Reviewerly/exHarmony huggingface dataset:
CNN-Based Automatic Modulation Recognition of Wireless Signal

Based on simulation samples of open source data collection and communication system signal pretreatment methods, we design a Convolution Neural Network (CNN) that contains three full connection layers and three convolution layers to recognize 11 different kinds of wireless signal modulation. In additive white Gaussian noise(AWGN) model, CNN networks can realize automatic modulation classification of the wireless signal by learning the deep characters of sample data, and the average recognition accuracy is up to 81% under different ratio of signal to noise (SNR). We analyze the influence between modulation recognition accuracy and network hyperparameters such as network structures, convolutional layers, dropout value, batch size, and network training methods.
Generalized Iterative Bayesian Update and Applications to Mechanisms for Privacy Protection

The iterative Bayesian update (IBU) and the matrix inversion (INV) are the main methods to retrieve the original distribution from noisy data resulting from the application of privacy protection mechanisms. We show that the foundations of IBU established in the literature are flawed, as they rely on an assumption that in general is not satisfied in typical datasets. We then propose an extension of the method, covering a more general privacy model, where different users are allowed to apply different privacy mechanisms. We call our algorithm GIBU, for Generalized IBU, and we prove its convergence to the maximum likelihood estimate, constructing a proof that does not rely on the problematic assumption, thus fixing also the theory of IBU. Finally we evaluate the precision of GIBU on data sanitized with k-RR, Rappor, geo-indistinguishability and exponential mechanisms. We show that, while GIBU and INV are comparable in the first two cases, the performance of GIBU is definitely superior in the latter cases.
On the Impact of Exception Handling Compatibility on Binary Instrumentation

To support C++ exception handling, compilers generate metadata that is a rich source of information about the code layout. On Linux, this metadata is also used to support stack tracing, thread cleanup and other functions. For this reason, Linux binaries contain code-layout-revealing metadata for C-code as well. Even hand-written assembly in low-level system libraries is covered by such metadata. We investigate the implications of this metadata in this paper, and show that it can be used to (a) improve accuracy of disassembly, (b) achieve significantly better accuracy at function boundary identification as compared to previous research, and (c) as a rich source of information for defeating fine-grained code randomization.
WeakAL: Combining Active Learning and Weak Supervision

Supervised Learning requires a huge amount of labeled data, making efficient labeling one of the most critical components for the success of Machine Learning (ML). One well-known method to gain labeled data efficiently is Active Learning (AL), where the learner interactively asks human experts to label the most informative data point. Nevertheless, even by applying AL in labeling tasks the amount of human effort is still too high and should be minimized further.
Local path planning for unmanned surface vehicle with improved artificial potential field method

Abstract Aiming at the drawbacks of traditional artificial potential field method in local path planning for USV (unmanned surface vehicle), an improved artificial potential field method is proposed. By improving the gravitational potential field model and restraining the distant gravitational potential field, the problem of USV colliding with obstacles is solved. In the repulsion field model, the influence factors such as relative position and relative velocity are introduced to reduce the repulsion potential field intensity of the obstacle near the target, so as to solve the problem of goal nonreachable with obstacle nearby. The regulation mechanism of random perturbation potential energy is introduced to solve the problem that it is difficult to move at local minimum points. The rationality and superiority of the proposed improved artificial potential field method are verified by simulation and comparison experiments.
Evaluation of Structural Hyperparameters for Text Classification with LSTM Networks

In natural language processing, most problems can be interpreted as text classification tasks, which makes this type of task a central one. A natural subdivision of more complex types of text classification can be made according to whether the classification is multilabel or multiclass, where both of these can be tackled with either a multiclass classifier or with a combination of several binary classifiers. An example of the problem which offers a natural way of comparing multiclass and binary classification on the same data, which makes the results comparable, is a classification of text author MBTI personality type. The dataset used is PersonalityCafe MBTI. We focus our comparison on structural hyperparameters, which are the hyperparameters pertaining to network structure. Structural hyperparameters are necessary for specifying the network itself, which makes them a primary concern in its construction. The hyperparameters investigated in this paper are the number of hidden layers and layer size. Through a number of experiments, we demonstrate the choice of hyperparameters and conclude with general hyperparameter selection recommendations based on our results.
Comparative Analysis of Supervised and Unsupervised Approaches Applied to Large-Scale “In The Wild” Face Verification

