Reviewerly/exHarmony huggingface dataset:
Extended Object Tracking Using Sets Of Trajectories with a PHD Filter

PHD filtering is a common and effective multiple object tracking (MOT) algorithm used in scenarios where the number of objects and their states are unknown. In scenarios where each object can generate multiple measurements per scan, some PHD filters can estimate the extent of the objects as well as their kinematic properties. Most of these approaches are, however, not able to inherently estimate trajectories and rely on ad-hoc methods, such as different labeling schemes, to build trajectories from the state estimates. This paper presents a Gamma Gaussian inverse Wishart mixture PHD filter that can directly estimate sets of trajectories of extended targets by expanding previous research on tracking sets of trajectories for point source objects to handle extended objects. The new filter is compared to an existing extended PHD filter that uses a labeling scheme to build trajectories, and it is shown that the new filter can estimate object trajectories more reliably.
Multi-scale Dynamic Network for Temporal Action Detection

In recent years, as the fundamental task in video understanding, Temporal Action Detection is attracting extensive attention. Most existing approaches use the same model parameters to process all input videos, which are not adaptive to the input video during the inference stage. In this paper, we propose a novel model termed Multi-scale Dynamic Network (MDN) to tackle this problem. The proposed MDN model incorporates multiple Multi-scale Dynamic Modules (MDMs). Each MDM can generate video-specific and segment-specific convolution kernels based on video content from different scales and adaptively capture rich semantic information for the prediction. Besides, we also design a new Edge Suppression Loss (ESL) function for MDN to pay more attention to hard examples. Extensive experiments conducted on two popular benchmarks ActivityNet-1.3 and THUMOS-14 show that the proposed MDN model achieves the state-of-the-art performance.
Image Restoration Based on End-to-End Unrolled Network

Recent studies on image restoration (IR) methods under unrolled optimization frameworks have shown that deep convolutional neural networks (DCNNs) can be implicitly used as priors to solve inverse problems. Due to the ill-conditioned nature of the inverse problem, the selection of prior knowledge is crucial for the process of IR. However, the existing methods use a fixed DCNN in each iteration, and so they cannot fully adapt to the image characteristics at each iteration stage. In this paper, we combine deep learning with traditional optimization and propose an end-to-end unrolled network based on deep priors. The entire network contains several iterations, and each iteration is composed of analytic solution updates and a small multiscale deep denoiser network. In particular, we use different denoiser networks at different stages to improve adaptability. Compared with a fixed DCNN, it greatly reduces the number of computations when the total parameters are equal and the number of iterations is the same, but the gains from a practical runtime are not as significant as indicated in the FLOP count. The experimental results of our method of three IR tasks, including denoising, deblurring, and lensless imaging, demonstrate that our proposed method achieves state-of-the-art performances in terms of both visual effects and quantitative evaluations.
Generalized approach to quantum interference in lossy N-port devices via a singular value decomposition

Modeling quantum interference in the presence of dissipation is a critical aspect of quantum technologies. Including dissipation into the model of a linear device enables for assessing the detrimental impact of photon loss, as well as for studying dissipation-driven quantum state transformations. However, establishing the input-output relations characterizing quantum interference at a general lossy N-port network poses important theoretical challenges. Here, we propose a general procedure based on the singular value decomposition (SVD), which allows for the efficient calculation of the input-output relations for any arbitrary lossy linear device. In addition, we show how the SVD provides an intuitive description of the principle of operation of linear optical devices. We illustrate the applicability of our method by evaluating the input-output relations of popular reciprocal and nonreciprocal lossy linear devices, including devices with singular and nilpotent scattering matrices. Our method also enables the analysis of quantum interference in large lossy networks, as we exemplify with the study of an N-port epsilon-near-zero (ENZ) hub. We expect that our procedure will motivate future research on quantum interference in complex devices, as well as the realistic modelling of photon loss in linear lossy devices.
Dynamic Group Recommendation Algorithm Based on Member Activity Level

The rapid development of social networks has led to an increased desire for group entertainment consumption, making the study of group recommender systems a hotspot. Existing group recommender systems focus too much on member preferences and ignore the impact of member activity level on recommendation results. To this end, a dynamic group recommendation algorithm based on the activity level of members is proposed. Firstly, the algorithm predicts the unknown preferences of members using a time-series-oriented rating prediction model. Secondly, considering the dynamic change of member activity level, the group profile is generated by designing a sliding time window to investigate the recent activity level of each member in the group at the recommended moment, and preference is aggregated based on the recent activity level of members. Finally, the group recommendations are generated based on the group profile. The experimental results show that the algorithm in this paper achieves a better recommendation result.
A Study on Building a “Real-Time Vehicle Accident and Road Obstacle Notification Model” Using AI CCTV

