Reviewerly/exHarmony huggingface dataset:
Dual Population Coding for Path Planning in Graphs with Overlapping Place Representations

Topological schemes for navigation from visual snapshots have been based on graphs of panoramic images and action links allowing the transition from one snapshot point to the next; see, for example, Cartwright and Collett [5] or Franz et al. [9]. These algorithms can only work if at each step a unique snapshot is recognized to which a motion decision is associated. Here, we present a population coding approach in which place is encoded by a population of overlapping “firing fields”, each of which is activated by the recognition of an unspecific “micro-snapshot” (i.e. feature), and associated to a subsequent action. Agent motion is then computed by a voting scheme over all activated snapshot-to-action associations. The algorithm was tested in a large virtual environment (Virtual Tübingen [24]) and shows biologically plausible navigational abilities.
Bio-Inspired Predator-Prey Large Spacial Search Path Planning

Traditional path-planning algorithms are entirely reliant on a priori information to function, meaning user interaction is always required. This rigidity constrains the functionality of Unmanned Aerial Systems in search, rescue, and reconnaissance missions. A new adaptive scalable and intelligent path-planning algorithm is developed based on biological evolution and metaheuristic methods that can operate independently of predefined waypoints using an energy constraint cost function to optimize scalability of number of Unmanned Aerial Systems search pathways in dynamically unknown areas. To demonstrate the capabilities of the proposed optimization techniques, several simulations are performed for various number of agents attempting to track both static and dynamic targets which remain unknown to all agents until they are within a range of UAS. Results show that the agents are able to find the desired targets without any a priori information.
Design and Implementation of a Privacy-Aware Smart Mirror System

The vision of the Internet-of-Things is rapidly changing the way we interact with everyday life objects, which can become not only data producers but also means for displaying information. This paper describes the design and implementation of such an object, a Smart Mirror that can be used to display personalised information to users in its proximity. An Android-based application is used to let users register to the Smart Mirror service, personalise the content they want to display and select whether such contents are public or private. To protect user's privacy and avoiding to disclose sensitive information to potential shoulder-surfers, the system is enriched with a BLE-based presence detection module following a device-free approach (i.e., without making use of any information from devices carried by users), which hides automatically private contents upon the detection of multiple people. The whole system is described in details and its performance are evaluated in realistic environments. In order to allow for reproducible research, the dataset used in this paper is made publicly available.
Multi-Hop Memory Network with Graph Neural Networks Encoding for Proactive Dialogue

Dialogue system has made great progress recently, but it is still in the initial stage of passive reply. How to build a dialogue model with proactive reply ability is a great challenge. This paper proposes an End-to-End dialogue model based on Memory network and Graph Neural Network, which uses memory network to store conversation history and knowledge, and uses Graph Neural Network to encode background knowledge. We propose a soft weighting mechanism to integrate the dialogue goal information into the query pointer, so as to enhance the dynamic topic transfer ability during decoding. Experimental results indicate that our model outperforms various kinds of generation models under automatic evaluations and can accomplish the conversational target more actively
Quantifying Aristotle’s Fallacies

Fallacies are logically false statements which are often considered to be true. In the “Sophistical Refutations”, the last of his six works on Logic, Aristotle identified the first thirteen of today’s many known fallacies and divided them into linguistic and non-linguistic ones. A serious problem with fallacies is that, due to their bivalent texture, they can under certain conditions disorient the nonexpert. It is, therefore, very useful to quantify each fallacy by determining the “gravity” of its consequences. This is the target of the present work, where for historical and practical reasons—the fallacies are too many to deal with all of them—our attention is restricted to Aristotle’s fallacies only. However, the tools (Probability, Statistics and Fuzzy Logic) and the methods that we use for quantifying Aristotle’s fallacies could be also used for quantifying any other fallacy, which gives the required generality to our study.
Joint-Sparse Heterogeneous Data Fusion for Target State Estimation with Weak Signals

