Reviewerly/exHarmony huggingface dataset:
CNN-Based RGB-D Salient Object Detection: Learn, Select, and Fuse

The goal of this work is to present a systematic solution for RGB-D salient object detection, which addresses the following three aspects with a unified framework: modal-specific representation learning, complementary cue selection, and cross-modal complement fusion. To learn discriminative modal-specific features, we propose a hierarchical cross-modal distillation scheme, in which we use the progressive predictions from the well-learned source modality to supervise learning feature hierarchies and inference in the new modality. To better select complementary cues, we formulate a residual function to incorporate complements from the paired modality adaptively. Furthermore, a top-down fusion structure is constructed for sufficient cross-modal cross-level interactions. The experimental results demonstrate the effectiveness of the proposed cross-modal distillation scheme in learning from a new modality, the advantages of the proposed multi-modal fusion pattern in selecting and fusing cross-modal complements, and the generalization of the proposed designs in different tasks.
AROC

We present a MATLAB toolbox for <u>A</u>utomated <u>R</u>eachset <u>O</u>ptimal <u>C</u>ontrol (AROC) that automatically synthesizes verified controllers for solving reach-avoid problems using reachability analysis. The toolbox implements two different types of control approaches: When using our verified model predictive controller, a feasible control law is constructed and verified on-the-fly during online application of the system. For motion-primitive-based control, on the other hand, controllers for many motion primitives are synthesized offline and then used for online motion planning with a maneuver automaton. Since our toolbox considers general nonlinear systems with input constraints, state constraints, and bounded disturbances, it is applicable to a very broad class of systems, as we demonstrate with several numerical examples. AROC is available at https://aroc.in.tum.de.
Awareness and Perception of the Artificial Intelligence in the Management of University Libraries in Nigeria

Due to the growing presence of artificial intelligence (AI) in developed countries, this study investigates the awareness and perception about artificial intelligence in the management of university libraries in Nigeria. The study is a survey design that is based on both qualitative and quantitative approaches. The population of the study comprised eighty academic librarians from eight purposively selected university libraries in the country. The finding of the study revealed that academic librarians are aware of the existence of AI usage in the university libraries and that the fear of job loss is the major constraint they face in the adoption of the technologies; even though they are aware that the innovative technologies will enable efficient user satisfaction. The study recommends the need for academic librarians to make themselves relevant in this era of the fourth industrial revolution, by acquiring necessary skills that are in line with the technologies. It is also critical that library management educate academic librarians to understand that the adoption of AI in the library does not translate to the loss of jobs.
Proximal Methods Avoid Active Strict Saddles of Weakly Convex Functions

We introduce a geometrically transparent strict saddle property for nonsmooth functions. This property guarantees that simple proximal algorithms on weakly convex problems converge only to local minimizers, when randomly initialized. We argue that the strict saddle property may be a realistic assumption in applications, since it provably holds for generic semi-algebraic optimization problems.
Vertex Feature Encoding and Hierarchical Temporal Modeling in a Spatio-Temporal Graph Convolutional Network for Action Recognition

Spatio-temporal Graph Convolutional Networks (ST-GCNs) have shown great performance in the context of skeleton-based action recognition. Nevertheless, ST-GCNs use raw skeleton data as vertex features. Such features have low dimensionality and might not be optimal for action discrimination. Moreover, a single layer of temporal convolution is used to model short-term temporal dependencies but can be insufficient for capturing both long-term. In this paper, we extend the Spatio-Temporal Graph Convolutional Network for skeleton-based action recognition by introducing two novel modules, namely, the Graph Vertex Feature Encoder (GVFE) and the Dilated Hierarchical Temporal Convolutional Network (DH-TCN). On the one hand, the GVFE module learns appropriate vertex features for action recognition by encoding raw skeleton data into a new feature space. On the other hand, the DH-TCN module is capable of capturing both short-term and long-term temporal dependencies using a hierarchical dilated convolutional network. Experiments have been conducted on the challenging NTU RGB-D 60, NTU RGB-D 120 and Kinetics datasets. The obtained results show that our method competes with state-of-the-art approaches while using a smaller number of layers and parameters; thus reducing the required training time and memory.
A Hybrid Adaptive Quantum Behaved Particle Swarm Optimization Algorithm Based Multilevel Thresholding for Image Segmentation

