Reviewerly/exHarmony huggingface dataset:
DETERMINING THE SEMANTIC PROXIMITY OF NEWS MESSAGES BASED ON TITLES ANALYSIS

The paper is devoted to the peculiarities of using data from unstructured sources, such as social networks, online media, etc. for the development of management decisions. The issue of analyzing such sources in the process of developing and making managerial decisions in the socio-economic sphere is considered. The difficulties of processing unstructured data are described, such as the impossibility of fully automatic evaluation of the semantics of data, the presence of a large amount of duplicate information. Various approaches to determining ratings and various metrics of sources and messages are described. The problem of identifying duplicate messages is considered using the example of online media both by full texts and by titles. Under the duplicate of a news message in this context refers a repetition of a significant amount of information from it in another article. If the text of a news message (article) is not repeated verbatim, it is impossible to determine the degree of duplication of the meaning of the article without involving human expertise. There are various metrics for assessing the similarity (semantic proximity) of textual information that can help in this matter. Some of them are described in the article. An adaptation of the Word Mover Distance method for the Russian language is proposed and the Word2Vec model is trained for its use. A hybrid approach to identifying and eliminating duplicate messages as part of the preprocessing of unstructured data in the context of managerial decision-making is proposed. According to the results of the experiments, depending on the chosen method, it was possible to automatically determine based on the publication time and the analysis of titles from 43 to 74 % duplicates.
An improved firefly algorithm with distance-guided selection strategy and its application

In this paper, to reduce the redundant attractions and incorrect directions of firefly algorithm (FA), a distance-guided selection approach (DSFA) is proposed, which consists of a distance-guided mechanism and selection strategy. Where the designed distance-guided mechanism reduces the attractions and plays as a classifier for global search and local search, the suggested selection strategy can avoid local search falling into traps, thereby increasing the probability of correct direction. With the good cooperation of these two approaches, DSFA obtains a good balance of exploration and exploitation. To confirm the performance of the proposed algorithm, excessive experiments are conducted on CEC2013 benchmark functions, large-scale optimization problems CEC2008, and software defect prediction (SDP). In the comparison with the 5 advanced FA variants, DSFA provides the optimal solutions to most CEC2013 problems. Besides, when facing the problems of class imbalance and the dimensional explosion of datasets, DSFA greatly improves the performance of machine learning classifiers employed by SDP. It can be concluded that DSFA is an effective method for global continuous optimization problems.
Image Steganography Based on DNA Encoding and Bayer Pattern

Steganography is a method of concealing sensitive data while transmitting it over public networks and it is regarded as one of the invisible security techniques. This paper proposed a new method to encrypt and hide a 24-bit color secret image within a larger 24-bit color cover image using the Bayer pattern principle and LSB algorithm. The secret image goes through a series of special steps of DNA encryption. Then hides to ensure more confidentiality and be more difficult for the attackers’ attempts. In this method, each pixel in the secret image is hidden in a 3 × 3 block from the cover image except the center of the block that is used as a public key. These blocks randomly select from the rows and columns of the cover images. The secret image pixels and the center selected block pixels are encrypted in a special method using DNA rules. Then, encoding the resulted encrypted image and encrypted center blocks selected pixels using the DNA-XOR operation. Finally, hiding the resulted image pixels using the Least Significant Bit (LSB) algorithm with locations determined by a modified Bayer pattern. The experimental results show the efficiency of the proposed method to produce a high-quality stego image and the results are acceptable when applying the Structural Similarity Index Metric (SSIM) and Peak Signal to Noise Ratio (PSNR) quality metrics and compared with other method.
Use energy and sunlight to produce light and heat water

Due to air pollution and its effects on the environment and fuel price, a method is recommended here that can heat both the space of the relevant place and the hot water used to heat or cool the facility. Install plastic magnifying glasses with dimensions of one meter by one meter on the roof of the desired place to the required area for purifying seawater. Using a transparent plastic pipe network, both the light of the structure is provided, and the water inside the pipe network is heated, which in winter could heat the radiators. In summer, it could warm up the fluid detergent in the pipe, which cools up the fluid inside; the tube is conveyed to a blower to spread the cool air; in this method, the architecture and construction of structures are determined according to the environment and climate of the area and the amount of energy needed, which saves energy and preserves the environment.
The Annual Quiz [Microwave Surfing]

Various puzzles, quizzes, games, humorous definitions, or mathematical that should engage the interest of readers.
Towards Temporally Uncertain Explainable AI Planning

