Reviewerly/exHarmony huggingface dataset:
Empirical Study of the Geoecological State of Selected Landscapes in North-Central Bulgaria

The chapter presents the results of field research in the central segments of Stara Planina Mountain, the Predbalkan region and the Danube Plain. The study of the geoecological state was based on semi-stationary and field observations made during the summer seasons of three consecutive years. The main objective of the research was to detect and map the contemporary geoecological problems in the area. This objective has been achieved through preliminary study of literature and web-based sources, and has been verified through field studies. Cases of negative impact of human activity—the main cause of the deteriorated geoecological state of the nature-territorial complexes—were detected in some of the preliminary selected “hot spots”, as well as in zones that were supposed to be relatively unspoiled, or zones of insignificant human intervention. Examples of such zones in the studied area are the unregulated clearing in the vicinity of “Pleven” hut, as well as the existing illegal landfill along the river passing by the Keryatsite hamlet of the Boazat village.
Cross Aggregation of Multi-head Attention for Neural Machine Translation

Transformer based encoder has been the state-of-the-art model for the latest neural machine translation, which relies on the key design called self-attention. Multi-head attention of self-attention network (SAN) plays a significant role in extracting information of the given input from different subspaces among each pair of tokens. However, that information captured by each token on a specific head, which is explicitly represented by the attention weights, is independent from other heads and tokens, which means it does not take the global structure into account. Besides, since SAN does not apply an RNN-like network structure, its ability of modeling relative position and sequential information is weakened. In this paper, we propose a method named Cross Aggregation with an iterative routing-by-agreement algorithm to alleviate these problems. Experimental results on the machine translation task show that our method help the model outperform the strong Transformer baseline significantly.
Integration of Feature Selection with Data Level Approach for Software Defect Prediction

The dataset of software metrics in general are not balanced (unbalanced).An imbalance distribution of classes and attributes that are not relevant may decrease the performance of the model prediction software defect, because the majority of the class predictions tend to produce than minority class. This research uses a public dataset from NASA (National Aeronautics and Space Administration) MDP (Metrics Data Program) repository. This research aims to reduce the influence of class imbalance in the dataset, so that performance can be improved in the classification of defect prediction software. The model proposed in this research is applying the technique feature selection with particle swarm optimization (PSO), approaches the level of data using Random Under Sampling (RUS) and SMOTE (Synthetic Minority Over-sampling Technique) and (ensemble) Bagging with Naive Bayes Classifier. Research results show that the proposed model can improve the performance of naive bayes of the overall value of the AUC reached &gt; 0.8. Statistical tests indicate that there is a significant difference between a naive bayes model with the model proposed by the p value (0.043) smaller than the alpha values (0.05) which means there is a significant difference between the two models.
Information support of agricultural sciences

The Central Scientific Agricultural Library’s system of the agrarian science information support is examined. The Library strives to make the content available online to the maximum. Most of its information services are offered both in conventional and online formats. For the recent 26 years the Library’s products have been being generated in digital formats and are accessible via the Library’s www-site in a varying degree. The www-site serves as a billboard promoting the Library’s events and activities, as well as the reading environment for the users retrieving information. The system of agrarian researcher awareness-building includes alert informing, current informing, and selective dissemination of information. The main information source is the CSAL collection comprising above 3 millions of depository items. On this basis of information products are generated: bibliographic, abstract and full-text databases, bibliographic and abstract publications, and electronic libraries. The key information product is the AGROS database comprising information on books and articles in 42 languages of the world of the total volume of over 1.95 million records. The database provides efficient identifying and subject-specific search owing to target- developed information retrieval languages: the information retrieval thesaurus in agriculture and foods, RUSMARC format. The authors conclude that agrarian science information support comprises diverse information products; well-developed awareness-building system enables to receive information in print and digital formats, online or in the Library’s physical premises, independently or through librarian’s assistance.
Sparse and online null proximal discriminant analysis for one class learning in large-scale datasets

