Reviewerly/exHarmony huggingface dataset:
DESIGN OF A SELF-TIMED LOGIC FUNCTIONS GENERATOR

Currently, despite some difficulties, the self-timed technique and the corresponding field of science proposed by D. Muller at the end of the 50s of the XX century continue to develop actively.In the A. Kamensky thesis a significant step was taken towards the fault tolerance of self-timed circuits (2017).However, the synthesis of self-timed universal logic modules has not yet been fully considered.Purpose: development of strictly self-timed generators of logical functions for one, two and three variables.Methods: Self-timed circuitry, synthesis of a self-synchronous multiplexer for two, four and eight channels using EDA for IC Kovcheg.Results: The study synthesized strictly self-timed logic function generators using 2AND-2OR-NOT elements, transition completion indicators, and hysteresis triggers.The proposed elements are almost equivalent to the LUT FPGA gates, however, they are built according to CMOS technology and implement self-timed operation, which allows them to be used in self-timed s base matrix crystals with either constants or additional memory cells configured.The simulation was performed in the EDA for IC Kovcheg system and in the NI Multisim circuit simulation system of the National Instruments Electronics Workbench Group firm and in the Microwind topological modeling system, which confirmed the operability of the proposed elements.Practical relevance: the developed elements can be used as additional universal blocks in the library of self-timed circuitry developed at the IPI RAS for basic matrix crystals produced at the MIET Technological Center.
SRC3: A Video Dataset for Evaluating Domain Mismatch

In this paper we introduce new video datasets to investigate the gaps between synthetic and real imagery in object detection and depth estimation. Currently, synthetic image datasets with real-world counterparts largely focus on computer vision applications for autonomous driving in unconstrained environments. The high scene complexity of such datasets pose challenges for systematic studies of domain disparities. We aim to create a set of paired datasets to study the discrepancies between the two domains in a more controlled setting. To this end, we have created Synthetic-Real Counterpart 3 (SRC3), which contains multiple datasets with varying levels of scene and object complexity. These versatile datasets span multiple environments and consist of ground-truthed, real-world, and synthetic videos generated using a gaming engine. In addition to the dataset, we present an in-depth analysis and provide comparison benchmarks of these datasets using state-of-the-art detection algorithms. Our results show contrasting performance during cross-domain testing due to differences in image quality and statistics, indicating a need for domain adapted datasets and models.
Efficient Translation of Safety LTL to DFA Using Symbolic Automata Learning and Inductive Inference

Safety LTL properties are ubiquitous in the verification of safety critical systems. There is already evidence that translating safety properties into DFA rather than Büchi automata results in faster verification times. Conventional translation strategies can in some cases use unnecessarily large amounts of resources. We develop a symbolic adaptation of the $$L^*$$ active learning algorithm tailored to efficiently translate safety LTL properties into symbolic DFA. We demonstrate how an inductive inference procedure can be used to provide additional input to the algorithm that greatly improves performance for certain important families of properties. For completeness, we also provide an outline and examples of how such a procedure can be implemented. Finally, we compare with state of the art LTL translators and provide experimental evidence where our approach significantly outperforms conventional translation strategies.
Context-aware Event Type Identification Based on Context Fusion and Joint Learning

Automatic Event Type Identification from a text document is a particularly challenging task in event extraction. Consideration of the event triggers' context has been shown effective in this task. However, existing methods suffer from insufficient consideration of the semantic information in the event trigger context and the dependencies between event triggers. To fill this gap, we propose a novel joint learning model called CAED for event type identification. Given a text document, first, CAED carries out event context fusion by sending the sentence embeddings to BiLSTMs for capturing the weighted average of its hidden states and aggregates them using the self-attention mechanism to obtain the document-level context embedding. Then, CAED introduces a dynamic memory vector to record the occurrences of event triggers and their dependencies in each sentence. Finally, CAED concatenates the documentlevel context embedding, the dynamic memory vector, and the pre-trained word embedding into a joint word-level embedding. CAED carries out joint learning by sending the joint word-level embeddings to a LSTM based event type classifier that iteratively uses the dynamic memory vector to achieve Context-aware Event Type Identification. Experiment results on the CEC benchmark dataset and a case study demonstrate the superiority of CAED over six state-of-the-art event type identification models.
Research on Search Intent Prediction for Big Data of National Grid System Standards

