Reviewerly/exHarmony huggingface dataset:
Constrained stochastic optimal control with learned importance sampling: A path integral approach

Modern robotic systems are expected to operate robustly in partially unknown environments. This article proposes an algorithm capable of controlling a wide range of high-dimensional robotic systems in such challenging scenarios. Our method is based on the path integral formulation of stochastic optimal control, which we extend with constraint-handling capabilities. Under our control law, the optimal input is inferred from a set of stochastic rollouts of the system dynamics. These rollouts are simulated by a physics engine, placing minimal restrictions on the types of systems and environments that can be modeled. Although sampling-based algorithms are typically not suitable for online control, we demonstrate in this work how importance sampling and constraints can be used to effectively curb the sampling complexity and enable real-time control applications. Furthermore, the path integral framework provides a natural way of incorporating existing control architectures as ancillary controllers for shaping the sampling distribution. Our results reveal that even in cases where the ancillary controller would fail, our stochastic control algorithm provides an additional safety and robustness layer. Moreover, in the absence of an existing ancillary controller, our method can be used to train a parametrized importance sampling policy using data from the stochastic rollouts. The algorithm may thereby bootstrap itself by learning an importance sampling policy offline and then refining it to unseen environments during online control. We validate our results on three robotic systems, including hardware experiments on a quadrupedal robot.
The Tales of Alphanumerical Symbols in Media: The Case of Bitcoin

Bitcoin, a decentralized cryptocurrency, has not only given rise to a wave of digital innovations but also stirred up considerable controversy. Some have hailed it as the most significant innovation since the Internet, while others have dismissed it as a Ponzi scheme that should be abandoned and forbidden. Regardless of these varying views, this is an innovation in need of scrutiny. In this paper we present a metastory of Bitcoin, based on an interpretative study of 737 news articles between 2011–2019. Through our analysis, we identified five narratives, including The Dark Side, The Bright Side, The Tulip Mania, The Idea, and The Normality. Our analysis demonstrates the interpretive flexibility of technology as influenced by ideologies, and we construct a theoretical model demonstrating media’s role as constructor and conduit. The metastory provides an institutional look at the broader interpretations of digital innovations as well as the multifaceted nature of digital innovations and how their interpretation evolve over time.
Secure data outsourcing in presence of the inference problem: A graph-based approach

In light of the emergence of Database-as-a-Service paradigm, secure data outsourcing has become one of the crucial challenges which strongly imposes itself. In such a scenario, access control is considered as a major challenge. In fact, access control policies of the data owner must be preserved when data is moved to the cloud. Here, we address this problem by considering inference leakage that could be produced by exploiting functional dependencies. The proposed approach is based on vertical partitioning to produce a set of secure sub-schemas stored in separated partitions in the distributed system. Then, we extend this approach by presenting a secure query processing model to preserve access control policies when querying data from distributed partitions. The effectiveness of our algorithms is confirmed through observations from a variety of conducted experiments.
Diff-Net: Image Feature Difference Based High-Definition Map Change Detection for Autonomous Driving

Up-to-date High-Definition (HD) maps are essential for self-driving cars. To achieve constantly updated HD maps, we present a deep neural network (DNN), Diff-Net, to detect changes in them. Compared to traditional methods based on object detectors, the essential design in our work is a parallel feature difference calculation structure that infers map changes by comparing features extracted from the camera and rasterized images. To generate these rasterized images, we project map elements onto images in the camera view, yielding meaningful map representations that can be consumed by a DNN accordingly. As we formulate the change detection task as an object detection problem, we leverage the anchor-based structure that predicts bounding boxes with different change status categories. To the best of our knowledge, the proposed method is the first end-to-end network that tackles the high-definition map change detection task, yielding a single stage solution. Furthermore, rather than relying on single frame input, we introduce a spatio-temporal fusion module that fuses features from history frames into the current, thus improving the overall performance. Finally, we comprehensively validate our method's effectiveness using freshly collected datasets. Results demonstrate that our Diff-Net achieves better performance than the baseline methods and is ready to be integrated into a map production pipeline maintaining an up-to-date HD map.
Compact Harris Hawks Optimization Algorithm

The intelligent optimization algorithm performs well in solving complex optimization problems in engineering. However, the intelligent optimization algorithm usually takes long execution time and large memory usage, which is not suitable for the engineering with limited hardware conditions. To handle this difficulty, in this paper, a novel compact harris hawks optimization (cHHO) algorithm is proposed, which is an modified version of the harris hawks optimization (HHO) algorithm. The cHHO algorithm searches the optimal solution using only one hawk, instead of a whole swarm of hawks in the HHO algorithm, by means of the compact optimization theory. Numerical experiments based on two commonly used benchmark functions show that the execution time of the cHHO algorithm is shorter and the memory usage is smaller, comparing with the HHO algorithm and the compact particle swarm optimization algorithm. Further, the cHHO algorithm is employed to the thrust allocation of a dynamic positioning vessel, and the simulation illustrates the effectiveness of the cHHO algorithm.
A Novel Cryptosystem for Color Images Based on Chaotic Maps Using a Random Controller

