Reviewerly/exHarmony huggingface dataset:
Imbalanced Clustering With Theoretical Learning Bounds

Imbalanced clustering, where the number of samples varies in different clusters, has arisen from many real data mining applications. It has gained increasing attention. Nevertheless, due to its unsupervised nature, imbalanced clustering is more challenging than its supervised counterpart, i.e., imbalanced classification. Furthermore, existing imbalanced clustering methods are empirically designed and they often lack solid theoretical guarantees, e.g., the excess risk estimation. To solve these important but rarely studied problems, we first propose a novel <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula> -Means algorithm for imbalanced clustering problem with Adaptive Cluster Weight (MACW), together with its excess clustering risk bound analysis. Inspired by this theoretical result, we further propose an improved algorithm called Imbalanced Clustering with Theoretical Learning Bounds (ICTLB). It refines the weights and encourages the optimal trade-off among per-cluster weights by optimizing the excess clustering risk bound. A theoretically-principled justification of ICTLB is provided for verification. Comprehensive experiments on many imbalanced datasets verify the effectiveness of ICTLB in solving cluster imbalanced problems.
Intent Prediction in Human–Human Interactions

The human ability to infer others' intent is innate and crucial to development. Machines ought to acquire this ability for seamless interaction with humans. In this article, we propose an agent model for predicting the intent of actors in human–human interactions. This requires <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">simultaneous generation and recognition of an interaction</i> at any time, for which end-to-end models are scarce. The proposed agent actively samples its environment via a sequence of glimpses. At each sampling instant, the model infers the observation class and completes the partially observed body motion. It learns the sequence of body locations to sample by jointly minimizing the classification and generation errors. The model is evaluated on videos of two-skeleton interactions under two settings: (first person) one skeleton is the modeled agent and the other skeleton's joint movements constitute its visual observation, and (third person) an audience is the modeled agent and the two interacting skeletons' joint movements constitute its visual observation. Three methods for implementing the attention mechanism are analyzed using benchmark datasets. One of them, where attention is driven by sensory prediction error, achieves the highest classification accuracy in both settings by sampling less than 50% of the skeleton joints, while also being the most efficient in terms of model size. This is the first known attention-based agent to learn end-to-end from two-person interactions for intent prediction, with high accuracy and efficiency.
Context Label Learning: Improving Background Class Representations in Semantic Segmentation

Background samples provide key contextual information for segmenting regions of interest (ROIs). However, they always cover a diverse set of structures, causing difficulties for the segmentation model to learn good decision boundaries with high sensitivity and precision. The issue concerns the highly heterogeneous nature of the background class, resulting in multi-modal distributions. Empirically, we find that neural networks trained with heterogeneous background struggle to map the corresponding contextual samples to compact clusters in feature space. As a result, the distribution over background logit activations may shift across the decision boundary, leading to systematic over-segmentation across different datasets and tasks. In this study, we propose context label learning (CoLab) to improve the context representations by decomposing the background class into several subclasses. Specifically, we train an auxiliary network as a task generator, along with the primary segmentation model, to automatically generate context labels that positively affect the ROI segmentation accuracy. Extensive experiments are conducted on several challenging segmentation tasks and datasets. The results demonstrate that CoLab can guide the segmentation model to map the logits of background samples away from the decision boundary, resulting in significantly improved segmentation accuracy. Code is available at https://github.com/ZerojumpLine/CoLab.
Improved Error Bounds for the Distance Distribution of Reed-Solomon Codes

We use the generating function approach to derive simple expressions for the factorial moments of the distance distribution over Reed-Solomon codes. We obtain better upper bounds for the error term of a counting formula given by Li and Wan, which gives nontrivial estimates on the number of polynomials over finite fields with prescribed leading coefficients and a given number of linear factors. This improvement leads to new results on the classification of deep holes of Reed-Solomon codes.
Obstacle Avoidance Learning for Robot Motion Planning in Human–Robot Integration Environments