Smart grids are becoming more complex due to the development of big data., and technical documents and institutional standards are constantly updated. As a result, It is difficult for workers in different positions to obtain the required information and data. This thesis is oriented towards this problem, and combined with deep learning algorithms to build a user intent prediction model based on the existing knowledge map. By extracting user characteristics and using a dynamic matching algorithm, the purpose of intent prediction is achieved. In this way, the required standards and requirements can be found faster and more directly in the work process, which effectively improves the working efficiency of employees and reduces the difficulty of learning and training.
Comparative Analysis of Text Mining Results With Tf ldf Features and SQL Like Operator in Indonesian News Search

Research on the implementation of text mining uses the TF IDF method to be used in the Information retrieval / Indonesian news search feature. The dataset used was sourced from NewsAPI and built a Codeigniter based website named "News Plus Six Dua". This study also uses the Vertor Space Model (VSM) method to overcome the weaknesses of the TF IDF method at the time of the sorting process. The results of this study explain that the search by the TF IDF method has higher accuracy when compared to SQL like operators. TF IDF produces a percentage of precision 100% and recall (sensitivity) 66.7% on searches with the keyword "Indonesian soccer schedule" while SQL like operators do not display search results or equal to 0%. But the TF IDF method has the disadvantage of running slower than SQL like operators. This has been tested using either the number of words or terms entered, the number of datasets, and the location of access. At the location of access, access via hosting is monitored faster when compared via localhost.
Observation Points-Based Particle Swarm Optimization Algorithm

Particle swarm optimization is a heuristic random optimization algorithm, in which each particle pursues its own optimal particle and the global optimal position. In the random search process of particle swarm optimization, these particles will eventually converge to the population optimal particle. The algorithm has the shortcoming of premature convergence because it is a stochastic approximate optimization algorithm. In this paper, the concept of random observation points is defined. Based on the standard PSO algorithm, a particle swarm optimization algorithm based on random observation points (OBPSO) which replaces the particles in the original particle swarm optimization by setting random observation points in each algorithm iteration is proposed. By comparing the experimental results of this algorithm with those of standard PSO and other variants on benchmark functions, OBPSO algorithm has the advantages of avoiding premature convergence and improving the global search ability of standard PSO algorithm, which effectively improves the search performance of standard PSO algorithm.
Supervised Deep Second-Order Covariance Hashing for Image Retrieval

Recently, deep hashing methods play a pivotal role in image retrieval tasks by combining advanced convolutional neural networks (CNNs) with efficient hashing. Meanwhile, second-order representations of deep convolutional activations have been established to effectively improve network performance in various computer vision applications. In this work, to obtain more compact hash codes, we propose a supervised deep second-order covariance hashing (SDSoCH) method by combining deep hashing with second-order statistic model. SDSoCH utilizes a powerful covariance pooling to model the second-order statistics of convolutional features, which is naturally integrated into the existing point-wise hashing network in an end-to-end manner. The embedded covariance pooling operation well captures the interaction of convolutional features and produces global feature representations with more discriminant capability, leading to the more informative hash codes. Extensive experiments conducted on two benchmarks demonstrate that the proposed SDSoCH outperforms its first-order counterparts and achieves superior retrieval performance.
Morphology for grey-scale images

The mathematical morphology of grey-level images has many special features, which are the subject of this chapter. A general procedure for proceeding from binary to grey-level morphology was presented in an earlier chapter. Here, each aspect of the morphological analysis of grey-level images is examined in depth.
The Big Data Approach Using Bio-Inspired Algorithms: Data Imputation

In this chapter, the concept of big data is defined based on the five characteristics namely velocity, volume, value, veracity, and variety. Once defined, the sequential phases of big data are denoted, namely data cleansing, data mining, and visualization. Each phase consists of several sub-phases or steps. These steps are briefly described. In order to manipulate data, a number of methods may be employed. In this chapter, we look at an approach for data imputation or the extrapolation of missing values in data. The concept of genetic algorithms along with its off-shoot, meta-heuristic algorithms, is presented. A specialized type of meta-heuristic algorithm, bio-inspired algorithms, is introduced with several example algorithms. An example, a bio-inspired algorithm, the kestrel, is introduced using the steps outlined for the development of a bio-inspired algorithm (Zang et al. ). This kestrel algorithm will be used as an approach for data imputation within the big data phases framework.
Closed-Form Distance Estimators under Kalman Filtering Framework with Application to Object Tracking