Hraoui, Said Gouiouez, Mounir Gmira, Faiq Berrada, Mohammed Jarjar, Abdellatif Jarrar, A. OulidiA new encryption technique for multicolor images is outlined in this study. After vectorization of the clear image, an initialization value will be calculated. This value, allows changing only the first pixel. This pixel will start the encryption process. In parallel, in a first step, we will make a confusion by chaotic vectors entirely controlled by another chaotic vector. In a second step, three chaotic substitution matrices of size (256, 256) will be generated. The passage of each pixel through these matrices will also be controlled by another chaotic vector. Finally, a strong link will be set up between the new pixel state and the previous ones to set up better diffusion/confusion. This step increases the avalanche effect. Testing performed through our algorithm on standard images shows the durability of our system.
Replay Overshooting: Learning Stochastic Latent Dynamics with the Extended Kalman Filter

This paper presents replay overshooting (RO), an algorithm that uses properties of the extended Kalman filter (EKF) to learn nonlinear stochastic latent dynamics models suitable for long-horizon prediction. We build upon overshooting methods used to train other prediction models and recover a novel variational learning objective. Further, we use RO to extend another objective that acts as a surrogate for the true log-likelihood, and show that this objective empirically yields better models than the variational one. We evaluate RO on two tasks: prediction of synthetic video frames of a swinging motorized pendulum and prediction of the planar position of various objects being pushed by a real manipulator (MIT Push Dataset). Our model outperforms several other prediction models on both quantitative and qualitative metrics.
Non-Linear Fusion for Self-Paced Multi-View Clustering

With the advance of the multi-media and multi-modal data, multi-view clustering (MVC) has drawn increasing attentions recently. In this field, one of the most crucial challenges is that the characteristics and qualities of different views usually vary extensively. Therefore, it is essential for MVC methods to find an effective approach that handles the diversity of multiple views appropriately. To this end, a series of MVC methods focusing on how to integrate the loss from each view have been proposed in the past few years. Among these methods, the mainstream idea is assigning weights to each view and then combining them linearly. In this paper, inspired by the effectiveness of non-linear combination in instance learning and the auto-weighted approaches, we propose Non-Linear Fusion for Self-Paced Multi-View Clustering (NSMVC), which is totally different from the the conventional linear-weighting algorithms. In NSMVC, we directly assign different exponents to different views according to their qualities. By this way, the negative impact from the corrupt views can be significantly reduced. Meanwhile, to address the non-convex issue of the MVC model, we further define a novel regularizer-free modality of Self-Paced Learning (SPL), which fits the proposed non-linear model perfectly. Experimental results on various real-world data sets demonstrate the effectiveness of the proposed method.
AutoBraid: A Framework for Enabling Efficient Surface Code Communication in Quantum Computing

Quantum computers can solve problems that are intractable using the most powerful classical computer. However, qubits are fickle and error prone. It is necessary to actively correct errors in the execution of a quantum circuit. Quantum error correction (QEC) codes are developed to enable fault-tolerant quantum computing. With QEC, one logical circuit is converted into an encoded circuit.
The Status of Hyperspectral Imager Suite (HISUI): One Year After Launch

Hyperspectral Imager Suite (HISUI) was launched in December 2019 and successfully attached to Japan Experiment Module Exposed Facility, International Space Station. Due to data communication problems, its initial check out activities have been delayed, but its first light image was acquired and released in September 2020. HISUI data delivery to Research Users will start in 1Q of Japanese FY2021 (April - June 2021).
Ammonia Nitrogen Monitoring of Urban Rivers with UAV-Borne Hyperspectral Remote Sensing Imagery

Ammonia nitrogen (NH4-N) can cause water eutrophication and is the main oxygen-consuming pollutant in water bodies. Remote sensing methods are more macroscopic than traditional measurement methods. However, due to the weak optical characteristics of NH4-N, traditional remote sensing data cannot meet the needs of NH4-N monitoring. In response to this problem, this paper attempts to use unmanned aerial vehicles (UAV) hyperspectral imagery combined with extreme gradient boosting (XGBoost)regression algorithm to quantitatively retrieve NH4-N in urban rivers. The results show that compared with the traditional empirical semi-empirical model, the accuracy of using the XGBoost algorithm to estimate the NH4-N in the water body is significantly improved, and is consistent with the field measurement.
Relational nullable types with Boolean unification

