Reviewerly/exHarmony huggingface dataset:
Spoofing Linear Cross-Entropy Benchmarking in Shallow Quantum Circuits

The linear cross-entropy benchmark (Linear XEB) has been used as a test for procedures simulating quantum circuits. Given a quantum circuit $C$ with $n$ inputs and outputs and purported simulator whose output is distributed according to a distribution $p$ over $\{0,1\}^n$, the linear XEB fidelity of the simulator is $\mathcal{F}_{C}(p) = 2^n \mathbb{E}_{x \sim p} q_C(x) -1$ where $q_C(x)$ is the probability that $x$ is output from the distribution $C|0^n\rangle$. A trivial simulator (e.g., the uniform distribution) satisfies $\mathcal{F}_C(p)=0$, while Google's noisy quantum simulation of a 53 qubit circuit $C$ achieved a fidelity value of $(2.24\pm0.21)\times10^{-3}$ (Arute et. al., Nature'19). In this work we give a classical randomized algorithm that for a given circuit $C$ of depth $d$ with Haar random 2-qubit gates achieves in expectation a fidelity value of $Ω(\tfrac{n}{L} \cdot 15^{-d})$ in running time $\textsf{poly}(n,2^L)$. Here $L$ is the size of the \emph{light cone} of $C$: the maximum number of input bits that each output bit depends on. In particular, we obtain a polynomial-time algorithm that achieves large fidelity of $ω(1)$ for depth $O(\sqrt{\log n})$ two-dimensional circuits. To our knowledge, this is the first such result for two dimensional circuits of super-constant depth. Our results can be considered as an evidence that fooling the linear XEB test might be easier than achieving a full simulation of the quantum circuit.
Indonesian news classification using convolutional neural network

&lt;span&gt;Every language has unique characteristics, structures, and grammar. Thus, different styles will have different processes and result in processed in Natural Language Processing (NLP) research area. In the current NLP research area, Data Mining (DM) or Machine Learning (ML) technique is popular, especially for Deep Learning (DL) method. This research aims to classify text data in the Indonesian language using Convolutional Neural Network (CNN) as one of the DL algorithms. The CNN algorithm used modified following the Indonesian language characteristics. Thereby, in the text pre-processing phase, stopword removal and stemming are particularly suitable for the Indonesian language. The experiment conducted using 472 Indonesian News text data from various sources with four categories: ‘hiburan’ (entertainment), ‘olahraga’ (sport), ‘tajuk utama’ (headline news), and ‘teknologi’ (technology). Based on the experiment and evaluation using 377 training data and 95 testing data, producing five models with ten epoch for each model, CNN has the best percentage of accuracy around 90,74% and loss value around 29,05% for 300 hidden layers in classifying the Indonesian News data.&lt;/span&gt;
How to Train Your Energy-Based Model for Regression

Energy-based models (EBMs) have become increasingly popular within computer vision in recent years. While they are commonly employed for generative image modeling, recent work has applied EBMs also for regression tasks, achieving state-of-the-art performance on object detection and visual tracking. Training EBMs is however known to be challenging. While a variety of different techniques have been explored for generative modeling, the application of EBMs to regression is not a well-studied problem. How EBMs should be trained for best possible regression performance is thus currently unclear. We therefore accept the task of providing the first detailed study of this problem. To that end, we propose a simple yet highly effective extension of noise contrastive estimation, and carefully compare its performance to six popular methods from literature on the tasks of 1D regression and object detection. The results of this comparison suggest that our training method should be considered the go-to approach. We also apply our method to the visual tracking task, achieving state-of-the-art performance on five datasets. Notably, our tracker achieves 63.7% AUC on LaSOT and 78.7% Success on TrackingNet. Code is available at https://github.com/fregu856/ebms_regression.
Directed Graph Convolutional Network

Graph Convolutional Networks (GCNs) have been widely used due to their outstanding performance in processing graph-structured data. However, the undirected graphs limit their application scope. In this paper, we extend spectral-based graph convolution to directed graphs by using first- and second-order proximity, which can not only retain the connection properties of the directed graph, but also expand the receptive field of the convolution operation. A new GCN model, called DGCN, is then designed to learn representations on the directed graph, leveraging both the first- and second-order proximity information. We empirically show the fact that GCNs working only with DGCNs can encode more useful information from graph and help achieve better performance when generalized to other models. Moreover, extensive experiments on citation networks and co-purchase datasets demonstrate the superiority of our model against the state-of-the-art methods.
Trotterized adiabatic quantum simulation and its application to a simple all-optical system

