Reviewerly/exHarmony huggingface dataset:
Improved Particle Swarm Optimization-Based BP Neural Networks for Aero-Optical Imaging Deviation Prediction

In this article, an improved particle swarm optimization (IPSO) algorithm based on similarity and random mutation is raised.The diversity of particles in the population is decided by the size of the aggregation.When the aggregation degree of particles in the population surpass a certain threshold, the concept of similarity is used to measure the similarity between particles and global extremum, and the particles with higher similarity are discretized by mutation strategy.By increasing the particle swarm's diversity, the population's local and global search ability tend to balance.The weight and threshold of the back propagation (BP) neural networks are optimized by the IPSO algorithm.Then, the model of the improved particle swarm optimization back propagation neural network (IPSO-BP) is applied to the aerooptical imaging deviation prediction.The results show that the prediction accuracy of the IPSO-BP model is superior to the PSO-BP model, the extreme learning machine (ELM) model, and the least square support vector machine (LSSVM) model, and its convergence speed is faster than that of the PSO-BP neural network model.Finally, the application of deep learning in aero-optical imaging deviation prediction is analyzed compared with the IPSO-BP neural network model.
Simultaneous Hand–Eye/Robot–World/Camera–IMU Calibration

The problem of calibrating an extrinsic parameter between a camera and an inertial measurement unit (IMU) using an industrial robotic manipulator has been studied. This generates a result of hand–eye/robot–world/camera–IMU calibration in a simultaneous fashion. The developed method is free of inertial integration over time and, thus, is robust to uncertain IMU biases. It is derived that the problem can be solved via a simultaneous optimization of hand–eye/robot–world/camera–IMU transformations. The resulted optimization is highly nonconvex on the special Euclidean group, and we give globally optimal solutions. Experiments verify that the proposed method is capable of estimating accurate calibration parameters. Comparative studies between representatives show the global optimality of the proposed method. The new simultaneous method is capable of conducting calibration of a robot/camera/IMU combination. The designed method guarantees the global optimality; thus, the accuracy is ensured. The developed globally optimal solutions will also be computationally efficient on modern industrial computers. Finally, we show that the proposed method can give accurate calibration results for a stereo/IMU sensor combination.
Privacy-Preserving Data Sharing Scheme with FL via MPC in Financial Permissioned Blockchain

Each bank has different clients and each client may have transactions with multiple banks. Hence, clients’ data in a single bank may be partial and incomplete. If the data can be combined, each bank obtains comprehensive information, so as to better carry out business and enhance the quality of service, such as recommending financial products and inquiring about personal credit records. However, after the promulgation of GDPR by European Union in 2018, it is illegal to directly consolidate data crossing enterprises due to privacy and security concerns, especially for privacy-sensitive industries. Emerging federated learning(FL) is very suitable for secure data sharing for distributed banks in privacy. To prevent from connection of clients’ data and the certain bank, we adopt anonymity mechanism to hide the real identity of banks. In this paper, we first propose blockchain-empowered secure federated learning for distributed banks based on multi-party computation(MPC) with multi-key fully-homomorphic encryption(FHE) scheme. Then, we give detailed description of multi-key FHE based MPC protocol, anonymity mechanism and permissioned blockchain consensus protocol. Finally, we analyze the security and compare our scheme with several existed schemes. Numerical results show that the proposed data sharing scheme has good performance in terms of computational overhead and model accuracy.
Proximal Policy Optimization for Radiation Source Search

Rapid search and localization for nuclear sources can be an important aspect in preventing human harm from illicit material in dirty bombs or from contamination. In the case of a single mobile radiation detector, there are numerous challenges to overcome such as weak source intensity, multiple sources, background radiation, and the presence of obstructions, i.e., a non-convex environment. In this work, we investigate the sequential decision making capability of deep reinforcement learning in the nuclear source search context. A novel neural network architecture (RAD-A2C) based on the advantage actor critic (A2C) framework and a particle filter gated recurrent unit for localization is proposed. Performance is studied in a randomized 20×20 m convex and non-convex simulation environment across a range of signal-to-noise ratio (SNR)s for a single detector and single source. RAD-A2C performance is compared to both an information-driven controller that uses a bootstrap particle filter and to a gradient search (GS) algorithm. We find that the RAD-A2C has comparable performance to the information-driven controller across SNR in a convex environment. The RAD-A2C far outperforms the GS algorithm in the non-convex environment with greater than 95% median completion rate for up to seven obstructions.
Indian Regional Language Abstractive Text Summarization using Attention-based LSTM Neural Network