This research produced a model that detects abnormal phenomena on the road, based on deep learning, and proposes a service that can prevent accidents because of other cars and traffic congestion. After extracting accident images based on traffic accident video data by using FFmpeg for model production, car collision types are classified, and only the head-on collision types are processed by using the deep learning object-detection algorithm YOLO (You Only Look Once). Using the car accident detection model that we built and the provided road obstacle-detection model, we programmed, for when the model detects abnormalities on the road, warning notification and photos that captures the accidents or obstacles, which are then transferred to the application. The proposed service was verified through application notification simulations and virtual experiments using CCTVs in Daegu, Busan, and Gwangju. By providing services, the goal is to improve traffic safety and achieve the development of a self-driving vehicle sector. As a future research direction, it is suggested that an efficient CCTV control system be introduced for the transportation environment.
A low light natural image statistical model for joint contrast enhancement and denoising

We study the problem of joint low light image contrast enhancement and denoising using a statistical approach. The low light natural image in the band pass domain is modeled by statistically relating a Gaussian scale mixture model for the pristine image, to the low light image, through a detail loss coefficient and Gaussian noise. The detail loss coefficient is statistically described using a posterior distribution with respect to its estimate based on a prior contrast enhancement algorithm. We then design our low light enhancement and denoising (LLEAD) method by computing the minimum mean squared error estimate of the pristine image band pass coefficients. We create the Indian Institute of Science low light image dataset of well-lit and low light image pairs to learn the model parameters and evaluate our enhancement method. We show through extensive experiments on multiple datasets that our method helps better enhance the contrast while simultaneously controlling the noise when compared to other state of the art joint contrast enhancement and denoising methods.
Optimal stroke learning with policy gradient approach for robotic table tennis

Learning to play table tennis is a challenging task for robots, as a wide variety of strokes are required. Recent advances have shown that deep Reinforcement Learning (RL) is able to successfully learn the optimal actions in a simulated environment. However, the applicability of RL in real scenarios remains limited due to the high exploration effort. In this work, we propose a realistic simulation environment in which multiple models are built for the dynamics of the ball and the kinematics of the robot. Instead of training an end-to-end RL model, a novel policy gradient approach with TD3 backbone is proposed to learn the racket strokes based on the predicted state of the ball at the hitting time. In the experiments, we show that the proposed approach significantly outperforms the existing RL methods in simulations. Furthermore, to cross the domain from simulation to reality, we adopt an efficient retraining method and test it in three real scenarios. The resulting success rate is 98% and the distance error is around 24.9 cm. The total training time is about 1.5 hours.
Acute Lymphoblastic Leukemia Detection Using Transfer Learning Techniques

Leukemia is a type of cancer that affects the body's blood forming tissues, including bone marrow. It is a dangerous illness prevalent in children under the age of five. The present diagnosis includes microscopic examination of blood cells by the hematologist. Recently, deep learning methods are extensively employed in many medical imaging applications for the diagnosis of diseases. However, one of the key issues is the limited availability of microscopic images for training the models. To overcome this difficulty, transfer learning techniques are put forward. This paper presents a comparative analysis of different transfer learning models like Xception, Inceptionv3, DenseNet201, ResNet50, and MobileNet to detect acute lymphocytic leukemia (ALL) from blood smear cells. All models were trained on ALL-IDB2 dataset and achieved an accuracy of 87.97%, 88.92%, 88.92%, 95.28%, and 97.88%, respectively.
Assessing Non-Intrusive Vulnerability Scanning Methodologies for Detecting Web Application Vulnerabilities on Large Scale

The online presence of organizations is increasing at a rapid pace with the major adaptation of work from home. Organizations have new digital infrastructure being added to their digital portfolio on a regular basis. The threats that organizations face are also increasing at an alarming rate. It is crucial in such a scenario to safeguard and test the new and growing digital infrastructure without causing any interruption to the services provided by these organizations. Through this paper, we would like to inspect six non-intrusive detection methodologies which are useful in the identification of web application vulnerabilities without hampering the organization’s service model. The dataset that was used for real-world analysis consisted of government agencies and reputable organizations. We have assessed these vulnerabilities on the criterion of the statistical probability of finding these vulnerabilities on the dataset. We were able to identify 44 sub-domain takeover vulnerability in 1628 domains (0.02%), 263 cryptographic vulnerabilities (0.17%) and 167 open port vulnerabilities (0.10%) in 1538 domains, and 10,491 GitHub API Leaks were found within 27 organization’s GitHub repository, 3 Cross-Origin Resource Sharing (CORS) misconfigurations in 1213 domains (0.002%) and 0 Wayback machine API leaks from 800 domains.
Robotic Lime Picking by Considering Leaves as Permeable Obstacles