Deep learning-based feature extraction methods and transfer learning have become common approaches in the field of pattern recognition. Deep convolutional neural networks trained using tripled-based loss functions allow for the generation of face embeddings, which can be directly applied to face verification and clustering. Knowledge about the ground truth of face identities might improve the effectiveness of the final classification algorithm; however, it is also possible to use ground truth clusters previously discovered using an unsupervised approach. The aim of this paper is to evaluate the potential improvement of classification results of state-of-the-art supervised classification methods trained with and without ground truth knowledge. In this study, we use two sufficiently large data sets containing more than 200,000 “taken in the wild” images, each with various resolutions, visual quality, and face poses which, in our opinion, guarantee the statistical significance of the results. We examine several clustering and supervised pattern recognition algorithms and find that knowledge about the ground truth has a very small influence on the Fowlkes–Mallows score (FMS) of the classification algorithm. In the case of the classification algorithm that obtained the highest accuracy in our experiment, the FMS improved by only 5.3% (from 0.749 to 0.791) in the first data set and by 6.6% (from 0.652 to 0.718) in the second data set. Our results show that, beside highly secure systems in which face verification is a key component, face identities discovered by unsupervised approaches can be safely used for training supervised classifiers. We also found that the Silhouette Coefficient (SC) of unsupervised clustering is positively correlated with the Adjusted Rand Index, V-measure score, and Fowlkes–Mallows score and, so, we can use the SC as an indicator of clustering performance when the ground truth of face identities is not known. All of these conclusions are important findings for large-scale face verification problems. The reason for this is the fact that skipping the verification of people’s identities before supervised training saves a lot of time and resources.
Multi-Scale Cross-Modal Spatial Attention Fusion for Multi-label Image Recognition

Multi-label image recognition aims to jointly predict multiple tags for an image. Despite great progress achieved, there are still two limitations for existing methods: 1) can not accurately locate the object regions due to the lack of adequate supervision information or semantic guidance; 2) can not effectively identify the target categories of small-size object due to only employing the high-level feature of deep CNN. In this paper, we propose a Multi-Scale Cross-Modal Spatial Attention Fusion (MCSAF) network to accurately locate more informative regions by introducing a spatial attention module, and our model can effectively recognize target classes of different scales with multi-scale cross-modal feature fusion. Furthermore, we develop an adaptive graph convolutional network (Adaptive-GCN) to capture the complex correlations among labels in depth. Empirical studies on benchmark datasets validate the superiority of our proposed model over state-of-the-art methods.
A Design of Image Dehazing Engine Using DTE and DAE Techniques

An image dehazing technique can restore hazy images to clear ones. Under hazy conditions, it can be a preceding stage of advanced-driver assistance systems (ADAS) to keep driving images clear. ADAS pursues a larger image resolution with a higher frame rate to keep its reliability for driving safety. This trend drives the image dehazing to face a severe challenge of considerable throughput. In this study, a hardware-efficient image dehazing engine with low computational burdens is proposed. It comprises two core techniques: Depth-Refinement Transmission-Rate Estimation (DTE) and Distribute Airlight Estimation (DAE). DTE provides an accurate depth map to estimate transmission rate, where the depth of white-objects is carefully calibrated. Base on the depth information, DAE predicts the airlight with the average of distant illumination sources to compensate nonuniform haze concentration. DTE and DAE can reduce the number of line buffers by 33% at least, while keeping comparable image dehazing quality. This study is implemented using TSMC CMOS <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$0.18~\mu \text{m}$ </tex-math></inline-formula> technology. To construct a smooth data scheduling, it is organized into a six-stage pipelinging strategy with an interleaving access technique. The logic gate count is 14.4K, and the power consumption is 15.2mW@250MHz. With a two-level parallelism, its throughput can support 3, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$840\times 2.160$ </tex-math></inline-formula> @60fps. The experiment results show this study presents the superior combined performance of energy efficiency, area efficiency and line buffer reduction.
Application of Improved Rapidly-exploring Random Trees (RRT) algorithm for Obstacle Avoidance of Snake-like Manipulator

With the development of science and technology, more and more redundant manipulators have been applied in various fields. Aim to realize autonomous obstacle avoidance in unstructured environment for snake-like manipulator, an improved Rapidly-exploring Random Trees (RRT) algorithm is proposed. Establish the Kinematic model of snake-like manipulator and obstacle. Collision detection is also performed. In order to improve the efficiency of the algorithm, build together multiple random trees and generate a valid path without collision. Next the path is optimized to make the path smoother. Finally, simulation is performed in MATLAB environment to verify the effectiveness and practicability of the algorithm. This method has important practical significance for the redundant manipulator.
Exploiting Self-Supervised and Semi-Supervised Learning for Facial Landmark Tracking with Unlabeled Data

