Reviewerly/exHarmony huggingface dataset:
Discover Life Skills for Planning as Bandits via Observing and Learning How the World Works

We propose a novel approach for planning agents to compose abstract skills via observing and learning from historical interactions with the world. Our framework operates in a Markov state-space model via a set of actions under unknown pre-conditions. We formulate skills as high-level abstract policies that propose action plans based on the current state. Each policy learns new plans by observing the states' transitions while the agent interacts with the world. Such an approach automatically learns new plans to achieve specific intended effects, but the success of such plans is often dependent on the states in which they are applicable. Therefore, we formulate the evaluation of such plans as infinitely many multi-armed bandit problems, where we balance the allocation of resources on evaluating the success probability of existing arms and exploring new options. The result is a planner capable of automatically learning robust high-level skills under a noisy environment; such skills implicitly learn the action pre-condition without explicit knowledge. We show that this planning approach is experimentally very competitive in high-dimensional state space domains.
Modeling Cybercrime with UFO: An Ontological Analysis of Non-Consensual Pornography Cases

The legal domain has challenging aspects. One is that legislation is written to regulate an unpredictable number of cases and, therefore, the language of the law is made of open texture and ambiguous terms. Another peculiarity is that each country has its own legislation, often using the common legal expressions but with different meanings. This results in difficulty in understanding supranational cases and in the interoperability of knowledge bases as well. For example, cybercrimes, in particular, image-based sexual abuses (also known as "revenge porn" or non-consensual pornography) are on the rise globally. Several countries are responding to this social issue by adopting dedicated regulations. However, these regulations are still fragmented, do not share a common conceptualization. In this work, we face these challenges by proposing the application of an ontology-based conceptual modeling to represent a set of concepts and legal relations in cybercrime law. To evaluate the model built, we analyzed the subdomain of cybercrimes, in particular, cases of non-consensual pornography on digital platforms. The result is a conceptualization capable of being shared with other models, increasing the interoperability and clarity of the meaning of common terms and relations found in the various laws studied.
Attention-Based Adversarial Robust Distillation in Radio Signal Classifications for Low-Power IoT Devices

Due to great success of transformers in many applications such as natural language processing and computer vision, transformers have been successfully applied in automatic modulation classification.We have shown that transformer-based radio signal classification is vulnerable to imperceptible and carefully crafted attacks called adversarial examples.Therefore, we propose a defense system against adversarial examples in transformer-based modulation classifications.Considering the need for computationally efficient architecture particularly for Internet of Things (IoT)-based applications or operation of devices in environment where power supply is limited, we propose a compact transformer for modulation classification.The advantages of robust training such as adversarial training in transformers may not be attainable in compact transformers.By demonstrating this, we propose a novel compact transformer that can enhance robustness in the presence of adversarial attacks.The new method is aimed at transferring the adversarial attention map from the robustly trained large transformer to a compact transformer.The proposed method outperforms the state-of-the-art techniques for the considered white-box scenarios including fast gradient method and projected gradient descent attacks.We have provided reasoning of the underlying working mechanisms and investigated the transferability of the adversarial examples between different architectures.The proposed method has the potential to protect the transformer from the transferability of adversarial examples.
The review of natural language processing (technology to communicate and understand the contents through human languages)

The emerging technologies of the information technology is changing the life of common man and therefore, there is huge requirements from the common man too. This demand of change in market evolve the industry revolution and therefore, The Industry 4.0 came in picture. The target and objectives of the Industry 4.o is to connect the electronic devices to each other through internet so that, the things can be managed remotely. Now, the technology is emerged to next level which is Industry 5.0 in.which the humans, bots, Artificial Intelligence and machines are working together. For this development, the interaction between the machines and human become possible through minimum efforts. The solution of this interaction is possible through two solutions as either human learn machine’s language or machine learn and understand the human’s language. First solution is not possible while the other can be easy to implement. This is the core of Natural Language processing, in which user instruct the machine through human language and machine understand the instructions through a software which implements on the concepts of Natural Language Processing. Therefore, this manuscript targets to to explain the emergence and development of the field Natural Language Processing includes its history, development, applications and challenges.
Mining the usage of reactive programming APIs

