Reviewerly/exHarmony huggingface dataset:
Deep Learning Based OFDM Physical-Layer Receiver for Extreme Mobility

In this paper, we propose a machine learning (ML) aided physical layer receiver technique for demodulating OFDM signals that are subject to very high Doppler effects and the corresponding distortion in the received signal. Specifically, we develop a deep learning based convolutional neural network receiver system that absorbs proper two-dimensional received signal entities in time and frequency, while containing convolutional neural network layers to efficiently and reliably demodulate the bits — when properly trained — despite the substantial Doppler distortion. Representative set of numerical results is provided, in the context of 5G NR mobile communication network and corresponding base-station demodulation performance for uplink. The obtained results show that the proposed receiver system is able to clearly outperform classical LMMSE receivers that operate on subcarrier level and neglect the Doppler-induced intercarrier interference (ICI). Additionally, the proposed ML receiver has the advantage over ICI cancellation based receivers in terms of the reference signal overhead. This paper provides the description of the method and vast set of numerical results in 5G NR network context.
A Planted Clique Perspective on Hypothesis Pruning

Hypothesis pruning is an important prerequisite while working with outlier-contaminated data in many computer vision problems. However, the underlying random data structures are barely explored in the literature, limiting designing efficient algorithms. To this end, we provide a novel graph-theoretic perspective on hypothesis pruning exploiting invariant structures of data. We introduce the planted clique model, a central object in computational statistics, to investigate the information-theoretical and computational limits of the hypothesis pruning problem. In addition, we propose an inductive learning framework for finding hidden cliques that learns heuristics on synthetic graphs with planted cliques and generalizes to real vision problems. We present competitive experimental results with large runtime improvement on synthetic and widely used vision datasets to show its efficacy.
Email phishing detection based on naïve Bayes, Random Forests, and SVM classifications: A comparative study

Emails are frequently utilized as a way of personal and professional communication. Banking information, credit reports, login data, and other sensitive personal information are commonly transmitted over email. This makes them valuable to cybercriminals, who can exploit the knowledge for their gain. Phishing is a technique used by con artists to steal sensitive information from people by impersonating well-known sources. The sender of a phished email can persuade you to disclose personal information under pretenses. The detection of a phished email is treated as a classification problem in this research, and this paper shows how machine learning methods are used to categorize emails as phished or not. SVM classifier attains a maximum accuracy of 0.998 percent in email classification.
Expert Knowledge-Driven Bayesian Network Modeling for Marine Disaster Assessment Under the Small Sample Condition

Risk assessment and management of marine disasters are the prerequisite of ocean exploitation and utilization. Marine disaster assessment is a complicated system engineering with high non-linearity and uncertainty. To deal with the problem, Bayesian network (BN) has become a powerful model used for disaster assessment due to its capability of expressing complex relationships and reasoning with uncertainty. However, scarce data sets and case samples of marine disasters pose an obstacle to BN modeling, particularly for structure and parameter learning. In our research, we combine expert knowledge with small sample to propose a new BN-based assessment model. Expert knowledge is regularly expressed and quantitatively incorporated into BN learning with DS evidence theory. Then, the genetic algorithm is adopted to search the optimal network parameters. Comparative experiments show that the new model has a better assessment accuracy (91.03%) than BPNN (61.34%) and SVM (70.67%), especially with small samples. The proposed model achieves the risk assessment of marine disasters under the small sample condition, providing the technical support for marine disaster prevention and mitigation.
A Dual-Robot Welding Path Planning Method Based on Kmeans and Ant Colony Algorithms

For the problem of double robots working together in the welding process, this project proposes a path planning method based on Kmeans algorithm and ant colony algorithm. The Kmeans algorithm is used to classify all weld joint tasks and reasonably assign them to individual robots. The ant colony algorithm is used to sort the weld joints assigned to individual robots and also to plan the work route for each robot. The simulation results show that the task assignment to this method is more scientific and reasonable than the existing methods, and the path planning results are shorter than the existing ones.
Application of Differential Privacy for Collaborative Filtering Based Recommendation System: A Survey

Collaborative Filtering (CF) is the main technology in recommendation systems. It requires more users' private information to predict personal preferences, so exists the risk of privacy disclosure. Differential Privacy (DP) is a powerful privacy-preserving approach, and it has been widely applied in CF-based recommendation system. However, a comprehensive summary of DP in CF-based recommendation system is lack although many privacy-preserving CF algorithms have been proposed. This paper reviews the existing research based on the principle of DP and the application of CF algorithms. We firstly introduce the theoretical basis and the mechanism employed by DP. Then, two types of CF-based recommendation algorithms with DP are summarized including memory-based and model-based algorithms. Moreover, the optimized CF algorithms with DP in specific application fields are illustrated.
Hybrid Rule-Based Classification by Integrating Expert Knowledge and Data

