Reviewerly/exHarmony huggingface dataset:
LT-Fall

Falls are the leading cause of fatal injuries to elders in modern society, which has motivated researchers to propose various fall detection technologies. We observe that most of the existing fall detection solutions are diverging from the purpose of fall detection: timely alarming the family members, medical staff or first responders to save the life of the human with severe injury caused by fall. Instead, they focus on detecting the behavior of human falls, which does not necessarily mean a human is in real danger. The real critical situation is when a human cannot get up without assistance and is thus lying on the ground after the fall because of losing consciousness or becoming incapacitated due to severe injury. In this paper, we define a life-threatening fall as a behavior that involves a falling down followed by a long-lie of humans on the ground, and for the first time point out that a fall detection system should focus on detecting life-threatening falls instead of detecting any random falls. Accordingly, we design and implement LT-Fall, a mmWave-based life-threatening fall detection and alarming system. LT-Fall detects and reports both fall and fall-like behaviors in the first stage and then identifies life-threatening falls by continuously monitoring the human status after fall in the second stage. We propose a joint spatio-temporal localization technique to detect and locate the micro-motions of the human, which solves the challenge of mmWave's insufficient spatial resolution when the human is static, i.e., lying on the ground. Extensive evaluation on 15 volunteers demonstrates that compared to the state-of-the-art work (92% precision and 94% recall), LT-Fall achieves zero false alarms as well as a precision of 100% and a recall of 98.8%.
A Survey of Indoor UAV Obstacle Avoidance Research

UAV obstacle avoidance technology is one of the key factors to realize UAV autonomous flight, efficient and accurate obstacle avoidance is significant to complete the UAV autonomous flight task.In contrast, the dynamic, real-time and uncertainty of the environment in which the UAV is located makes the problem very tricky, especially in the indoor environment.At present, a large number of scholars have shown strong interest in the indoor UAV obstacle avoidance problem.With the rapid development of computer technology and hardware devices, many intelligent algorithms have been proposed to solve the obstacle avoidance problem.However, the research on indoor UAV obstacle avoidance technology is not comprehensive enough, and there is a lack of summarization of the research results in recent years.This paper introduces the sensor modules commonly used for indoor UAV environment sensing, related obstacle avoidance methods based on sensory detection.Classifies and composes the commonly used UAV path planning obstacle avoidance algorithms, and gives several representative UAV flight control research methods.This paper summarizes the advantages and disadvantages of different perception modules and detection methods applied to UAV obstacle avoidance tasks, and compares various current path planning methods.Finally, the critical difficulties and challenges faced in the field of indoor UAV obstacle avoidance are discussed, and future research in the field of UAV obstacle avoidance has prospected.
CRNet: A Fast Continual Learning Framework With Random Theory

Artificial neural networks are prone to suffer from catastrophic forgetting. Networks trained on something new tend to rapidly forget what was learned previously, a common phenomenon within connectionist models. In this work, we propose an effective and efficient continual learning framework using random theory, together with Bayes' rule, to equip a single model with the ability to learn streaming data. The core idea of our framework is to preserve the performance of old tasks by guiding output weights to stay in a region of low error while encountering new tasks. In contrast to the existing continual learning approaches, our main contributions concern (1) closed-formed solutions with detailed theoretical analysis; (2) training continual learners by one-pass observation of samples; (3) remarkable advantages in terms of easy implementation, efficient parameters, fast convergence, and strong task-order robustness. Comprehensive experiments under popular image classification benchmarks, FashionMNIST, CIFAR-100, and ImageNet, demonstrate that our methods predominately outperform the extensive state-of-the-art methods on training speed while maintaining superior accuracy and the number of parameters, in the class incremental learning scenario. Code is available at https://github.com/toil2sweet/CRNet.
A Novel Framework to Generate Synthetic Video for Foreground Detection in Highway Surveillance Scenarios