Recently, we developed a joint-sparse data-level fusion (JSDLF) approach to fuse heterogeneous sensor data for target detection and estimation. In this approach, the target state space is discretized and the data fusion problem is formulated as a joint sparse signal reconstruction problem, and solved by simultaneous orthogonal matching pursuit. In this paper, we continue our work on JSDLF, and our new work is different from our previous work in two aspects. First, the performance of the JSDLF is investigated for cases with very low signal to noise ratio (SNR). Further, to compare the JSDLF approach with decision/feature level fusion approaches, we develop a new maximum likelihood (ML) decision-level fusion approach. In this approach, first each sensor makes its local detections, by comparing the signal's discrete Fourier transform amplitude at each frequency bin (or the image intensity at each pixel) with a threshold. The sensor detection results provide target Doppler shift estimates from radio frequency sensors and target location estimates from the video camera. Then a likelihood function is derived to statistically characterize the detected frequency bins or image pixels, by considering missed detections and false alarms. With the likelihood function, the sensor detections are fused using an ML estimator for target state estimation. We name this fusion approach, which considers both missed detections and false alarms, MLE-MDFA. The MLE-MDFA has assumed the perfect knowledge of the signals' distributions under both hypotheses, which in practice is very difficult to obtain. In contrast, the JSDLF approach requires minimum prior knowledge of the signal model. Hence, the MLE-MDFA only serves as a benchmark for the JSDLF approach. Our numerical results show that at high SNR, the MLE-MDFA provides similar performance as the JSDLF approach. At low SNR values, the JSDLF approach significantly outperforms the MLE-MDFA algorithm. This clearly demonstrates the benefits of data-level fusion over decision-level fusion.
High-Dimensional Data Clustering Algorithm Based on Stacked-Random Projection

This study focuses on high dimensional data, which are characterized by sparsity, redundancy, and high computational complexity. It is impossible to obtain expected results via clustering with traditional algorithms due to the “Curse of Dimensionality”. In this study, we propose a Stacked-Random Projection dimensionality reduction framework and a dimensionality reduction evaluation index based on distance preservation. The algorithm uses Stacked-Random Projection to reduce the dimensionality of the high-dimensional data, and then spectral clustering and fast search and find density peak clustering are used to cluster the processed data. The algorithm is validated using two high-dimensional data sets. Experimental results show that this algorithm can improve the performance of clustering algorithm significantly.
Supporting Data Science in Automotive and Robotics Applications with Advanced Visual Big Data Analytics

Handling Big Data requires new techniques with regard to data access, integration, analysis, information visualization, perception, interaction, and insight within innovative and successful information strategies supporting informed decision making. After deriving and qualitatively evaluating the conceptual IVIS4BigData Reference Model as well as defining a Service-Oriented Architecture, two prototypical reference applications for demonstrations and hands-on exercises for previous identified e-Science user stereotypes with special attention to the overall user experience to meet the users’ expectation and way-of-working will be outlined within this book chapter. In this way and based on the requirements as well as data know-how and other expert know-how of an international leading automotive original equipment manufacturer and a leading international player in industrial automation, two specific industrial Big Data analysis application scenarios (anomaly detection on car-to-cloud data and (predictive maintenance analysis on robotic sensor data) will be utilized to demonstrate the practical applicability of the IVIS4BigData Reference Model and proof this applicability through a comprehensive evaluation. By instantiation of an IVIS4BigData infrastructure and its exemplary prototypical proof-of-concept reference implementation, both application scenarios aim at performing anomaly detection on real-world data that empowers different end user stereotypes in the automotive and robotics application domain to gain insight from car-to-cloud as well as from robotic sensor data.
Resource Optimal Realization of Fault-Tolerant Quantum Circuit

Encoding of quantum information and carrying out computation on encoded state is an essential requirement for improving the reliability of a quantum computer. Resource limitation in today's noisy intermediate scale quantum (NISQ) processors further restricts carrying out fault-tolerant quantum gate operations on such systems. Recent experiments conducted on physical qubits of superconducting transmon type and trapped atomic ions using the fault-tolerant scheme based on [[4, 2, 2]] code have shown a systematic improvement in the fidelity of all logical quantum gate operations except the logical controlled-NOT (CNOT) operation that requires 3 physical SWAP operations for fault-tolerant realization. In this present work we propose an optimal realization of logical CNOT operations on a single or two separate [[4, 2, 2]] code-words using 4 physical CNOT operations and an additional qubit. We further introduce logical two-qubit positive and negative controlled-phase operations with varying rotation angle, and also propose the fault-tolerant realization of logical 2-controlled-phase (C <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> Z) and 2-controlled-NOT (C <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> NOT) operations that are required for universal computation using [[4, 2, 2]] encoding. The implementation requires less number of encoded operations and one additional qubit. Through experiments conducted on the 15-qubit IBM Quantum Experience processor and QASM simulator the fidelity and validity of all these proposed gate operations have been verified.
Supervisable Anonymous Management of Digital Certificates for Blockchain PKI