Abstract As first proposed for the adiabatic quantum information processing by Wu et al (2002 Phys. Rev. Lett. 89 057904), the Trotterization technique is a very useful tool for universal quantum computing, and in particular, the adiabatic quantum simulation of quantum systems. Given a boson Hamiltonian involving arbitrary bilinear interactions, we propose a static version of this technique to perform an optical simulation that would enable the identification of the ground state of the Hamiltonian. By this method, the dynamical process of the adiabatic evolution is mapped to a static linear optical array which is robust to the errors caused by dynamical fluctuations. We examine the cost of the physical implementation of the Trotterization, i.e. the number of discrete steps required for a given accuracy. Two conclusions are drawn. One is that the number of required steps grows much more slowly than the system size if the number of non-zero matrix elements of Hamiltonian is not too large. The second is that small fluctuations of the parameters of optical elements do not affect the first conclusion. This implies that the method is robust against the certain type of errors as we considered. Last but not least, we present an example of implementation of the simulation on a photonic chip as well as an optimized scheme. By such examples, we show a reduction of the costs compared to its classical counterpart and the potential for further improvement, which promotes a more general application.
Collision Avoidance of Multi Modal Moving Objects for Mobile Robot Using Hybrid Velocity Obstacles

Mobile robotic systems must have the ability to guarantee safety for operating in close proximity with other moving objects.This paper aims to develop collision avoidance method based on velocity control with respect to several modal of moving objects in the vicinity of wheeled mobile robot.We propose a method that is called Hybrid Velocity Obstacles to avoid multi modal moving objects.This is different from Hybrid Reciprocal Velocity Obstacles approaches in the assumption that objects in the robot's surrounding move with arbitrary speed and directions.The main advantage of the method is in the ability to avoid unknown nonlinear trajectories of moving objects.Collision avoidance is achieved by considering static objects, other mobile robots, and moving objects with nonlinear trajectories.These different characteristics of robot's surrounding objects are calculated to produce avoidance velocity.Our approach is implemented in simulations for a two-wheeled differential-steering mobile robot in the environment contains moving objects.The results show that our approach is capable to avoid collision with multi modal moving objects by maintaining safety with average value 0.17 of Proximity Index.It is envisaged that the proposed method can be very useful for developing transport robot that operate in human environment.
A comparison on visual prediction models for MAMO (multi activity-multi object) recognition using deep learning

Abstract Multi activity-multi object recognition (MAMO) is a challenging task in visual systems for monitoring, recognizing and alerting in various public places, such as universities, hospitals and airports. While both academic and commercial researchers are aiming towards automatic tracking of human activities in intelligent video surveillance using deep learning frameworks. This is required for many real time applications to detect unusual/suspicious activities like tracking of suspicious behaviour in crime events etc. The primary purpose of this paper is to render a multi class activity prediction in individuals as well as groups from video sequences by using the state-of-the-art object detector You Look only Once (YOLOv3). By optimum utilization of the geographical information of cameras and YOLO object detection framework, a Deep Landmark model recognize a simple to complex human actions on gray scale to RGB image frames of video sequences. This model is tested and compared with various benchmark datasets and found to be the most precise model for detecting human activities in video streams. Upon analysing the experimental results, it has been observed that the proposed method shows superior performance as well as high accuracy.
Exploring Fine-tuning Techniques for Pre-trained Cross-lingual Models via Continual Learning

Recently, fine-tuning pre-trained language models (e.g., multilingual BERT) to downstream cross-lingual tasks has shown promising results. However, the fine-tuning process inevitably changes the parameters of the pre-trained model and weakens its cross-lingual ability, which leads to sub-optimal performance. To alleviate this problem, we leverage continual learning to preserve the original cross-lingual ability of the pre-trained model when we fine-tune it to downstream tasks. The experimental result shows that our fine-tuning methods can better preserve the cross-lingual ability of the pre-trained model in a sentence retrieval task. Our methods also achieve better performance than other fine-tuning baselines on the zero-shot cross-lingual part-of-speech tagging and named entity recognition tasks.
An Intelligent Calculation Method of Volterra Time-Domain Kernel Based on Time-Delay Artificial Neural Network

To solve the problems of high complexity and low accuracy in Volterra time-domain kernel calculation of a nonlinear system, this paper proposes an intelligent calculation method of Volterra time-domain kernel by time-delay artificial neural networks (TDANNs) and also designs a root mean square error (RMSE) index to choose the neuron number of the network input layer. Firstly, a three-layer TDANN is designed according to the characteristics of the Volterra model. Secondly, the relationship between parameters of TDANN and Volterra time-domain kernel is analyzed, and then three-order expressions of Volterra time-domain kernel are derived. The calculation of Volterra time-domain kernel is completed by network training. Finally, it is verified by a nonlinear system. Simulation results indicate that compared with traditional methods, the new method has higher accuracy, and it can realize the batch calculation of Volterra kernel, which not only improves the calculation efficiency but also provides accurate data for fault diagnosis based on Volterra kernel in further research work.
Towards a Semantic Layer Design for an Advanced Intelligent Multimodal Transportation System

