these works only ruled out classical black-box reductions among cryptographic primitives. Therefore it may be possible to overcome these impossibility results by using quantum reductions. To exclude such a possibility, we have to extend these impossibility results to the quantum setting. In this paper, we study black-box impossibility in the quantum setting. We first formalize a quantum counterpart of fully-black-box reduction following the formalization by Reingold, Trevisan and Vadhan (TCC 2004). Then we prove that there is no quantum fully-black-box reduction from collision-resistant hash functions to one-way permutations (or even trapdoor permutations). We take both of classical and quantum implementations of primitives into account. This is an extension to the quantum setting of the work of Simon (Eurocrypt 1998) who showed a similar result in the classical setting.
Dynamical System Segmentation for Information Measures in Motion

Motions carry information about the underlying task being executed. Previous work in human motion analysis suggests that complex motions may result from the composition of fundamental submovements called movemes. The existence of finite structure in motion motivates information-theoretic approaches to motion analysis and robotic assistance. We define task embodiment as the amount of task information encoded in an agent's motions. By decoding task-specific information embedded in motion, we can use task embodiment to create detailed performance assessments. We extract an alphabet of behaviors comprising a motion without \textit{a priori} knowledge using a novel algorithm, which we call dynamical system segmentation. For a given task, we specify an optimal agent, and compute an alphabet of behaviors representative of the task. We identify these behaviors in data from agent executions, and compare their relative frequencies against that of the optimal agent using the Kullback-Leibler divergence. We validate this approach using a dataset of human subjects (n=53) performing a dynamic task, and under this measure find that individuals receiving assistance better embody the task. Moreover, we find that task embodiment is a better predictor of assistance than integrated mean-squared-error.
Disrupting Agro-industry Supply Chain in Indonesia With Blockchain Technology: Current and Future Challenges

This paper provides an overview of the problems of agro-industrial supply chains in Indonesia. This becomes important because Indonesia has a very diverse agro-industrial. Starting from the agro-agricultural industry, agro-plantation industry, agro-livestock industry to the agro-fishing industry. The problem that is often encountered in agro-industrial supply chains in Indonesia is the length of the supply chain which involves many intermediaries, communication of data and information that is still lacking, to the lack of quality assurance and product safety. Blockchain technology has characteristics that can disrupt the agro-industry supply chain. Its ability to store data and information makes transactions made through blockchain technology more transparent, reliable, and more secure. The research methods used include identifying agro-industry supply chain problems, identifying potential blockchain, mapping problems into their potential solutions, and providing a recommendation model. At the end of the conclusion of this study, a recommendation was made to apply blockchain technology to overcome the agro-industry supply chain problems in Indonesia today and in the future.
Deep Learning Based Prediction of Signal-to-Noise Ratio (SNR) for LTE and 5G Systems

Deep learning (DL) is applied to predict signal-to-noise ratio (SNR) in de facto LTE and 5G systems in a non-data-aided (NDA) manner. Various channel conditions and impairments are considered, including modulation types, path delays, and Doppler shifts. Both time-domain and frequency-domain signal grids are evaluated as inputs for SNR prediction. A combination of convolutional neural network (CNN) and long short term memory (LSTM) - CNN-LSTM - is used as the SNR predictor. Learning both spatial and temporal features is known to improve DL prediction accuracy. Techniques employed to enhance performance are SNR range/resolution manipulation, binary prediction, and multiple input prediction. Computer simulation is conducted using MATLAB LTE, 5G, and DL toolboxes to generate OFDM signals, model fading channels with AWGN noise, and construct CNN-LSTM. Simulation results show, with off-line training, DL based prediction of SNR in LTE and 5G systems has better accuracy and latency than traditional estimation techniques. Specifically, SNR prediction for SNR range of [-4, 32] dB and resolution of 2 dB utilizing time-domain signals has an accuracy of 100%, hence normalized mean square error (NMSE) of zero, and a latency of 1 millisecond or less.
Evolutionary Detection Accuracy of Secret Data in Audio Steganography for Securing 5G-Enabled Internet of Things