Recently, null proximal discriminant analysis (NPDA) has been introduced in [1] to identify anomalies in a target data set. However, the proposed algorithm involves high computational burden and memory requirement which limit its use to moderate sized static datasets. The goal of this paper is to propose an incremental and sparse NPDA which is able both to process sequentially a large volume of data and remove the redundant information. More precisely, our solution lies in computing the null projection direction by a recursive singular value decomposition in the kernel induced feature space. Sparsity of the solution is achieved by thresholding the null score between the already processed data and newly injected data. The combined action of these two steps introduces a significant gain in terms of time complexity and memory burden while offering comparable classification performance as the batch method. Experimental results on large-scale data sets confirm the effectiveness of the proposed algorithm.
A Robust Face Recognition Method for Occluded and Low-Resolution Images

Face images that appear in multimedia applications, such as digital entertainments usually exhibit dramatic nonuniform illumination, occlusions, low-resolution, and pose/expression variations that result in substantial performance degradation for traditional face recognition algorithms. Recent research is focused to develop robust face recognition algorithms to solve the aforementioned issues with maximum effort to mimic the human vision system. This paper presents a near real-time and novel face recognition method to recognize the occluded and low-resolution face images. Proposed face recognition algorithm initially uses 68 points to locate a face in the input image. Meanwhile, the adaptive boosting and Linear Discriminant Analysis (LDA) are used to extract face features. In the final stage, classic nearest centre classifier is used for face classification. Detailed experiments are performed on two publicly available LFW and the AR databases. Simulation results reveal that the proposed method outperforms recent state-of-the-art face recognition algorithms by producing high recognition rate.
Encoding of a Chaotic Attractor in a Reservoir Computer: A Directional Fiber Investigation

In this work, we study the dynamical properties of a machine learning technique called reservoir computing in order to gain insight into how representations of chaotic signals are encoded through learning. We train the reservoir on individual chaotic Lorenz signals. The Lorenz system is characterized by a set of equations and known to have three fixed points, all of which are unstable in the chaotic regime of the strange attractor. Exploration of the fixed points of the reservoir whose outputs are trained allows us to understand whether inherent Lorenz dynamics are transposed onto reservoir dynamics during learning. We do so by using a novel fixed point finding technique called directional fibers. Directional fibers are mathematical objects that systematically locate fixed points in a high dimensional space, and are found to be competitive and complementary with other traditional approaches. We find that the reservoir, after training of output weights, contains a higher dimensional projection of the Lorenz fixed points with matching stability, even though the training data did not include the fixed points. This tells us that the reservoir does indeed learn dynamical properties of the Lorenz attractor. We also find that the directional fiber also identifies additional fixed points in the reservoir space outside the projected Lorenz attractor region; these amplify perturbations during prediction and play a role in failure of long-term time series prediction.
An Experimental Study of Multi-Layer Multi-Valued Neural Network

Complex numbers are used to represent data in many practical applications such as in telecommunications, image processing, and speech recognition. In this work, we examine the efficiency of complex-valued neural networks and compare that with their real-valued counterpart. Specifically, we examine the performance of neural network with Multi Layer Multi-Valued Neuron (MLMVN) for classification on several benchmark datasets such as Iris and MNIST datasets. It is shown that in applications where complex numbers occur naturally, complex-valued neural networks such as MLMVN network could offer advantages such as more efficient embedding and processing of information over their real-valued counterparts. It is also observed that complex-valued neural networks have a tendency of overfitting especially in applications involving large datasets. Potential solution to the overfitting problem has been discussed.
Radar Signal Sorting Based on Core Cluster Support Vector Clustering

With the increasing complexity of the modern electronic countermeasure environment, radar signal sorting faces serious challenges in ensuring effectiveness. At present, the conventional inter-pulse parameters or intra-pulse features are used, or the two directly form a vector to achieve signal sorting, but they all have certain problems. This paper proposes a radar signal sorting technology based on core cluster support vector clustering (CCSVC), which not only overcomes the existing technology or limited application scope, or computational complexity, or is not conducive to engineering implementation.
Analysis of Wavelet Transform Design via Filter Bank Technique

The technique of filter banks has been extensively applied in signal processing in the last three decades. It provides a very efficient way of signal decomposition, characterization, and analysis. It is also the main driving idea in almost all frequency division multiplexing technologies. With the advent of wavelets and subsequent realization of its wide area of application, filter banks became even more important as it has been proven to be the most efficient way a wavelet system can be implemented. In this chapter, we present an analysis of the design of a wavelet transform using the filter bank technique. The analysis covers the different sections which make up a filter bank, i.e., analysis filters and synthesis filters, and also the upsamplers and downsamplers. We also investigate the mathematical properties of wavelets, which make them particularly suitable in the design of wavelets. The chapter then focuses attention to the particular role the analysis and the synthesis filters play in the design of a wavelet transform using filter banks. The precise procedure by which the design of a wavelet using filter banks can be achieved is presented in the last section of this chapter, and it includes the mathematical techniques involved in the design of wavelets.
Generative Adversarial Networks Based on Denoising and Reconstruction Regularization

