Reviewerly/exHarmony huggingface dataset:
Improvement of Multichannel Image Classification by Combining Elementary Classifiers

A post-classification processing technique for multi-channel images that includes three stages is proposed. The purpose of the first stage is to correct decisions of pixel-by-pixel classifiers based on estimates of classes’ posterior probabilities. At the second stage, a logical convolution of the classification layers is performed which makes it possible to select the most probable class. At the final stage, local spatial filtering of pre-segmented image is done which is performed in the neighborhood of detected segments’ edges. The post-classification processing effectiveness is verified for satellite images. It is demonstrated that the proposed post-classification processing procedure can significantly increase the probability of recognizing poorly distinguishable classes and improve overall accuracy of image classification.
Denoising Adversarial Autoencoder for Obfuscated Traffic Detection and Recovery

Traffic classification is key for managing both QoS and security in the Internet of Things (IoT). However, new traffic obfuscation techniques have been developed to thwart classification. Traffic mutation is one such obfuscation technique, that consists of modifying the flow's statistical characteristics to mislead the traffic classifier. In fact, this same technique can also be used to hide normal traffic characteristics for the sake of privacy. However, the concern is its use by attackers to bypass intrusion detection systems by modifying the attack traffic characteristics. In this paper, we propose an unsupervised Deep Learning (DL)-based model to detect mutated traffic. This model is based on generative DL architectures, namely Autoencoders (AE) and Generative Adversarial Network (GAN). This model consists of a denoising AE to de-anonymize the mutated traffic and a discriminator to detect it. The implementation results show that the traffic can be denoised when different mutation techniques are applied with a reconstruction error less than $$10^{-1}$$. In addition, the detection rate of fake traffic reaches 83.7%.
Woodwind instrument design optimization based on impedance characteristics with geometric constraints

Computational optimization algorithms coupled with acoustic models of wind instruments provide instrument makers with an opportunity to explore new designs. Specifically, they enable the automatic discovery of geometries exhibiting desired resonance characteristics. In this paper, the design optimization of woodwind instruments with complex geometrical features (e.g., non-cylindrical bore profile and side holes with various radii and chimney heights) is investigated. Optimal geometric designs are searched so that their acoustic input impedance has peaks with specific target frequencies and amplitudes. However, woodwind instruments exhibit complex input impedance whose features, such as resonances, might have non-smooth evolution with respect to design variables, thus hampering gradient-based optimization. For this reason, this paper introduces new formulations of the impedance characteristics (resonance frequencies and amplitudes) using a regularized unwrapped angle of the reflection function. The approach is applied to an illustrative instrument subjected to geometric constraints similar to the ones encountered by manufacturers (a key-less pentatonic clarinet with two-registers). Three optimization problems are considered, demonstrating a strategy to simultaneously adjust several impedance characteristics on all fingerings.
The Mertens Unrolled Network (MU-Net): A high dynamic range fusion neural network for through the windshield driver recognition

Face recognition of vehicle occupants through windshields in unconstrained environments poses a number of unique challenges ranging from glare, poor illumination, driver pose and motion blur. In this paper, we further develop the hardware and software components of a custom vehicle imaging system to better overcome these challenges. After the build out of a physical prototype system that performs High Dynamic Range (HDR) imaging, we collect a small dataset of through-windshield image captures of known drivers. We then reformulate the classical Mertens-Kautz-Van Reeth HDR fusion algorithm as a pre-initialized neural network, which we name the Mertens Unrolled Network (MU-Net), for the purpose of fine-tuning the HDR output of through-windshield images. Reconstructed faces from this novel HDR method are then evaluated and compared against other traditional and experimental HDR methods in a pre-trained state-of-the-art (SOTA) facial recognition pipeline, verifying the efficacy of our approach.
Image Zooming Based on Two Classes of C1-Continuous Coons Patches Construction with Shape Parameters over Triangular Domain

Image interpolation is important in image zooming. To improve the quality of image zooming, in this work, we proposed a class of rational quadratic trigonometric Hermite functions with two shape parameters and two classes of C 1 -continuous Coons patches constructions over a triangular domain by improved side–side method and side–vertex method. Altering the values of shape parameters can adjust the interior shape of the triangular Coons patch without influencing the function values and partial derivatives of the boundaries. In order to deal with the problem of well-posedness in image zooming, we discussed symmetrical sufficient conditions for region control of shape parameters in the improved side–side method and side–vertex method. Some examples demonstrate the proposed methods are effective in surface design and digital image zooming. C 1 -continuous Coons patches constructed by the proposed methods can interpolate to scattered 3D data. By up-sampling to the constructed interpolation surface, high-resolution images can be obtained. Image zooming experiment and analysis show that compared to bilinear, bicubic, iterative curvature-based interpolation (ICBI), novel edge orientation adaptive interpolation scheme for resolution enhancement of still images (NEDI), super-resolution using iterative Wiener filter based on nonlocal means (SR-NLM) and rational ball cubic B-spline (RBC), the proposed method can improve peak signal to noise ratio (PSNR) and structural similarity index (SSIM). Edge detection using Prewitt operator shows that the proposed method can better preserve sharp edges and textures in image zooming. The proposed methods can also improve the visual effect of the image, therefore it is efficient in computation for image zooming.
Overlapping Hierarchical Clustering (OHC)