In this paper, the minimum mean square error (MMSE) estimation problem for calculation of distances between two signals via the Kalman filtering framework is considered. The developed algorithm includes two stages: the Kalman estimate of a state vector computed at the first stage is nonlinearly transformed at the second stage based on a distance function and the MMSE criterion. In general, the most challenging aspect of application of the distance estimator is calculation of the multivariate Gaussian integral. However, it can be successfully overcome for the specific metrics between two points in line, between point and line, between point and plane, and others. In these cases, the MMSE estimator is defined by an analytical closed-form expression. We derive the exact closed-form bilinear and quadratic MMSE estimators that can be effectively applied for calculation of an inner product, squared norm, and Euclidean distance. A novel low-complexity suboptimal estimator for special composite functions of linear, bilinear, and quadratic forms is proposed. Radar range-angle responses are described by the functions. The proposed estimators are validated through a series of experiments using real models and metrics. Experimental results show that the MMSE estimators outperform existing estimators that calculate distance and angle in nonoptimal manner.
FCDnet

Image denoising is to estimate a latent clean image from the noisy image. Existing denoising algorithms generally neglect smooth edges (missing details) while removing noises. In order to solve this problem, we propose an image denoising algorithm called fusion canny-edge operator image denoising based on CNN (FCDnet), which is composed of a denoising module based on Convolutional neural network (CNN), a canny edge module based on canny operator and a fusion module based on residual block. In addition, the edge extracted by canny edge extraction module is fused with the denoised image extracted by the denoising module to get a clearer and more detailed image. Experimental results show that the proposed algorithm obtains higher PSNR with more edge details and textures features than state-of-the-art methods on multiple datasets, i.e., Set5, Set14 and McMaster.
Zero-shot Learning with Cross-Layer Neural Network for Emitter Pattern Recognition

The existing emitter pattern recognition methods depend on a large number of labeled samples and are unable to handle unknown samples. Zero-shot Learning (ZSL) can migrate from source classes to target categories by learning a common embedding space, thus realizing the generalization to unknown samples. In this paper, a novel Cross-Layer Neural Network (CLNN) is proposed that integrates different embedding methods into an end-to-end deep learning architecture. The experimental results demonstrate that the proposed method can achieve excellent performances in the presence of unseen radar patterns with either new feature combinations or new feature ranges.
Design of Target Behavior Intelligent Analysis System Based on Distributed Computing

Through the fusion processing of target data obtained by radar, electronic reconnaissance, space reconnaissance, technical reconnaissance and civil cooperative system in early warning and surveillance system, multidimensional target data including explicit state information such as target attributes, type, position, speed and course could be obtained. The latent potential information, such as regular behavior, intention and threat degree, need to be mastered by human intelligence and experience analysis. It is urgent to use big data mining technology to realize intelligent analysis of target behavior. Based on the analysis of big data related technologies, this paper designs an intelligent target behavior analysis system based on distributed computing, which provides theoretical and technical support for intelligent upgrading of intelligence processing systems and command decision systems.
A model-theoretic approach to descriptive general frames: the van Benthem characterization theorem

Abstract The celebrated van Benthem characterization theorem states that on Kripke structures modal logic is the bisimulation-invariant fragment of first-order logic. In this paper, we prove an analogue of the van Benthem characterization theorem for models based on descriptive general frames. This is an important class of general frames for which every modal logic is complete. These frames can be represented as Stone spaces equipped with a ‘continuous’ binary relation. The proof of our theorem generalizes Rosen’s proof of the van Benthem theorem for finite frames and uses as an essential technique a new notion of descriptive unravelling. We also develop a basic model theory for descriptive general frames and show that in many ways it behaves like the model theory of finite structures. In particular, we prove the failure of the compactness theorem, of the Beth definability theorem, of the Craig interpolation theorem and of the upward Löwenheim–Skolem theorem.1
SERIES

Requirement defects are one of the major sources of failure in any software development process, and the main objective of this chapter is to make requirement analysis phase exhaustive by estimating risk at requirement level by analyzing requirement defect and requirement inter-relationships as early as possible to using domain modeling to inhibit them from being incorporated in design and implementation. To achieve this objective, this chapter proposes a tool to assist software developers in assessing risk at requirement level. The proposed tool, software risk estimator, SERIES in short, helps in early identification of potential risk where preventive actions can be undertaken to mitigate risk and corrective actions to avoid project failure in collaborative manner. The entire process has been supported by a software case study. The results of the proposed work are promising and will help software engineers in ensuring that all business requirements are captured correctly with clear vision and scope.
Multi-turn response selection with multi-level granularity representations and 3D convolutional neural network

