Reviewerly/exHarmony huggingface dataset:
Multi-Objective Deep Inverse Reinforcement Learning through Direct Weights and Rewards Estimation

Inverse reinforcement learning (IRL) methods effectively imitate complex behaviors and analyze intentions by estimating rewards from expert demonstrations. However, while most real-world problems are multi-objective problems (MOPs), most IRL methods target the estimation of a single-objective reward, thus creating a need for multi-objective IRL (MOIRL) methods to estimate rewards for MOPs. Previous MOIRL methods used matrix factorization, an indirect estimation method with a two-step learning process. We propose a novel MODIRL framework that directly estimates weights and rewards in MOIRL. Further, we experimentally verify that the proposed method can simultaneously estimate both: appropriate weights and rewards.
Motion Planner based on CNN with LSTM through Mediated Perception

For autonomous navigation, a mobile robot is required to move toward a destination while avoiding obstacles. In this paper, we present a motion planner based on CNN. In terms of obstacle avoidance, since a position of a dynamic obstacle changes with time, it is important for the robot to plan the avoidance motions in consideration of the time series variation in the images. For this purpose, an LSTM block is applied to the CNN. The policy of the motion planner represented by CNN with LSTM is trained through imitation learning. In this regard, however, it is difficult for the robot to recognize unknown objects as obstacles. For obstacle recognition, a perception process is further provided between the image inputs and CNN with LSTM in the motion planner. In the navigation experiments, we show that the robot based on the proposed motion planner is able to move toward a destination autonomously while avoiding a standing and walking person, respectively.
Centralized Training with Decentralized Execution Reinforcement Learning for Cooperative Multi-agent Systems with Communication Delay

In cooperative multi-agent systems, efficient coordination among agents is important when accomplishing tasks. VFFAC is a method that learns the communication system between agents and their interactions with the environment to obtain policies with high performance. However, this method results in decreased performance of policy in environments with a delay in communication. Furthermore, there is no formulation of the control problem of a cooperative multi-agent system with communication delays in unknown environments. In this study, we formulated a decision-making problem in a cooperative multi-agent system with an unknown environment model and a certain length delay in communication. We also propose a method to handle communication delays by using the history of information obtained through communication. We demonstrated that the proposed method successfully learns policy with high rewards through simulated experiments in an environment with a communication delay.
A square-root formulation of the sliding innovation filter for target tracking

The sliding innovation filter (SIF) is a state and parameter estimation strategy based on sliding mode concepts. It has seen significant development and research activity in recent years. In an effort to improve upon the numerical stability of the SIF, a square-root formulation is derived. The square-root SIF is based on Potter’s algorithm. The proposed formulation is computationally more efficient and reduces the risks of failure due to numerical instability. The new strategy is applied on target tracking scenarios for the purposes of state estimation. The results are compared with the popular Kalman filter.
Data postprocessing for the one-way heterodyne protocol under composable finite-size security

The performance of a practical continuous-variable (CV) quantum key distribution (QKD) protocol depends significantly, apart from the loss and noise of the quantum channel, on the post-processing steps which lead to the extraction of the final secret key. A critical step is the reconciliation process, especially when one assumes finite-size effects in a composable framework. Here, we focus on the Gaussian-modulated coherent-state protocol with heterodyne detection in a high signal-to-noise ratio regime. We simulate the quantum communication process and we post-process the output data by applying parameter estimation, error correction (using high-rate, non-binary low-density parity-check codes), and privacy amplification. This allows us to study the performance for practical implementations of the protocol and optimize the parameters connected to the steps above. We also present an associated Python library performing the steps above.
A novel stock counting system for detecting lot numbers using Tesseract OCR

Counting stock is one of the warehouse's methods for preventing insatiable stock. Moreover, it could help the company forecast how many products they need to store and predict the replenished goods for customers. However, stock count in the medical business, which sells specialized medical equipment, needs more focus on, because it uses to treat the patient. So that lack of inventory should not happen. In a normal situation, stock count at some hospitals is quite hard for salespeople, especially hospitals in upcountry that far away. During the COVID-19 situation, many limits need to be strict. At this point, it causes a shortage of goods in many hospitals. In this paper, we represent how computer vision can help this process. When the hospital's officer sends images of stock to our system. The system will recognize the quantity and lot number of goods that remain in the hospital. Therefore, salespeople can decrease the times to visit hospitals. The result showed that for text detection and text recognition in a specific use case. Our prototype system achieves 84.17% in accuracy.
Synaptic delay plasticity based on frequency-switched VCSELs for optical delay-weight spiking neural networks