Agglomerative clustering methods have been widely used by many research communities to cluster their data into hierarchical structures. These structures ease data exploration and are understandable even for non-specialists. But these methods necessarily result in a tree, since, at each agglomeration step, two clusters have to be merged. This may bias the data analysis process if, for example, a cluster is almost equally attracted by two others. In this paper we propose a new method that allows clusters to overlap until a strong cluster attraction is reached, based on a density criterion. The resulting hierarchical structure, called a quasi-dendrogram, is represented as a directed acyclic graph and combines the advantages of hierarchies with the precision of a less arbitrary clustering. We validate our work with extensive experiments on real data sets and compare it with existing tree-based methods, using a new measure of similarity between heterogeneous hierarchical structures.
Improving situational awareness with collective artificial intelligence over knowledge graphs

Situational awareness (SA) was defined as perception of environmental elements within the situation, comprehension, and projection of future status. Here the perception is the representation of sensory information that includes multi-typed interacting objects forming &#107;nowledge graphs." A typical problem in artificial intelligence (AI) research is to learn representations of objects that preserve structural information in knowledge graphs (KGs). Existing methods assume an AI agent has a complete knowledge graph, and any kind of prediction can be made accurately by a single AI. However, the real world needs multiple AI agents (e.g., warfighters, citizens) to collectively make a prediction. Each AI has a different, incomplete view of the knowledge graph with noise. In this work, I present a novel approach to improve representation learning and thus to improve SA with collective AI over KGs. I present the approach in four parts. First, I introduce knowledge graph and its nature of heterogeneity with multiple examples in the real world. Second, I discuss four ideas of making prediction with collective AI: prediction ensemble, data aggregation, representation aggregation, and joint representation learning. Third, I describe two state-of-the-art models to learn object representations from heterogeneous graphs: one is path- based embedding and the other is a graph neural network (GNN). Lastly, I present a new GNN framework that jointly learns object representations from multiple agents. Experimental results demonstrate that collective AI performs significantly better than individual AI. In future work, I discuss about federated learning that may improve security and privacy of the framework, which is quite necessary when any type of sharing (e.g., raw data, object representations, or learning process) is sensitive.
A Novel Data Cleansing Approach for Sensitive Applications of Wireless Sensor Networks

In many applications of wireless sensor networks, data collection happens to be generally unreliable and inaccurate. In order to improve the quality and the accuracy of this collected data, a deep analysis should be applied in order to spot and remove abnormalities that this data may contain, generally referred to as data outliers. This paper tackles this problem and presents a novel Distributed Outlier Detection Approach, dubbed DODA, that works in two steps: (1) first, it models the problem of detecting outliers with a Naïve Bayesian network within each cluster of sensors, and (2) then detects these outliers using a Mahalanobis distance at the cluster heads. Through extensive experiments, conducted on real datasets from Intel-Berkeley Laboratory, we demonstrated that the proposal performs better than the recent state-of-the-art algorithms under several metrics such energy consumption, false alarm rate and detection accuracy.
A Low Cost High Performance VLSI Architecture for Image Scaling in Multimedia Applications

Image scaling refers to the resizing of an image and is done quite often during Digital Image Processing. In this paper, an efficient image scaling algorithm and its architecture is developed which produce good quality resized image with lesser area and high performance. It uses a linear space-variant edge detector for edge enhancement and a spatial sharp filter for reducing the blurring effects produced by the bilinear interpolation. A simplified bilinear interpolation is used which is hardware efficient. The proposed and the existing algorithms are implemented in Matlab. Quality of the image is assessed by the Peak Signal to Noise Ratio (PSNR) and Structural Similarity (SSIM) parameters. Hardware implementation is done using Verilog hardware description language and synthesized in Cadence Genus tool using GPDK 90-nm CMOS process.
Integrating Computer Vision Technologies for Smart Surveillance Purpose

