Reviewerly/exHarmony huggingface dataset:
Modeling and Minimizing Spontaneous Raman Scattering for QKD Secured DWDM Networks

Quantum key distribution (QKD) provides information-theoretic security based on quantum mechanics. Integrating QKD with classical data traffic by using wavelength division multiplexing (WDM) techniques in a single fibre is a cost-efficient way to improve security in legacy infrastructure. In such a system, the main noise source to the quantum channel is spontaneous Raman scattering (SRS) caused by the classical channels. In this letter we introduce a channel allocation strategy for both quantum and classical signals to minimize the SRS noise. A use case that quantum and classical channels co-exist in a dense WDM system is investigated. The results show >26% increase of achievable transmission distance for the QKD system when implementing the introduced channel allocation strategy. Moreover, a network updating plan is proposed, which provides a guideline to light the new wavelengths for classical communications while minimizing the SRS noise to quantum channels.
Construction of Chinese-English Cross-language Information Retrieval Model Based on Dictionary Learning

Using the method of semantic ontology information fusion, this paper obtains the distribution fusion model of Chinese and English cross-language information from the perspective of systemic functional linguistics. A data distributed structure model of Chinese and English cross-language information under Systemic Functional Linguistics is constructed. Structural semantic hierarchical feature analysis method is used to fuse Chinese and English cross-language information under Systemic Functional Linguistics, feature extraction of Chinese and English cross-language information under Systemic Functional Linguistics is carried out in the reorganized feature space, and a fuzzy clustering model of Chinese and English cross-language information retrieval under Systemic Functional Linguistics is established by combining big data mining method. According to the semantic extension of verb-resultative phrases and dictionary learning results, the feature matching of Chinese and English cross-language data in personalized retrieval under the vision of Systemic Functional Linguistics is realized, and the Chinese and English cross-language information retrieval model under the vision of Systemic Functional Linguistics is established by adopting the method of grammatical attribute matching of semantic factors. Tests show that this method has higher accuracy and better matching in cross-language information retrieval between Chinese and English under the vision of Systemic Functional Linguistics.
Object detection of face mask recognition based on improved faster rcnn

Object detection is a hot talking point in computer vision. Recently, as COVID-19 is spreading globally, the epidemic prevention and control has entered a normalization, wearing masks when entering and leaving public places and taking public transportation has now become normalized. The recognition of face mask is also of increasing concern. Then fast and accurate mask identification is essential. The Faster R-CNN is currently a more advanced object detection algorithm. It has the advantages of fast detection speed and high detection accuracy and is widely used in various fields. However, this method often fails to demonstrate its excellent performance in detecting small objects. This paper is based on the Faster R-CNN object detection algorithm and introduces FPN to solve multi-scale mask recognition and detection. The feature map of each resolution is introduced into the latter resolution feature map for element-wise summation operation. Fusing shallow layers with high resolution and deep layers with rich semantic information to improve the ability to detect small objects. The method is validated with 2000 face mask images as the dataset. Experimentally, the improved method proposed in this paper proves to be effective and better than the original algorithm.
Variational Information Bottleneck on Few Shot Model based on Weight Imprinting for Image Classification

Few shot learning remains an open issue in computer vision. Among several recently proposed approaches, Weight Imprinting (WI) achieves superior performance on many challenging benchmarks. The performance of the imprinted weights heavily depends on the quality of the representations generated by the encoder. However, it is not known what characteristics are required for weight imprinting. The representations learned during the pre-training phase are optimized for classification accuracy for the pre-training base classes and not necessarily suitable for the downstream, imprinted tasks. In this paper, we investigate the effect of Variational Information Bottleneck on the few shot learning with weight imprinting. Variational Information Bottleneck strongly regularizes the representation by minimizing the mutual information between input data and representation while keeping the classification accuracy for pretraining task. We demonstrate that the encoder regularized by VIB achieves significantly better performance on few-shot learning tasks with imprinting. Furthermore, we comprehensively investigate the effect of combining VIB with other regularization methods including data augmentation and auxiliary data. We confirmed that with a proper auxiliary dataset, we can achieve even better accuracy on the downstream task.
A Deep Learning Approach to Subject Identification Based on Walking Patterns

