Reviewerly/exHarmony huggingface dataset:
Recurrent Neural Network Based Prediction to Enhance Satellite Telemetry Compression

Because of the gradually increasing number of remote measured low and/or high frequency sampled parameters in space applications, aerospace mission operators have to make hard choices on which parameters at which sampling rates should be downlinked. On-board aerospace applications are characterized by limited storage and communication budgets, while lossless data compression schemes should be sufficient enough to enhance transmission efficiency and hence the whole aerospace mission. In this paper, a proposed two-stage lossless compression method for telemetry data is presented. The proposed method consists of a decorrelation stage and an entropy coding one. The Long-Short-Term Memory (LSTM) Recurrent Neural Network (RNN) is implemented as a predictor in the decorrelation stage of the proposed method, and an illustrative method of applying LSTM network for telemetry data samples prediction is presented and figured out. In experiments, different entropy coders: Rice codes, arithmetic method and Huffman algorithm are separately implemented at the second stage. The proposed method is tested by different real telemetry data sets of FUNcube satellite in frames of data words of 8-,10-,16-bits widths. Experimental results show that the proposed method improved compression efficiency based on a single stage of entropy coder: Rice codes, arithmetic code, and Huffman algorithm by a ratio up to: 98%, 21%, and 1.6%, respectively.
Specific Emitter Identification Based on Instantaneous Frequency Characteristics

The extraction of specific emitter characteristics is the crucial factor and foundation of implementation of specific emitter identification technology. This paper provides one method for extraction of specific emitter characteristics based on intra-pulse instantaneous frequency, and validates the coherence and distinguishability of intra-pulse instantaneous frequency characteristics of specific emitter by field test data. The test results indicate that instantaneous frequency characteristics of specific emitter can be successfully extracted from pulse compressed signal, including both linear frequency modulated pulse signal and non-linear frequency modulated pulse signal, and specific emitter identification can be achieved for such kinds of emitters, which shows the potential of instantaneous frequency characteristics of specific emitter becoming one important dimension of specific emitter characteristics space.
Sun's Position Tracking by Solar Angles Using MATLAB

The current paper revolves around a MATLAB project for calculation of solar incidence angle, solar altitude angle and solar azimuth angle of any specific point on earth at any time of day for any date. This model helps us to know the optimum position of the solar panel for the maximum power generation from sun at any point of time for specific location. This model also helps us in comparing the difference in the given angles at any day or time of year.
SysGen

Security investigations often rely on forensic tools to deliver the necessary supporting evidence. It is therefore imperative that forensic tools are scientifically tested in both their accuracy and capabilities. The primary means to develop and validate forensic tools is by evaluating them against a set of known answers (i.e., a data corpus). While researchers have long recognized the need for standardized forensic corpora, there are few such tools or datasets available, particularly for database management systems (DBMS). In fact, there are currently no publicly available tools that can generate a DBMS dataset for forensic testing. In this paper, we share SysGen, a customizeable data generator and a pre-built corpus that offers a reference for most major relational DBMSes. The pre-built corpus includes individual DBMS files, the full disk snapshot, the RAM snapshot, and network packets taken from a set of clean virtual machines. SysGen can be easily adapted to execute a custom workload scenario, capturing a new data corpus; it can also create other variations of full system snapshots, even beyond DBMS testing.
QoC enhanced semantic IoT model

The miniaturization of computers, coupled with a constant increase in computing power, led to the emergence of new sources of context information. We are facing a new paradigm, the Internet of Things (IoT). Today, this latter improves the quality of life in multiple areas. However, the heterogeneity of objects used in such environments makes their interoperability difficult. In addition, the observations produced by context providers (connected objects) are generated with different vocabularies and data formats. This heterogeneity of technologies in the IoT world makes it necessary to adopt generic solutions. Therefore, it is important to transform the raw data from these context producers into knowledge and information based on ontologies. The use of ontologies solves the challenges of heterogeneity and interoperability of IoT systems. In this paper, we propose a semantic IoT model that aims to overcome the semantic interoperability challenges introduced by the variety of objects potentially used in IoT systems. Furthermore, we enhanced this ontology with quality of context meta-data. These meta-data helps in dealing with imperfection and inconsistency of the collected IoT data.
Designing high performance, power-efficient, reconfigurable compute structures for specialized applications

Abstract In this paper, a new approach to design high-performance and power-efficient computing structures are proposed for machine learning tasks. Such structures can be very useful in some specialized applications such as autonomous robots, mobile devices, smart sensors for the Internet of Things (IoT) and so on. This approach is based on the concept of reconfigurable homogeneous computing environments. Major advantages of this approach are discussed. The process of designing a set of elementary operations for such structures is described in detail using an example with a typical Feed-Forward Neural Network (FFNN) and its training module.
Evaluation of Interaction Techniques for Early Phase Satellite Design in Immersive AR

