Reviewerly/exHarmony huggingface dataset:
Variational quantum simulation of thermal statistical states on a superconducting quantum processer

Quantum computers promise to solve finite-temperature properties of quantum many-body systems, which is generally challenging for classical computers due to high computational complexities. Here, we report experimental preparations of Gibbs states and excited states of Heisenberg XX and XXZ models by using a 5-qubit programmable superconducting processor. In the experiments, we apply a hybrid quantum–classical algorithm to generate finite temperature states with classical probability models and variational quantum circuits. We reveal that the Hamiltonians can be fully diagonalized with optimized quantum circuits, which enable us to prepare excited states at arbitrary energy density. We demonstrate that the approach has a self-verifying feature and can estimate fundamental thermal observables with a small statistical error. Based on numerical results, we further show that the time complexity of our approach scales polynomially in the number of qubits, revealing its potential in solving large-scale problems.
Research data mismanagement – from questionable research practice to research misconduct

Good record keeping practice and research data management underlie responsible research conduct and promote reproducibility of research findings in the sciences. In many cases of research misconduct, inadequate research data management frequently appear as an accompanying finding. Findings of disorganized or otherwise poor data archival or loss of research data are, on their own, not usually considered as indicative of research misconduct. Focusing on the availability of raw/primary data and the replicability of research based on these, we posit that most, if not all, instances of research data mismanagement (RDMM) could be considered a questionable research practice (QRP). Furthermore, instances of RDMM at their worst could indeed be viewed as acts of research misconduct. Here, we analyze with postulated scenarios the contexts and circumstances under which RDMM could be viewed as a significant misrepresentation of research (ie. falsification), or data fabrication. We further explore how RDMM might potentially be adjudicated as research misconduct based on intent and consequences. Defining how RDMM could constitute QRP or research misconduct would aid the formulation of relevant institutional research integrity policies to mitigate undesirable events stemming from RDMM.
Research on behaviour planning for power substation inspection robot based on fuzzy cognitive map

Abstract A fuzzy cognitive map based behavior planning method for mobile robot is proposed in this paper. Aiming at the intelligentialize advanced application problems of power substation equipment inspection robot, a logical structure of inspection tasks planning based on hierarchical hybrid complex system is presented, and the operating principle of task decomposition and task planning are given in detail. The dependent events in inspecting tasks are considered as concept nodes to present the connection weights between nodes and establish the fuzzy cognitive map model of task and behavior planning, so as to realize task decomposition and generate the action logic sequences. Meanwhile, the action sequences can be adjusted in real time with the sensor information. The proposed method is simple without introducing complicated calculations and much training. Simulation results verify the feasibility and effectiveness.
How AI Can be Used for Governance of Messaging Services: A Study on Spam Classification Leveraging Multi-Channel Convolutional Neural Network

Over the past decade, there has been a meteoric evolution in Internet Messaging Services and although these services have become ingrained in our everyday life, SMS service remains an essential form of communication service. The omnipresence of SMS has also given rise to unsolicited and junk messages which has motivated researchers to use machine learning and deep learning to detect such spam messages. Studies using deep learning have shown promising results for spam classification, and in this paper, extending these studies, we have proposed a Multi-Channel CNN architecture with static and dynamic embeddings for SMS spam classification. UCI’s SMS spam collection dataset along with several personally collected text messages are used to create a rich dataset for training the models. The proposed model has an accuracy of 96.12% and overcomes certain disadvantages associated with some of the state-of-the-art deep learning models.
Deep Learning–Based Objective and Reproducible Osteosarcoma Chemotherapy Response Assessment and Outcome Prediction

Osteosarcoma is the most common primary bone cancer, whose standard treatment includes pre-operative chemotherapy followed by resection. Chemotherapy response is used for prognosis and management of patients. Necrosis is routinely assessed after chemotherapy from histology slides on resection specimens, where necrosis ratio is defined as the ratio of necrotic tumor/overall tumor. Patients with necrosis ratio ≥90% are known to have a better outcome. Manual microscopic review of necrosis ratio from multiple glass slides is semiquantitative and can have intraobserver and interobserver variability. In this study, an objective and reproducible deep learning-based approach was proposed to estimate necrosis ratio with outcome prediction from scanned hematoxylin and eosin whole slide images (WSIs). To conduct the study, 103 osteosarcoma cases with 3134 WSIs were collected. Deep Multi-Magnification Network was trained to segment multiple tissue subtypes, including viable tumor and necrotic tumor at a pixel level and to calculate case-level necrosis ratio from multiple WSIs. Necrosis ratio estimated by the segmentation model highly correlates with necrosis ratio from pathology reports manually assessed by experts. Furthermore, patients were successfully stratified to predict overall survival with P = 2.4 × 10-6 and progression-free survival with P = 0.016. This study indicates that deep learning can support pathologists as an objective tool to analyze osteosarcoma from histology for assessing treatment response and predicting patient outcome.
Face Morphing Attack Detection and Localization Based on Feature-Wise Supervision

