Reviewerly/exHarmony huggingface dataset:
Research on abnormal behavior recognition of the elderly based on spatial-temporal feature fusion

With the deepening of the aging society, the health care of the elderly and disabled people has been widely concerned by society. It is of great practical significance to monitor the physical conditions and abnormal behaviors of elderly and disabled people based on sensor devices to prevent diseases and dangerous behaviors. However, most of the current algorithms (classical machine learning algorithms, CNN, RNN, LSTM) are only limited to the traditional Euclidean space, with little expansion in Non-Euclidean space. The translation invariance limits the expression ability of spatial data. The signals collected by the wearable sensor based on human abnormal behavior recognition often come from different parts and different directions of the human body. The traditional Euclidean algorithm is used to solve the problem, which makes the classification results easy to ignore the spatial characteristics of the sensor signals. The emergence of graph neural networks provides a new method to solve this problem. A large number of studies have proved that the graph neural network has a great role in dealing with Non-Euclidean space problems. Therefore, this paper proposes a new method for abnormal behavior recognition of the elderly based on spatiotemporal feature fusion (SCGAT). The Non-Euclidean spatial features of sensor signals are extracted by graph attention neural network, and the classification model is constructed by combining the extracted time domain features of the time-series coding network. In the experimental results, the proposed method achieves better classification results than most of the current algorithms.
To Medical Ontology Fuzzification Purpose: COVID-19 Study Case

Clearly, ontology components are distinguished depending on features of inexactitudes and uncertainties. Such shortcomings are mostly the outcome an indistinct inaccurate semantic lingual representation, supplied by professionals. So as to tackle this lack of exactness issue, the notion of ”fuzziness” has to be considered. In this respect, fuzzy ontologies have been shown to be useful tools to represent specific knowledge (crisp and fuzzy) and reasoning over it. Thus, an advanced fuzzy ontology called the COVID-19 Fuzzy Ontology (CFO) is exhibited in this work. The latter licenses a semantically meaningful representation of fuzzy crusty medical data particulars related to the diagnosis of COVID-19. The CFO also takes into account the imprecise aspects associated to the induced knowledge of this disease. The CFO is grounded from a domain ontology about COVID-19. The evaluation of the CFO ontology shows that it is accurate, consistent, and that it covers COVID-19 terminologies.
What Could Possibly Go Wrong? Identification of Current Challenges and Prospective Opportunities for Anomaly Detection in Internet of Things

The era of the Internet of Things (IoT) is now. Wide-spread adoption of this technology has transformed the way the world, people, and businesses are connected. However, with the adoption of IoT, comes the increased threat of security risks which can negatively impact industry productivity and performance. Companies and organizations, that rely on IoT for operations, are specifically vulnerable to anomalies in their data. Over the years, anomaly detection has evolved, and algorithms have continually taken new forms for better performance. However, there are still challenges that need to be addressed to make IoT anomaly detection more robust in the future. Within this article, we highlight a high-level description of the current IoT architecture. Following this, we identify some current challenges to anomaly detection in IoT. Finally, we propose prospective opportunities that can be investigated to address these identified modern-day challenges of anomaly detection.
Adaptive Cross-Camera Video Analytics at the Edge

Cross-camera video analytics is a major video ana-lytic task that associates and analyzes information across multiple cameras. However, the searching cost for existing cross-camera tracking tasks grows linearly with the number of cameras, leading to substantial cost in large-scale camera systems. Although correlation among cameras can greatly reduce the searching cost, our empirical analysis reveals that the correlation actually changes over time, leading to sub-optimal performance for schemes leveraging rigid correlation information. Furthermore, adjusting the correlations to dynamically guide the searching process is extremely challenging due to the high construction cost. In this paper, we propose an adaptive cross-camera video analytics framework under the guidance of fine-grained estimated correlation information. Specifically, we propose a mean-field game approach to estimate the dynamic correlation with only the initial correlation and the destination correlation. We first carefully craft the cost functions and constraint functions to model the dynamics of the users in the camera systems, and formulate the correlation estimation problem as a tracking-cost minimization problem. Considering the enormous number of interactions embedded in the problem, we further reformulate the proposed problem by introducing the correlation as the mean-field term. Given the complexity to solve the equilibrium, we adopt a G-prox primal-dual hybrid gradient algorithm to solve our problem efficiently. Consequently, the correlation from the initial to the destination can also be inferred over time. Extensive experiments on a real-world dataset demonstrate that our adaptive cross-camera video analytics framework based on fine-grained correlation can reduce the overall workload by 36 % in general. For queries with a large searching space, the overall workload reduction can even be reduced by 40 times with 6 % precision improvement.
Colorectal Cancer Disease Classification Using Mobilenetv2 Based on Deep Learning