Over time, intelligent transport systems (ITS) have shown their relevance and have become essential for good urban mobility management within smart cities.They facilitate traffic management and make flow more fluid and autonomous.Indeed, the data manipulated in the ITS is large and diversified which makes the information less exploitable and leads us to a considerable loss of time before obtaining the desired information.In order to manage these different sources of information, their semantic conflicts and ensure interoperability between all these sources involved in the multi-modal transport network, we decided to integrate all this information in a way that it is unified, flexible and understandable by all the agents of our multimodal information system.Thus, the objective of this semantic approach is to localize the different resources of the WEB, to standardize the description models so that they are usable by the different agents of the system.In this article, we propose a new approach for the development of an architecture based on Multi-Agent systems (SMA) coupled with semantic Web and this in order to help decision-making and improve the management in the context of urban mobility.The system to develop covers all modes of transport and capable of managing planned trips as well as answering questions related to safety during travel.
Reversible Fragile Watermarking Scheme for Relational Database Based on Prediction-Error Expansion

The protection of database systems content using digital watermarking is nowadays an emerging research direction in information security. In the literature, many solutions have been proposed either for copyright protection and ownership proofing or integrity checking and tamper localization. Nevertheless, most of them are distortion embedding based as they introduce permanent errors into the cover data during the encoding process, which inevitably affect data quality and usability. Since such distortions are not tolerated in many applications, including banking, medical, and military data, reversible watermarking, primarily designed for multimedia content, has been extended to relational databases. In this article, we propose a novel prediction-error expansion based on reversible watermarking strategy, which not only detects and localizes malicious modifications but also recovers back the original data at watermark detection. The effectiveness of the proposed method is proved through rigorous theoretical analysis and detailed experiments.
Stream processing data decision model for higher environmental performance and resilience in sustainable logistics infrastructure

Purpose As the global freight transport network has experienced high vulnerability and threats from both natural and man-made disasters, as a result, a huge amount of data is generated in freight transport system in form of continuous streams; it is becoming increasingly important to develop sustainable and resilient transport system to recover from any unforeseen circumstances quickly and efficiently. The aim of this paper is to develop a stream processing data driven decision-making model for higher environmental performance and resilience in sustainable logistics infrastructure by using fifteen dimensions with three interrelated domains. Design/methodology/approach A causal and hierarchical stream processing data driven decision-making model to evaluate the impact of different attributes and their interrelationships and to measure the level of environmental performance and resilience capacity of sustainable logistics infrastructure are proposed. This work uses fuzzy cognitive maps (FCMs) and fuzzy analytic hierarchy process (FAHP) techniques. A real-life case under a disruptive event scenario is further conducted. Findings The result shows which attributes have a greater impact on the level of environmental performance and resilience capacity in sustainable logistics infrastructure. Originality/value In this paper, causal and hierarchical stream processing data decision and control system model was proposed by identified three domains and fifteen dimensions to assess the level of environmental performance and resilience in sustainable logistics infrastructure. The proposed model gives researchers and practitioners insights about sustainability trade-offs for a resilient and sustainable global transport supply chain system by enabling to model interdependencies among the decision attributes under a fuzzy environment and streaming data.
Knowledge Graph-Augmented Abstractive Summarization with Semantic-Driven Cloze Reward

Sequence-to-sequence models for abstractive summarization have been studied extensively, yet the generated summaries commonly suffer from fabricated content, and are often found to be near-extractive. We argue that, to address these issues, the summarizer should acquire semantic interpretation over input, e.g., via structured representation, to allow the generation of more informative summaries. In this paper, we present ASGARD, a novel framework for Abstractive Summarization with Graph-Augmentation and semantic-driven RewarD. We propose the use of dual encoders---a sequential document encoder and a graph-structured encoder---to maintain the global context and local characteristics of entities, complementing each other. We further design a reward based on a multiple choice cloze test to drive the model to better capture entity interactions. Results show that our models produce significantly higher ROUGE scores than a variant without knowledge graph as input on both New York Times and CNN/Daily Mail datasets. We also obtain better or comparable performance compared to systems that are fine-tuned from large pretrained language models. Human judges further rate our model outputs as more informative and containing fewer unfaithful errors.
Natural Language Interface to Database Using the DialogFlow Voice Recognition and Text Conversion API