Conventionally, callbacks and inversion of control have been the main tools to structure event-driven applications. Sadly, those patterns constitute a well-known source of design problems. The Reactive Programming (RP) paradigm has arisen as an approach to mitigate these problems. Yet, little evidence has been provided regarding the advantages of RP, and concerns have also arisen about the API usability of RP libraries given their disparate number of operators. In this work, we conduct a study on GitHub (GH) and Stack Overflow (SO) and explore three Reactive Extensions (Rx) libraries (RxJava, RxJS, and RxSwift) with the most GH projects to understand how much the vast Rx operators are being used. Also, we examine Rx SO posts to complement the results from the GH exploration by understanding the problems faced by RP developers and how they relate with the operators' frequencies found in open source projects. Results reveal that, in spite of its API size, the great majority of the Rx operators are actually being used (95.2%), with only a few, mostly related to RxJava, not being utilized. Also, we unveil 23 topics from SO with more posts concerning the Stream Abstraction (36.4%). Posts related to Dependency Management, Introductory Questions, and iOS Development figure as relevant topics to the community. The findings herein present can not only stimulate advancements in the field by understanding the usage of RP API and the main problems faced by developers, but also help newcomers in identifying the most important operators and the areas that are the most likely to be relevant for a RP application.
Recognition-Free Question Answering on Handwritten Document Collections

In recent years, considerable progress has been made in the research area of Question Answering (QA) on document images. Current QA approaches from the Document Image Analysis community are mainly focusing on machine-printed documents and perform rather limited on handwriting. This is mainly due to the reduced recognition performance on handwritten documents. To tackle this problem, we propose a recognition-free QA approach, especially designed for handwritten document image collections. We present a robust document retrieval method, as well as two QA models. Our approaches outperform the state-of-the-art recognition-free models on the challenging BenthamQA and HW-SQuAD datasets.
Some Developments in Bayesian Hierarchical Linear Regression Modeling

Considering the flexibility and applicability of Bayesian modeling, in this work we revise the main characteristics of two hierarchical models in a regression setting. We study the full probabilistic structure of the models along with the full conditional distribution for each model parameter. Under our hierarchical extensions, we allow the mean of the second stage of the model to have a linear dependency on a set of covariates. The Gibbs sampling algorithms used to obtain samples when fitting the models are fully described and derived. In addition, we consider a case study in which the plant size is characterized as a function of nitrogen soil concentration and a grouping factor (farm).
Object-Oriented Extraction of Land Occupation Types in Mining Areas by Using Densenet

The acquisition of information on land occupation in mining areas is important for ecological management and restoration of mining areas. The current work has the problem of low extraction accuracy due to the small number of typical land category samples and simple network structure. In order to improve the accuracy of extraction of land use information in mining areas, this paper uses the data from the Gaofen-2 satellite, combines the dense connected neural network (DenseNet) with object-oriented ideas to extract mining areas in Yuzhou City, Henan Province. The results are compared with convolutional neural network (CNN) and support vector machine (SVM). The results show that the overall accuracy and kappa coefficient of the proposed DenseNet is 87.46% and 0.84, which are 0.06%, 2.63% and 0.004, 0.039 higher than that of the CNN and SVM respectively. The proposed DenseNet performs the best in the extraction of open-pit and waste-dump area, which indicating that it can provide promising advantages in mine information extraction, and can provide technical support for environmental monitoring and scientific management of mining areas.
Skeleton-based Adaptive Visual Servoing for Control of Robotic Manipulators in Configuration Space

This paper presents a novel visual servoing method that controls a robotic manipulator in the configuration space as opposed to the classical vision-based control methods solely focusing on the end effector pose. We first extract the robot's shape from depth images using a skeletonization algorithm and represent it using parametric curves. We then adopt an adaptive visual servoing scheme that estimates the Jacobian online relating the changes of the curve parameters and the joint velocities. The proposed scheme does not only enable controlling a manipulator in the configuration space, but also demonstrates a better transient response while converging to the goal configuration compared to the classical adaptive visual servoing methods. We present simulations and real robot experiments that demonstrate the capabilities of the proposed method and analyze its performance, robustness, and repeatability compared to the classical algorithms.
EGCN: An Efficient GCN Accelerator for Minimizing Off-Chip Memory Access