Automatic detection of dangerous situations in order to ensure the safety of residents is a new step in the development of video surveillance systems in cities. And dangerous situations are often caused by deviant behavior of people: robbery, brawl, vandalism and etc. But due to the strong variability of such scenes, their detection is a challenging problem, which still remains unresolved. The key to solving this problem is the recognition of fine-grained features and events of scenes and the application of knowledge management technologies. In this paper, three computer vision technologies for detecting people, tracking people and estimating three-dimensional human poses were integrated with the aim of recognizing the actions and interactions of people in three-dimensional space. For all technologies an open source implementations were used that showed high results in popular computer vision challenges. A dataset was also created using computer graphics to test the developed system, containing scenes of the interaction of people in the city, shot under different point of views. This dataset showed that additional teaching of the human pose estimation component to handle challenging poses of people and camera viewpoints is required.
Discovering Human Activities from Binary Data in Smart Homes

With the rapid development in sensing technology, data mining, and machine learning fields for human health monitoring, it became possible to enable monitoring of personal motion and vital signs in a manner that minimizes the disruption of an individual’s daily routine and assist individuals with difficulties to live independently at home. A primary difficulty that researchers confront is acquiring an adequate amount of labeled data for model training and validation purposes. Therefore, activity discovery handles the problem that activity labels are not available using approaches based on sequence mining and clustering. In this paper, we introduce an unsupervised method for discovering activities from a network of motion detectors in a smart home setting. First, we present an intra-day clustering algorithm to find frequent sequential patterns within a day. As a second step, we present an inter-day clustering algorithm to find the common frequent patterns between days. Furthermore, we refine the patterns to have more compressed and defined cluster characterizations. Finally, we track the occurrences of various regular routines to monitor the functional health in an individual’s patterns and lifestyle. We evaluate our methods on two public data sets captured in real-life settings from two apartments during seven-month and three-month periods.
A WEB RESOURCE MAP FOR A UNIVERSITY COURSE IN THE HISTORY OF THE ENGLISH LANGUAGE

ICT and the Internet have revolutionized education, in terms of opportunities for distance learning, independent study and resource sharing in particular. However, these new opportunities are not equally utilized across university curricula, with much slower progress in the Humanities than in STEM disciplines. The paper looks at how teaching the History of the English language, a mandatory course for university students majoring in English, can be enhanced through the use of web resources. The use of high quality study resources can help solve a threefold task: shifting the emphasis from the lecturer to the student; raising students’ motivation and engagement in the course through independent work and research; and increasing the content quality of the course. The authors developed a map of the Internet resources that can be used in the course in the History of the English language. The map has four segments: digital texts; online dictionaries and translation tools; videos, podcasts, textual materials, databases, timelines, etc.; and online activities. Each of the segments offers a list of the Internet resources which can be recommended for the course. The authors see the major benefits of using these resources in facilitating students’ preparation for lectures, practical classes, tests and examinations, changing their overall attitude to the History of English, which is traditionally stigmatized by students as insurmountably hard to master, helping students to see that the course can be engaging and even fun, motivating them to explore the history of the language they study on their own. The critical assessment of the web resources available for the course has revealed that the Internet can offer little for the online activities segment of the developed map, which defines the necessity for academics to invest their effort in developing and sharing exercises, practical assignments, tests and other activities to assist students in mastering the course and provide tools for knowledge assessment.
Dynamics of entanglement of atoms with two-photon transitions induced by a thermal field

In this paper, we investigate the entanglement between two two-level atoms non-resonantly in-teracting with a thermal field of a lossless one-mode resonator via degenerate two-photon transi-tions. On the basis of the exact solution of the time-dependent density matrix we calculate the negativity as a measure of atomic entanglement. We show that for separable initial atomic states a slight atom-field detuning may generate the high amount of atom-atom entanglement. The re-sults also show that for non-resonant atom-field interaction the entanglement induced by nonlin-ear two-photon interaction is smaller than that induced by one-photon interaction in contrast to the resonant interaction situation. For a Bell-type entangled initial atomic state we obtain that if the detuning increases, there is an appreciable decrease in the amplitudes of the negativity oscilla-tions. The results also show that elimination of the sudden death of entanglement for non-resonant two-photon atom-field interaction may take place.
[Concentration Characteristics, Source Analysis, and Potential Ecological Risk Assessment of Heavy Metals in a Peak-cluster Depression Area, Southeast of Yunnan Province].