In this paper, we present a new controller-based interaction technique on the Microsoft HoloLens to support communication for the early phase satellite design at the Concurrent Engineering Facility (CEF). We design a virtual satellite with virtual moveable objects utilizing two different interaction methods: the default hand gesture-based interaction method and a novel controller-based interaction method for rotation and translation of satellite components in immersive augmented reality. In order to evaluate our method, we conduct a perceptual study with 12 participants. We apply multiple performance metrics for each user on both methods. Additionally, we measure user preferences and ease of use. Our results show that our controller-based method is significantly more precise for placing objects (consisting of position and orientation). Furthermore, it is less time consuming than the hand gesture-based method and more preferred by the participants.
An Improved Breast Cancer Nuclei Segmentation Method Based on UNet++

Nuclei segmentation plays an important role in medical image analysis but it is also a challenging area due to the tiny size of nuclei especially for breast cancer nuclei. To address these challenges, in this paper we present an improved UNet++ architecture, a more powerful architecture for nuclei segmentation. The original UNet++, which is an encoder-decoder architecture with a series of nested and dense skip pathways, is used as the framework in our work. The main reason for the increase in ability is that the Inception-ResNet-V2 network is added as backbone, which is a very deep network with brilliant performance in object detection. We have evaluated our improved UNet++ in comparison with UNet and the original UNet++ architectures in breast cancer nuclei segmentation dataset. The experiments demonstrate that our improved UNet++ is superior to U-Net and the original U-Net++.
Radar Specific Emitter Identification Using Carrier Frequency Feature

Radar Specific Emitter Identification (SEI) technology extracts individual features from received pulses to distinguish radar emitters of the same kind. In this paper, we first analyze the mechanism of carrier frequency feature caused by radar transmitters, realizing that the carrier frequency offset is unavoidable and unique to individual emitters. Then a carrier frequency feature extraction algorithm for the single-tone frequency signals and chirp signals is proposed and used to characterize the real Emitter pulses. Experimental results show that the proposed methodology is efficient for carrier frequency feature extraction and can be used to distinguish radar emitters.
Radar Jamming Recognition Method Based on Fuzzy Clustering Decision Tree

Aiming at the problem that the traditional classifier based on decision tree for radar jamming recognition needs prior information and manual intervention, a jamming signal recognition method based on fuzzy clustering decision tree is proposed in this paper. After establishing multidimensional sets of parameter features of the jamming signal which are extracted from both time and frequency domain, the Fuzzy C-means (FCM) clustering algorithm is then employed in building the decision tree. Moreover, the improved ID3 standard based on information gain is also used to complete the fuzzy clustering decision tree. The improved method realizes an automatic design of decision trees which avoids using any prior knowledge in selecting the decision threshold. Computer simulation verifies the effectiveness of this method.
Multi-modal Visual-Thermal Saliency-based Object Detection in Visually-degraded Environments

In this work we present a new approach on the fusion of multi-modal data from visible-light and thermal camera sensors in order to efficiently detect objects in visually-degraded environments using aerial robots. Through a bottom-up approach inspired by the principles of visual saliency and how it models the attention mechanism of mammals, a novel algorithm that builds individual conspicuity maps across color channels and across visible-light and longwave infrared spectra is proposed. More specifically, two input images - a radiometric thermal image and a visible-light RGB image - are fed respectively into an intensity channel, and a set of intensity and color-opponent channels. For every channel, image pyramids are computed to allow searching in different scales. The eventual feature maps are subsequently combined into one saliency map. The approach is computationally efficient and tailored to the limited processing capacities found onboard Micro Aerial Vehicles, while at the same time providing fast real-time object detections as required for the purposes of autonomous navigation and object search. Integrating thermal vision together with visible-light data, the method builds on top of a rich representation that allows resilient operation even in conditions of visual degradation and specifically in cases of low-light or obscurant-filled (e.g., dust, fog) settings which can be penetrated using thermal vision. The presented algorithm and overall system-level solution is built to facilitate efficient, fast and reliable object detection during autonomous aerial robotic exploration of subterranean environments, contributing novel capacity beyond the context of aerial robotic navigation. Our approach is extensively evaluated in the framework of real-life field deployments in underground metal mines in Northern Nevada.
Spatio Temporal Joint Distance Maps for Skeleton-Based Action Recognition Using Convolutional Neural Networks