Text summarization is a process of compiling a block of text into a short, precise, and understandable text which provides the complete interpretation of the original text in fewer words whilst retaining the context of the original content. Literature and texts in regional languages are often difficult to comprehend due to a lack of corresponding summaries conveying the idea of the text. Abstractive text summarization is widely studied for the English language, however, it is in nascent stages for Indian Regional languages. There is an acute paucity of regional data sets, a challenge for researchers working in this field. In this paper, we try to resolve the data set scarcity in Indian Regional Languages like Hindi and Marathi, and we have proposed two new deep learning architectures to perform text summarization using the Abstractive approach which is Attention-based and Stacked LSTM based Sequence To Sequence (Seq2Seq) Neural Network. These models are backed by Hindi and Marathi stop-words and rare words list that we have created for pre-processing. Our novel approach enables the model to accept text in Hindi and Marathi languages and to produce a succinct summary correspondingly able to explain the gist of the original text lucidly.
Using state abstractions to compute personalized contrastive explanations for AI agent behavior

There is a growing interest within the AI research community in developing autonomous systems capable of explaining their behavior to users. However, the problem of computing explanations for users of different levels of expertise has received little research attention. We propose an approach for addressing this problem by representing the user's understanding of the task as an abstraction of the domain model that the planner uses. We present algorithms for generating minimal explanations in cases where this abstract human model is not known. We reduce the problem of generating an explanation to a search over the space of abstract models and show that while the complete problem is NP-hard, a greedy algorithm can provide good approximations of the optimal solution. We empirically show that our approach can efficiently compute explanations for a variety of problems and also perform user studies to test the utility of state abstractions in explanations.
Multimodal Dependent Type Theory

We introduce MTT, a dependent type theory which supports multiple modalities. MTT is parametrized by a mode theory which specifies a collection of modes, modalities, and transformations between them. We show that different choices of mode theory allow us to use the same type theory to compute and reason in many modal situations, including guarded recursion, axiomatic cohesion, and parametric quantification. We reproduce examples from prior work in guarded recursion and axiomatic cohesion, thereby demonstrating that MTT constitutes a simple and usable syntax whose instantiations intuitively correspond to previous handcrafted modal type theories. In some cases, instantiating MTT to a particular situation unearths a previously unknown type theory that improves upon prior systems. Finally, we investigate the metatheory of MTT. We prove the consistency of MTT and establish canonicity through an extension of recent type-theoretic gluing techniques. These results hold irrespective of the choice of mode theory, and thus apply to a wide variety of modal situations.
Enterprise Problems and Proposed Solutions Using the Concept of E-Commerce

The noise of e-commerce businesses has seen the extension of number of the online businesses global. Buyers now are opted with online business commerce which is greatly benefit compared to traditional businesses. It is undeniable e-commerce enterprises contribute more profits, comparing to "bricks-and-mortal" traditional enterprises, but not to be avoided the problems and challenges faced by the e- commerce marketplace. This study emphasizes the problems and challenges of e-commerce and a set of recommended solutions to those problems and challenges. Those problems are trust, readiness and security. For each of the problems, proposed solutions are presented. Companies have conducted business electronically by applying a range of electronic commerce solutions. In the current situation, a corporation enters the electronic market by building trading partner agreements with sellers or wholesalers of their choice. These deals may include any items that cannot be agree electronically, like terms of transfer, payment devices, or implementation rules. After discovering the right market relationships, a company must choose the elements of their electronic commerce system. In this paper have been formulate current ecommerce problems, challenges and their effective solutions. In future, e-commerce marketplace should concentrate more on securing payment, e-banking and e-government. With that, problems and challenges in e-commerce can be reduce.
A Survey of Advances in Evolutionary Neural Architecture Search

Deep neural networks (DNNs) have been frequently and widely applied for intelligent systems such as object detection, natural language understanding and speech recognition. Given a specific problem, we always aim to construct the most suitable DNN to solve it, which requires choosing the most appropriate model architecture and seeking the best model parameters values. However, most existing works focus on model parameters learning under the assumption that the model architecture can be manually specified as per prior knowledge and/or trial-and-error experimentation. To overcome this problem, evolutionary algorithms (EAs) have been widely used to design model architectures automatically. Further, EAs have been used for neural network optimization for more than 30 years. Therefore, in this paper, we review the evolutionary neural architecture search (ENAS) from the view of the advanced techniques. We hope this work can provide a comprehensive understanding of EAs' roles for the readers and focus themselves on ENAS.
Parsing Digitized Vietnamese Paper Documents