The common methods for dealing with classification problems include data-driven models and knowledge-driven models. Recently, some methods were proposed to combine the data-driven model with the knowledge-driven model to construct a hybrid model, which improves the classification performance by complementing each other. However, most of the existing methods just assume that the expert knowledge is known in advance, and do not indicate how to obtain it. To this end, this paper proposes a way to obtain knowledge from experts represented by rules through active learning. Then, a hybrid rule-based classification model is developed by integrating the knowledge-driven rule base and the rule base learned from the training data using genetic algorithm. Experiments based on real datasets demonstrate the superiority of the proposed classification model.
Path Planning Based on Inflated Medial Axis and Probabilistic Roadmap for Duct Environment

This paper proposes an inflated medial axis based probabilistic roadmap (PRM) for the duct environment. The study intends to explore a sampling strategy for PRM toward clearance-driven path planning. Sampled nodes are constrained only inside the inflated medial axis area. The medial axis is generated from skeletonizing the map and inflate the skeletal diagram. To evaluate effectiveness of the proposed method, experiments were carried out on a reference map generated by simultaneous localization and mapping (SLAM) from an actual mockup duct environment. Experiments conducted showed the proposed method able to produce a path trajectory with clearance-driven behavior. The method is very useful in narrow spaces such as ducting, which required a safe distance when navigating autonomously in the environment.
A Phase-Derived Velocity Measurement Method Based on the Generalized Radon–Fourier Transform With a Low SNR

The phase-derived velocity measurement (PDVM) technique can achieve a high measurement accuracy at the phase level and thus has great application prospects in the field of micromotion feature extraction and target recognition. To achieve a PDVM with a low signal-to-noise ratio (SNR), a PDVM method based on the generalized Radon&#x2013;Fourier transform (GRFT) is proposed in this article. The main challenges that we overcome are phase extraction and phase ambiguity resolving under the condition of a low SNR. By utilizing the GRFT to estimate the target motion parameters, the echo peak position can be reconstructed, and then the peak phase value can be extracted. In the meantime, the phase ambiguity integer can be resolved based on the rough velocity estimation results obtained by the GRFT, and the phase ambiguity resolving can be realized at a low SNR. In addition, to suppress the influence of noise on the extracted phase, a filter design method based on the target motion characteristics is proposed to further improve the accuracy of the PDVM. In the simulation, the performance of the proposed method under different motion models and different SNR conditions is analyzed, and the effectiveness of the proposed method under low-SNR conditions is verified. Compared with directly using the GRFT, the proposed method has the advantages of strong applicability to different motion models and low computational load.
Renormalization of multipartite entanglement near the critical point of two-dimensional XXZ model with Dzyaloshinskii–Moriya interaction

We investigate the multipartite entanglement and the trace distance for the two-dimensional anisotropic spin-12 XXZ lattice with Dzyaloshinskii–Moriya interaction. It is found that for a many-body quantum system the multipartite entanglement is more advantageous than the bipartite entanglement due to the monogamy property. Both the quantifiers, the multipartite entanglement and the trace distance, decreases with an increase in the anisotropy and the Dzyaloshinskii–Moriya interaction tends to restore the spoiled entanglement. The quantum reormalization group method is used to compute the stable and the unstable fixed points. We observe that the quantum phase transition point is independent of the chosen quantifier as the thermodynamic limit is reached. After sufficient iterations of the quantum renormalization group, we observe two different saturated values of both the quantifiers that represent two separate phases, the spin-fluid phase and the Néel phase. The first derivative and the scaling behavior of the renormalized entanglement quantifiers are calculated. At quantum phase transition point, the non-analytic behavior of the first derivative of the two quantifiers as a function of lattice size is examined and it is found that the universal finite-size scaling law is obeyed. Furthermore, we observe that at the critical point the scaling exponent for the multipartite entanglement and the trace distance can describe the correlation length of the model.
Energy-Efficient Application-Specific Instruction-Set Processor for Feature Extraction in Smart Vision Systems