The third most commonly diagnosed cancer behind breast and lung cancers is colorectal cancer. Specifically, in the minimization of health inequalities, it can be supported by the clinical care of AI guidance. To develop generalizable deep learning approaches, an enormous amount of data is essential. In this paper, cycleGAN is used to do data augmentation supposed to overcome the issue of data imbalance. Moreover, segmentation and classification of colorectal cancers are proposed.
A Context-aware Interface for Immersive Sports Spectating

Novel Augmented Reality sports spectating interfaces allow on-site sports spectators to access game-related information by overlaying relevant digital data into their field of view. However, displaying all game-related information at once would overload the user. There-fore it is important to develop a suitable interface that is aware of the game context, the user's context, and is able to display rele-vant information at the right time. We developed a state inference model based on spectators' behavior and game states to provide a context-aware sports spectating interface. The interface gradually reveals information using different levels of detail that is based on the context of the game. As an implementation of our model, we created a prototype featuring a context -aware adaptive interface for a sports spectating scenario. Although our implementation is just a preliminary prototype, the goal of this research is to begin the ex-ploration of intelligent context-aware interfaces to be used in on-site sports spectating.
frances: A Deep Learning NLP and Text Mining Web Tool to Unlock Historical Digital Collections: A Case Study on the Encyclopaedia Britannica

This work presents frances, an integrated text mining tool that combines information extraction, knowledge graphs, NLP, deep learning, parallel processing and Semantic Web techniques to unlock the full value of historical digital textual collections, offering new capabilities for researchers to use powerful analysis methods without being distracted by the technology and middleware details. To demonstrate these capabilities, we use the first eight editions of the Encyclopaedia Britannica offered by the National Library of Scotland (NLS) as an example digital collection to mine and analyse. We have developed novel parallel heuristics to extract terms from the original collection (alongside metadata), which provides a mix of unstructured and semi-structured input data, and populated a new knowledge graph with this information. Our Natural Language Processing models enable frances to perform advanced analyses that go significantly beyond simple search using the information stored in the knowledge graph. Furthermore, frances also allows for creating and running complex text mining analyses at scale. Our results show that the novel computational techniques developed within frances provide a vehicle for researchers to formalize and connect findings and insights derived from the analysis of large-scale digital corpora such as the Encyclopaedia Britannica.
D-AnoGAN: Anomaly Detection in Disconnected Data Manifolds with Generative Adversarial Networks

We present a novel GAN-based method, D-AnoGAN, for unsupervised anomaly detection in multi-class, disconnected data manifolds. Current state of the art methods learn the non-anomalous data as a single, continuous manifold, limiting their use in multi-class problems. The key contribution of D-AnoGAN is specifically accounting for potential discontinuities between manifolds through clustering. To achieve this, we implement a multi-generator network, where each generator is responsible for learning a unique manifold of data. We also implement a bandit to find the optimal set of generators required to cover all data manifolds through unsupervised prior-learning. Finally, we use this multi-generator and bandit within a GAN architecture to reconstruct queried images using distinct manifolds, ensuring that queries falling outside these manifolds are labeled as anomalous. We demonstrate our method's effectiveness on three publicly available datasets. We also introduce a software package for producing fully parameterized, disconnected manifold datasets of simulated automobile wheel images. We use this software to further show D-AnoGAN's anomaly detection capabilities in a real-world application.
Tiny Asymmetric Feature Normalized Network for Person Re-Identification System

Person re-identification (ReID) identifies object IDs in a multicamera environment based on local tracking of city surveillance cameras in public places. This method can improve the performance by learning various features using a convolutional neural network. However, ReID methods are limited in terms of application in practical surveillance environments as the ReID model is trained on public datasets and lacks the ability to generalize images acquired using other cameras. Moreover, although various methods to improve the ReID performance have been proposed, most existing studies did not evaluate ReID performances according to the number of parameters, i.e., ReID models that can be used in a limited memory environment were not considered. In this study, we propose a Tiny Asymmetric Feature Normalized Network, which can be generalized to test atasets acquired in real surveillance environments considering various scale features and can be operated with a limited number of parameters. Moreover, the Gwangju Institute of Science and Technology Practical Person ReID (GPP-reID) dataset, which was used to evaluate the performance of the ReID model, has been distributed and made available to enable applications in real-world surveillance environments. Our proposed method achieved mean average precision (mAP), Rank-1 values of 86.2, 94.7 and 74.8, 85.9 on the Market1501 and Duke Multitracking Multicamera ReID datasets, respectively. In addition, mAP and Rank-1 values of 44.2 and 64.1, respectively, were achieved on the cross-validated, new benchmark dataset, GPP-reID, using a network with one-tenth the number of parameters as the 50-layer residual network.
MID3A: Microscopy Image Denoising meets Differentiable Data Augmentation