Foreground detection (FD) plays an important role in the domain of video surveillance for highway. The design of advanced FD algorithms requires large-scale and diverse video dataset. However, collecting and labeling real dataset is still time-consuming, labor-intensive, and highly subjective. To address this issue, we first use computer graphics (CG) to clone real highway scenarios (HS) and generate synthetic multi-challenge video datasets, called “Synthetic-HS (CG)”, automatically labeled with accurate pixel-level ground truth. The Synthetic-HS (CG) dataset contains eight imaging condition sequences for computer vision research. Then, we design an image translation (IT) model that translates source domain (Synthetic-HS (CG)) to target domain (real). This model uses skip connections and attention module to generate realistic synthetic images “Synthetic-HS (IT)”. We use publicly available Synthetic-HS in combination with the corresponding real video sequence to conduct experiments. The experiment results suggest that: 1) The Synthetic-HS (CG) dataset enables us to provide precise quantitative evaluation of the drawbacks of foreground detection methods 2) The realistic Synthetic-HS (IT) images can be used to promote the visual perception in highway video surveillance.
Multiple Levels Perceptual Noise Backed Visual Information Fidelity for Picture Quality Assessment

Image Quality Assessment (IQA) is a crucial aspect of image processing. A great deal of works have improved the performance of IQA algorithms, however, the existing classical Visual Information Fidelity (VIF) algorithm has minor limitations: at low scales, fidelity information of images is under-extracted; the variation of perceptual noise of Human Visual System (HVS) with scale is not fully taken into account. In response, this paper proposes an improved VIF-Multiple Levels Perceptual Noise Backed Visual Information Fidelity (MPNVIF), which optimizes the internal algorithm of image information extraction and utilizes multiple levels perceptual noise to enhance the evaluation performance of VIF. Finally, we perform a comparison experiment of MPNVIF algorithm, VIF algorithm and some other objective IQA algorithms on the Industrial Scene Image Database (ISID) database. The results show that compared to related algorithms, the MPNVIF proposed in our paper has better performance on the ISID database.
Object Detection in Traffic Videos: A Survey

Traffic video analytics has become one of the core components in the evolution of transportation systems. Artificially intelligent traffic management systems apply computer vision techniques to alleviate the monotony of manually monitoring the video feeds from surveillance cameras. Object detection is the most important step in these systems, and much research has been done on identifying objects in traffic scenes. This paper reviews various algorithms used for object detection in traffic surveillance, in addition to the recent trends and future directions. Based on the approaches used in the related studies, the object detection methods are categorized into motion-based and appearance-based techniques. Each group of techniques is further classified into a number of subcategories and the advantages and disadvantages of each method are finally analyzed. The major challenges, limitations, and potential solutions are also discussed along with the future directions.
Using Smart Home Technologies to Promote Physical Activity Among the General and Aging Populations: Scoping Review