Automated planning is able to handle increasingly complex applications, but can produce unsatisfactory results when the goal and metric provided in its model does not match the actual expectation and preference of those using the tool. This can be ameliorated by including methods for explainable planning (XAIP), to reveal the reasons for the automated planner’s decisions and to provide more in-depth interaction with the planner. In this paper we describe at a high-level two recent pieces of work in XAIP. First, plan exploration through model restriction, in which contrastive questions are used to build a tree of solutions to a planning problem. Through a dialogue with the system the user better understands the underlying problem and the choices made by the automated planner. Second, strong controllability analysis of probabilistic temporal networks through solving a joint chance constrained optimisation problem. The result of the analysis is a Pareto optimal front that illustrates the trade-offs between costs and risk for a given plan. We also present a short discussion on the limitations of these methods and how they might be usefully combined.
Secure Digital Signature Validated by Ambient User’s Wi-Fi-enabled devices

In cyberspace, a digital signature is a mathematical technique that plays a significant role, especially in validating the authenticity of digital messages, emails, or documents. Furthermore, the digital signature mechanism allows the recipient to trust the authenticity of the received message that is coming from the said sender and that the message was not altered in transit. Moreover, a digital signature provides a solution to the problems of tampering and impersonation in digital communications. In a real-life example, it is equivalent to a handwritten signature or stamp seal, but it offers more security. This paper proposes a scheme to enable users to digitally sign their communications by validating their identity through users&#x2019; mobile devices. This is done by utilizing the user&#x2019;s ambient Wi-Fi-enabled devices. Moreover, the proposed scheme depends on something that a user possesses (i.e., Wi-Fi-enabled devices), and something that is in the user&#x2019;s environment (i.e., ambient Wi-Fi access points) where the validation process is implemented, in a way that requires no effort from users and removes the "weak link" from the validation process. The proposed scheme was experimentally examined.
LAFS: Learning-Based Application-Agnostic Flow Scheduling for Datacenters

Many cloud applications in modern datacenters have very demanding latency requirements, making flow completion time (FCT) an important metric for evaluating the network performance. Existing network flow scheduling methods either base on pre-known information or have poor performance. Therefore, we present LAFS, an efficient learning-based flow scheduling approach which minimizes the FCT with estimated information of flows. LAFS combines system call monitoring and learning methods to learn the flow size and implements the Shortest Remaining Processing Time (SRPT) principle with in-network priorities. Moreover, LAFS adopts flowlets to alleviate the packets disorder problem in fine-grained flow scheduling. Our theoretical analysis and extensive simulations show that LAFS is a practical design and significantly outperforms other information-agnostic designs like DCTCP and PIAS under diverse workloads.
BenchSubset: A framework for selecting benchmark subsets based on consensus clustering

The redundancy in the benchmark suite will increase the time for computer system performance evaluation and simulation. The most typical method to solve this problem is to select subsets based on clustering. However, it is a challenge to validate benchmark subsetting results for unlabeled benchmark suites when using the clustering method, and existing research has not considered this problem. Also, there is no quantitative evaluation method for subsetting which can reflect the universal and the diversity characteristics of the benchmark suite at the same time. To solve the above problems, we propose BenchSubset, a framework for selecting benchmark subsets based on consensus clustering, which includes Group Principal Components Analysis, consensus clustering, and a new evaluation method considering the universal and the diversity characteristics of the benchmark suite. We conducted SPEC CPU2017 subsetting experiments on Huawei's Taishan 200, then verified the effectiveness of BenchSubset in selecting a benchmark subset. Compared with the mainstream principal components analysis with hierarchical clustering (PCA-H) method, the benchmark subset selected by BenchSubset performs better in representing the universal and the diversity characteristics of SPEC CPU2017.
Introduction

Two important areas of mathematics are related by modeling purposes: Biomathematics and Fuzzy Sets Theory. Both have a point in common in their history of development: a respected background in formal theory and a wide range of applications, both attained in the twentieth century. That is, both areas are new scientific tendencies in comparison to those mathematical areas with centuries of discussion and establishment.
Manifold Discriminative Transfer Learning for Unsupervised Domain Adaptation

This paper proposes manifold discriminative transfer learning (MDTL) for traditional unsupervised domain adaptation. It first utilizes manifold subspace learning to reconstruct the original data in both source and target domains, which can reduce the domains shifts, then performs simultaneously structural risk minimization, discriminative class level alignment, and manifold regularization for transfer learning. More specifically, it minimizes the intra-class distribution discrepancy to improve the domain transferability, maximizes the inter-class distribution discrepancy to improve the class discriminability, and also maintains geometrical structures of the data samples. Remarkably, MDTL is a traditional transfer learning approach and it has a closed-form solution, so the computational cost is low. Extensive experiments showed that MDTL outperforms several state-of-the-art traditional domain adaptation approaches.
Boosting Exploration of Low-Dimensional Game Spaces with Stale Human Demonstrations