In the human–robot integration environment, it is essential for mobile robots to pass through the crowd and complete the navigation task smoothly. However, the current mainstream robot navigation algorithms only treat pedestrians as dynamic obstacles and passively avoid pedestrians in local planning. When encountering fast-moving pedestrians, local path planning often fails, causing the robot to stagnate, spin or shake in place, which in turn reduces the navigation efficiency and results in unnatural navigation trajectories. To address this problem, it is desirable for the robot to find a safe and convenient temporary target to avoid the collision with fast-moving pedestrians. In this article, we propose an obstacle avoidance learning method with the temporary target for the robot motion planning in the human–robot integration environment. The temporary target distribution is learned from imitations by using a conditional variational autoencoder (CVAE) framework, whereby the dynamic scenario information, including pedestrian information, the environmental information, and the robot information, is considered as the generation conditions. With the proposed method, the mobile robot first navigates to the temporary target area, and then plans the path toward the final target point. Experimental studies reveal that the proposed method can achieve satisfactory performance with respect to different scenario conditions.
Fabric Variation and Visualization Using Light Dependent Factor

In the present-day scenario, we wear a variety of clothing but never know its fabric until unless a person touches or feels it. But is there any possibility to find its fabric without touching it? The answer for this query is yes, and we can find a fabric type of clothing without any touch. Here comes an application of IoT, where each problem can be solved by things in real-world problems. Fabric variation is a factor of recognition for a sensor where its resonance is the type and gives the sensor feedback. This type of mechanism scenario is used for regular period identifications of clothing. Pattern analysis in fabric can speak a matter a lot in differentiation of types in it.
Alternated Greedy-Step Deterministic Policy Gradient

The greedy-step <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula> -learning (GQL) can effectively accelerate the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula> -value updating process. However, since it is an improved version of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula> -learning, the problem of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula> -value overestimation also exists. Since there are in total two max operators used to iteratively calculate <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula> -value in GQL, many existing solutions to reduce the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula> -value estimation bias are invalid for GQL. To address the issue, an alternated greedy-step update (AGU) framework that consists of two independent <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula> -value estimators is proposed in this study. In the proposed AGU framework, one estimator is to determine the time step that can maximize the estimated <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> -step return and the other estimator is to update the prior estimator using the target value calculated on the basis of the determined time step. The convergence of the AGU framework is proved in theoretical. In addition, an alternated greedy-step deterministic policy gradient (AGDPG) that can be applied to continuous-action tasks is proposed by combining the AGU framework with deep deterministic policy gradient (DDPG). Experiments of AGDPG on continuous-action tasks of the MuJoCo platform highlight its superior performance.
Design and Implementation of Deep Learning Based Illicit Drug Supplier Detection System

Video-based face recognition is likely exigent for the reason that a large amount of data has to be processed and it involves intricate computations. This paper provides a novel video-based face recognition system, designed using the convolution neural network (CNN) algorithm. The CNN is trained with the database generated from a video stream to recognize the drug dealers. If a drug dealer is detected, the system alerts the school or college management. The proposed drug distributor detection system is implemented using a 64-bit raspberry Pi 3 microcontroller and a Pi camera module that captures the video stream in real-time. If the detected face is a drug dealer, the system alerts the school or college management through SMS. The experimental results show that the CNN is capable to classify authorized students and drug dealers correctly and detect abnormal activities without using huge amount of training data. A top recognition accuracy of 99.58% was acquired for CNN.
On the Discriminability of Samples Using Binarized ReLU Activations

Binarized ReLU activations are considered as a metric space equipped with the Hamming distance. While for two-layer ReLU networks with random Gaussian weights it can be shown theoretically that local metric properties are approximately preserved, we experimentally study the discrimination capability in this Hamming space for deeper ReLU networks and look also at the non-local behavior. It turns out that the discrimination capability is approximately preserved as expected.
An Efficient Integrity Checking Scheme with Full Identity Anonymity for Cloud Data Sharing

Cloud storage services can support data sharing for a group of users. However, the cloud server may lose some of the shared data when attacked. The integrity of stored data is a key issue for cloud storage. However, identity anonymity, an important problem in data integrity, has not been fully investigated yet. Furthermore, the existing schemes of data integrity checking are usually based on public key infrastructure and thus have to perform complex certificate management. In this paper, we propose an efficient integrity checking scheme to achieve full identity anonymity. Additionally, the proposed scheme can reduce the workloads of certificate management and simplify key management. Inspired by the idea of attribute-based signature, we devise a common predicate to manage the identities of legitimate users and disable illegal users from joining the data sharing. By adopting the technique of the monotone span program, the legitimate user can compute the valid authenticators of shared data for all attributes in the common predicate without the need of binding his attribute set to the shared data. Whereupon, the user's identity remains anonymous to all parties in the data sharing because he can share the data and authenticators without revealing his attribute set to other parties. The extensive experimental results demonstrate that the proposed scheme has less computational and communication overhead compared with the existing schemes while achieving full identity anonymity.
Designing Learning-Based Adversarial Attacks to (MIMO-)OFDM Systems With Adaptive Modulation