Health-monitoring smart homes are becoming popular, with experts arguing that 9-to-5 health care services might soon become a thing of the past. However, no review has explored the landscape of smart home technologies that aim to promote physical activity and independent living among a wide range of age groups.This review aims to map published studies on smart home technologies aimed at promoting physical activity among the general and aging populations to unveil the state of the art, its potential, and the research gaps and opportunities.Articles were retrieved from 6 databases (PubMed, CINAHL, Scopus, IEEE Xplore, ACM Library, and Web of Science). The criteria for inclusion were that the articles must be user studies that dealt with smart home or Active Assisted Living technologies and physical activity, were written in English, and were published in peer-reviewed journals. In total, 3 researchers independently and collaboratively assessed the eligibility of the retrieved articles and elicited the relevant data and findings using tables and charts.This review synthesized 20 articles that met the inclusion criteria, 70% (14/20) of which were conducted between 2018 and 2020. Three-quarters of the studies (15/20, 75%) were conducted in Western countries, with the United States accounting for 25% (5/20). Activities of daily living were the most studied (9/20, 45%), followed by physical activity (6/20, 30%), therapeutic exercise (4/20, 20%), and bodyweight exercise (1/20, 5%). K-nearest neighbor and naïve Bayes classifier were the most used machine learning algorithms for activity recognition, with at least 10% (2/20) of the studies using either algorithm. Ambient and wearable technologies were equally studied (8/20, 40% each), followed by robots (3/20, 15%). Activity recognition was the most common goal of the evaluated smart home technologies, with 55% (11/20) of the studies reporting it, followed by activity monitoring (7/20, 35%). Most studies (8/20, 40%) were conducted in a laboratory setting. Moreover, 25% (5/20) and 10% (2/20) were conducted in a home and hospital setting, respectively. Finally, 75% (15/20) had a positive outcome, 15% (3/20) had a mixed outcome, and 10% (2/20) had an indeterminate outcome.Our results suggest that smart home technologies, especially digital personal assistants, coaches, and robots, are effective in promoting physical activity among the young population. Although only few studies were identified among the older population, smart home technologies hold bright prospects in assisting and aiding older people to age in place and function independently, especially in Western countries, where there are shortages of long-term care workers. Hence, there is a need to do more work (eg, cross-cultural studies and randomized controlled trials) among the growing aging population on the effectiveness and acceptance of smart home technologies that aim to promote physical activity.
Optimized Privacy-Preserving CNN Inference With Fully Homomorphic Encryption

Inference of machine learning models with data privacy guarantees has been widely studied as privacy concerns are getting growing attention from the community. Among others, secure inference based on Fully Homomorphic Encryption (FHE) has proven its utility by providing stringent data privacy at sometimes affordable cost. Still, previous work was restricted to shallow and narrow neural networks and simple tasks due to the high computational cost incurred from FHE. In this paper, we propose a more efficient way of evaluating convolutions with FHE, where the cost remains constant regardless of the kernel size, resulting in 12–46× timing improvement on various kernel sizes. Combining our methods with FHE bootstrapping, we achieve at least 18.9% (and 48.1%) timing reduction in homomorphic evaluation of 20-layer CNN classifiers (and a part of it) on CIFAR10/100 (and ImageNet, respectively) datasets. Furthermore, in consideration of our methods being effective for evaluating CNNs with intensive convolutional operations and exploring such CNNs, we achieve at least 5× faster inference on CIFAR10/100 with FHE than the prior works having the same or less accuracy.
Dynamic S-Box Generation Using Novel Chaotic Map with Nonlinearity Tweaking

A substitution box (S-Box) is a crucial component of contemporary cryptosystems that provide data protection in block ciphers. At the moment, chaotic maps are being created and extensively used to generate these S-Boxes as a chaotic map assists in providing disorder and resistance to combat cryptanalytical attempts. In this paper, the construction of a dynamic S-Box using a cipher key is proposed using a novel chaotic map and an innovative tweaking approach. The projected chaotic map and the proposed tweak approach are presented for the first time and the use of parameters in their working makes both of these dynamic in nature. The tweak approach employs cubic polynomials while permuting the values of an initial S-Box to enhance its cryptographic fort. Values of the parameters are provided using the cipher key and a small variation in values of these parameters results in a completely different unique S-Box. Comparative analysis and exploration confirmed that the projected chaotic map exhibits a significant amount of chaotic complexity. The security assessment in terms of bijectivity, nonlinearity, bits independence, strict avalanche, linear approximation probability, and differential probability criteria are utilized to critically investigate the effectiveness of the proposed S-Box against several assaults. The proposed S-Box's cryptographic performance is comparable to those of recently projected S-Boxes for its adaption in real-world security applications. The comparative scrutiny pacifies the genuine potential of the proposed S-Box in terms of its applicability for data security.
LIAAD: Lightweight attentive angular distillation for large-scale age-invariant face recognition