Automated game exploration methods benefit from having human demonstration data, but this kind of data is not available for each incremental build of a videogame. If we want to make use of exploration inside of continuous integration (CI) workflows, we need to leverage stale human data (from a recent version of the game). In this paper, we show how to train a goal-conditioned action policy from stale human data used in the context of RRT-based exploration of a modestly changed game version. We demonstrate the benefit of this transfer with experiments in the MiniGrid environment (which has a three-dimensional agent configuration space).
A Study on the Word Usage of L2 Korean Learners through Text Mining - Based on Analysis of Spoken Corpus -

This study aims to examine a spoken corpus of Korean learners based on each learner's proficiency and nationality to identify the relational networks and main topics of core vocabulary and to confirm that Korean language education is being conducted appropriately in accordance with the grade goals suggested in the international Korean standard curriculum. To this end, core vocabulary was extracted using text mining techniques and data structures and common topics were found and then visualized and analyzed as relational networks. Text mining found that most Korean learners, other than advanced learners, use vocabulary according to proficiency and use vocabulary differently depending on nationality. These results demonstrate the need to provide vocabulary education to advanced learners according to their proficiency and show learners' purposes for learning Korean and values according to their nationality.
Category Map Guided Ordinal Depth Prediction for 3D Human Pose Estimation

In this paper, we propose a two-stage method to estimate 3D human pose, which focuses on the uncertainty of lifting 2D detected pose to 3D pose. Firstly, a novel category map is introduced to predict the ordinal depth category which depicts three kinds of depth ordering relationship for linked joints. Compared with the common probability of vector, our category map can provide better association between prediction with image appearance, and lead to a higher classification accuracy. Secondly, taking predicted 2D pose and ordinal depth category as input, we put forward a temporal convolution network to regress 3D pose, which exploits the temporal context to alleviate the 2D-to-3D uncertainty and reduce prediction errors rate from single image further. Experimental results show that our method can outperform promising results on several benchmarks.
FCSR connection integers: efficient generation, testing and counting

The period of a binary sequence generated by feedback with carry shift register (FCSR) depends on the connection integer (q) of the register. We define FCSR primes as primes generating maximum possible period sequences when used as connection integer of an FCSR. Hence, to design good FCSR pseudorandom generators, one needs suitable feedback primes. The authors of this article study some properties about these primes and give algorithms to generate some of them. The first algorithm is somewhat straightforward and is based on the criterion given in Arnault and Berger. We propose two new algorithms for the efficient generation of FCSR primes. Comparison between these algorithms, supported by experimental and analytical results is presented. Further, we address the question of quantification of such primes and derive some explicit bounds.
Importance and Usefulness of The Forensic Audit: A Critical Review

The article aims to highlight the importance and usefulness of forensic audits. The methodology employed for the study is a systematic review of the literature. The study findings revealed that forensic audit plays a critical role in minimizing and preventing financial fraud. The forensic audit has established itself as a strategic tool for the success of the business.
A Novel Sequential Quadratic Programming Based Material Allocation Optimization Algorithm

Material allocation is a nonlinear optimization problem. For this problem, a quadratic programming based material allocation optimization algorithm is proposed. In the proposed algorithm, firstly, the objective function of support effectiveness and its approximate model of quadratic programming are constructed with target importance and material requirements. Secondly, by using SQP (sequential quadratic programming), the proposed algorithm quickly converge to the optimal solution. For the initial allocation, selective rounding and iterative adjustment according to the material requirements is implemented, and then obtaining the final allocation. Simulation results show that through combing the target parameters and actual requirements constrains, the proposed algorithm can realize reasonable, balanced and fast allocation.
Application Discuss of Block Chain Technology in Power Consumption Information Power consumption information acquisition system

Blockchain is a kind of distributed storage technology based on cryptography, P2P communication and consensus mechanism. It has been widely developed in various fields because of its non-tampering, open and transparent, decentralized and other characteristics. The application of this technology can deal with the difficult problems such as storage trustworthiness and traceability in power information collection system. This paper firstly analyzes the current situation of power consumption information power consumption information acquisition system, and interprets the blockchain technology in detail, then proposes an intelligent terminal integration scheme based on blockchain technology to realize the integration of acquisition terminal and blockchain technology, finally analyzes the application of this technology in power consumption information power consumption information acquisition system, and prospects the application of blockchain technology.
Boosting Boundary Representation for Gland Instance Segmentation