A Natural Language Interface to Database using the DialogFlow voice recognition and text conversion API is presented in this work. The user’s ability to retrieve information stored in databases is limited by the lack of understanding of the SELECT statement and the clauses that can make it up. If a user does not have sufficient knowledge about this statement, he is unable to obtain information from the database. This is why the need arises to provide a mechanism to users that facilitates the interaction and data acquisition with the databases using a natural language. Users without experience in database languages will be enabled to retrieve information with this interface; while experienced users will reduce query creation time.
Automated Motion Tracking and Data Extraction for Red Blood Cell Biomechanics

Abstract Red blood cell biomechanics can provide us with a deeper understanding of macroscopic physiology and have the potential of being used for diagnostic purposes. In diseases like sickle cell anemia and malaria, reduced red blood cell deformability can be used as a biomarker, leading to further assays and diagnoses. A microfluidic system is useful for studying these biomechanical properties. We can observe detailed red blood cell mechanical behavior as they flow through microcapillaries using high‐speed imaging and microscopy. Microfluidic devices are advantageous over traditional methods because they can serve as high‐throughput tests. However, to rapidly analyze thousands of cells, there is a need for powerful image processing tools and software automation. We describe a workflow process using Image‐Pro to identify and track red blood cells in a video, take measurements, and export the data for use in statistical analysis tools. The information in this protocol can be applied to large‐scale blood studies where entire cell populations need to be analyzed from many cohorts of donors. © 2020 The Authors. Basic Protocol 1 : Enhancing raw video for motion tracking Basic Protocol 2 : Extracting motion tracking data from enhanced video
Low-Latency Sequence-to-Sequence Speech Recognition and Translation by Partial Hypothesis Selection

Encoder-decoder models provide a generic architecture for sequence-to-sequence tasks such as speech recognition and translation. While offline systems are often evaluated on quality metrics like word error rates (WER) and BLEU, latency is also a crucial factor in many practical use-cases. We propose three latency reduction techniques for chunk-based incremental inference and evaluate their efficiency in terms of accuracy-latency trade-off. On the 300-hour How2 dataset, we reduce latency by 83% to 0.8 second by sacrificing 1% WER (6% rel.) compared to offline transcription. Although our experiments use the Transformer, the hypothesis selection strategies are applicable to other encoder-decoder models. To avoid expensive re-computation, we use a unidirectionally-attending encoder. After an adaptation procedure to partial sequences, the unidirectional model performs on-par with the original model. We further show that our approach is also applicable to low-latency speech translation. On How2 English-Portuguese speech translation, we reduce latency to 0.7 second (-84% rel.) while incurring a loss of 2.4 BLEU points (5% rel.) compared to the offline system.
Framework for Monitoring and Recognition of the Activities for Elderly People from Accelerometer Sensor Data Using Apache Spark

Analysis of daily human activities becomes more open and prevalent with the advancement of sensors embedded in mobile devices. A number of applications such as fitness tracking, health analysis or user-adaptive systems, has been developed by tracking down detailed analysis of complex human activities. In this paper, a framework for the monitoring and recognition of the elderly people’s activities using Apache-Spark Big-data processing tool is proposed. In the proposed framework, different classification techniques such as Logistic Regression, Decision Tree and Random Forest Classifier of ML Machine Learning library of Apache Spark are used to recognize human activities. In order to evaluate the proposed framework, two commonly known KAGGLE-UCI and WISDM Smartphone accelerometer datasets are used. Performance analysis of classification based human activities recognition schemesis evaluated in terms of training time, testing time, accuracy and F1-score. Results show that Logistic Regression classification after tuning it with Cross Fold Validation has provided better performance compared to remaining classification methods.
Data Efficient Segmentation of Various 3D Medical Images Using Guided Generative Adversarial Networks

The recent significant increase in accuracy of medical image processing is attributed to the use of deep neural networks as manual segmentation generates errors in interpretation besides, is very arduous and inefficient. Generative adversarial networks (GANs) is a particular interest to medical researchers, as it implements adversarial loss without explicit modeling of the probability density function. Medical image segmentation methods face challenges of generalization and over-fitting, as medical data suffers from various shapes and diversity of organs. Furthermore, generating a sufficiently large annotated dataset at a clinical site is costly. To generalize learning with a small amount of training data, we propose guided GANs (GGANs) that can decimate samples from an input image and guide networks to generate images and corresponding segmentation mask. The decimated sampling is the key element of the proposed method employed to reduce network size using only a few parameters. Moreover, this method yields promising results by generating several outputs, such as bagging approach. Furthermore, errors of loss function increase, during the generation of original images and corresponding segmentation mask, in comparison to generating only the segmentation mask. Minimization of increased error leads (GGANs) to enhance the performance of segmentation using smaller datasets and less testing time. This method can be applied to a wide range of segmentation problems for different modalities and various organs (such as aortic root, left atrium, knee cartilage, and brain tumors) during a real-time crisis in hospitals. The proposed network also yields high accuracy compared to state-of-the-art networks.