The problem of robotic lime picking is challenging; lime plants have dense foliage which makes it difficult for a robotic arm to grasp a lime without coming in contact with leaves. Existing approaches either do not consider leaves, or treat them as obstacles and completely avoid them, often resulting in undesirable or infeasible plans. We focus on reaching a lime in the presence of dense foliage by considering the leaves of a plant as permeable obstacles with a collision cost. We then adapt the rapidly exploring random tree star (RRT*) algorithm for the problem of fruit harvesting by incorporating the cost of collision with leaves into the path cost. To reduce the time required for finding low-cost paths to goal, we bias the growth of the tree using an artificial potential field (APF). We compare our proposed method with prior work in a 2-D environment and a 6-DOF robot simulation. Our experiments and a real-world demonstration on a robotic lime picking task demonstrate the applicability of our approach.
DeFiHap

The emergence of Hive greatly facilitates the management of massive data stored in various places. Meanwhile, data scientists face challenges during HiveQL programming - they may not use correct and/or efficient HiveQL statements in their programs; developers may also introduce anti-patterns indeliberately into HiveQL programs, leading to poor performance, low maintainability, and/or program crashes. This paper presents an empirical study on HiveQL programming, in which 38 HiveQL anti-patterns are revealed. We then design and implement DeFiHap, the first tool for automatically detecting and fixing HiveQL anti-patterns. DeFiHap detects HiveQL anti-patterns via analyzing the abstract syntax trees of HiveQL statements and Hive configurations, and generates fix suggestions by rule-based rewriting and performance tuning techniques. The experimental results show that DeFiHap is effective. In particular, DeFiHap detects 25 anti-patterns and generates fix suggestions for 17 of them.
An Optimized Approach to Vehicle-Type Classification Using a Convolutional Neural Network

Vehicle type classification is considered a central part of an intelligent traffic system. In recent years, deep learning had a vital role in object detection in many computer vision tasks. To learn high-level deep features and semantics, deep learning offers powerful tools to address problems in traditional architectures of handcrafted feature-extraction techniques. Unlike other algorithms using handcrated visual features, convolutional neural network is able to automatically learn good features of vehicle type classification. This study develops an optimized automatic surveillance and auditing system to detect and classify vehicles of different categories. Transfer learning is used to quickly learn the features by recording a small number of training images from vehicle frontal view images. The proposed system employs extensive data-augmentation techniques for effective training while avoiding the problem of data shortage. In order to capture rich and discriminative information of vehicles, the convolutional neural network is fine-tuned for the classification of vehicle types using the augmented data. The network extracts the feature maps from the entire dataset and generates a label for each object (vehicle) in an image, which can help in vehicle-type detection and classification. Experimental results on a public dataset and our own dataset demonstrated that the proposed method is quite effective in detection and classification of different types of vehicles. The experimental results show that the proposed model achieves 96.04% accuracy on vehicle type classification.
Insights on an automated fall detection device designed for older adult wheelchair and scooter users: A qualitative study

Falls are a concern for older adults who use wheelchairs and scooters. Many wheelchair and scooter users require assistance to recover from a fall and often lie on the ground waiting for assistance for 10 min or more. An automated fall detection device may facilitate communication with care partners and expedite recovery; however, there is limited research on the specifications and features of an automated fall detection device preferred by older adults who use wheelchair and scooter.To examine the desired specifications, perceived ease of use and perceived usefulness of an automated fall detection device desired by older adults who use a wheelchair or scooter through semi-structured interviews.Fifteen full-time wheelchair and scooter users (9 females; age: 68 ± 5 years) were interviewed from July to November 2020. Interviews were transcribed, coded, and analyzed.Preferred features include wireless charging, a watch form, ability to change the individual who is contacted in the event of a fall, and the ability to disable a notification in the event of a false alarm. Participants felt that an automated fall detection device would be useful and easy to use.Older adults who use a wheelchair or scooter indicated the need for an automated fall detection device to facilitate recovery from a fall. Participants reported challenges with previous fall detection devices and the need for specific design requirements to facilitate ongoing use. Participants' insights inform the design of a fall detection device to maximize usability and prevent technology abandonment.
ER-SQL: Learning enhanced representation for Text-to-SQL using table contents