With the development of 5G, 6G, and beyond, wireless communications have taken a vital role in our daily life. The wide adoption of wireless devices and applications makes security of the wireless transmissions more critical. Many attacks aiming at wireless communications have been proposed (e.g., jamming attack). Orthogonal Frequency Division Multiplexing (OFDM) together with multi-input and multi-output (MIMO) design and adaptive modulation (AM) becomes an important technique to achieve high data rates. However, most of the attacks are not tailored for OFDM based systems. In this paper, we reveal a potential security issue of (MIMO-)OFDM-AM systems by developing three learning-based attack methods to (MIMO-) OFDM-AM systems. Based on the principle of AM, channel state information (CSI) is inferred from the modulation type of each subcarrier without any prior knowledge of the desired transmission. With the inferred CSI, we develop three adversarial attacking methods by maximizing error rate, minimizing capacity, or maximizing outage probability. Simulation results show that our learning-based method can detect the modulation type of each subcarrier in high accuracy and then successfully infer the CSI range. Furthermore, simulations also demonstrate that the proposed attacks cause severe performance degradation of (MIMO-)OFDM-AM systems (e.g., error rate, capacity) with fairly low power.
Mobile Application for Predicting Diseases

There might be a lot of times that the user needs to know what he/she might have been going down with and keen to know what kind of disease they might be having so it is really easy for the user to contact the required doctor. The health disease prediction application comes in handy at many times. A lot of applications out there are concerned with a particular disease or some set of symptoms. Here, we propose a system that solves all the issues mentioned above. It is a mobile application where the user is instructed to add his/her symptoms and the disease that they might be having or tends to have will be displayed on the user screen. The user can access the latest news related to the medical. Here, we tend to use the machine learning algorithm that uses the trained data for predicting the disease. The user interface is built on JavaScript language using react native framework. Once the prediction is done using the major machine learning models, the result is being displayed to the user on the mobile application.
Content-Based Retrieval Using Autoencoder and Transfer Learning

The use of deep neural networks is a recent research area in content-based image retrieval that has enhanced results on many datasets and outperformed handcrafted for fine-tuning of the network. The image feature is computed by combining different feature descriptors like shape with color and texture, so that it describes the image more effectively. In this paper, image retrieval on database images is done to get the top-k most similar database images using kNN on the image embeddings with cosine similarity as the distance metric. Two unsupervised methods are performed by generating image embeddings using a pre-trained network such as simple autoencoder, convolution autoencoder, and transfer learning such as VGG19, VGG16, and ResNET152. This is done by removing its last few layers and performing inference on our image vectors for the generation of flattened embeddings. No training is needed throughout this entire processing, only the loading of the pre-trained weights is needed. We train both a simple autoencoder and a convolutional autoencoder on our database images with the objective of minimizing reconstruction loss. After sufficient training, we extract the encoder part of the autoencoder and use it during inference to generate flattened embeddings. This work is using transfer learning and autoencoder-based retrieval system with classification. Experiments are carried out on flower and fruit datasets. Results are recorded using GPU on Google Colaboratory platform. Visualization is shown using “t-distributed stochastic neighbor embedding (t-SNE)”.
Real-Time Traffic Management System Using Machine Learning and Image Processing

In major and rapidly developing urban regions across the world, traffic congestion is unavoidable. Congestion during rush hour is an unavoidable consequence, especially in large metropolitan. In the current circumstance, the traditional technique works well only if the count is low, as the density of vehicles on one side of the lane road grows if the traffic is heavier on one side of the road than on the other; otherwise, the approach fails. As a result, our goal is to create a traffic system that can switch signals as well as track and handle signals in real time. Signal switching will be done in this project based on real-time image detection and accuracy of vehicular traffic. Another goal is to put in place a traffic management system that gives priority to the lane where the ambulance arrives that offers ambulance detection.
Attendance System Based on Face Recognition Using Haar Cascade and LBPH Algorithm