For the time being, smartphone devices rely on direct interaction from the users for unlocking and authentication purposes through implicit authentication systems such as PINs, facial recognition or fingerprint scanning. While different passive two-factor authentication systems based on machine learning were explored in recent work, all require an implicit authentication system. In this study, the focus is to develop and introduce a passive authentication system based on walking patterns. In this scenario, the authentication system continuously authenticates the user in the background, without any further action. To the best of our knowledge, this is the first study in which the data sets are processed with the aim to generate better performing gait-based motion signals. Compared to previous studied work, we employ a processing stage in which we extract tiny frames of data from the motion signals. Our contribution of processing gait data, allows for more robust learning of the subject movement and lowers the number of samples required to classify a user thereafter. Hence, our approach is more robust compared to using raw gait signals. Further, we transform them into gray-scale images for deep neural network training and feature extraction. Conducting the experiments, the empirical results demonstrate that subjects can be identified with a very high accuracy through walking patterns employing the presented techniques. Empirical results outline that a system based on gait data can be utilized as a passive authentication system. Therefore, it is concluded that deep neural networks employing the technique described in this work for gait-based feature representation are well suited for continuous and unobtrusive authentication systems.
The Role of Entropy in Guiding a Connection Prover

In this work we study how to learn good algorithms for selecting reasoning steps in theorem proving. We explore this in the connection tableau calculus implemented by leanCoP where the partial tableau provides a clean and compact notion of a state to which a limited number of inferences can be applied. We start by incorporating a state-of-the-art learning algorithm — a graph neural network (GNN) – into the plCoP theorem prover. Then we use it to observe the system’s behavior in a reinforcement learning setting, i.e., when learning inference guidance from successful Monte-Carlo tree searches on many problems. Despite its better pattern matching capability, the GNN initially performs worse than a simpler previously used learning algorithm. We observe that the simpler algorithm is less confident, i.e., its recommendations have higher entropy. This leads us to explore how the entropy of the inference selection implemented via the neural network influences the proof search. This is related to research in human decision-making under uncertainty, and in particular the probability matching theory. Our main result shows that a proper entropy regularization, i.e., training the GNN not to be overconfident, greatly improves plCoP ’s performance on a large mathematical corpus.
Single image super-resolution based on mapping-vector clustering and nonlinear pixel-reconstruction

Single image super-resolution (SR) aims to estimate high-resolution (HR) images from low-resolution (LR) ones with high reconstruction quality and low time cost. In this paper, we propose a new pixel-wise SR method by combining a classification tree of external samples and a learning stage of nonlinear pixel-reconstruction mapping-kernels. To obtain more reasonable sample sets for mapping-learning, we classify external samples by clustering the mapping-vector of LR-HR patch pairs based on fractional-norm. Then, a decision-tree branched by lightweight networks is learned to choose one reasonable class for each testing LR patch. In our mapping-learning stage, pixel-wise nonlinear mappings are represented as several full connected networks, which can provide satisfying generalization ability for LR patch reconstruction. Therefore, our method can simultaneously guarantee the reconstruction quality and execution speed at the stage of sample representation and mapping. Experiment results demonstrate the effectiveness of our approach on quantitative visual quality assessment and time cost. • To obtain more reasonable sample sets for mapping-learning, we classify external samples by clustering the mapping-vector of LR-HR patch pairs based on fractional-norm. • A decision-tree branched by lightweight networks is learned to choose one reasonable class for each testing LR patch. • In our mapping-learning stage, pixel-wise nonlinear mappings are represented as several full connected networks, which can provide satisfying generalization ability for LR patch reconstruction.
Interpretation on Deep Multimodal Fusion for Diagnostic Classification

Fusion of multimodal imaging data with nonimaging data is critically important for a more complete understanding of the disease characteristics and therefore essential to accurate computer-aided diagnosis. However, there are two major challenges. 1) Effective discovery of the discriminative multimodal information during the fusion process is hindered by the large dimension gap between raw medical images and clinical factors. 2) Interpreting the complex nonlinear cross-modal association, especially in deep-network-based fusion models, remains an unsolved challenge, which is essential for uncovering the disease mechanism. To address the two challenges, we propose an Interpretable Deep Multimodal Fusion (DMFusion) Framework based on Deep Canonical Correlation Analysis (CCA). Specifically, a novel DMFusion loss is proposed to optimize the discovery of discriminative multimodal representations in low-dimensional latent fusion space. It is achieved by jointly exploiting intermodal correlational association via CCA loss and intra-modal structural and discriminative information via reconstruction loss and cross-entropy loss. For interpreting the nonlinear cross-modal association in DMFusion network, we propose a cross-modal association (CA) score to quantify the importance of input features towards the correlated association, by harnessing integrated gradients in deep networks and canonical loading in CCA projection. The proposed fusion framework was validated on the differential diagnosis of demyelinating diseases in Central Nervous System (CNS) and outperformed six state-of-the-art methods on three fusion tasks.
A stepper for a functional JavaScript sublanguage