Smart vision sensor systems enable many computer vision applications such as autonomous drones and wearable devices. These battery-powered gadgets have very stringent power consumption requirements. Close-to-sensor feature extraction compressing the full image into descriptive keypoints, is crucial as it allows for several design optimizations. First, the amount of necessary on-chip memory can be lessened. Second, the volume of data that needs to be exchanged between nodes in Internet of Things (IoT) applications can also be reduced. This work explores the usage of an Application Specific Instruction Set Processor (ASIP) tailored to perform energy-efficient feature extraction in real-time. The ASIP features a Very Long Instruction Word (VLIW) central core comprising one RV32I RISCV and three vector slots. The on-chip memory sub-system implements parallel multi-bank memories with near-memory data shuffling to enable single-cycle multi-pattern vector access. As a case study, Oriented FAST and Rotated BRIEF (ORB) is used to evaluate the proposed architecture. We show that the architecture supports VGA-resolution images at 140 Frames-Per-Second (FPS), for one scale, reducing the number of memory accesses by 2 orders of magnitude comparing to other embedded general-purpose architectures.
Multi-scale synergy approach for real-time semantic segmentation

In deep convolution neural network based models for semantic segmentation, diverse receptive fields improve the performance by capturing disparate context information. Multiscale inference is good for both thin and large objects. However, the final result is not optimal through averaging or Max pooling combination. In this paper, we propose an approach to take advantage of multi-scale predictions. Our uncertain-pixels part discovers the worse prediction of a low scale and chooses the complement from a high scale. The final output is effectively merged from two scales. We validate our proposed model with a series of experiments on different datasets. The results achieve the accuracy and speed for real-time semantic segmentation. On Cityscapes dataset, our network achieves 76.3 % mIoU at 50 FPS, and on Mapillary, 42.6 % mIoU.
Multiple Resolution Prediction With Deep Up-Sampling for Depth Video Coding

The depth video contains large smooth contents with sharp edges. Since the deep learning-based color video orientated intra prediction methods pay no attention to the characteristics of depth video, they are unsuitable for optimizing the coding efficiency of depth video. In this paper, a multiple resolution prediction method with deep up-sampling is proposed to promote the coding efficiency of depth video. To efficiently encode the depth blocks of different complexity, the depth block is selectively encoded at different resolutions, including <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\times 1$ </tex-math></inline-formula> , <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\times 1$ </tex-math></inline-formula> /2, and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\times 1$ </tex-math></inline-formula> /4 resolutions. If the block is encoded with a low-resolution (LR), the resolution of reconstructed LR depth block is recovered by an up-sampling network. To constrain the quality of both reconstructed high-resolution depth block and its synthesized view, a view synthesis distortion guidance mechanism is proposed for the up-sampling network. In addition, a distillation-based lightweight up-sampling network is proposed to reduce the computational complexity. Experimental results demonstrate that the proposed multiple resolution prediction method obtains an average of 10.84% BD-rate saving in comparison with 3D-HEVC.
Detection of Invasive Ductal Carcinoma using Transfer Learning with Deep Residual Network

Invasive Ductal Carcinoma (IDC) is a most com-mon subtype of Breast Cancer. This is a cancer to which any gender is sensitive. The early diagnosis can help to start timely treatment for this rapidly spreading cancer which gradually infects the other parts of the body. This malignancy can be detected by the pathologists by some specialised pathological tasks. But this can be a tedious and time-consuming task. So, to speed up the detection, computational techniques are being employed in the pathology labs. The Deep Learning based approaches prove to be a reliable solution as they are generally accurate and less error prone. In this we propose a classification model which involves the pretrained Residual model for the feature extraction. The IDC datasets are generally huge, so most of the researchers train their models over a subset of the dataset. But in this paper a fold based training approach was used to train the model over the entire dataset. The model seems to be performing better than the other works and is reliable as it is trained and over the complete dataset distribution.
Handwritten Digit Recognition using Convolution Neural Networks

Deep learning has witnessed a significant evolution recently with growth in high-performance devices and research in the neural network. The central aspect of this paper is to discuss the deep learning concept ideas and problems faced during training the model and come with a solution for better accuracy, illustrated by digit recognition and prediction using a convolution neural network. The first part of the research focused on the techniques used for training models in neural networks and enlightened the previous study in digit recognition. The second half of the paper detailed the concept of the proposed model for digit recognition with increased accuracy by preprocessing samples and ensemble of two or more models with different architectures and preprocessing. Preprocessing of samples and implementation of the model architecture is done using python.
College Agent: The Machine Learning Chatbot for College Tasks

A chatbot is a computer program that can understand the human language and respond like humans. The more it acts like a human, the more helpful the chatbot is. There are several attempts to make the chatbot intelligent or human-like. In college, the routine task is to answer the questions that have been asked repeatedly or found on the official website. This task is time-consuming and a wastes task. The chatbot is one of the solutions to this problem. A good chatbot can naturally answer such questions quickly and tirelessly. However, producing an intelligent or human-like chatbot is a challenge. The chatbot should be able to answer the fundamental questions in the domain and the advanced opinion questions. In this research, we proposed the chatbot system using the machine learning technique. Therefore, the chatbot can learn from the user to improve its performance. We evaluate our chatbot by testing with real-world conversation. The result shows that our chatbot can respond to the fundamental questions with a higher accuracy rate than the advanced questions.
An Investigation on Deep Learning-Based Activity Recognition Using IMUs and Stretch Sensors