With the unprecedented growing demand for communication between Internet of Things (IoT) devices, the upcoming 5G and 6G technologies will pave the path to a widespread use of ultra-reliable low-latency applications in such networks. However, with most of the sensitive data being transmitted over wireless links, security, privacy and trust management are emerging as big challenges to handle. IoT applications vary, from self-driving vehicles, drone deliveries, online shopping, IoT smart cities, e-healthcare and robotic assisted surgery, with many applications focused on Voice over IP (VoIP) and require securing data from potential eavesdroppers and attackers. One well-known technique is a hidden exchange of secret data between the devices for which security can be achieved with audio steganography. Audio steganography is an efficient, reliable and low-latency mechanism used for securely communicating sensitive data over wireless links. MPEG-1 Audio Layer 3’s (MP3’s) bit rate falls within the acceptable sound quality required for audio. Its low level of noise distortion does not affect its sound quality, which makes it a good carrier medium for steganography and watermarking. The strength of any embedding technique lies with its undetectability measure. Although there are many detection techniques available for both steganography and watermarking, the detection accuracy of secret data has been proven erroneous. It has yet to be confirmed whether different bit rates or a constant sampling rate for embedding eases detection. The accuracy of detecting hidden information in MP3 files drops with the influence of the compression rate or increases. This drop or increase is caused by either the increase in file track size, the sampling rate or the bit rate. This paper presents an experimental study that evaluates the detection accuracy of the secret data embedded in MP3. Training data were used for the embedding and detection of text messages in MP3 files. Several iterations were evaluated. The experimental results show that the used approach was effective in detecting the embedded data in MP3 files. An accuracy rate of 97.92% was recorded when detecting secret data in MP3 files under 128-kbps compression. This result outperformed the previous research work.
Deep Learning Models for Road Passability Detection during Flood Events Using Social Media Data

During natural disasters, situational awareness is needed to understand the situation and respond accordingly. A key need is assessing open roads for transporting emergency support to victims. This can be done via analysis of photos from affected areas with known location. This paper studies the problem of detecting blocked/open roads from photos during floods by applying a two-step approach based on classifiers: does the image have evidence of road? If it does, is the road passable or not? We propose a single double-ended neural network (NN) architecture which addresses both tasks simultaneously. Both problems are treated as a single class classification problem with the use of a compactness loss. The study was performed on a set of tweets, posted during flooding events, that contain (i) metadata and (ii) visual information. We studied the usefulness of each data source and the combination of both. Finally, we conducted a study of the performance gain from ensembling different networks. Through the experimental results, we prove that the proposed double-ended NN makes the model almost two times faster and the load on memory lighter while improving the results with respect to training two separate networks to solve each problem independently.
Convex resource theory of non-Markovianity

Abstract We establish a convex resource theory of non-Markovianity inducing information backflow under the constraint of small time intervals within the temporal evolution. We identify the free operations and a generalized bona-fide measure of non-Markovian information backflow. The framework satisfies the basic properties of a consistent resource theory. The proposed resource quantifier is lower bounded by the optimization free Rivas–Huelga–Plenio (RHP) measure of non-Markovianity. We next define the robustness of non-Markovianity and show that it can directly linked with the RHP measure of non-Markovianity through a lower bound. This enables a physical interpretation of the RHP measure. We further relate robustness of non-Markovianity with the quantum capacity of dephasing channels.
Personalized Music Recommendation Simulation Based on Improved Collaborative Filtering Algorithm

Collaborative filtering technology is currently the most successful and widely used technology in the recommendation system. It has achieved rapid development in theoretical research and practice. It selects information and similarity relationships based on the user’s history and collects others that are the same as the user’s hobbies. User’s evaluation information is to generate recommendations. The main research is the inadequate combination of context information and the mining of new points of interest in the context-aware recommendation process. On the basis of traditional recommendation technology, in view of the characteristics of the context information in music recommendation, a personalized and personalized music based on popularity prediction is proposed. Recommended algorithm is MRAPP (Media Recommendation Algorithm based on Popularity Prediction). The algorithm first analyzes the user’s contextual information under music recommendation and classifies and models the contextual information. The traditional content-based recommendation technology CB calculates the recommendation results and then, for the problem that content-based recommendation technology cannot recommend new points of interest for users, introduces the concept of popularity. First, we use the memory and forget function to reduce the score and then consider user attributes and product attributes to calculate similarity; secondly, we use logistic regression to train feature weights; finally, appropriate weights are used to combine user-based and item-based collaborative filtering recommendation results. Based on the above improvements, the improved collaborative filtering recommendation algorithm in this paper has greatly improved the prediction accuracy. Through theoretical proof and simulation experiments, the effectiveness of the MRAPP algorithm is demonstrated.