Accurate and automated gland instance segmentation on histology images can assist pathologists to analyze the malignancy degree of adenocarcinoma. Recently, deep-learning-based segmentation networks have been significantly developed to achieve this goal. However, the gland instances are generally proximate to each other and have indiscernible boundaries (i.e., homogeneous intensity values). Most of the existed networks do not define discriminative boundaries representation as context information, resulting in segmenting proximate instances incorrectly. In this paper, to improve the segmentation accuracy between proximate instances, we propose a Boundary Definition Module to boost boundaries feature representation by the guidance of the intra-and-extra glandular features. Moreover, we propose to use the Gumbel-Softmax distribution estimator to clarify the final prediction of boundaries further. Finally, we embed the Boundary Definition Module and Gumbel-Softmax distribution estimator into the gland instance network(FullNet) for performance verification. Experiments on the 2015 MICCAI Gland Segmentation Challenge dataset demonstrate that our proposed method achieves state-of-the-art performance.
A Physically-Informed Deep-Learning Model Using Time-Reversal for Locating a Source from Sparse and Highly Noisy Sensors Data

We approximate the underwater acoustic wave problem for locating sourcesin that medium. We create a time dependent synthetic data-set of sensorsrecorded pressures, based on a small set of sensors placed in the domain, andperturb this data with high random multiplicative noise. We propose a method,based on physically-informed neural-network and time-reversal, for approximatingthe source location even in the presence of high sensors noise.
Design of Warning System Based on Knowledge Graph

Knowledge graph is the current main method of knowledge representation in the field of AI. It takes entities as the central node, connects with attribute nodes through edges, and expresses the real-world entities and the relationship between entities in the form of graphs. Google proposed the conception of knowledge graph in 2012; it has been applied in many fields. Based on the entity nodes and attribute nodes in the knowledge graph, we propose the addition of event nodes to construct a domain knowledge graph. The new knowledge graph contains warning events nodes as well as response event nodes, and based on the knowledge graph an early warning system was builded.
Effect of Kernel in Support Vector Regression Method to Predict Surabaya Consumer Price Index Trend

Staple Food is a necessity that is needed by the people of Indonesia. This is very sensitive when there is an increase or decrease in the price of basic foodstuffs. In the calculations at the cooperative and trade industry offices, some constants are used as benchmarks to assess the increase and decrease in prices of basic foodstuffs, namely the CPI (Consumer Price Index).Support vector regression (SVR) is one of the appropriate methods to solve regression problems based on the concept of maximizing the hyperplane to obtain data that is a support vector. The selection of this method is based on the level of MSE (mean square error) which is lower than the neural network method. As a test material, a sample dataset of staple food prices for the past 5 years was used. Where for training data using a sample of 2013-2014 with a total of 24 records, and testing data using a sample of 2015-2016 as many as 24 records.In this study, the researcher tried to apply multiple kernels in the Support Vector Regression method to predict the CPI (Consumer Price Index). The result of this research is like this.SPLine and Polynomial have less MSE than other kernels it means These two kernels have good usage when use in time-series data.
The Development of Cloud Computing Promotes the Research and Design of Network Teaching System

Cloud computing is a kind of computer technology that takes the Internet as the center, public standards, and services as the basis and provides secure and reliable computing services. Based on the research on the concept and service level of cloud computing, this paper applies cloud computing to the network teaching platform of colleges and universities, puts forward the implementation plan of this application, and analyzes the positive impact of the combination of cloud computing and education on education informatization ring.
A Comparative Study of Local and Global Feature Detector and Descriptors for Image and Video Retrieval

In this paper, we are studying about two leading approaches. Step one, we will define the local features detection and description method for classifying pattern recognition, image tracking, clustering object recognition, or identification images based on local image patches or key points in the images of an object. Secondly, we can define global feature detection and description methods for an image that are used in image retrieval and object detection for a whole image. Local and global feature methods are comprehensive, and their output is compared using a dataset. The images were selected according to the hypothesis that they could be better described using global features. Selecting the algorithms, that all depends on the feature’s detection and description for the data. This led to a detailed review of local and global video or image detectors and new descriptors. We presented a summary of existing performance evaluation and databases of benchmarks. Lastly, in terms of future directions, we have completed the investigation. This research study may assist and provide a framework for image recognition/processing and machine learning.