We present a simple, practical, and expressive relational nullable type system. A relational nullable type system captures whether an expression may evaluate to null based on its type, but also based on the type of other related expressions. The type system extends the Hindley-Milner type system with Boolean constraints, supports parametric polymorphism, and preserves principal types modulo Boolean equivalence. We show how to support full Hindley-Milner style type inference with an extension of Algorithm W. We conduct a preliminary study of open source projects showing that there is a need for relational nullable type systems across a wide range of programming languages. The most important findings from the study are: (i) programmers use programming patterns where the nullability of one expression depends on the nullability of other related expressions, (ii) such invariants are commonly enforced with run-time exceptions, and (iii) reasoning about these programming patterns requires not only knowledge of when an expression may evaluate to null, but also when it may evaluate to a non-null value. We incorporate these observations in the design of the proposed relational nullable type system.
A Superpixel Aggregation Method Based on Multi-Direction Gray Level Co-Occurrence Matrix for Sar Image Segmentation

SAR image segmentation is a key step of SAR image interpretation, boosting target detection and recognition. Since similar targets may exist in complex and changeable scenes, under-segmentation and over-segmentation often occur in SAR image segmentation. To solve the above deficiencies, we propose a superpixel aggregation method based on multi-direction gray level co-occurrence matrix (GLCM) for SAR image segmentation. Firstly, a linear similarity judgment based on gray feature and spatial distance of pixels is introduced. In this stage, we expand the search range of clustering centers and add constraints to reduce the deviation, so as to alleviate over-segmentation. Then, for the spatial adjacent su-perpixels, we use multi-direction GLCM to measure texture similarity between them, merging homogeneous superpixel-s to solve under-segmentation. Experimental results based on satellite-borne SAR images from different scenes illustrate that the proposed method performs well with excellent pixel accuracy, effectively solving under-segmentation and over-segmentation.
Deepfake Detection using GAN Discriminators

Deepfake videos are videos where the features of a person are replaced with the features of another person. Videos can be manipulated using powerful Deep Learning techniques. This technology may be used maliciously as a means of misinformation, manipulation, and persuasion. There are currently not many solutions to identify products of Deepfake technology, although there is significant research being conducted to tackle this problem. One often researched deep learning technology is the Generative Adversarial Network (GAN). These networks are commonly used to generate Deepfake videos but not used for their detection. In this work, we explore solutions based on GAN discriminators as a means to detect Deepfake videos. Using MesoNet as a baseline, we train a GAN and extract the discriminator as a dedicated module to detect Deepfakes. We test several discriminator architectures using multiple datasets to explore how the efficacy of the discriminator varies with different setups and training methods. Finally, we propose a model to boost the efficacy of a group of GAN discriminators using ensemble methods. Our results show that GAN discriminators, even augmented by ensemble methods, do not perform well on videos from unknown sources.
Chinese Macro Discourse Parsing on Dependency Graph Convolutional Network

The macro-level discourse parsing, as a fundamental task of macro discourse analysis, mainly focuses on converting a document into a hierarchical discourse tree at paragraph level. Most existing methods follow micro-level studies and suffer from the issues of semantic representation and the semantic interaction of the larger macro discourse units. Therefore, we propose a macro-level discourse parser based on the dependency graph convolutional network to enhance the semantic representation of the large discourse unit and the semantic interaction between those large discourse units. Experimental results on both the Chinese MCDTB and English RST-DT show that our model outperforms several state-of-the-art baselines.
Efficient Multi-scale POMDPs for Robotic Object Search and Delivery

We present a novel hierarchical POMDP framework to solve an object search and delivery task where the agent is given a prior belief about the possible item locations. Solving POMDPs is computationally demanding and, as such, applications have typically been limited to small environments. The proposed hierarchical POMDP framework performs reasoning on multiple spatial scales in order to reduce computation time. The problem is first solved in the top layer of the hierarchy with a coarsely discretized state space. Its solution is refined in the lower layers with increasing resolution. Three different methods for propagating information down the spatial hierarchy are discussed and validated in simulation. We show that a two-layer multi-scale POMDP decreases computation time by an order of magnitude allowing for real-time applications while maintaining high solution quality. For large problems that require three layers to reach the desired resolution, computation time speedups by two orders of magnitude are achieved.
A Vision for Composing, Integrating, and Deploying AI Planning Functionalities