In this Letter, we propose an optical delay-weight spiking neural network (SNN) architecture constructed by cascaded frequency and intensity-switched vertical-cavity surface emitting lasers (VCSELs). The synaptic delay plasticity of frequency-switched VCSELs is deeply studied by numerical analysis and simulations. The principal factors related to the delay manipulation are investigated with the tunable spiking delay up to 60 ns. Moreover, a two-layer spiking neural network based on the delay-weight supervised learning algorithm is applied to a spiking sequence pattern training task and then a classification task of the Iris dataset. The proposed optical SNN provides a compact and cost-efficient solution for delay weighted computing architecture without considerations of extra programmable optical delay lines.
Multi-Robot Weighted Coverage Path Planning: a Solution based on the DARP Algorithm

Covering a given area with a team of mobile robots in a minimum time is a well-studied problem with many real-world applications. A rarely studied subject, however, is the case of a weighted plane: due to the necessity of taking time-consuming measurements or having to traverse different kinds of terrains, the coverage time may vary over the environment and the path planning needs to be adapted accordingly. In this paper, we present an adapted version of a state-of-the-art mCPP (multi-robot coverage path planning) approach, the DARP algorithm, to make it suitable to deal with weighted environments. In particular, we propose several modifications to DARP that allow overcoming some of its limitations and, as a result, obtain an increased convergence rate and decreased convergence time with respect to the original version. Furthermore, as proved by extensive simulations, these improvements are also noticed in the unweighted version of the problem.
Subject Priorities of Academic Research in Russia and the World: Analysis of Publication Activity Based on SciVal Data

In the situation of global threats and international social and economic pressure on the part of certain countries the development of science becomes very important for Russia and turns into a factor significant both for attaining national goals and effective adaptation to global challenges. In these circumstances the task of making subjects of academic research more acute with due account for national interests and global academic agenda acquires paramount importance. Such methods as qualitative and quantitative analysis, comparison, grouping, ranging and data aggregation were used in the research. The authors on the basis of SciVal data carried out a comparative analysis of subject priorities in academic research in Russian and the world that took into account publication activity indicators, top subject fields and topic clusters, which gave an opportunity to identify specificity of content trends of Russian science in view of global tendencies and find out promising lines of academic research. Sciencemetric tools were widely used for deep and detailed assessment of the most promising trends in academic research.
A Closer Look at Debiased Temporal Sentence Grounding in Videos: Dataset, Metric, and Approach

Temporal Sentence Grounding in Videos (TSGV) , which aims to ground a natural language sentence that indicates complex human activities in an untrimmed video, has drawn widespread attention over the past few years. However, recent studies have found that current benchmark datasets may have obvious moment annotation biases, enabling several simple baselines even without training to achieve state-of-the-art (SOTA) performance. In this paper, we take a closer look at existing evaluation protocols for TSGV, and find that both the prevailing dataset splits and evaluation metrics are the devils that lead to untrustworthy benchmarking. Therefore, we propose to re-organize the two widely-used datasets, making the ground-truth moment distributions different in the training and test splits, i.e., out-of-distribution (OOD) test. Meanwhile, we introduce a new evaluation metric “dR@ n ,IoU= m ” that discounts the basic recall scores especially with small IoU thresholds, so as to alleviate the inflating evaluation caused by biased datasets with a large proportion of long ground-truth moments. New benchmarking results indicate that our proposed evaluation protocols can better monitor the research progress in TSGV. Furthermore, we propose a novel causality-based Multi-branch Deconfounding Debiasing (MDD) framework for unbiased moment prediction. Specifically, we design a multi-branch deconfounder to eliminate the effects caused by multiple confounders with causal intervention. In order to help the model better align the semantics between sentence queries and video moments, we enhance the representations during feature encoding. Specifically, for textual information, the query is parsed into several verb-centered phrases to obtain a more fine-grained textual feature. For visual information, the positional information has been decomposed from the moment features to enhance the representations of moments with diverse locations. Extensive experiments demonstrate that our proposed approach can achieve competitive results among existing SOTA approaches and outperform the base model with great gains.
Rényi Cross-Entropy Measures for Common Distributions and Processes with Memory

Two Rényi-type generalizations of the Shannon cross-entropy, the Rényi cross-entropy and the Natural Rényi cross-entropy, were recently used as loss functions for the improved design of deep learning generative adversarial networks. In this work, we derive the Rényi and Natural Rényi differential cross-entropy measures in closed form for a wide class of common continuous distributions belonging to the exponential family, and we tabulate the results for ease of reference. We also summarise the Rényi-type cross-entropy rates between stationary Gaussian processes and between finite-alphabet time-invariant Markov sources.
Sentiment analysis on 2D images of urban and indoor spaces using deep learning architectures