In this paper, a fast and easy implementation method is proposed, which adopts the denoising loss of real data and the reconstruction loss of latent codes to deal with the training instability and mode collapse of generative adversarial networks (GAN). The features learned from the corrupted real data by the discriminator are used by the generator to recover real data. Since the denoising loss can estimate the local properties of data generation density, adding denoising loss to the GAN objective functions can improve the quality and diversity of generated samples. Adding reconstruction loss of latent codes to the generator further improves the performance of GAN. Therefore, the optimizing signal of the generator comes from three aspects: the adversarial loss like in standard GAN, the denoising loss of real data and the reconstruction loss of latent codes, which help to prevent the no gradient problem. We evaluate the quality and diversity of generated samples with the robust metric Fréchet Inception Distance, a measure that can detect the intra-class mode dropping and is consistent with human assessment. In addition, we conduct a battery of experiments with the widely used architecture DCGAN and the complex architecture ResNet to demonstrate the effectiveness and robustness of the proposed method.
Root-cause analysis of alarms based on data segments with similar qualitative trends

This paper proposes a method to search similar data segments in order to assist plant operators in analyzing the root causes of an occurring alarm for a pivot process variable. The proposed method is composed by three major steps: (i) piece-wise linear representations are applied to obtain increasing, decreasing and steady trends of all related process variables; (ii) density peaks cluster analysis is used to cluster historical data segments with similar qualitative trends; (iii) the most similar historical data segments are found when the pivot variable is currently on the alarm state. An industrial example is provided to illustrate the effectiveness of the proposed method.
Accelerating HotSpots in Deep Neural Networks on a CAPI-Based FPGA

This paper introduces a new energy-efficient FPGA accelerator targeting the hotspots in Deep Neural Network (DNN) applications. Our design leverages the Coherent Accelerator Processor Interface (CAPI) which provides a coherent view of system memory to attached accelerators. Our implementation bypasses the need for device driver code and significantly reduces the communication and I/O overhead. Performance is further improved by a tiling transformation that exploits data locality in the computation kernel via the CAPI Power Service Layer (PSL) cache. A new adder tree configuration is proposed which achieves a tunable balance between resource utilization and power consumption. An implementation on a CAPI-supported Kintex FPGA board achieves up to 155 GOPs/s and 15.79 GOPs/watt, improving on the state-of-the-art of FPGA-based DNN implementations.
Towards Decentralized Equilibrium Asset Trading Based on Blockchain

The last decade has witnessed the explosive growth of digital tokens and cryptocurrencies including Bitcoin, reward programs and various tokens on Ethereum. However, trading these heterogeneous and non-financial digital assets often encounters substantial liquidity barriers due to the lack of interoperable standards, and faces an equilibrium dilemma in pricing strategies with a risk of market disequilibrium. In this sense, we introduce a novel decentralized equilibrium trading mechanism for assets across value systems with a combination of blockchain and economic theories that optimizes storage, equilibrium, and circulation of assets. To demonstrate the practical feasibility of the proposed mechanism, we design, implement, and evaluate an end-to-end prototype system on Ethereum platform with carefully-crafted smart contracts. Our extensive experimental evaluations and the trading simulation suggest that the proposed mechanism settles exchanges and tradings among different assets with equilibrium prices in a decentralized fashion, providing both practical and theoretical foundation for the autonomous equilibrium of asset trading market without human interventions.
A Model-based Time-to-Compromise Estimator to Assess the Security Posture of Vulnerable Networks

Several operational and economic factors impact the patching decisions of critical infrastructures. The constraints imposed by such factors could prevent organizations from fully remedying all of the vulnerabilities that expose their (critical) assets to risk. Therefore, an involved decision maker (e.g. security officer) has to strategically decide on the allocation of possible remediation efforts towards minimizing the inherent security risk. This, however, involves the use of comparative judgments to prioritize risks and remediation actions. Throughout this work, the security risk is quantified using the security metric Time-To-Compromise (TTC). Our main contribution is to provide a generic TTC estimator to comparatively assess the security posture of computer networks taking into account interdependencies between the network components, different adversary skill levels, and characteristics of (known and zero-day) vulnerabilities. The presented estimator relies on a stochastic TTC model and Monte Carlo simulation (MCS) techniques to account for the input data variability and inherent prediction uncertainties.
A new anchor word selection method for the separable topic discovery