The pollution characteristics, sources, and ecological risk of heavy metals in farmland soils were studied in a peak-cluster depression area southeast of Yunnan Province. In total, 199 topsoil samples were collected using the grid method and contents of eight heavy metals (As, Cd, Cr, Cu, Pb, Ni, Zn, and Hg) were determined. The geoaccumulation index and potential ecological risk index (RI) were used to evaluate soil pollution levels and potential ecological risks, respectively. The absolute principal component scores-multivariate linear regression (APCS-MLR) model and cluster analysis were used to identify the pollution sources and apportion the heavy metals in the soils. The results show that the mean content of Cd, Hg, Cr, Ni, and Zn was higher than their respective background values, exceeding them by up to 16.00, 7.29, 1.46, 1.45, and 2.39 times, respectively. No evidence of the occurrence of other metals was found. The average RI was 1039.3, which represents a high potential ecological risk, with Cd and Hg being the most crucial elements. The APCS-MLR model revealed that natural sources (PC1), traffic sources (PC2), fossil fuel combustion sources (PC3), and agricultural sources (PC4) were the four main contributors of soil heavy metals. The primary source of Cd, Cr, and Ni was PC1, while PC2 was the main source of Pb and Zn. PC3 and PC4 were the main sources of Cu and Hg with contribution rates of 77.86% and 70.37%, respectively. The main sources of As were PC1 (46.71%) and PC4 (49.46%).
Algebraic Analysis of a Simplified Encryption Algorithm GOST R 34.12-2015

In January 2016, a new standard for symmetric block encryption was established in the Russian Federation. The standard contains two encryption algorithms: Magma and Kuznyechik. In this paper we propose to consider the possibility of applying the algebraic analysis method to these ciphers. To do this, we use the simplified algorithms Magma ⊕ and S-KN2. To solve sets of nonlinear Boolean equations, we choose two different approaches: a reduction and solving of the Boolean satisfiability problem (by using the CryptoMiniSat solver) and an extended linearization method (XL). In our research, we suggest using a security assessment approach that identifies the resistance of block ciphers to algebraic cryptanalysis. The algebraic analysis of an eight-round Magma (68 key bits were fixed) with the CryptoMiniSat solver demanded four known text pairs and took 3029.56 s to complete (the search took 416.31 s). The algebraic analysis of a five-round Magma cipher with weakened S-boxes required seven known text pairs and took 1135.61 s (the search took 3.36 s). The algebraic analysis of a five-round Magma cipher with disabled S-blocks (equivalent value substitution) led to getting only one solution for five known text pairs in 501.18 s (the search took 4.92 s). The complexity of the XL algebraic analysis of a four-round S-KN2 cipher with three text pairs was 236.33 s (took 1.191 Gb RAM).
Memory-Efficient and Skew-Tolerant MapReduce Over MPI for Supercomputing Systems

Data analytics has become an integral part of large-scale scientific computing. Among various data analytics frameworks, MapReduce has gained the most traction. Although some efforts have been made to enable efficient MapReduce for supercomputing systems, they are often limited to fairly homogeneous workloads where equal partitioning of input data across tasks results in essentially equal output or temporary data generated on each task. For workloads that are more skewed, however, current implementations can result in imbalance in memory usage and, consequently, can cause a slowdown in execution time and a loss in data scalability. To tackle this problem, we enhance a previously published memory-conscious MapReduce over MPI framework called Mimir. Our enhancements to Mimir include combiner and dynamic repartition optimizations to minimize and balance memory usage and to achieve close to optimal balance of the memory usage across processes and to reduce the execution time by up to 12 times. Experimental results show that Mimir can scale to at least 3072 processes on the Tianhe-2 supercomputer on skewed datasets.
Pengaruh Persepsi Siswa atas Lingkungan Belajar dan Penguasaan Kosa Kata Terhadap Kemampuan Berbicara Bahasa Inggris Siswa Sekolah Menengah Atas Swasta Balaraja

This study aims to determine the effect of students' perceptions on the learning environment and vocabulary mastery of English speaking ability at Balaraja Private High School. The method used in this study is a survey with correlation and regression analysis, which is connecting data that shows the level of students' perceptions of the learning environment and vocabulary mastery with the ability to speak English in Balaraja Private High Schools. Sampling is done by random sampling technique. Total population of 806 with respondents in this study was 60 students. The research instruments used were questionnaires and multiple choice test questions. The results showed that: 1) there was a significant influence on students' perceptions of the learning environment and shared vocabulary mastery of the ability to speak English in private high school students in Kec. Balaraja, Banten. This is evidenced by the acquisition of Sig. 0,000 <0.05 and Fh = 14,160. Together, the perception of the learning environment and vocabulary mastery contributed 33.2% to the variable in English speaking ability. 2) There is a significant influence of perceptions on the learning environment on the ability to speak English in high school students in Kec. Balaraja, Banten. This is evidenced by the acquisition of Sig. 0.013 <0.05 and th = 2.578. Perception variable on learning environment contributed 14.32% in improving English speaking ability. 3) There was a significant influence on vocabulary mastery on English speaking ability of private high school students in Kec. Balaraja, Banten. This is evidenced by the acquisition of Sig. 0.003 <0.05 and th = 3.144. The vocabulary mastery variable contributed 18.90% in improving the ability to speak English.