Building an intelligent chatbot has always been a difficult point in the research of artificial intelligence. Multi-turn response selection is one of the most important tasks in the retrieval-based chatbots researches. The challenges of the task are how to build relationships among the utterances in context and how to identify key information in the context. To address the problems, we propose a model with multi-level granularity representations and 3D convolutional neural network (MR3DCNN). MR3DCNN first use word embedding and Bi-directional Gated Recurrent Unit (BiGRU) to create multi-level granularity representations for each utterance and response. Then MR3DCNN matches a response with each utterance at multi-level granularity representations that mentioned before to construct similarity matrices. We combine these similarity matrices into a 3D matching cube. Utilizing a 3D convolutional neural network(3DCNN), our model distills important matching features from the cube and build the relationships among the utterances. Sending the matching features to a logit model, we get the final matching score. Our experimental results demonstrate MR3DCNN is an effective approach to solve the difficulties of multi-turn response selection.
Quantification of Unextendible Entanglement and Its Applications in Entanglement Distillation

The unextendibility or monogamy of entangled states is a key property of quantum entanglement. Unlike conventional ways of expressing entanglement monogamy via entanglement measure inequalities, we develop a state-dependent resource theory to quantify the unextendibility of bipartite entangled states. First, we introduce a family of entanglement measures called unextendible entanglement. Given a bipartite state ρ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">AB</sub> , the key idea behind these measures is to minimize a divergence between ρ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">AB</sub> and any possibly reduced state ρ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">AB</sub> ' of an extension ρ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ABB</sub> ' of ρ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">AB</sub> . These measures are intuitively motivated by the fact that the more a bipartite state is entangled, the less each of its individual systems can be entangled with a third party. Second, we show that the unextendible entanglement is an entanglement monotone under two-extendible operations, which include local operations and one-way classical communication as a special case. Unextendible entanglement has several other desirable properties, including normalization and faithfulness. As applications, we show that the unextendible entanglement provides efficiently computable benchmarks for the rate of perfect entanglement distillation, as well as for the overhead of entanglement distillation.
Affordable Security or Big Guy vs Small Guy

When we design a security protocol we assume that the humans (or organizations) playing Alice and Bob do not make a difference. In particular, their financial capacity seems to be irrelevant. In the latest trend to guarantee that secure multi-party computation protocols are fair and not vulnerable to malicious aborts, a slate of protocols has been proposed based on penalty mechanisms. We look at two well-known penalty mechanisms, and show that the so-called see-saw mechanism (Kumaresan et al., CCS 15), is only fit for people with deep pockets, well beyond the stake in the multi-party computation itself. Depending on the scheme, fairness is not affordable by everyone which has several policy implications on protocol design. To explicitly capture the above issues, we introduce a new property called financial fairness.
An Efficient Model Compression Method of Pruning for Object Detection

In this paper, we propose an efficient model compression method for object detection network. The key to this method is that we combine pruning and training into a single process. This design benefits in two aspects. First, we have a full control on pruning of convolution kernel, which ensures the model's accuracy to maximum extent. Second, compared with previous works, we overlap pruning with the training process instead of waiting for the model to be trained before pruning. In such a way, we can directly get a compressed model that is ready to use once training finished. We took experiments based on SSD(Single Shot MultiBox Detector) for verification. Firstly, when compressing the ssd300 model with dataset of Pascal VOC, we got model compression of 7.7X while the model accuracy only drops by 1.8%. Then on the COCO dataset, under the premise that the accuracy of the model remains unchanged, we got the model compressed by 2.8X.
MQWAGS

With the rapid development of digital technology, from the application of traditional databases and scientific computing to the emerging cloud computing services, the analysis, and processing of massive data has become the focus of society. Providing low-cost, scalable, and configurable shared cloud services to users on cloud service platforms is a new hotspot for the development of major cloud service providers. Job scheduling plays an important role in improving the overall system performance of cloud service capabilities. Simple job scheduling strategies (such as Fair and FIFO scheduling) do not consider job size and may degrade performance when jobs of different sizes arrive. This paper proposes the MQWAG (Multi-Queue Load-Sensitive Greedy Scheduling Algorithm) job scheduling algorithm to reorder multi-queue jobs so that short jobs are executed preferentially in multiple queues. In our experiments, our algorithm shortened the average job completion time by about 26% compared with other algorithms.