To manage attendance of each student is much more complex when it’s done manually, and it has to be done for each class. To overcome this, we can use face recognition application where it takes less time comparatively, and it is stored in the database; this avoids confusions, proxies, and error of storing data when done manually. The students need to fill their data along with parent’s details, so when the students are absent, the message will be passed to them. After taking attendance through the application, mails and message of the data entry of attendance will be sent to the respective teachers. In advance, the details need to be entered beforehand, so when the students come in front of the camera, their face is recognized by comparing them from database containing faces. When it is unable to recognize, the faces will be stored in unknown, so when there is a mishap of being absent, it can be checked later on. This method of attendance is much more successful. Compare to other algorithms, haar cascade and local binary pattern histogram is best due to their robustness and less false rate.
Decentralized Motion Planning for Load Carrying and Manipulating by a Robotic Pack

In many cases, a pack of robots holds an advantage over a single robot such when an oversized or over-weighted load is to be carried. In such cases, a single robot will not do. Nevertheless, this may not be an easy task for a pack of robots as well, especially when the load needs to be lifted off the ground making the cooperative task less tolerant of errors. The limited research on such a load can be attributed to the mechanical complexity of the problem. Notably, previous studies have not considered the spatial, decentralized, communication-free scenario. We, therefore, consider a robotic pack of six agents that assumes the task of spatially moving a load through a cluttered space. As it transports the load, the pack carefully avoids planar obstacles while maintaining its orientation. To do so, we model the whole system as a six Prismatic-Prismatic-Spherical-Spherical (6-PPSS) redundant mobile platform, having twelve degrees of freedom. This paper focuses on a decentralized control scheme where no mutual communication is needed. Each agent calculates its ego movements according to the height of its corresponding load-node; the surrounding obstacles, and; the goal’s relative position. To avoid numerical errors appearing in the vicinity of singular configurations, we calculate the platform’s forward kinematics in the model’s full configuration space. We then show how this rationale can be further extended to formulate a distributed control scheme for the motion planner. We demonstrate our algorithms in several simulated scenarios and in a set of real-world experiments using specially designed omnidirectional robot agents. We test the ability of the pack to maintain the load’s orientation just by measuring the load’s height at the holding node of each agent. Lastly, we measured the time required for the pack to assume a desired load orientation. Results indicated that even in the presence of a 10-degree tilt error, the load was able to be restabilized within a maximum of 15 seconds in simulated conditions and 20 seconds in real-life experiments.
Observations on K-Image Expansion of Image-Mixing Augmentation

Image-mixing augmentations (e.g., Mixup and CutMix), which typically involve mixing two images, have become the de-facto training techniques for image classification. Despite their huge success in image classification, the number of images to be mixed has not been elucidated in the literature: only the naive K-image expansion has been shown to lead to performance degradation. This study derives a new K-image mixing augmentation based on the stick-breaking process under Dirichlet prior distribution. We demonstrate superiority of our K-image expansion augmentation over conventional two-image mixing augmentation methods through extensive experiments and analyses: (1) more robust and generalized classifiers; (2) a more desirable loss landscape shape; (3) better adversarial robustness. Moreover, we show that our probabilistic model can measure the sample-wise uncertainty and boost the efficiency for network architecture search by achieving a 7-fold reduction in the search time.
Towards Long-Term Retrieval-Based Visual Localization in Indoor Environments With Changes

Visual localization is a challenging task due to the presence of illumination changes, occlusion, and perception from novel viewpoints. Re-localizing the camera pose in long-term setups raises difficulties caused by changes in scene appearance and geometry introduced by human or natural deterioration. Many existing methods use static scene assumptions and fail in dynamic indoor scenes. Only a few works handle scene changes by introducing outlier awareness with pure learning methods. Other recent approaches use semantics to robustify camera localization in changing setups. However, to the best of our knowledge, no method has yet used scene graphs in feature-based approaches to introduce change awareness. In this work, we propose a novel feature-based camera re-localization method that leverages scene graphs within retrieval and feature detection and matching. Semantic scene graphs are used to estimate scene changes by matching instances and relationship triplets. The knowledge of scene changes is then used for our change-aware image retrieval and feature correspondence verification. We show the potential of integrating higher-level knowledge about the scene within a retrieval-based localization pipeline. Our method is evaluated on the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">RIO10</i> benchmark with comprehensive evaluations on different levels of scene changes.