In recent years, the need to exploit digitized document data has been increasing. In this paper, we address the problem of parsing digitized Vietnamese paper documents. The digitized Vietnamese documents are mainly in the form of scanned images with diverse layouts and special characters introducing many challenges. To this end, we first collect the UIT-DODV dataset, a novel Vietnamese document image dataset that includes scientific papers in Vietnamese derived from different scientific conferences. We compile both images that were converted from PDF and scanned by a smartphone in addition a physical scanner that poses many new challenges. Additionally, we further leverage the state-of-the-art object detector along with the fused loss function to efficiently parse the Vietnamese paper documents. Extensive experiments conducted on the UIT-DODV dataset provide a comprehensive evaluation and insightful analysis.
Clustering by Orthogonal NMF Model and Non-Convex Penalty Optimization

The non-negative matrix factorization (NMF) model with an additional orthogonality constraint on one of the factor matrices, called the orthogonal NMF (ONMF), has been found a promising clustering model and can outperform the classical K-means. However, solving the ONMF model is a challenging optimization problem because the coupling of the orthogonality and non-negativity constraints introduces a mixed combinatorial aspect into the problem due to the determination of the correct status of the variables (positive or zero). Most of the existing methods directly deal with the orthogonality constraint in its original form via various optimization techniques, but are not scalable for large-scale problems. In this paper, we propose a new ONMF based clustering formulation that equivalently transforms the orthogonality constraint into a set of norm-based non-convex equality constraints. We then apply a non-convex penalty (NCP) approach to add them to the objective as penalty terms, leading to a problem that is efficiently solvable. One smooth penalty formulation and one non-smooth penalty formulation are respectively studied. We build theoretical conditions for the penalized problems to provide feasible stationary solutions to the ONMF based clustering problem, as well as proposing efficient algorithms for solving the penalized problems of the two NCP methods. Experimental results based on both synthetic and real datasets are presented to show that the proposed NCP methods are computationally time efficient, and either match or outperform the existing K-means and ONMF based methods in terms of the clustering performance.
I3CL: Intra- and Inter-Instance Collaborative Learning for Arbitrary-Shaped Scene Text Detection

Existing methods for arbitrary-shaped text detection in natural scenes face two critical issues, i.e., (1) fracture detections at the gaps in a text instance; and (2) inaccurate detections of arbitrary-shaped text instances with diverse background context. To address these issues, we propose a novel method named Intra- and Inter-Instance Collaborative Learning (I3CL). Specifically, to address the first issue, we design an effective convolutional module with multiple receptive fields, which is able to collaboratively learn better character and gap feature representations at local and long ranges inside a text instance. To address the second issue, we devise an instance-based transformer module to exploit the dependencies between different text instances and a global context module to exploit the semantic context from the shared background, which are able to collaboratively learn more discriminative text feature representation. In this way, I3CL can effectively exploit the intra- and inter-instance dependencies together in a unified end-to-end trainable framework. Besides, to make full use of the unlabeled data, we design an effective semi-supervised learning method to leverage the pseudo labels via an ensemble strategy. Without bells and whistles, experimental results show that the proposed I3CL sets new state-of-the-art results on three challenging public benchmarks, i.e., an F-measure of 77.5% on ArT, 86.9% on Total-Text, and 86.4% on CTW-1500. Notably, our I3CL with the ResNeSt-101 backbone ranked the 1st place on the ArT leaderboard. Code is available at www.github.com/ViTAE-Transformer/ViTAE-Transformer-Scene-Text-Detection .
Gaze Behavior During Navigation and Visual Search of an Open-World Virtual Environment