AI planning deals with automatically selecting and organising actions to achieve some user objective. The field offers planning tools not only to solve planning problems but also to address other planning aspects, such as modeling problems and validating found solutions. Despite the need for a fully-fledged planning functionality in real-world applications, however, no tool exists supporting all aspects. As a result, there is a need to manually combine several planning tools that are heterogeneous in terms of software, design, and configuration requirements. While the manual process of combining, integrating and, finally, deploying these systems is time-consuming and error-prone, it also demands immense planning and technical expertise from the practitioners. In this paper, we introduce a vision for composing, integrating, and deploying AI planning functionalities contemplated given several prerequisites for the design of planning services. The objective is to enable straightforward composition and integration of various modular planning services provided by different tools into advanced AI planning systems by intuitive modelling and automatic deployment. Our vision can lead to new research and development opportunities in both AI planning and system engineering, ultimately contributing to the industrialisation of AI.
Understanding Research Data Repositories as Infrastructures

Abstract This study discusses the properties of research data repositories and explores metadata about 2,646 entries in the Registry of Research Data Repositories ( re3data.org ) to identify the characteristics attributed to infrastructures they exhibit. The results reveal how research data repositories function as information infrastructure for scientific community members and contribute to the small body of literature that examines data repositories through a socio‐technical lens.
Machine Learning and Deep Learning Integrated Model to Predict, Classify and Analyze Crime in Indore City

In this paper, we proposed an approach to predict, classify and analyse crime in Indore city from multiple data sources, in particular publicly available Indore police dataset. Also, we examined criminal mentality and behaviour to tackle the problem of crime. While previous research efforts approach the issue with Statistical Models, our findings support an integrated model which will use Machine Learning Algorithms combined with Deep Learning Algorithms to classify, analyse and predict crime in Indore. Using our experimental approach with crime data from Indore police we can achieve an accuracy of almost 75% which can specify crime hotspots in the city.
Maximal Gap Between Local and Global Distinguishability of Bipartite Quantum States

We prove a tight and close-to-optimal lower bound on the effectiveness of local quantum measurements (without classical communication) at discriminating any two bipartite quantum states. Our result implies, for example, that any two orthogonal quantum states of a $n_A\times n_B$ bipartite quantum system can be discriminated via local measurements with an error probability no larger than $\frac12 \left(1 - \frac{1}{c \min\{n_A, n_B\}} \right)$, where $1\leq c\leq 2\sqrt2$ is a universal constant, and our bound scales provably optimally with the local dimensions $n_A,n_B$. Mathematically, this is achieved by showing that the distinguishability norm $\|\cdot\|_{LO}$ associated with local measurements satisfies that $\|\cdot\|_1\leq 2\sqrt2 \min\{n_A,n_B\} \|\cdot\|_{LO}$, where $\|\cdot\|_1$ is the trace norm.
Multi-Modal Pre-Training for Automated Speech Recognition

Traditionally, research in automated speech recognition has focused on local-first encoding of audio representations to predict the spoken phonemes in an utterance. Unfortunately, approaches relying on such hyper-local information tend to be vulnerable to both local-level corruption (such as audio-frame drops, or loud noises) and global-level noise (such as environmental noise, or background noise) that has not been seen during training. In this work, we introduce a novel approach that leverages a self-supervised learning technique based on masked language modeling to compute a global, multi-modal encoding of the environment in which the utterance occurs. We then use a new deep-fusion framework to integrate this global context into a traditional ASR method, and demonstrate that the resulting method can outperform baseline methods by up to 7% on Librispeech; gains on internal datasets range from 6% (on larger models) to 45% (on smaller models).
Adaptive Echo State Network Robot Control with Guaranteed Parameter Convergence

Most existing adaptive neural network (NN) robot control methods suffer from poor convergence performance of network weights, which cannot fully exploit the approximation ability of NNs. A sufficient condition to ensure parameter convergence of NNs in these control methods is termed persistent excitation, which is difficult to be satisfied in practice. In this paper, an adaptive echo state network (ESN) robot control method enhanced by composite learning is proposed for a trajectory tracking problem of robotic arms with multiple degrees of freedom (DoFs), where a chaotic ESN is used to accurately model and compensate for robot uncertainty online. In the proposed method, a generalized prediction error is defined based on memory regressor extension, and both the prediction error and the generalized prediction error emerge into the weight update law of ESNs such that exponential convergence of parameter estimation, implying exponential convergence of trajectory tracking, is guaranteed under a weaker condition termed interval excitation. Simulation results based on a 7-DoF collaborative robot have verified the effectiveness and superiority of the proposed method.
Efficient verification of entangled continuous-variable quantum states with local measurements

Continuous-variable quantum states are of particular importance in various quantum information processing tasks including quantum communication and quantum sensing. However, a bottleneck has emerged with the fast increasing in size of the quantum systems which severely hinders their efficient characterization. In this work, we establish a systematic framework for verifying entangled continuous-variable quantum states by employing local measurements only. Our protocol is able to achieve the unconditionally high verification efficiency which is quadratically better than quantum tomography as well as other nontomographic methods. Specifically, we demonstrate the power of our protocol by showing the efficient verification of entangled two-mode and multimode coherent states with local measurements.