The first two chapters of the introductory computer science textbook Structure and Interpretation of Computer Programs, JavaScript Adaptation (SICP JS), use a subset of JavaScript called Source §2. The book introduces the reduction-based "substitution model" as a first mental model for the evaluation of Source §2 programs. To support the learner in adopting this mental model, we built an algebraic stepper—a tool for visualizing the evaluation of Source §2 programs according to the model. As a sublanguage of JavaScript, Source §2 differs from other purely functional programming languages by using a statement-oriented syntax, with statement sequences, return statements, and block-scoped declarations. For the purpose of this tool description, we distill these distinguishing features—along with explicit recursion—into a Source §2 sublanguage that we call Source §0, and focus on a stepper for this language. We formalize the substitution model of Source §0 as a lambda-calculus-style reduction semantics that handles explicit recursion by term graph rewriting and faithfully implements the JavaScript specification, when restricted to that language. Our implementation of the stepper represents term graphs by persistent data structures that maximize sharing and enable random access to all steps. This work presents the first reduction-based semantics for a JavaScript sublanguage and the first algebraic stepper for a language with return statements and block-scoped declarations. The tool supports the learner with step-level explanations, redex highlighting, and function-level skipping and can also be used for teaching the applicative-order-reduction lambda calculus.
SDCDet: Robust Remote Sensing Object Detection Based on Instance Segmentation Direction Correction

Aerial and satellite images usually contain complex background interference and dense small objects. The difficulties of remote sensing target detection tasks include complex scenes, large amounts of data, and the arbitrariness of target directions. Differing from the dominant regression-based approaches for orientation estimation, this paper combines example segmentation methods to design a dense small target detection and segmentation model. After passing through the feature extraction module, our method is divided into three branches: (1) Multiple Box Offset Regression Branch: used to perform dense small object frame regression; (2) Discriminative Classification Branch: can obtain more accurate classification results; (3) Segmentation correction branch: used to obtain instance segmentation output and correct the final Bounding box. We conducted training and testing on a large high-resolution remote sensing dataset (DOTA). Compared with the benchmark model, our model can obtain more accurate results.
Semantic Modeling Framework for Mission-Oriented Military Systems and Combat Cloud Control

Based on the characteristics and applications of military systems, we propose a new semantic description method for military systems, as well as a mission dynamic control mechanism. Rather than the "data-centric" research, this paper adopts the "mission-oriented" approach to build the Task Model, which is employed as the basic unit to model military systems and combat cloud control semantics. And then the framework of the Task Model is put forward, as well as the ontology constitution and the relationship among the ontologies. By using the Task Model, this paper explores the approach to dynamic task decomposition, combination, and adjustment. Additionally, this paper proposes semantic description paradigm and scalability methods respectively. These works paved the ways to further research on the interconnection and control of heterogeneous military systems.
An Offensive Approach for Hiding Malicious Payloads in an Image

Steganography is the oldest technique that is been used from century, steganography purpose has not changed, i.e., all these techniques aim at hiding data or protecting data. With the help of steganalysis, the media can be analyzed to check for the presence of any secret information. Nowadays, attackers are making the use of advanced steganography approaches to conceal the secret information and communicate in a stealth manner. In this paper, the authors have discussed about the novel approach to hide malicious payload into image metadata. Therefore, metadata is a data that describes about the image rights and its administration. Hacker generally uses this metadata to perform various malicious attacks such embedding malicious script inside the image metadata and many more.
Salaries in general education in Russian regions

One of the functions of a social state is providing decent wages for labor. This is especially topical for the most important social areas — healthcare and education. In recent years, raising teacher’s salary has become one of the most important issues, but the problem is still far from being resolved. The aim of the work is to analyze the level of teachers’ salaries in municipal schools in 2013–2017 in the RF subjects with different levels of economic development. As the main indicators there were selected nominal average monthly salary of teachers, their salary in relation to the average wage in the region, and their salary in relation to the subsistence minimum of the working age population. On the basis of cluster analysis using the Ward method, there were created cartograms for the first two indicators presenting their dynamics. The analysis shows that in the period under review, the salary of teaching staff grew at a slower pace than the average wage across the country and in the majority of the RF subjects. The level of economic development of a region has a significant impact on the salary of teachers. In financial centers, industrialized and mining regions, work remuneration of teachers grew at a higher rate than in less developed agricultural ones. As a result, the interregional differentiation of teachers’ salaries in general education has increased. This indicates that the state policy aimed at raising salaries in the general education system does not reach its goal, and it is necessary to increase the state funding of general education.
Prophet: Speeding up Distributed DNN Training with Predictable Communication Scheduling