Text-to-SQL emerges to play an important role in interactive data analysis, which provides a friendly interface for converting natural language into relational database language (i.e., SQL). In order to translate a user's query into an executable SQL statement, semantic parsing is essential to the transformation process. In particular, existing efforts provide some feasible solutions, and state-of-the-art models mainly adopt the sketch-based paradigm such that template values are to be filled. To this end, most methods extract values based on column representations. However, if the query contains multiple values that belong to different columns, these methods may fail to extract the values accurately. Moreover, it can be difficult to infer the right values when the query does not explicitly mention the corresponding column names. To bridge the gap, we propose a novel neural architecture, namely, ER-SQL for learning enhanced representations for Text-to-SQL. Based on pre-trained model BERT, ER-SQL uses column contents to better extract features of columns. Moreover, ER-SQL harnesses the column representations to latently reformulate the query. To verify the effectiveness of ER-SQL, comprehensive experiments demonstrate that ER-SQL achieves better results than existing models on the benchmark dataset WikiSQL, as well as on a representative Chinese dataset TableQA.
Holo-Box: Level-of-Detail Glanceable Interfaces for Augmented Reality

Glanceable interfaces are Augmented Reality (AR) User Interfaces (UIs) for information retrieval ”at a glance” relying on eye gaze for implicit input. While they provide rapid information retrieval, they often occlude a large part of the real-world. This is compounded as the amount of virtual information increases. Interacting with complex glanceable interfaces often results in unintentional eye gaze interaction and selections due to the Midas Touch problem. In this work, we present Holo-box, an innovative AR UI design that combines 2D compact glanceable interfaces with 3D virtual ”Holo-boxes”. We can utilize the glanceable 2D interface to provide compact information at a glance while using Holo-box for explicit input such as hand tracking activated when necessary, surpassing the Midas Touch problem and resulting in Level-of-Detail(LOD) for AR glanceable UIs. We test our proposed system inside a real-world machine shop to provide on-demand virtual information while minimizing unintentional real-world occlusion.
A Multiple-Choice Machine Reading Comprehension Model with Multi-Granularity Semantic Reasoning

To address the problem of poor semantic reasoning of models in multiple-choice Chinese machine reading comprehension (MRC), this paper proposes an MRC model incorporating multi-granularity semantic reasoning. In this work, we firstly encode articles, questions and candidates to extract global reasoning information; secondly, we use multiple convolution kernels of different sizes to convolve and maximize pooling of the BERT-encoded articles, questions and candidates to extract local semantic reasoning information of different granularities; we then fuse the global information with the local multi-granularity information and use it to make an answer selection. The proposed model can combine the learned multi-granularity semantic information for reasoning, solving the problem of poor semantic reasoning ability of the model, and thus can improve the reasoning ability of machine reading comprehension. The experiments show that the proposed model achieves better performance on the C3 dataset than the benchmark model in semantic reasoning, which verifies the effectiveness of the proposed model in semantic reasoning.
Robust Event Detection based on Spatio-Temporal Latent Action Unit using Skeletal Information

This paper proposes a novel dictionary learning approach to detect event anomalities using skeletal information extracted from RGBD video. The event action is represented as several latent action atoms and composed of latent spatial and temporal attributes. We aim to construct a network able to learn from few examples and also rules defined by the user. The skeleton frames are clustered by an initial K-means method. Each skeleton frame is assigned with a varying weight parameter and fed into our Gradual Online Dictionary Learning (GODL) algorithm. During the training process, outlier frames will be gradually filtered by reducing the weight that is inversely proportional to a cost. To strictly distinguish the event action from similar actions and robustly acquire its action units, we build a latent unit temporal structure for each sub-action.We validate the method at the example of fall event detection on NTU RGB+D dataset, because it provides a benchmark available for comparison. We present the experimental validation of the achieved accuracy, recall, and precision. Our approach achieves the best performance in precision and accuracy of human fall event detection, compared with other existing dictionary learning methods. Our method remains the highest accuracy and the lowest variance, with increasing noise ratio.
Secure Rotation Invariant Face Detection System for Authentication

Biometric applications widely use the face as a component for recognition and automatic detection. Face rotation is a variable component and makes face detection a complex and challenging task with varied angles and rotation. This problem has been investigated, and a novice algorithm, namely RIFDS (Rotation Invariant Face Detection System), has been devised. The objective of the paper is to implement a robust method for face detection taken at various angle. Further to achieve better results than known algorithms for face detection. In RIFDS Polar Harmonic Transforms (PHT) technique is combined with Multi-Block Local Binary Pattern (MBLBP) in a hybrid manner. The MBLBP is used to extract texture patterns from the digital image, and the PHT is used to manage invariant rotation characteristics. In this manner, RIFDS can detect human faces at different rotations and with different facial expressions. The RIFDS performance is validated on different face databases like LFW, ORL, CMU, MIT-CBCL, JAFFF Face Databases, and Lena images. The results show that the RIFDS algorithm can detect faces at varying angles and at different image resolutions and with an accuracy of 99.9%. The RIFDS algorithm outperforms previous methods like Viola-Jones, Multi-block Local Binary Pattern (MBLBP), and Polar Harmonic Transforms (PHTs). The RIFDS approach has a further scope with a genetic algorithm to detect faces (approximation) even from shadows.