Eye tracking has been an essential tool within the vision science community for many years. However, the majority of studies involving eye-tracking technology employ a relatively passive approach through the use of static imagery, prescribed motion, or video stimuli. This is in contrast to our everyday interaction with the natural world where we navigate our environment while actively seeking and using task-relevant visual information. For this reason, an increasing number of vision researchers are employing virtual environment platforms, which offer interactive, realistic visual environments while maintaining a substantial level of experimental control. Here, we recorded eye movement behavior while subjects freely navigated through a rich, open-world virtual environment. Within this environment, subjects completed a visual search task where they were asked to find and count occurrence of specific targets among numerous distractor items. We assigned each participant into one of four target conditions: Humvees, motorcycles, aircraft, or furniture. Our results show a statistically significant relationship between gaze behavior and target objects across Target Conditions with increased visual attention toward assigned targets. Specifically, we see an increase in the number of fixations and an increase in dwell time on target relative to distractor objects. In addition, we included a divided attention task to investigate how search changed with the addition of a secondary task. With increased cognitive load, subjects slowed their speed, decreased gaze on objects, and increased the number of objects scanned in the environment. Overall, our results confirm previous findings and support that complex virtual environments can be used for active visual search experimentation, maintaining a high level of precision in the quantification of gaze information and visual attention. This study contributes to our understanding of how individuals search for information in a naturalistic (open-world) virtual environment. Likewise, our paradigm provides an intriguing look into the heterogeneity of individual behaviors when completing an un-timed visual search task while actively navigating.
Cloud computing approaches in health care

Cloud computing and health monitoring is increasingly being employed together as the healthcare equipment's are giving proper monitoring and collection of patient health record is being transferred and collected using cloud computing services like SaaS which stands for storage as a service. The need of tech equipped. equipment's are increasing day by day as equipment's are highly technical and the software and configuration which need to keep equipment's work progressively can easily be served by cloud services which named under infrastructure as a service and software as a service. Our paper is a literature survey for how the cloud web services are merging with health care and in how many ways it is beneficial for human being for adding the cloud web services in healthcare in terms of facility and industry utilization. The digitalization of everything gives the benefit of availability of each record about a disease or patient, this makes the data available for all and all experts can look and monitor without having limitation of physically present there. Also, there are some challenges in employment of cloud services they are also mentioned in this paper with possible solution.
DeepFake Videos Detection Using Self-Supervised Decoupling Network

With wide applications of facial manipulation technology, fake images and videos are becoming a great public concern. Although existing methods for face forgery detection could achieve fairly good results on public database, most of them perform poorly when the fake images/videos are compressed as they are usually done in social networks. To tackle this issue, a self-supervised decoupling network (SSDN), that incorporates compression irrelevance, is proposed in this paper. The proposed model learns two separate feature representations for the suspect videos, i.e., authenticity and compression. A joint self-supervised strategy is then utilized for feature decoupling, in which, the similarity decoupling is carried out by similarity learning on authentic features, whereas for adversarial decoupling, the proposed SSDN model is trained in an adversarial manner for robust feature learning. Experimental results show that the SSDN outperforms the state-of-the-art methods for deepfake detection against compression attacks on public datasets, e.g., FaceForensics++.
Federated K-Means Clustering: A Novel Edge AI Based Approach for Privacy Preservation

Edge AI is the result of a more fundamental thought, “bring code to data, not data to code”. The assumption here is that the data is stored on devices which are computationally powerful and are therefore `edge devices'. Our paper deals with the technique of Federated Averaging applied to `Federated k-Means' algorithm. To the best of our knowledge, at the time of writing, there is no published research outcome of applying Federated k-Means, a computing paradigm aligned towards this concept of Edge AI. The data generated at the edge device never leaves it, thus ensuring privacy preservation. Each device has a ML model on it, thus reducing the latency associated with a centralized model deployed on the cloud. Our paper aims to provide a holistic analysis of the performance metrics at the edge devices as well as at the central server after each federation step. Ours is an early publication of the research results, aiming to excite the researcher community on the immense possibilities to explore this new area, a possible solution to the concerns of user data privacy and the inherent latency associated with traditional ML approaches. The findings of our paper can potentially be applied to a privacy-preserving real-world use case, such as EMR (Electronic Medical Record) based ML applications.
Efficient Task Planning for Mobile Manipulation: a Virtual Kinematic Chain Perspective

We present a Virtual Kinematic Chain (VKC) perspective, a simple yet effective method, to improve task planning efficacy for mobile manipulation. By consolidating the kinematics of the mobile base, the arm, and the object being manipulated collectively as a whole, this novel VKC perspective naturally defines abstract actions and eliminates unnecessary predicates in describing intermediate poses. As a result, these advantages simplify the design of the planning domain and significantly reduce the search space and branching factors in solving planning problems. In experiments, we implement a task planner using Planning Domain Definition Language (PDDL) with VKC. Compared with conventional domain definition, our VKC-based domain definition is more efficient in both planning time and memory. In addition, abstract actions perform better in producing feasible motion plans and trajectories. We further scale up the VKC-based task planner in complex mobile manipulation tasks. Taken together, these results demonstrate that task planning using VKC for mobile manipulation is not only natural and effective but also introduces new capabilities.