Image restoration is an important task in a wide variety of topics. Especially in the domain of microscopy images, several content-aware image restoration (CARE) methods have arisen to improve the interpretability of acquired data. One of the main problems is the presence of high levels of noise that must be removed before any further post-processing or analysis happens. To solve this issue, we propose a simple and yet effective framework consisting of a generative adversarial network (GAN) coupled with a regularization term (differentiable data augmentation) that highly increases the quality of the denoising for three different well-known microscopy imaging data sets. We also introduce two structure preserving loss terms (Structural Similarity Index and Total Variation loss) that, added to our framework, help to further improve the quality of the results. In addition, we prove that our method is able to generalize well when trained on one dataset and used in another. Finally, we show that we can drastically reduce the amount of training data while retaining the quality of the denoising, thus alleviating the burden of acquiring paired data and enabling few-shot learning.
Design of Proof-of-Stake PBFT Algorithm for IoT Environments

Blockchain ledgers are being increasingly used in Internet of Things (IoT) and Internet of Vehicles (IoV) applications. However, Proof of Work consensus is unsuitable in an IoV setting, which is why other paradigms need to be investigated. In this work we integrate Proof of Stake (PoS) consensus technique with multiple entry Practical Byzantine Fault Tolerance voting in a permissioned blockchain network. We introduce several PoS classes based on combination of initial stake and truthfulness of voting. We also describe a differentiated medium access approach similar to Enhanced Distribution Function (EDCA). Using a Semi Markov Process-based model, we derive the probability of reaching consensus and highlight the impact of the populations of individual stake/priority classes on achieving consensus. Our results show the impact of numbers of nodes from classes with different voting behavior on consensus probability. Results also show differentiation through the number of blocks linked per second, success probability in leadership application and delay in leader selection for node belonging to certain class. These values show how much revenue a node from given class can collect from its clients depending on initial stake and voting behavior.
Visualization Error Analysis for Augmented Reality Stereo Video See-Through Head-Mounted Displays in Industry 4.0 Applications

Abstract Under the fourth industrial revolution (Industry 4.0), Augmented Reality (AR) provides new affordances for a variety of applications, such as AR-based human-robot interaction, virtual assembly assistance, and workforce virtual training. The see-through head-mounted displays (STHMDs), based on either optical see-through or video see-through technologies, are the primary AR device to augment the visual perception of the real environment with computer-generated contents through a hand-free headset. Specifically, the video see-through STHMDs process the superimposing of the real environment and virtual contents based on the digital images and output it to users, while optical see-through STHMDs display virtual contents through the optics-based near-eyes display with users’ normal view of the real scene kept. For both types of AR devices, the accuracy of visualization is essential. For example, in AR-based human-robot interaction, the inaccurate rendering of 3D virtual objects with respect to the real environment, will lead to users’ mistaking operations, and therefore, causes an invalid tool path planning result. In spite of many works related to system calibration and error reduction for optical see-through STHMDs, there are few efforts at figuring out the nature and factors of those errors in video see-through STHMDs. In this paper, taking consumer-available AR video see-through STHMDs as an example, we identify error sources of registration and build a mathematical model of the display progress to describe the error propagation in the stereo video see-through systems. Then, based on the mathematical model of the system, the sensitivity of each error source to the final registration error is analyzed. Finally, possible solutions of error correction are suggested and summarized in the general video see-through STHMDs.
Aspect-level Sentiment Analysis Research based on XLN et-LCF

At present, the commonly used aspect-level sentiment analysis method is to combine the neural network model and the attention mechanism, use the neural network to mine the semantic features of the sentence, and the attention mechanism assigns the weight of the emotional words in the sentence. However, when there are multiple aspects in a sentence, and the aspect words are uncertain, methods that rely solely on the attention mechanism cannot effectively distinguish sentiment words from different aspects. Therefore, an aspect-level sentiment analysis model based on XLNet-LCF is proposed. The model obtains contextual semantic features bidirectionally through XLNet pre-training, and introduces a context focus mechanism to capture the local context of the context with the aspect word as the focus, which can effectively reduce the impact of different aspects. To solve the problem of mutual interference between emotional words, we combined the multi-head self-attention mechanism to deeply extract the semantic features in the global context to construct an emotional weight matrix. Finally, the matrix was normalized to improve the training speed and input the emotional analysis layer to judge the emotional polarity. The model is tested on three public datasets, Laptop, Restaurant, and Twitter. The results show that the accuracy rates of sentiment analysis of the XLNet-LFC model reach 80.12%, 85.24%, and 81.2%, and the F1 values reach 76.59%, 76.9%, and 73.37%, the overall performance is better than the comparison model.
Efficient Pairing-Free Attribute-Based Blind Signature Scheme Based on Ordered Binary Decision Diagram