With the advancement and ubiquitousness of wearable devices, wearable sensor-based human activity recognition (HAR) has become a prominent research area in the healthcare domain and human-computer interaction. Inertial measurement unit (IMU) which can provide a wide range of information such as acceleration, angular velocity has become one of the most commonly used sensors in HAR. Recently, with the growing demand for soft and flexible wearable devices, mountable stretch sensors have become a new promising modality in wearable sensor-based HAR. In this paper, we propose a deep learning-based multi-modality HAR framework which consists of three IMUs and two fabric stretch sensors in order to evaluate the potential of stretch sensors independently and in combination with IMU sensors for the activity recognition task. Three different deep learning algorithms: long short-term memory (LSTM), convolutional neural network (CNN) and hybrid CNN-LSTM are deployed to the sensor data for automatically extracting deep features and performing activity classification. The impact of sensor type on recognition accuracy of different activities is also examined in this study. A dataset collected from the proposed framework, namely iSPL IMU-Stretch and a public dataset called w-HAR are used for experiments and performance evaluation.
Increasing Accuracy of Hand Gesture Recognition using Convolutional Neural Network

Human gestures play important roles in the interaction between humans and machines. These human gestures are becoming more important, yet complex gesture input and noise induced by external elements are important problems to solve in order to improve the accuracy of hand gesture recognition methods. Convolutional Neural Networks (CNN) are offered as a technology that can solve this problem in this research. CNN has the advantage of being able to learn image data, and this technology will greatly improve human-machine interaction accuracy. Data was extracted using Vivaldi antennas with a frequency bandwidth of 7.4-9.0 GHz and gain characteristics of 8 dB in five sign language operations, and data that went through the preprocessing process was learned through CNN. The classification results of the proposed CNN showed about 90% accuracy.
On the Round Complexity of Randomized Byzantine Agreement

We prove lower bounds on the round complexity of randomized Byzantine agreement (BA) protocols, bounding the halting probability of such protocols after one and two rounds. In particular, we prove that: (1) BA protocols resilient against $n/3$ [resp., $n/4$] corruptions terminate (under attack) at the end of the first round with probability at most $o(1)$ [resp., $1/2+ o(1)$]. (2) BA protocols resilient against a fraction of corruptions greater than $1/4$ terminate at the end of the second round with probability at most $1-\Theta(1)$. (3) For a large class of protocols (including all BA protocols used in practice) and under a plausible combinatorial conjecture, BA protocols resilient against a fraction of corruptions greater than $1/3$ [resp., $1/4$] terminate at the end of the second round with probability at most $o(1)$ [resp., $1/2 + o(1)$]. The above bounds hold even when the parties use a trusted setup phase, e.g., a public-key infrastructure (PKI). The third bound essentially matches the recent protocol of Micali (ITCS'17) that tolerates up to $n/3$ corruptions and terminates at the end of the third round with constant probability.
Advanced Retinex-Net image enhancement method based on value component processing

When capturing images under low-light lighting conditions, the resulting images often suffer low visibility. Such low-visibility images not only affect the visual effect but also cause many difficulties in practical application. Therefore, image enhancement technology under low-light conditions has always been a challenging problem in image algorithms. Considering that most of the existing image enhancement methods are based on the RGB color space enhancement technology, the correlation among the RGB three primary colors is ignored, which makes the color distortion phenomenon easy to occur when the image is enhanced. To solve the problems of poor image visibility and color deviation under low-light conditions, in this paper an advanced Retinex network enhancement method is proposed. In the method, firstly the low-light RGB image is transformed into HSV color space, the Retinex decomposition network is used to decompose and enhance the value component separately, and thus increasing the resolution of the value component through up-sampling operation; then, for the hue component and saturation component, the nearest neighbor interpolation is used to increase their resolutions, and the enhanced value component is combined to convert back to RGB color space to obtain the initial enhanced image; finally, the wavelet transform image fusion technology is used to fuse with the original low-light image to eliminate the over-enhanced part in the initial enhanced image. The analysis of experimental results shows that the method proposed in this paper has obvious advantages in brightness enhancement and color restoration of low-light images. Especially, comparing with the original Retinex network method, the NIQE value decreases by an average of 19.49%, and the image standard deviation increases by an average of 41.35%. The algorithm proposed in this paper is expected to be effectively used in the fields of security monitoring and biomedicine.