As Graph Convolutional Networks (GCNs) have emerged as a promising solution for graph representation learning, designing specialized GCN accelerators has become an important challenge. An analysis of GCN workloads shows that the main bottleneck of GCN processing is not computation but the memory latency of intensive off-chip data transfer. Therefore, minimizing off-chip data transfer is the primary challenge for designing an efficient GCN accelerator. To address this challenge, optimization is initialized by considering GCNs as tiled matrix multiplication. In this paper, we optimize off-chip memory access from both the in- and out-of-tile perspectives. From the out-of-tile perspective, we find optimal tile configurations of given datasets and on-chip buffer capacity, then observe the dataflow across phases and layers. Inter-layer phase fusion dataflow with optimal tile configuration reduces data transfer of intermediate outputs. From the in-tile perspective, due to the sparsity of tiles, tiles have redundant data which does not participate in computation. Redundant data load is eliminated with hardware support. Finally, we introduce an efficient GCN inference accelerator, EGCN, specialized for minimizing off-chip memory access. EGCN achieves 41.9% off-chip DRAM access reduction, 1.49× speedup, and 1.95× energy efficiency improvement on average over the state-of-the-art accelerators.
Procedural Game Level Design to Trigger Spatial Exploration

Synthesizing game levels that evoke players' curiosity, driving them to explore different level parts, is time-consuming and tedious. Typically, game level designers manually perform this synthesis using trial and error. In this paper, we propose a method with which to replace this manual, time-consuming process. We benefited from recent work that had proposed game level design patterns to evoke curiosity, and we propose an approach to automatically synthesizing game levels in order to encourage players to pursue designer-specified exploration goals. We started by creating a dataset of level assets, based on the four design patterns that evoke curiosity-driven exploration in games (reaching extreme points, resolving visual obstructions, out-of-place objects, and understanding spatial connections). We annotated the assets in our dataset with spatial exploration measurements (the time players took to explore an asset over their total time spent in the game level). We then formulated game level design as an optimization problem, encoding both spatial exploration (mean spatial exploration, spatial exploration variance, and spatial exploration distribution) and game level design (occupied area, adjacent penalty, and height distribution) decisions. Then, we solved this problem by implementing a reversible-jump Markov chain Monte Carlo method. We demonstrate our method's ability to synthesize game level variations with different spatial exploration and level design decisions. Finally, a user study showed that our approach can automatically synthesize game levels, encouraging a certain amount of spatial exploration by players.
Multi-granularity Weighted Federated Learning in Heterogeneous Mobile Edge Computing Systems

As a promising framework for distributed learning in mobile edge computing scenarios, federated learning (FL) allows multiple mobile devices to train a model collaboratively without transferring raw data and exposing user privacy. However, vanilla FL schemes are still facing to problems in edge computing, where the diversity of tasks and devices causes the non-IID and multi-granularity data with model heterogeneity. It becomes a pressing challenge to jointly training edge devices accompanied by these problems, while vanilla FL only discusses them separately. To this end, we consider tailoring FL to adapt to mobile edge environments, which focus on solving the problems of collaborative training of edge devices with multi-granularity heterogeneous models under different data distributions. In particular, we proposed a distance-based FL for the same type of edge devices that provides personalized models to avoid the negative impact of non-IID data on model aggregation. Further, we design a bi-directional guidance method with a prior attention mechanism, which can transfer knowledge among edge devices with multi-granulairty and multi-scale models. The experimental results show that our proposed mechanisms significantly improve training performance compared to other baselines on IID and non-IID data. Furthermore, the bi-directional guidance significantly improves convergence efficiency and accuracy performance for finer and coarser granularity edge devices, respectively.
Fold–unfold lemmas for reasoning about recursive programs using the Coq proof assistant

Abstract Fold–unfold lemmas complement the rewrite tactic in the Coq Proof Assistant to reason about recursive functions, be they defined locally or globally. Each of the structural cases gives rise to a fold–unfold lemma that equates a call to this function in that case with the corresponding case branch. As such, they are “boilerplate” and can be generated mechanically, though stating them by hand is a learning experience for a beginner, to say nothing about explaining them. Their proof is generic. Their use is precise (e.g., in terms with multiple calls) and they scale seamlessly (e.g., to continuation-passing style and to various patterns of recursion), be the reasoning equational or relational. In the author’s experience, they prove effective in the classroom, considering the clarity of discourse in the subsequent term reports and oral exams, and beyond the classroom, considering their subsequent use when continuing to work with the Coq Proof Assistant. Fold–unfold lemmas also provide a measure of understanding as well as of control about what is cut short when one uses a shortcut, i.e., an automated simplification tactic. Since Version 8.0, the functional-induction plugin provides them for functions that are defined globally, i.e., recursive equations, and so does the Equations plugin now, both for global and for local declarations, a precious help for advanced users.
Intrinsic Satellite Video Decomposition With Motion Target Energy Constraint