Aiming at the requirement of anonymous supervision of digital certificates in blockchain public key infrastructure (PKI), this paper proposes a ring signature with multiple indirect verifications (RS-MIV). This mechanism can ensure multiple and indirect verification of certificate signer identity while preserving its anonymity. On this basis, a supervisable anonymous management scheme was designed based on smart contracts, which realizes the anonymity of certificate authority nodes, the anonymous issuance of digital certificates, the anonymous verification of digital certificates, and the traceability of illegal certificate issuers in the blockchain PKI. It is proved that the scheme can guarantee the anonymity and traceability of the certificate issuer's identity at an acceptable cost.
Proposal-Based Visual Tracking Using Spatial Cascaded Transformed Region Proposal Network

Region proposal network (RPN) based trackers employ the classification and regression block to generate the proposals, the proposal that contains the highest similarity score is formulated to be the groundtruth candidate of next frame. However, region proposal network based trackers cannot make the best of the features from different convolutional layers, and the original loss function cannot alleviate the data imbalance issue of the training procedure. We propose the Spatial Cascaded Transformed RPN to combine the RPN and STN (spatial transformer network) together, in order to successfully obtain the proposals of high quality, which can simultaneously improves the robustness. The STN can transfer the spatial transformed features though different stages, which extends the spatial representation capability of such networks handling complex scenarios such as scale variation and affine transformation. We break the restriction though an easy samples penalization loss (shrinkage loss) instead of smooth L1 function. Moreover, we perform the multi-cue proposals re-ranking to guarantee the accuracy of the proposed tracker. We extensively prove the effectiveness of our proposed method on the ablation studies of the tracking datasets, which include OTB-2015 (Object Tracking Benchmark 2015), VOT-2018 (Visual Object Tracking 2018), LaSOT (Large Scale Single Object Tracking), TrackingNet (A Large-Scale Dataset and Benchmark for Object Tracking in the Wild) and UAV123 (UAV Tracking Dataset).
4-D Scene Alignment in Surveillance Video

Designing robust activity detectors for fixed camera surveillance video requires knowledge of the 3-D scene. This paper presents an automatic camera calibration process that provides a mechanism to reason about the spatial proximity between objects at different times. It combines a CNN-based camera pose estimator with a vertical scale provided by pedestrian observations to establish the 4-D scene geometry. Unlike some previous methods, the people do not need to be tracked nor do the head and feet need to be explicitly detected. It is robust to individual height variations and camera parameter estimation errors.
A Token Level Multi-target Stance Detection Dataset

More and more people would like to express their opinions on the social media platform, such as Twitter, Sina Weibo, or Facebook. High-performance stance detection algorithm has become a meaningful and important application. The accuracy improvement of the stance detection model significantly relies on the quality of the training dataset. Previously, the traditional social media textual datasets with manual annotation are tagged on the sentence level mostly. It leads to lacking fine-grained analysis and generalization ability of the stance detection algorithm. Therefore, we propose a token level stance detection dataset with 2025 labeled tweets. The multiple targets of stances in the tweet are tagged at a token level. It uses the token level target annotations instead of using a list of hashtags to represent the stance target. The experiment shows that our dataset can improve the classification accuracy of the stance detection algorithm.
LCP-Aware Parallel String Sorting

When lexicographically sorting strings, it is not always necessary to inspect all symbols. For example, the lexicographical rank of europar amongst the strings eureka, eurasia, and excells only depends on its so called relevant prefix euro. The distinguishing prefix size D of a set of strings is the number of symbols that actually need to be inspected to establish the lexicographical ordering of all strings. Efficient string sorters should be D-aware, i.e. their complexity should depend on D rather than on the total number N of all symbols in all strings. While there are many D-aware sorters in the sequential setting, there appear to be no such results in the PRAM model. We propose a framework yielding a D-aware modification of any existing PRAM string sorter. The derived algorithms are work-optimal with respect to their original counterpart: If the original algorithm requires $${\mathcal {O}(w(N))}$$ work, the derived one requires $${\mathcal {O}(w(D))}$$ work. The execution time increases only by a small factor that is logarithmic in the length of the longest relevant prefix. Our framework universally works for deterministic and randomized algorithms in all variations of the PRAM model, such that future improvements in (D-unaware) parallel string sorting will directly result in improvements in D-aware parallel string sorting.
Windowed generalized phase-shifting algorithms

In this work, we introduce the windowed generalized phase-shifting algorithms (WG-PSAs) using static and dynamic weighting functions/widows. These algorithms are derived from a weighted least square fitted to the monochromatic temporal fringe, thereby, the selection of the window plays an important role due to the fact that it shall reduce the influence of those intensities jeopardizing the phase estimation. In order to make the best selection, we propose to employ an adaptive/dynamic window which has the ability to detect the fringe patterns that jeopardize the phase retrieval. This window is computed iteratively by analyzing the error between the measured and fitted intensities. Furthermore, we provide the analysis of our scheme using the frequency transfer function (FTF) formalism for phaseshifting algorithms. Finally, we executed numerical experiments with synthetic data in which we compare the performance of the dynamic window versus several static ones from the state-of-the-art; although our scheme is more computationally expensive due to the iterative procedure, it works better than the traditional generalized PSAs with a window included or not.
Real-time Cherry Color Grading Based on Machine Vision