Disentangled representations have been commonly adopted to Age-invariant Face Recognition (AiFR) tasks. However, these methods have reached some limitations with (1) the requirement of large-scale face recognition (FR) training data with age labels, which is limited in practice; (2) heavy deep network architectures for high performance; and (3) their evaluations are usually taken place on age-related face databases while neglecting the standard large-scale FR databases to guarantee robustness. This work presents a novel Lightweight Attentive Angular Distillation (LIAAD) approach to Large-scale Lightweight AiFR that overcomes these limitations. Given two high-performance heavy networks as teachers with different specialized knowledge, LIAAD introduces a learning paradigm to efficiently distill the age-invariant attentive and angular knowledge from those teachers to a lightweight student network making it more powerful with higher FR accuracy and robust against age factor. Consequently, LIAAD approach is able to take the advantages of both FR datasets with and without age labels to train an AiFR model. Far apart from prior distillation methods mainly focusing on accuracy and compression ratios in closed-set problems, our LIAAD aims to solve the open-set problem, i.e. large-scale face recognition. Evaluations on LFW, IJB-B and IJB-C Janus, AgeDB and MegaFace-FGNet with one million distractors have demonstrated the efficiency of the proposed approach on light-weight structure. This work also presents a new longitudinal face aging (LogiFace) database 1 for further studies in age-related facial problems in future.
Improved relation span detection in question answering systems over extracted knowledge bases

In recent years, AI studies have been focused on developing question answering systems to deal with automatic answering natural language questions. Knowledge based open domain question answering systems can generate accurate answers to questions posed by users in various fields. However, these systems need further development to scale the domain of answer retrieval systems and question interpretation. Deep learning methods are one of the current approaches in this research area. Existing knowledge-based question answering systems use either manually curated knowledge bases such as Freebase or knowledge bases automatically extracted from unstructured texts such as Reverb, or a combination of both. In the case of open domain question answering systems, limited access to knowledge bases reduces the expandability of the system. Systems that use only curated knowledge bases have high precision with limited coverage; while systems that use extracted knowledge bases have higher coverage with generally lower precision. To improve the precision of question answering over extracted knowledge bases, this paper presents a solution to enhance detection of the relation span in the question, corresponding to the triples of the extracted knowledge base. First, a dataset with 16,675 simple questions is introduced along with answers based on the Reverb triples. Then, a method based on a fine-tuned BERT model for relation span detection in the questions is proposed. The results showed an increase in the precision of the relation span detection, so that the precision reached 99.65%.
Enhanced Differential Evolution and Particle Swarm Optimization Approaches for Discovering High Utility Itemsets

Mining patterns from High-utility itemsets (HUIs) have been exploited recently in place of frequent itemset mining (FIMs) or association-rule mining (ARMs) as they highlight profitability of products where quantity and profits are taken into account. Several techniques for HUIs have been proposed and they encounter exponential search spaces which have more distinct items or voluminous databases. Alternatively, Evolutionary Computations (ECs)-based meta-heuristics algorithms can be effective in solving issues in HUIs since a set of near-optimal solutions can be obtained within restricted periods. Current ECs-based techniques consume more time to identify HUIs in transactional databases, discover unacceptable combinations of HUIs, and finally fail to discover HUIs when neighborhood searches are not executed locally and globally. To overcome these challenges, a HUI mining algorithm based on Differential Evolution (DE) and Particle Swarm Optimization (PSO) using multiple strategies including elitism, population diversifications, exclusive preservations, and neighborhood exploration techniques has been proposed. Thus, this work defines mining patterns based on DE and PSO to identify HUIs in voluminous transactional databases. The HUIM-DE-PSO-DE algorithm proposed in this work discovers more number of HUIs which is revealed in experimental results obtained from a set of benchmark data instances. Results are compared with existing approaches using several performance metrics including convergence speeds, minimum utility threshold values, and execution time consumed.
Fast cancer metastasis location based on dual magnification hard example mining network in whole-slide images