This paper focuses on the determination of the evoked sentiments to people by observing outdoor and indoor spaces, aiming to create a tool for designers and architects that can be utilized for sophisticated designs. Since sentiment is subjective, the design process can be facilitated by an ancillary automated tool for sentiment extraction. Simultaneously, a dataset containing both real and virtual images of vacant architectural spaces is introduced, while the SUN attributes are also extracted from the images in order to be included throughout training. The dataset is annotated towards both valence and arousal, while five established and two custom architectures, one which has never been used before in classifying abstract concepts, are evaluated on the collected data.
A Virtual Reality Talking Avatar for Investigative Interviews of Maltreat Children

Interviews conducted with the maltreated children are often the primary source of evidence in prosecution. Many alleged incidents of abuse are not prosecuted because the children’s testimony is collected in an unreliable way. Research shows the consistent poor quality of these interviews and highlights the need for better training of Child Protection Services (CPS) and police personnel who interview abused child witnesses. The currently available systems for training of CPS and police personnel are developed in a rigid way that lag behind in generating dynamic responses. Moreover, these systems require human input such as employing an actor mimicking a child or an operator controlling prerecorded child responses during the interactions. This paper demonstrates the prototype of an interview training program with an artificial intelligent Child Avatar in Virtual Reality (VR), enabling CPS and police personnel to practice interviewing with abused children. The program is developed using Unity game engine and artificial intelligence-based technologies such as dialogue models, talking visual avatars, text-to-speech, and speech-to-text components.
Learning to Detect Fallen People in Virtual Worlds

Falling is one of the most common causes of injury in all ages, especially in the elderly, where it is more frequent and severe. For this reason, a tool that can detect a fall in real time can be helpful in ensuring appropriate intervention and avoiding more serious damage. Some approaches available in the literature use sensors, wearable devices, or cameras with special features such as thermal or depth sensors. In this paper, we propose a Computer Vision deep-learning based approach for human fall detection based on largely available standard RGB cameras. A typical limitation of this kind of approaches is the lack of generalization to unseen environments. This is due to the error generated during human detection and, more generally, due to the unavailability of large-scale datasets that specialize in fall detection problems with different environments and fall types. In this work, we mitigate these limitations with a general-purpose object detector trained using a virtual world dataset in addition to real-world images. Through extensive experimental evaluation, we verified that by training our models on synthetic images as well, we were able to improve their ability to generalize. Code to reproduce results is available at https://github.com/lorepas/fallen-people-detection.
FEDResNet: a flexible image encryption and decryption scheme based on end-to-end image diffusion with dilated ResNet

Image encryption has emerged as a method of disguising an image with a noisy or meaningless appearance to prevent its content from being accessed by unauthorized users. We propose an architecture named flexible image encryption and decryption ResNet (FEDResNet) for diffusing an image in end-to-end mode. The architecture consists of an encryption network for diffusing the image and a decryption network for restoring the plaintext image from the diffused image. To enhance the security of the encrypted image, the diffused image is further processed with two optional operations: parallel scrambling and serial diffusion. Two key planes are constructed based on a user-defined key with a chaotic map to control the authority to access images. The structure and parameters of FEDResNet can be shared publicly by different users; hence, it is more flexible and convenient than previous deep-learning-based image encryption methods. A classification network is trained to classify medical images in ciphertext environments. The proposed FEDResNet is trained and tested on the ImageNet data set. Extensive experiments have been performed, and the experimental results suggest that the proposed model can achieve a high level of security with satisfactory efficiency. The experimental results also show that FEDResNet-encrypted images can be classified directly in the ciphertext domain by authorized users as accurately as plaintext images, which is a superior property that is not possessed by traditional image encryption methods.
A hybrid Chinese word segmentation model for quality management-related texts based on transfer learning