Current work of facial landmark tracking usually requires large amounts of fully annotated facial videos to train a landmark tracker. To relieve the burden of manual annotations, we propose a novel facial landmark tracking method that makes full use of unlabeled facial videos by exploiting both self-supervised and semi-supervised learning mechanisms. First, self-supervised learning is adopted for representation learning from unlabeled facial videos. Specifically, a facial video and its shuffled version are fed into a feature encoder and a classifier. The feature encoder is used to learn visual representations, and the classifier distinguishes the input videos as the original or the shuffled ones. The feature encoder and the classifier are trained jointly. Through self-supervised learning, the spatial and temporal patterns of a facial video are captured at representation level. After that, the facial landmark tracker, consisting of the pre-trained feature encoder and a regressor, is trained semi-supervisedly. The consistencies among the tracking results of the original, the inverse and the disturbed facial sequences are exploited as the constraints on the unlabeled facial videos, and the supervised loss is adopted for the labeled videos. Through semi-supervised end-to-end training, the tracker captures sequential patterns inherent in facial videos despite small amount of manual annotations. Experiments on two benchmark datasets show that the proposed framework outperforms state-of-the-art semi-supervised facial landmark tracking methods, and also achieves advanced performance compared to fully supervised facial landmark tracking methods.
Aggregated Deep Saliency Prediction by Self-attention Network

The data-driven method has recently obtained great success on saliency prediction thanks to convolutional neural networks. In this paper, a novel end-to-end deep saliency prediction method named VGG-SSM is proposed. This model identifies three key components: feature extraction, self-attention module, and multi-level integration. An encoder-decoder architecture is used to extract the feature as a baseline. The multi-level integration constructs a symmetric expanding path that enables precise localization. Global information of deep layers is refined by a self-attention module which carefully coordinated with fine details in distant portions of a feature map. Each component surely has its contribution, and its efficiency is validated in the experiments. Additionally, In order to capture several quality factors, the loss function is given by a linear combination of some saliency evaluation metrics. Through comparison with other works, VGG-SSM gains a competitive performance on the public benchmarks, SALICON 2017 version. The PyTorch implementation is available at https://github.com/caoge5844/Saliency.
MRS-Net: Multi-Scale Recurrent Scalable Network for Face Quality Enhancement of Compressed Videos

The past decade has witnessed the explosive growth of faces in video multimedia systems, e.g., videoconferencing and live shows. However, these videos are normally compressed at low bit-rates due to the bandwidth-hungry issue, leading to heavy quality degradation on face regions. This paper addresses the problem of face quality enhancement in compressed videos. Specifically, we establish a compressed face video (CFV) database, which includes 87,607 faces in 113 raw video sequences and their corresponding 904 compressed sequences. We find that the faces of compressed videos exhibit tremendous scale variation and quality fluctuation. Motivated by scalable video coding, we propose a multi-scale recurrent scalable network (MRS-Net) to enhance the quality of multi-scale faces in compressed videos. The MRS-Net is comprised by one base and two refined enhancement levels, corresponding to the quality enhancement of small-, medium- and large-scale faces, respectively. In the multi-level architecture of our MRS-Net, small-/medium-scale face quality enhancement serves as the basis for facilitating the quality enhancement of medium-/large-scale faces. Finally, experimental results show that our MRS-Net method is effective in enhancing the quality of multi-scale faces for compressed videos, significantly outperforming other state-of-the-art methods.
Grandtotem

The grandchild-grandparent relationship faces unique challenges among international students. We developed the Grandtotem, which functions as a focal point for family ritual, a photo frame, and a relationship media album. Research with international student grandchildren helped to identify three dimensions along which their relationships with grandparents must adapt: (1) technology, (2) physical barriers, and (3) changing roles. Initial design feedback suggests that the Grandtotem is a promising tool for strengthening the social fabric during a difficult time of cultural transition.
Recurrent-DC: A deep representation clustering model for university profiling based on academic graph

Universities play an important role in exploring new concepts and knowledge transfer. University research naturally forms heterogeneous graphs through all real-life academic communication activities. In recent years, there have been many large scholarly graph datasets containing web-scale nodes and edges. However, so far, for these graph data, characterizing research about university output is focusing on counting the volume or evaluating the excellence of research articles and providing a ranking. This paper proposes a novel University Profiling Framework (UPF) from the production and complexity point of view which is different from other straightforward solutions. The framework includes a novel Recurrent Deep Clustering Model (Recurrent-DC) for the learning of deep representations and clusters. In our model, successive operations in a clustering algorithm are expressed as steps in a recurrent process, stacked on top of representations output by a Stacked Autoencoder (SAE). Our key idea behind this model is that good representations for university clustering task-specific problem can be learned over multiple timesteps. Experimental results illustrate the stability and effectiveness of the proposed model comparing with the other deep clustering and classical clustering methods.
Sentiment Analysis of Indonesians Response to Influencer in Social Media