Satellite videos dynamically monitor the Earth’s surface by using staring imaging, which has gained increased attention and enabled target tracking applications. While various tracking methods are processed on the original video, the rapid light changes due to staring imaging are not considered and have a negative effect on tracking. To reduce the effects of illumination and improve the performance of satellite video target tracking, an intrinsic satellite video decomposition model with motion target energy constraint, called MTE-ISVD, is proposed in this paper. The proposed algorithm introduces two main constraints: The first is a temporal constraint of reflectance, which can solve the flicker problem by preserving reflectance coherence in the time domain with the property that the background pixels in satellite videos are nearly consistent between adjacent frames. The second is a motion target energy constraint, which can concentrate the signal energy of the motion targets in the reflectance by representing them with the surrounding background in the shading. The decomposition problem is reformulated as a quadratic function minimization, which can be addressed using the standard conjugate gradient in closed form. For visual and quantitative comparisons, we perform experiments on five Jilin-1 satellite videos and analyze the results in terms of visual comparison, target tracking improvement, stability evaluation and processing time comparison. The experimental results demonstrate that our proposed method outperforms the other representative intrinsic decomposition methods in terms of processing speed, stability, and motion target representation.
Crowd Counting Using Federated Learning and Domain Adaptation

Crowd counting is a technique used to estimate the number of people in an image at a particular instance. Accurate and quick estimation of crowd counts is a challenging yet meaningful task which has a wide range of applications in diverse fields. A CNN-based crowd counting approach which utilizes the first 13 layers of pre-trained VGG-16 model and dilated convolutional layers to generate quality density maps is proposed. The dilated layers allow for larger receptive fields without increasing the amount of computation. In addition, a federated learning-based approach involving the federated averaging algorithm is adopted to decentralize the training process, reduce the time taken and preserve privacy. The problem of domain-adaptation in crowd counting is also addressed by training a model using the abundant labelled data available in the source domain and transferring the parameters learnt to a target domain with relatively fewer labelled data using neuron linear transformation, thereby minimizing the domain gap and improving performance.
Deep Learning Based a Novel Method of Classroom Behavior Recognition

With the rapid rise of artificial intelligence, the application of artificial intelligence in classroom teaching has become more and more extensive. Combining the related technologies of artificial intelligence with the dynamic detection of classroom behavior is a hot research topic at present. In this article, a novel recognition method of students' action in classroom is proposed. The method first uses YOLOv3 to recognize a single target in the classroom, and then, according to the proposed combination method, combines each single target into a fixed student classroom behavior. The research was carried out in eight classes (215 students in total). The research results show that the proposed research method can accurately and quickly identify students' behaviors in classroom.
Continual Predictive Learning from Videos

Predictive learning ideally builds the world model of physical processes in one or more given environments. Typical setups assume that we can collect data from all environments at all times. In practice, however, different prediction tasks may arrive sequentially so that the environments may change persistently throughout the training procedure. Can we develop predictive learning algorithms that can deal with more realistic, non-stationary physical environments? In this paper, we study a new continual learning problem in the context of video prediction, and observe that most existing methods suffer from severe catastrophic forgetting in this setup. To tackle this problem, we propose the continual predictive learning (CPL) approach, which learns a mixture world model via predictive experience replay and performs test-time adaptation with non-parametric task inference. We construct two new benchmarks based on RoboNet and KTH, in which different tasks correspond to different physical robotic environments or human actions. Our approach is shown to effectively mitigate forgetting and remarkably outperform the naíve combinations of previous art in video prediction and continual learning.
An Adaptive Whale Optimization Algorithm Integrating Multiple Improvement Strategies

A self-adaptive whale optimization algorithm integrating four improvement strategies is proposed to overcome the shortcomings of the basic whale optimization algorithm that are easy to fall into local optima, slow convergence speed and low calculation accuracy. Firstly, the chaotic logistics mapping method is used to initialize the population randomly to increase the diversity of the population and the uniformity of individual distribution. Secondly, the convergence factor is non-linearized and dynamically adjusted to balance the global search and local search performance of the algorithm. Thirdly, the dynamic adjustment of the search step length is realized by setting different adaptive inertia weights according to stages. Finally, crossover and mutation operations are performed on the random dimensions of the population individuals to avoid the algorithm from falling into the local optimum. The results of comparative experiments with three related algorithms on six benchmark functions show that the proposed algorithm is superior to the others in terms of global search capability, convergence speed and calculation accuracy.