Optimizing performance for Distributed Deep Neural Network (DDNN) training has recently become increasingly compelling, as the DNN model gets complex and the training dataset grows large. While existing works on communication scheduling mostly focus on overlapping the computation and communication to improve DDNN training performance, the GPU and network resources are still under-utilized in DDNN training clusters. To tackle this issue, in this paper, we design and implement a predictable communication scheduling strategy named Prophet to schedule the gradient transfer in an adequate order, with the aim of maximizing the GPU and network resource utilization. Leveraging our observed stepwise pattern of gradient transfer start time, Prophet first uses the monitored network bandwidth and the profiled time interval among gradients to predict the appropriate number of gradients that can be grouped into blocks. Then, these gradient blocks can be transferred one by one to guarantee high utilization of GPU and network resources while ensuring the priority of gradient transfer (i.e., low-priority gradients cannot preempt high-priority gradients in the network transfer). Prophet can make the forward propagation start as early as possible so as to greedily reduce the waiting (idle) time of GPU resources during the DDNN training process. Prototype experiments with representative DNN models trained on Amazon EC2 demonstrate that Prophet can improve the DDNN training performance by up to 40% compared with the state-of-the-art priority-based communication scheduling strategies, yet with negligible runtime performance overhead.
Textual merge based on language-specific syntactic separators

In practice, developers mostly use purely textual, line-based, merge tools. Such tools, however, often report false conflicts. Researchers have then proposed AST-based tools that explore language syntactic structure to reduce false conflicts. Nevertheless, these approaches might negatively impact merge performance, and demand the creation of a new tool for each language. Trying to simulate the behavior of AST-based tools without their drawbacks, this paper proposes and analyzes a purely textual, separator-based, merge tool that aims to simulate AST-based tools by considering programming language syntactic separators, instead of just lines, when comparing and merging changes. The obtained results show that the separator-based textual approach might reduce the number of false conflicts when compared to the line-based approach. The new solution makes room for future studies and hybrid merge tools.
Ship detection in navigation based on broad learning system

Based on the broad learning system(BLS), this paper carries out the detection of ships in navigation. Firstly, grayscale, and histogram equalization are used to preprocess the image to enhance the detail of the image and improve the training accuracy. Secondly, randomly select a part of the photo set as the test set. Finally, BLS is introduced to train and test the data set. Two training methods, back propagation neural network(BPNN) and support vector machine (SVM), are used to compare the proposed method with the navigation ship data set. From the experimental results, it can be seen that the BLS in this paper has a better detection effect in ship detection. By applying the comparison on the face detection data set, the universality of BLS for image recognition is proved.
Website Fingerprinting Attacks Based on Homology Analysis

Website fingerprinting attacks allow attackers to determine the websites that users are linked to, by examining the encrypted traffic between the users and the anonymous network portals. Recent research demonstrated the feasibility of website fingerprinting attacks on Tor anonymous networks with only a few samples. Thus, this paper proposes a novel small-sample website fingerprinting attack method for SSH and Shadowsocks single-agent anonymity network systems, which focuses on analyzing homology relationships between website fingerprinting. Based on the latter, we design a Convolutional Neural Network-Bidirectional Long Short-Term Memory (CNN-BiLSTM) attack classification model that achieves 94.8% and 98.1% accuracy in classifying SSH and Shadowsocks anonymous encrypted traffic, respectively, when only 20 samples per site are available. We also highlight that the CNN-BiLSTM model has significantly better migration capabilities than traditional methods, achieving over 90% accuracy when applied on a new set of monitored sites with only five samples per site. Overall, our experiments demonstrate that CNN-BiLSTM is an efficient, flexible, and robust model for website fingerprinting attack classification.
Modeling and Querying Data in an Ontology-Based Data Access System

The Ontology-Based Data Access (OBDA) systems allows users to access external databases through a conceptual domain view, given in terms of an ontology. This semantic technology addresses such problems as conceptual modeling, query rewriting, an source-to-target mapping. We show how these issues have been defined and implemented in the DAFO system, highlighting those features that distinguish it from the standard OBDA system. We propose an original approach to cope with the trade-off between expressiveness of the ontology-based conceptual modelling and ontology-mediated query rewriting in the DAFO systems. For this purpose, we divide ontological rules into three parts: (a) rewriting rules, (b) rules defining intensional predicates (views), and (c) constraint rules (satisfied in the database). This allows to balance the semantic power of the conceptual modeling and the effciency of query answering in the DAFO system, where queries are based on the faceted query paradigm.