Social media as a space for information and thought exchange has many users who have outsized influence towards other users. Users with such influence are known by the term “influencers”. Their influence on social media mainly conveyed in the content they share, either in texts or images form. Such influence become an important aspect of societal life since majority of citizens are now social media user. This research analyzes best classification method to predict the sentiment contained in the response to contents shared by Indonesian influencer Fiersa Besari and Keanu in Twitter. The data used are tweets and comments in Bahasa Indonesia, gathered from Twitter API and cleaned up. The final dataset consists of 3,243 tweets with manual labeling of sentiment. The classification algorithm considered in this research are Naïve Bayes, Support Vector Machine (SVM), Logistic Regression, and Decision Tree. The main result of this research is that Naïve Bayes classification method has the best F-Score performance compared with other methods, with TP Rate for Fiersa Besari 88% and Keanu 86% & TN Rate for Fiersa Besari 76% and Keanu 60%.
Single- and Multi-Distribution Dimensionality Reduction Approaches for a Better Data Structure Capturing

In recent years, the huge expansion of digital technologies has vastly increased the volume of data to be explored, such that reducing the dimensionality of data is an essential step in data exploration. The integrity of a dimensionality reduction technique relates to the goodness of maintaining the data structure. Dimensionality reduction techniques such as Principal Component Analyses (PCA) and Multidimensional Scaling (MDS) globally preserve the distance ranking at the expense of neglecting small-distance preservation. Conversely, the structure capturing of some other methods such as Isomap, Locally Linear Embedding (LLE), Laplacian Eigenmaps t-Stochastic Neighbour Embedding (t-SNE), Uniform Manifold Approximation and Projection (UMAP), and TriMap rely on the number of neighbours considered. This paper presents a dimensionality reduction technique, Same Degree Distribution (SDD) that does not rely on the number of neighbours, thanks to using degree-distributions in both high and low dimensional spaces. Degree-distribution is similar to Student-t distribution and is less expensive than Gaussian distribution. As such, it enables better global data preservation in less processing time. Moreover, to improve the data structure capturing, SDD has been extended to Multi-SDDs (MSDD), which employs various degree-distributions on top of SDD. The proposed approach and its extension demonstrated a greater performance compared with eight other benchmark methods, tested in several popular synthetics and real datasets such as Iris, Breast Cancer, Swiss Roll, MNIST, and Make Blob evaluated by the co-ranking matrix and Kendall's Tau coefficient. For further work, we aim to approximate the number of distributions and their degrees in relation to the given dataset. Reducing the computational complexity is another objective for further work.
Use of the Method of Configuring the Interval Target in the Problem of Synthesis of the Neural Network Classifier for Diagnosing Cardiovascular Diseases

A new approach of using the paradigm of “interval teacher” in the classical scheme of training neural networks with a teacher is used to solve the problem of medical diagnosis of the disease coronary heart disease. An NN architecture has been created that implements a mechanism for selecting a specific “teacher” from a deterministic interval set. A comparative analysis of the proposed and classical training schemes of the neural network is shown, confirming the effectiveness of the proposed training concept. The created medical classifier based on a neural network with an interval teacher provides high classification accuracy.
WITHDRAWN: Clustering Yelp’s sentiment data through various approaches and estimating the error rate

Yelp is a platform that provides free access to use the data for educational and research purposes. The sentiment data which is used for this empirical research consists of two attributes namely sentiment text and sentiment count. The sentiment text contains the sentences and the sentiment counts contain their binary sentiment polarity as 0 and 1. 1 means the sentence is positive and 0 means negative. After performing clustering through various approaches like k-means, canopy, cobweb, etc. it is found that none of the approaches gave the exact results. Through this empirical research, we are putting forward the most appropriate approach among various approaches which gave better results for Yelp’s sentiment data and similar types of data. This research is concluded by recommending the clustering approach which is having less error rate and execution time as compared to others.
Extrovert and Introvert Classification based on Myers-Briggs Type Indicator(MBTI) using Support Vector Machine (SVM)

Personality is a characteristic of each individual who describes their behavior and influences their interactions with other individuals. Every individual has various way to express their feelings, one of them through social media. On social media, humans can create and share a variety of content about various objects, describe activities, to express their thoughts, opinions, and feelings. This study aims to classify human personalities based on the MBTI method that focused on Extrovert and Introvert class, seen from their tweets. Humans able to better understand and improve themselves by recognizing their weaknesses and strengths. The dataset used in this study is a public dataset from Kaggle, consists of 8676 data that posted on Twitter. Various feature combinations have been compared using Support Vector Machine (SVM) classifier. The deployment of solution have been described. Accuracies up to 84.07% were achieved using the methods detailed in this work.