Thresholding is a frequently used method in image processing because of its consistency and low computational cost. Kapur's method is an important threshold segmentation method. However, it is computationally expensive when extended to multilevel thresholding since it exhaustively searched the optimal thresholds to optimize the objective functions. Recently, metaheuristic algorithms have been successfully applied for thresholding problems. A multi-threshold segmentation of 2D Kapur's entropy based on hybrid adaptive quantum behaved particle swarm optimization (HAQPSO) algorithm is proposed. Then, the Gaussian chaotic map model and the Levy flight are employed to increase the search ability of HAQPSO algorithm and balance the exploitation and exploration. The HAQPSO algorithm optimizes the Kapur's multi-threshold method to conduct experiments on standard images, satellite images and sport images. The experimental results show that HAQPSO is an effective image segmentation method, with high segmentation accuracy, good convergence, strong anti-noise and certain engineering practicability.
Research on Tracking and Registration Algorithm Based on Natural Feature Point

In the augmented reality system, the position and direction of the user's point of view and line of sight in the real scene is acquired in real-time. The position and direction information will determine the exact position of the virtual object of the real scene. At the same time, various coordinate systems are established according to the user's line of sight. So registration tracking technology is very important. The paper proposes an accurate, stable, and effective augmented reality registration algorithm. The method adopts the method of ORB (oriented FAST and rotated BRIEF) features matching combined with RANSAC (random sample consensus) to obtain the homography matrix and then uses the KLT (kanade-lucas-tomasi) tracking algorithm to track the mark, which is a better solution. The error accumulation defect based on the natural feature tracking registration method is improved, and the stability and accuracy of the registration are improved. Experiments have proved that the algorithm in this paper is accurate, stable, and effective, and can complete the virtual and real registration tasks accurately and stably even when the marked part is not visible.
Energy Efficiency Analysis of Post-Quantum Cryptographic Algorithms

Classical cryptographic schemes in use today are based on the difficulty of certain number theoretic problems. Security is guaranteed by the fact that the computational work required to break the core mechanisms of these schemes on a conventional computer is infeasible; however, the difficulty of these problems would not withstand the computational power of a large-scale quantum computer. To this end, the post-quantum cryptography (PQC) standardization process initiated by the National Institute of Standards and Technology (NIST) is well underway. In addition to the evaluation criteria provided by NIST, the energy consumption of these candidate algorithms is also an important criterion to consider due to the use of battery-operated devices, high-performance computing environments where energy costs are critical, as well as in the interest of green computing. In this paper, the energy consumption of PQC candidates is evaluated on an Intel Core i7-6700 CPU using PAPI, the Performance API. The energy measurements are categorized based on their proposed security level and cryptographic functionality. The results are then further subdivided based on the underlying mechanism used in order to identify the most energy-efficient schemes. Lastly, IgProf is used to identify the most energy-consuming subroutines within a select number of submissions to highlight potential areas for optimization.
Adaptive Quantization Method for CNN with Computational-Complexity-Aware Regularization

Quantization is a typical approach toward reducing processing time for inference of convolutional neural networks (CNNs). The key to reducing inference times without drastic decreases in accuracy is allocating optimal bit widths according to each layer or filter. One of the most promising ways to find optimal bit allocations is to learn quantization step size and weight parameters through gradient descent. The conventional method optimizes those parameters under the constraint for model size or memory footprint of CNN. However, the bit allocations obtained by the conventional method are not always optimal for inference time because the arithmetic intensity of CNN is significantly high, and the time spent on computing is a bottleneck for the inference time. In this paper, we propose a regularization method using a computational-complexity metric (which we call MACxbit) that is correlated with the inference time of quantized CNN models. The proposed method can obtain an optimal bit allocation that achieves better recognition accuracy under a specified computational-complexity target than the conventional method. For similar recognition accuracy on the optimized ResNet-18 model, the proposed method achieves 21.0% less inference time compared to the conventional method.
TricubeNet: 2D Kernel-Based Object Representation for Weakly-Occluded Oriented Object Detection

We present a novel approach for oriented object detection, named TricubeNet, which localizes oriented objects using visual cues (i.e., heatmap) instead of oriented box offsets regression. We represent each object as a 2D Tricube kernel and extract bounding boxes using simple image-processing algorithms. Our approach is able to (1) obtain well-arranged boxes from visual cues, (2) solve the angle discontinuity problem, and (3) can save computational complexity due to our anchor-free modeling. To further boost the performance, we propose some effective techniques for size-invariant loss, reducing false detections, extracting rotation-invariant features, and heatmap refinement. To demonstrate the effectiveness of our TricubeNet, we experiment on various tasks for weakly-occluded oriented object detection: detection in an aerial image, densely packed object image, and text image. The extensive experimental results show that our TricubeNet is quite effective for oriented object detection. Code is available at https://github.com/qjadud1994/TricubeNet.
Over-the-Air Radar Emitter Signal Classification Based on SDR