Text information mining is a key step to data-driven automatic/semi-automatic quality management (QM). For Chinese texts, a word segmentation algorithm is necessary for pre-processing since there are no explicit marks to define word boundaries. Because of intrinsic characteristics of QM-related texts, word segmentation algorithms for normal Chinese texts cannot be directly applied. Hence, based on the analysis of QM-related texts, we summarized six features, and proposed a hybrid Chinese word segmentation model by means of integrating transfer learning (TL), bidirectional long-short term memory (Bi-LSTM), multi-head attention (MA), and conditional random field (CRF) to construct the mTL-Bi-LSTM-MA-CRF model, considering insufficient samples of QM-related texts and excessive cutting of idioms. The mTL-Bi-LSTM-MA-CRF model is composed of two steps. Firstly, based on a word embedding space, the Bi-LSTM is introduced for context information learning, and the MA mechanism is selected to allocate attention among subspaces, and then the CRF is used to learn label sequence constraints. Secondly, a modified TL method is put forward for text feature extraction, adaptive layer weights learning, and loss function correction for selective learning. Experimental results show that the proposed model can achieve good word segmentation results with only a relatively small set of samples.
Multi-Camera Collaborative Depth Prediction via Consistent Structure Estimation

Depth map estimation from images is an important task in robotic systems. Existing methods can be categorized into two groups including multi-view stereo and monocular depth estimation. The former requires cameras to have large overlapping areas and sufficient baseline between cameras, while the latter that processes each image independently can hardly guarantee the structure consistency between cameras. In this paper, we propose a novel multi-camera collaborative depth prediction method that does not require large overlapping areas while maintaining structure consistency between cameras. Specifically, we formulate the depth estimation as a weighted combination of depth basis, in which the weights are updated iteratively by a refinement network driven by the proposed consistency loss. During the iterative update, the results of depth estimation are compared across cameras and the information of overlapping areas is propagated to the whole depth maps with the help of basis formulation. Experimental results on DDAD and NuScenes datasets demonstrate the superior performance of our method.
SoDa: A Serverless-Oriented Deadline-Aware Workflow Scheduling Engine for IoT Applications in Edge Clouds

As a coordination tool, workflow with a large number of interdependent tasks has increasingly become a new paradigm for orchestrating computationally intensive tasks in large-scale and complex Internet of Things (IoT) applications. Serverless computing has also recently been applied to real-world problems at the network edge as well, primarily aimed at event based IoT applications. However, the existing workflow scheduling algorithm based on the virtual machine resource model is inefficient in ensuring the QoS (Quality of Service) of users on the serverless platform. In this paper, we design an elastic workflow scheduling framework in edge clouds called EWSF based on the serverless architecture. In addition, we propose a serverless-oriented deadline-aware workflow scheduling algorithm called SoDa. Furthermore, we implemented the EWSF prototype based on Knative and Kubernetes and integrated SoDa as the scheduling engine. The performance of SoDa has been verified on the experimental platform in comparison with six counterparts. The experiment results show that SoDa adapts to various scheduling environments and achieves better performance in terms of overall makespan and execution success rate. In the case of tight cluster resources, SoDa improves the overall makespan and success rate by 10.4% and 55%, respectively, compared with the second-best algorithm.
Smart contract assisted blockchain based public key infrastructure system

ABSTRACT Public key infrastructure (PKI) is a reliable solution for Internet communication. PKI finds applications in secure email, virtual private network (VPN), e‐commerce, e‐governance, and so on. It provides a secure mechanism to authenticate users and communications. The conventional PKI system is centralized, which exposes the infrastructure to many security issues. The digital certificate generation and validation processes in PKI suffer from high latency and inadequate authentication processes. Moreover, it needs enormous time and effort to mitigate the malfeasance of the certificate authority (CA). The complexity of employing the traditional key and certificate management increases by enforcing the centralized , which can compromise the transaction security. To overcome the aforementioned issues of PKI, three different solutions have been reported in the literature: Log based PKI (LBPKI), Web of Trust (WoT), and blockchain based PKI. The blockchain based PKI achieves more attention as it is the combination of LBPKI and WoT, which serves distributed trust, log of transactions, and constant sized data to verify the identity of users. Motivated by these facts, this article reports a blockchain‐based PKI system which has a lighter smart contract and less storage capacity and is also suitable for lightweight applications. The lighter smart contract in our infrastructure uses a , which validates the limit of one participating node for becoming the of any transaction inside the network. This approach can prevent distributed denial of service (DDoS) attacks. This smart contract also checks the signer node address. The proposed smart contract can prevent seven cyber attacks, such as Denial of Service (DoS), Man in the Middle Attack (MITM), Distributed Denial of Service (DDoS), 51%, Injection attacks, Routing Attack, and Eclipse attack. The Delegated Proof of Stake (DPoS) consensus algorithm used in this model reduces the number of validators for each transaction which makes it suitable for lightweight applications. The timing complexity of key/certificate validation and signature/certificate revocation processes do not depend on the number of transactions. The comparisons of various timing parameters with existing solutions show that the proposed PKI is competitively better.