Breast cancer has become the most common form of cancer among women. In recent years, deep learning has shown great potential in aiding the diagnosis of pathological images, particularly through the use of convolutional neural networks for locating lymph node metastasis under gigapixel whole slide images (WSIs). However, the massive size of these images at the highest magnification introduces redundant computation during the inference process. Additionally, the diversity of biological textures and structures within WSIs can cause confusion for classifiers, particularly in identifying hard examples. As a result, the trade-off between accuracy and efficiency remains a critical issue for whole-slide image metastasis localization. In this paper, we propose a novel two-stream network that takes a pair of low- and high-magnification image patches as input for identifying hard examples during the training phase. Specifically, our framework focuses on samples where the outputs of the two magnification networks are dissimilar. We adopt a dual magnification hard mining loss to re-weight the ambiguous samples. To more efficiently locate tumor metastasis cells in whole slide images, the two stream networks are decomposed into a cascaded network during the inference phase. The low magnification WSIs scanned by the low-mag network generate a coarse probability map, and the suspicious areas in the map are refined by the high-mag network. Finally, we evaluate our fast location dual magnification hard example mining network on the Camelyon16 breast cancer whole-slide image dataset. Experiments demonstrate that our proposed method achieves a 0.871 FROC score with a faster inference time, and our high magnification network also achieves a 0.88 FROC score.
Joint group dictionary-based structural sparse representation for image restoration

With the aid of image nonlocal self-similarity (NSS), recent studies have indicated that structural sparse representation (SSR) models with patch groups as processing units have a great potential in various image inverse problems. Most of existing works, however, only use the dictionary learned from internal or external patch groups for sparse representation. Consequently, such methods generally overfit degradation or do not adapt to the given image. In this paper, we propose a novel sparse representation model, termed joint group dictionary-based SSR (JGD-SSR) model, to alleviate the above problem. Compared with traditional SSR models, the proposed JGD-SSR provides an effective mechanism to exploit internal and external dictionaries jointly. Moreover, to make the proposed model more robust, the connection between JGD-SSR and maximum a posteriori (MAP) estimation is revealed to adaptively estimate regularization parameters. When we apply the proposed JGD-SSR to image restoration tasks, an alternating minimization scheme is developed to solve the resulting optimization problems. The experimental results, including image denoising, deblocking, and deblurring, demonstrate that the proposed JGD-SSR-based restoration algorithm outperforms many state-of-the-art competing methods in terms of both objective and visual qualities.
Analysis of cybercrimes and security in FinTech industries using the novel concepts of interval-valued complex q-rung orthopair fuzzy relations

Modeling the uncertainty has long been a crux for mathematicians which was resolved by the theory of fuzzy sets and logic initiated by Zadeh. Since its introduction, many developments and improvements have been made in the field. This research defines several innovative theories such as the Cartesian product (CP) of interval-valued complex q-rung orthopair fuzzy sets (IVCqROFSs), IVCqROF relations (IVCqROFRs), and their types. After deep analysis, related results and worthwhile properties have been stated. Moreover, a Hasse diagram for IVCqROFR is introduced with its properties. The proposed concepts are capable of modeling a wide range of problems of uncertain nature. The involvement of complex numbers enables these structures to cope with phase-altering problems and multidimensional problems, ultimately making them extremely powerful tools for handling ambiguity. An application has been proposed as an illustration. Since, in modern days, every business and industry is being digitalized, the business and finance departments are no exception. Digital systems have numerous risks and threats. Henceforth, for the first time in the theory of fuzzy logic, cybercrimes and cyber security in the industry of FinTech have been modeled and analyzed through fuzzy mathematics. The effects of different cybersecurity techniques on different threats in FinTech are analyzed by using the proposed methods. Finally, the selection and use of the proposed theories in the analysis of Fintech cybersecurity is justified and explained in contrast to other available competing fuzzy structures in the literature.