At present, in the field of radar emitter classification, theoretical simulation is mostly used to carry out algorithm research. However, there are few schemes to study signal classification in real electromagnetic environment using actual hardware. Therefore, this paper proposes a radar emitter classification scheme based on HackRF Software Defined Radio (SDR) and deep learning to solve the problem of weak engineering practice. Firstly, the GNU Radio development environment is used to realize the integration design of real space signal transceiver and time-frequency analysis algorithm application on HackRF hardware platform. Then, a classification model with 11 layers network is constructed to automatically extract the deep features of intra-pulse signal time-frequency image. Finally, the classification performance of eight kinds of signals in real electromagnetic environment is tested. The total recognition accuracy of this scheme is more than 83% under 6dB low Signal-to-Noise Ratio (SNR), which proves the effectiveness of the scheme, and provides an important basis for practical engineering application in the future.
A Unified Formulation of k-Means, Fuzzy c-Means and Gaussian Mixture Model by the Kolmogorov–Nagumo Average

Clustering is a major unsupervised learning algorithm and is widely applied in data mining and statistical data analyses. Typical examples include k-means, fuzzy c-means, and Gaussian mixture models, which are categorized into hard, soft, and model-based clusterings, respectively. We propose a new clustering, called Pareto clustering, based on the Kolmogorov–Nagumo average, which is defined by a survival function of the Pareto distribution. The proposed algorithm incorporates all the aforementioned clusterings plus maximum-entropy clustering. We introduce a probabilistic framework for the proposed method, in which the underlying distribution to give consistency is discussed. We build the minorize-maximization algorithm to estimate the parameters in Pareto clustering. We compare the performance with existing methods in simulation studies and in benchmark dataset analyses to demonstrate its highly practical utilities.
Positive unlabeled learning‐based anomaly detection in videos

Anomaly detection plays a critical role in intelligent video surveillance. However, real-world video data obtained always contains large numbers of normal video data, along with large numbers of unlabeled data. A promising solution with one-class classification and semi-supervised learning may not be satisfactory as they fail to make good use of unlabeled data with only normal data available. In this paper, we introduced a new framework, called Positive Unlabeled learning-based Anomaly event Detection (PU-AD), to exploit the weakly-supervised information. To the best of our knowledge, this is the first work that introduces the PU idea and achieves detecting abnormal events with a limited number of partially labeled data. Experiments on real-world surveillance videos show that the proposed method outperforms the existing state-of-the-art methods.
ITSO: a novel inverse transform sampling-based optimization algorithm for stochastic search

Optimization algorithms appear in the core calculations of numerous Artificial Intelligence (AI) and Machine Learning methods and Engineering and Business applications. Following recent works on AI's theoretical deficiencies, a rigour context for the optimization problem of a black-box objective function is developed. The algorithm stems directly from the theory of probability, instead of presumed inspiration. Thus the convergence properties of the proposed methodology are inherently stable. In particular, the proposed optimizer utilizes an algorithmic implementation of the n-dimensional inverse transform sampling as a search strategy. No control parameters are required to be tuned, and the trade-off among exploration and exploitation is, by definition, satisfied. A theoretical proof is provided, concluding that when falling into the proposed framework, either directly or incidentally, any optimization algorithm converges. The numerical experiments verify the theoretical results on the efficacy of the algorithm apropos reaching the sought optimum.
The Doge of Wall Street: Analysis and Detection of Pump and Dump Cryptocurrency Manipulations

Cryptocurrencies are increasingly popular. Even people who are not experts have started to invest in these assets, and nowadays, cryptocurrency exchanges process transactions for over 100 billion US dollars per month. Despite this, many cryptocurrencies have low liquidity and are highly prone to market manipulation. This paper performs an in-depth analysis of two market manipulations organized by communities over the Internet: The pump and dump and the crowd pump. The pump and dump scheme is a fraud as old as the stock market. Now, it has new vitality in the loosely regulated market of cryptocurrencies. Groups of highly coordinated people systematically arrange this scam, usually on Telegram and Discord. We monitored these groups for more than 3 years, detecting around 900 individual events. We report on three case studies related to pump and dump groups. We leverage our unique dataset of the verified pump and dumps to build a machine learning model able to detect a pump and dump in 25 seconds from the moment it starts, achieving the results of 94.5% of F1-score. Then, we move on to the crowd pump, a new phenomenon that hit the news in the first months of 2021, when a Reddit community inflated the price of the GameStop stocks (GME) by over 1,900% on Wall Street, the world’s largest stock exchange. Later, other Reddit communities replicated the operation on the cryptocurrency markets. The targets were DogeCoin (DOGE) and Ripple (XRP). We reconstruct how these operations developed and discuss differences and analogies with the standard pump and dump. We believe this study helps understand a widespread phenomenon affecting cryptocurrency markets. The detection algorithms we develop effectively detect these events in real-time and helps investors stay out of the market when these frauds are in action.
Visual Thinking of Neural Networks: Interactive Text to Image Synthesis