Skeleton-based action recognition has become popular with the recent developments in sensor technology and fast pose estimation algorithms. The existing research works have attempted to address the action recognition problem by considering either spatial or temporal dynamics of the actions. But, both the features (spatial and temporal) would contribute to solve the problem. In this paper, we address the action recognition problem using 3D skeleton data by introducing eight Joint Distance Maps, referred to as Spatio Temporal Joint Distance Maps (ST-JDMs), to capture spatio temporal variations from skeleton data for action recognition. Among these, four maps are defined in spatial domain and remaining four are in temporal domain. After construction of ST-JDMs from an action sequence, they are encoded into color images. This representation enables us to fine-tune the Convolutional Neural Network (CNN) for action classification. The empirical results on the two datasets, UTD MHAD and NTU RGB+D, show that ST-JDMs outperforms the other state-of-the-art skeleton-based approaches by achieving recognition accuracies 91.63% and 80.16%, respectively.
An efficient multi-label learning method with label projection

Multi-label classification (MLC) is a problem that each given sample is associated with more than one label simultaneously. There is a variety of application in our daily life, such as text categorization and image annotation. To date, many methodologies are proposed to do a multi-label learning task. According to the MLC setting, we put forward an MLC method called TPMLC (an MLC method with Two Parts) and propose a uniform loss function based on the variational inference with Bayesian and Gaussian distribution assumption. Moreover, this uniform loss function is composed of two parts. On one hand, the first part is about the determination of the relationship between sample and multiple labels, so we adopt a set of multiple support vector machines (SVMs) to determine this relationship. On the other hand, the second part in this uniform loss function is about the determination of the relationship among multiple labels, and thus we construct a projection matrix model to determine this relationship. Furthermore, this uniform loss function is optimized simultaneously, such that the two kinds of relationships can be optimized at the same time. Besides, we also present the convergence analysis and computational complexity analysis of the method. After that, in the experiment part, the comparison of TPMLC with state-of-the-art approaches manifests the feasibility and the competitive performance in classification. In addition, the statistic results show that the proposed method performs better than the state-of-the-art methods.
Distinguishing Political Parties From Inaugural Speeches Using Machine Learning

A massive amount of text data is available online; applying machine learning techniques to analyze the data helps human beings to grasp the status of previous and current eras. We analyze lexically all US presidential inaugural speeches from 1789 until now and use machine learning algorithms to learn patterns to categorize the authors of the speeches into their respective parties. We apply four different supervised learning approaches (Multinomial Naive Bayes, SVMs, Random Forest, and Logistic Regression) and evaluate their classification performance. The outcome is a 100% accurate classification based on our choice of lexical features (such as unigram and stopwords removal) and the parameters of SVM with linear, polynomial, and RBF kernels. Our study shows that the selected supervised machine learning algorithms can produce highly accurate association between political parties inaugural speech text without requiring any additional information such as metadata for authors or parties or semantics.
POI Recommendations Using Self-attention Based on Side Information

Point of interest (POI) recommendation is one of the most important tasks in location-based social networks (LBSN). The existing recommendation methods face two challenges: (1) the cold start problem caused by data sparsity; (2) underutilization of the abundant side information besides user-POI interaction in large-scale data. Recent research shows that a user's social relationship can be used to solve the cold start problem to some extent. The deep neural network learns users' long term and short term preferences to improve the recommendation quality. Therefore, this paper proposes a POI recommendation model called SSANet, applying side information (S) and self-attention (SA) to provide the high-satisfaction POI recommendations for users. Specifically, first, the user-POI interaction matrix were constructed by users history data to represents the user hidden representation; second, the side information includes rating scores, access frequency, social relationship, and geographic information were used to extract users preference; third, we use self-attention mechanism to learn user long term and short term preference. The experimental results on the real LBSN datasets show that the recommendation performance of the SSANet model is better than the existing POI recommendation model.
Real-time Motion Planning in Unknown Environments for Legged Robotic Planetary Exploration

Planetary surface robotic explorers currently implement limited amounts of autonomy, often relying on rigorously-developed offline plans. If deviation occurs, long communication delays often result in rover downtime and subsequent lost time for scientific exploration. Onboard robotic motion planning that is fast and accounts for obstacles and robot kinematics is one key piece of the autonomy pipeline required to bring more meaningful autonomy to planetary exploration. Current approaches normally rely on sampling-based planning methods like the rapidly exploring random tree (RRT) algorithm which has had considerable success for kinematic motion planning. However, global computation over the entire state space for high-dimensional systems in cluttered environments-like legged robots on a planetary surface-can be complex and too slow for practical use. What's more, complete environment information is often not available a priori. This work proposes a real-time combined global-local planner for a legged robot in a partially unobserved, cluttered environment. Large obstacles known beforehand (e.g., orbital imagery) are accounted for using a fast global planner on a low-dimensional model. Unknown small obstacles which restrict foot placements are dealt with as they are observed using a slower but real-time local planner, obeying the complex legged robot kinematics. This approach, called SweepingRRT, makes use of observed information locally as it becomes known, while providing the fast global replanning that may be necessitated by new obstacle observations. The planning algorithm is demonstrated in simulation for a standard four-legged, eight-jointed robot in some demonstrative obstacle environments consisting of large (known) and small (unknown) obstacles using a limited sensor range.
A Forensic Method for DeepFake Image based on Face Recognition