Attribute-based blind signature is used to realize the blind signature of messages by multiple people, and it is suitable for electronic payment services in the cloud. Most existing attribute-based blind signature schemes have complex bilinear pairing operations in the process of signature and verification, which leads to low algorithm efficiency. To solve this problem, in this study, an efficient pairing-free attribute-based blind signature scheme based on ordered binary decision diagram is proposed, and the security analysis of the new scheme is given under the difficulty of the elliptic curve discrete logarithm problem (ECDLP). The proposed scheme is based on elliptic curve cryptography (ECC). In this scheme, scalar multiplication on the elliptic curve, which is relatively lightweight, is used to replace bilinear pairing with a large computational cost in traditional attribute-based blind signature schemes. The computational overhead in the signature and verification processes is reduced, making the solution more suitable for resource-constrained cloud terminal user scenarios. On the other hand, in order to improve the computational efficiency of the attribute-based blind signature scheme, a more expressive and computationally efficient ordered binary decision diagram (OBDD) structure is used to construct the access structure of the signer. The OBDD access structure can simultaneously support the positive and negative values of attributes in the access structure at the same time. Compared with existing attribute-based blind signature schemes, the proposed scheme has better performance and storage, and meets the needs of resource-constrained cloud end users.
HetGRec: Heterogeneous Graph Attention Network for Group Recommendation

Due to the popularity of group activities in social media, group recommendation becomes increasingly significant. It aims to pursue a list of preferred items for a target group. Recently, some methods perform graph neural network on the interaction graph. However, these works ignore that the interaction relationships among groups, users, and items are heterogeneous, i.e., two objects can be connected via different paths. In this article, we propose a heterogeneous graph attention network for group recommendation. It first employs meta-path-based random walk with restart to search for strongly correlated neighbors for each node. Then, it performs a dual-hierarchical attention network to extract semantics existing in each meta-path and fuse them to obtain hybrid representation of groups and items. Extensive experiments on three public datasets demonstrate its superiority over the state-of-the-art methods for group recommendation.
The optimal strategy against Fictitious Play in infinitely repeated games

With the rapid development of Artificial Intelligence(AI), the game between human and machine/AI becomes more and more common and important. In this paper we will investigate such infinitely repeated games. We will study the optimal play of the human given the machine/AI adopting the Fictitious Play(FP) for all different kinds of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$2 \times 2$</tex> games under the assumption of a payoff parameter being rational. We give the optimal strategy of human which is proven to lead the system into a cycle after finite times. The results show that FP algorithm can be exploited easily and heavily. Analysis in this paper might shed some light on the result for more general and complicated situations.
Some Topics on the Gabor Wavelet Transformation

We studied the Gabor wavelet transform of analytic functionals on the sphere in general dimension. Then we studied the Gabor wavelet transformation on the two-dimensional sphere and its inverse transformation. In this note, we review our previous results and we consider the relationship among the Fourier transformation, the windowed Fourier transformation whose windows function is given by a Gaussian function, and the Gabor wavelet transformation on the two-dimensional unit sphere.
SMA-NBO: A Sequential Multi-Agent Planning with Nominal Belief-State Optimization in Target Tracking

In target tracking with mobile multi-sensor sys-tems, sensor deployment impacts the observation capabilities and the resulting state estimation quality. Based on a partially observable Markov decision process (POMDP) formulation comprised of the observable sensor dynamics, unobservable target states, and accompanying observation laws, we present a distributed information-driven solution approach to the multi-agent target tracking problem, namely, sequential multi-agent nominal belief-state optimization (SMA-NBO). SMA-NBO seeks to minimize the expected tracking error via receding horizon control including a heuristic expected cost-to-go (HECTG). SMA-NBO incorporates a computationally efficient approximation of the target belief-state over the horizon. The agent-by-agent decision-making is capable of leveraging on-board (edge) compute for selecting (sub-optimal) target-tracking maneuvers exhibiting non-myopic cooperative fleet behavior. The opti-mization problem explicitly incorporates semantic information defining target occlusions from a world model. To illustrate the efficacy of our approach, a random occlusion forest environment is simulated. SMA-NBO is compared to other baseline approaches. The simulation results show SMA-NBO 1) maintains tracking performance and reduces the computational cost by replacing the calculation of the expected target trajectory with a single sample trajectory based on maximum a posteriori estimation; 2) generates cooperative fleet decision by sequentially optimizing single-agent policy with efficient usage of other agents' policy of intent; 3) aptly incorporates the multiple weighted trace penalty (MWTP) HECTG, which improves tracking performance with a computationally efficient heuristic.