Reasoning, a trait of cognitive intelligence, is regarded as a crucial ability that distinguishes humans from other species.However, neural networks now pose a challenge to this human ability.Text-to-image synthesis is a class of vision and linguistics, wherein the goal is to learn multimodal representations between the image and text features.Hence, it requires a high-level reasoning ability that understands the relationships between objects in the given text and generates high-quality images based on the understanding.Text-to-image translation can be termed as the visual thinking of neural networks.In this study, our model infers the complicated relationships between objects in the given text and generates the final image by leveraging the previous history.We define diverse novel adversarial loss functions and finally demonstrate the best one that elevates the reasoning ability of the text-to-image synthesis.Remarkably, most of our models possess their own reasoning ability.Quantitative and qualitative comparisons with several methods demonstrate the superiority of our approach.
A feature fusion deep-projection convolution neural network for vehicle detection in aerial images

With the rapid development of Unmanned Aerial Vehicles, vehicle detection in aerial images plays an important role in different applications. Comparing with general object detection problems, vehicle detection in aerial images is still a challenging research topic since it is plagued by various unique factors, e.g. different camera angle, small vehicle size and complex background. In this paper, a Feature Fusion Deep-Projection Convolution Neural Network is proposed to enhance the ability to detect small vehicles in aerial images. The backbone of the proposed framework utilizes a novel residual block named stepwise res-block to explore high-level semantic features as well as conserve low-level detail features at the same time. A specially designed feature fusion module is adopted in the proposed framework to further balance the features obtained from different levels of the backbone. A deep-projection deconvolution module is used to minimize the impact of the information contamination introduced by down-sampling/up-sampling processes. The proposed framework has been evaluated by UCAS-AOD, VEDAI, and DOTA datasets. According to the evaluation results, the proposed framework outperforms other state-of-the-art vehicle detection algorithms for aerial images.
Comparison on Some Machine Learning Techniques in Breast Cancer Classification

Breast cancer is the second most common cancer after lung cancer and one of the main causes of death worldwide. Women have a higher risk of breast cancer as compared to men. Thus, one of the early diagnosis with an accurate and reliable system is critical in breast cancer treatment. Machine learning techniques are well known and popular among researchers, especially for classification and prediction. An investigation was conducted to evaluate the performance of breast cancer classification for malignant tumors and benign tumors using various machine learning techniques, namely k-Nearest Neighbors (k-NN), Random Forest, and Support Vector Machine (SVM) and ensemble techniques to compute the prediction of the breast cancer survival by implementing 10-fold cross validation. Additionally, the proposed methods are classified using 2-fold, 3-fold, and 5-fold cross validation to meet the best accuracy rate. This study used a dataset obtained from Wisconsin Diagnostic Breast Cancer (WDBC) with 23 selected attributes measured from 569 patients, from which 212 patients have malignant tumors and 357 patients have benign tumors. The performance evaluation of the proposed methods was computed to obtain accuracy, sensitivity, and specificity. Comparison results between all methods show that AdaBoost ensemble methods gave the highest accuracy at 98.77% for 10-fold cross validation, while 2-fold and 3-fold cross validation at 98.41% and 98.24%, respectively. Nevertheless, the result with 5-fold cross validation show SVM produced the best accuracy rate at 98.60% with the lowest error rate.
The Development of Android for Indonesian Sign Language Using Tensorflow Lite and CNN: An Initial Study

Abstract Deaf people in the world have different sign language according to their living place. They tend to use simple sign language to communicate with others. Whereas every country has their own sign language. Usually a country has one national sign language, but there are 2 kinds of sign languages (SIBI and BISINDO) in Indonesia. However, BISINDO (Indonesian Sign Language) had not recognized yet as a formal sign language like as SIBI (Indonesian Sign Language System), it is only a daily sign language among them and with normal people. Thus, researcher propose to promote BISINDO by developing BisAndro (BISINDO using Android) application. At the start, researcher prepare to compare BISINDO systems in cyberspace, develope basic BISINDO website by PHP and MySQL, train some gestures in TensorFlow lite, and make CNN (Convolutional Neural Network) plan before developing BisAndro application. BisAndro application will be built by Python programming, Tensorflow as an image recognition technology, and Android mobile phone as the launcher device. The application is expected can be used by deaf people to communicate with others easily because it facilitates two ways communication through mobile phone. So, the preparation is needed to know about initial requirement of the next BISINDO learning application using Android smartphone in order to be more useful for deaf and normal people directly.