Cherry color can reflect the sweetness and maturity of cherry to a certain extent, which is an important feature of cherry quality evaluation. At present, the grading of cherry color in China is almost manual entirely, which is slow and easy to get errors. In recent years, using machine vision technology to classify and sort agricultural products has gradually become a research hotspot, and achieved good results. In this paper, Convolutional Neural Network (CNN) was used to achieve real-time cherry color grading, and the validity of the method is verified by experiments. The experimental results show that the accuracy of color classification is over 99% and the recognition rate is 25 per second. Compared with the classical color threshold method and other research methods, this method has higher accuracy and applicability.
Temporal-Aware SfM-Learner: Unsupervised Learning Monocular Depth and Motion from Stereo Video Clips

Unsupervised Learning based monocular visual odometry is generating considerable interests due to its learning capability without needing labeled data and robustness to environmental variations. However, most of the existing unsupervised learning methods provide far less accurate results than geometry based counterparts. It is confirmed that lacking of drift correction procedure could cause significant adverse effects. To solve this problem, we propose a LSTM based end-to-end VO system, which mainly uses temporal/spatial photometric losses to generate depth maps and 6-DoF relative pose transformations. It can also leverage geometric consistencies to improve performance and robustness over SE(3) by constructing a new loss function on pose graph recursively. Detailed quantitative and qualitative evaluations on KITTI show that: 1) Our method can outperform most of unsupervised learning methods on pose estimation tasks. 2) Our system also gives competitive results on depth estimation tasks as well.
Attention-Based Graph Convolution Collaborative Filtering

The development of big data has brought changes to society and brought us challenges. How to extract useful information from complex data has become the focus of research in recent years. Personalized recommendation as an effective solution has received widespread attention in academia and industry. Collaborative filtering has been widely used by finding users with similar user behaviors as the preferences of similar users. However, the existing methods ignore the interaction information between the user and the item during feature extraction, which leads to imperfect feature extraction and affects the algorithm effect. This paper proposes a graph convolution collaborative filtering model based on the attention model, which uses the graph convolution network to embed user and item interaction information into feature vectors, and uses the attention model to highlight the relatively important interaction information among them, so as to obtain more excellent feature vector. The experimental results show that the model has a good effect on the two commonly used evaluation metrics: recall and normalized discounted cumulative gain(NDCG).
Trustworthy Inter-connected Cyber-Physical Systems

In this paper we identify some of the particular challenges that are encountered when trying to secure cyber-physical systems. We describe three of our current activities: the architecture of a system for monitoring cyber-physical systems; a new approach to modelling dependencies in such systems which leads to a measurement of the security of the system – interpreted as the least effort that an attacker has to expend to compromise the operation; and an approach to optimising the diversity of products used in a system with a view to slowing the propagation of malware. We conclude by discussing how these different threads of work contribute to meeting the challenges and identify possible avenues for future development, as well as providing some pointers to other work.
Research on Accelerating the Performance of SpMV Based on AVX2 Instruction Set

SpMV (Sparse Matrix-Vector Multiplication) has been widely used in various computing fields. Of course, the requirements for its performance also increase with the increase in the amount of data. Under the CPU processor, we can bring SpMV computing high-performance speed increase through multi-threaded programming. Besides, for CPU processors with SIMD (Single Instruction Multiple Data) vectorizations, you can still get additional performance improvements by using the SIMD instruction set. In this article, we used CSR (Compressed Sparse Row) to compress the sparse matrix and compared the performance of using the AVX2 (Advanced Vector Extension 2) instruction set and the serial calculation of SpMV in a single thread. By testing 16 sets of test data, we found that the average using the AVX2 instruction set can bring 6.67% performance improvement (up to 29.97%).
Energy Consumption for IoT Streaming Applications

Usage of Internet of Things (IoT) devices are growing rapidly, resulting in the evolution of IoT infrastructure and efficient energy consumption. Organizations processing IoT big data in real-time for insights require various platforms that process high-velocity data to obtain incremental results. Streaming workflow applications designed consists of different data pipelines to support the decision-making process. These applications run on virtual machines provided by IaaS (Infrastructure as a Service) similar to the on-premise data center. The I/O operations performed by storage systems on these virtual machines result in considerable energy consumption. This paper calculates the energy consumption of storage systems during I/O operations of streaming applications.