DeepFake digital images have serious negative impacts on news integrity, legal forensics, and social security. In order to detect the DeepFake digital images more accurately, a method based on face recognition is proposed. Face image feature vectors are extracted by Facenet, and the Euclidean distances among the vectors of different face images are calculated as classification principle. Then, the machine learning algorithms is trained to perform binary classification of real and fake face images. The experimental results on the Celeb-DF data set show that the proposed method has better detection effect than the existing detection methods.
Robust Text Clustering with Graph and Textual Adversarial Learning

Text clustering is a fundamental task that finds groups of similar texts in the corpus. Deep learning based models can capture the semantic and syntactic information in local word sequences to represent the texts but may ignore global text structures in the corpus. Recently, Graph Convolutional Networks (GCNs) have achieved the state-of-the-art performance in many applications, which may facilitate the text clustering task by exploiting the graph structures between texts via words and phrases. However, GCNs are vulnerable to adversarial attacks, e.g., small perturbations in graph structures and node attributes lead to poor performance. To address this issue, we propose a Robust Text Clustering (RTC) approach which consists of both autoencoder and GCN modules to exploit the local text features and global text structures respectively in one unified framework. We generate two types of adversarial perturbations targeting text structure and graph structure, and develop an adversarial training method to improve the robustness and generalization of the RTC framework. Extensive experiments on two real-world datasets demonstrate the effectiveness of our proposal in text clustering.
Automatic Detection of Amplitude-Distorted Samples from Clipped Seismic Waveforms

Abstract Seismic waveforms are essential for seismology but are clipped when their actual amplitudes are too high to be faithfully recorded by seismometers. The clipping effects are popular for both big earthquakes and small earthquakes within a short epicentral distance. Here, we illustrate potential risks of direct usage of clipped waveforms by examining the frequency leakage and show the failure of bandpass filtering for different clipping levels; then we summarize two characteristics of clipped records: (1) The temporal gradient is unusually large around the clipped segment compared with the unclipped portions, and (2) the clipped samples cluster into one segment or several if many samples are involved. Next, we propose three criteria for distinguishing clipped samples from the perfect samples based on these two characteristics. Finally, we design a numerical algorithm for automatic detection of clipped samples using constraints on the gradient, amplitude, and gradient-varying range. Numerical experiments show the excellent performance of our algorithm on automatically detecting the clipped samples. Our algorithm seamlessly integrates all necessary constraints for both flat-top type and back-to-zero type and thus can correctly recognize these two types simultaneously; in addition, it is basically data driven and thus can work well without considering seismometer configuration and instrument type, which would be helpful for real-time detection of clipped records without interruption from human operations. As a robust and swift tool of automatic detection on amplitude-clipped samples, our algorithm could identify most typical clipped records and reduce potential risks due to using unrecognizable clipped waveforms; furthermore, it would be helpful for fast detection and possible restoration of clipped waveforms in the presence of huge volumes of data.
HOPS: Probabilistic Subtree Mining for Small and Large Graphs

Frequent subgraph mining, i.e., the identification of relevant patterns in graph databases, is a well-known data mining problem with high practical relevance, since next to summarizing the data, the resulting patterns can also be used to define powerful domain-specific similarity functions for prediction. In recent years, significant progress has been made towards subgraph mining algorithms that scale to complex graphs by focusing on tree patterns and probabilistically allowing a small amount of incompleteness in the result. Nonetheless, the complexity of the pattern matching component used for deciding subtree isomorphism on arbitrary graphs has significantly limited the scalability of existing approaches. In this paper, we adapt sampling techniques from mathematical combinatorics to the problem of probabilistic subtree mining in arbitrary databases of many small to medium-size graphs or a single large graph. By restricting on tree patterns, we provide an algorithm that approximately counts or decides subtree isomorphism for arbitrary transaction graphs in sub-linear time with one-sided error. Our empirical evaluation on a range of benchmark graph datasets shows that the novel algorithm substantially outperforms state-of-the-art approaches both in the task of approximate counting of embeddings in single large graphs and in probabilistic frequent subtree mining in large databases of small to medium sized graphs.