Abstract Separable nonnegative matrix factorization (SNMF) is an important method for topic modeling, where “separable” assumes every topic contains at least one anchor word, defined as a word that has non‐zero probability only on that topic. SNMF focuses on the word co‐occurrence patterns to reveal topics by two steps: anchor word selection and topic recovery. The quality of the anchor words strongly influences the quality of the extracted topics. Existing anchor word selection algorithm is to greedily find an approximate convex hull in a high‐dimensional word co‐occurrence space. In this work, we propose a new method for the anchor word selection by associating the word co‐occurrence probability with the words similarity and assuming that the most different words on semantic are potential candidates for the anchor words. Therefore, if the similarity of a word‐pair is very low, the two words are very likely to be the anchor words. According to the statistical information of text corpora, we can get the similarity of all word‐pairs. We build the word similarity graph where the nodes correspond to words and weights on edges stand for the word‐pair similarity. Following this way, we design a greedy method to find a minimum edge‐weight anchor clique of a given size in the graph for the anchor word selection. Extensive experiments on real‐world corpus demonstrate the effectiveness of the proposed anchor word selection method that outperforms the common convex hull‐based methods on the revealed topic quality. Meanwhile, our method is much faster than typical SNMF‐based method. This article is categorized under: Algorithmic Development &gt; Text Mining Technologies &gt; Machine Learning
An End-to-End Goal-Oriented Dialog System with a Generative Natural Language Response Generation

Recently advancements in deep learning allowed the development of end-to-end trained goal-oriented dialog systems. Although these systems already achieve good performance, some simplificationsConstantin, Stefan limitNiehues, Jan theirWaibel, Alex usage in real-life scenarios. In this work, we address two of these limitations: ignoring positional information and a fixed number of possible response candidates. We propose to use positional encodings in the input to model the word order of the user utterances. Furthermore, by using a feedforward neural network, we are able to generate the output word by word and are no longer restricted to a fixed number of possible response candidates. Using the positional encoding, we were able to achieve better accuracies in the Dialog bAbI Tasks and using the feedforward neural network for generating the response, we were able to save computation time and space consumption.
Research on the Application of RFID in Equipment Management in Universities

In most universities, equipment is managed through barcode tags. This method is low in automation and inefficient and cannot improve the level of equipment management in universities. RFID tags have small volume, large storage capacity, strong resistance to breakage, fast scanning speed, strong penetrability, high reliability, and reusability. Through RFID tags, universities can achieve rapid inventory of equipment, real-time monitoring equipment, and big data analysis of equipment data.
Database Knowledge Discovery in Marketing Companies

In recent years, technological development in both the computing and data transmission fields has allowed the storage and management of large volumes of data. Today, businesses move in highly competitive and continuous changing environments. Market dynamics requires companies to handle the right information at the right time so that managers can make the appropriate business decisions. For this reason, companies have understood that the large volumes of data residing in their systems can, and must, be analyzed and exploited to gain new knowledge. This research develops a Database Knowledge Discovery process for assisting in the decision-making of a group of distribution companies. In this sense, subjects related to Data Mining and its application in the environment of economic, financial, and management indicators are described, obtaining models of association and grouping to support the work of managers at different levels of the organization.
Modeling Image Composition for Visual Aesthetic Assessment

Composition information is an important cue to characterize the aesthetic property of an image. We propose to model the image composition information as the mutual dependencies of its local regions, and design an architecture to leverage such information to boost aesthetics assessment. We adopt a Fully Convolutional Network (FCN) as the feature encoder of the input image and use the encoded feature map to represent the individual local regions and their spatial layout in the image. Then we build a region composition graph in which each node denotes one region and any two nodes are connected by an edge weighted by the similarity of the region features. We perform reasoning on this graph via graph convolution, in which the activation of each node is determined by its highly correlated neighbors. Our method achieves the state-of-the-art performance on the benchmark visual aesthetic dataset [15].