To strengthen the security of face recognition systems to morphing attacks (MAs), many countermeasures were proposed. However, in the existing face morphing attack detection (MAD), the deep networks trained by classical score-level losses are weak in characterizing the intrinsic morphing patterns of different MAs, and they also cannot be directly applied to differential MAD scenarios. To this end, this paper presents a method for detecting and locating face MAs by the use of feature-wise supervision. It constructs the fine-grained classification loss on the basis of different morphing patterns, and designs the similarity-based and distance-based differential losses according to the properties of differential MAD scenarios. The experimental results and analysis show that the fine-grained classification loss can locate the local morphed areas after detecting MAs, while the differential losses are able to improve the generalization ability of MAD methods to unseen MAs, and can enhance the robustness of MAD methods to low-resolution and non-frontal probe face images.
Chapter 9

Abstract The theory of natural selection has two forms. Deductive theory describes how populations change over time. One starts with an initial population and some rules for change. From those assumptions, one calculates the future state of the population. Deductive theory predicts how populations adapt to environmental challenge. Inductive theory describes the causes of change in populations. One starts with a given amount of change. One then assigns different parts of the total change to particular causes. Inductive theory analyzes alternative causal models for how populations have adapted to environmental challenge. This chapter emphasizes the inductive analysis of cause.
A Dataset for StarCraft AI and an Example of Armies Clustering

This paper advocates the exploration of the full state of recorded real-time strategy (RTS) games, by human or robotic players, to discover how to reason about tactics and strategy. We present a dataset of StarCraft games encompassing the most of the games' state (not only player’s orders). We explain one of the possible usages of this dataset by clustering armies on their compositions. This reduction of armies compositions to mixtures of Gaussian allow for strate- gic reasoning at the level of the components. We evaluated this clustering method by predicting the outcomes of battles based on armies compositions' mixtures components.
DBMS annihilator

Modern DBMS engines can achieve unprecedented transaction processing speeds thanks to the invention of clever data structures, concurrency schemes, and improvements in CPU and memory subsystems. However, developing realistic and efficient networked clients to benchmark these systems remains daunting. Simply put, traditional client-side networking stacks present high overheads and thus cannot exercise the high performance that modern DBMSs can, in principle, provide. In this demo, we propose a different approach to benchmarking; we showcase a new framework that leverages hardware-software co-design. With our system, which we call the DBMS Annihilator, workloads are specified using a high-level language that is then converted into hardware (FPGA) for execution. The hardware we use is a commodity Smart NIC, allowing workloads to be fully reproducible to anyone using such hardware. A software console and dashboard provide real-time visibility and interactivity, which we explore in this demo.
Transfer-Printed Photonic Crystal Nanobeam Laser With Unidirectional Coupling to SiN<sub>x</sub> Waveguide

We propose and study the hybrid integration of a 1D photonic crystal (PhC) nanobeam (NB) laser on a SiN <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">x</sub> waveguide. With modifications by reduced-PhCs and a tapered beam to the NB laser, this integration can exhibit over 50% directional coupling to the specific waveguide output facet while maintaining a sufficient quality factor for low-threshold lasing. Using the transfer printing technique, we realized the proposed hybrid integration with optimized parameters, which showed significant planar laser emission from the SiN <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">x</sub> waveguide facet in experiments while maintaining a low lasing threshold of 180 μW. In addition to verifying the unidirectionality of planar emission by simulation and experimental results, we further confirm its robustness by characterizing many transfer-printed NB lasers on SiN <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">x</sub> waveguides under generally occurring displacement and rotational misalignments in the present transfer printing systems. We believe that the hybrid integration with compact size, unidirectional coupling, and low lasing threshold demonstrated herein can provide a helpful reference for designing PhC NB lasers coupled with SiN <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">x</sub> waveguides in silicon-based photonic integrated circuits.
Multi-Robot Directed Coverage Path Planning in Row-based Environments

Multiple autonomous robots are deployed to fulfill tasks collaboratively in real-world applications with row-based settings as found in precision agriculture, warehouses, factory inspections, and wind farms. One batch of robots are assigned to explore, search and localize objects in large-scale row-based environments, while the other batch of robots move directly to the detected targets to retrieve the objects. In this paper, a multi-robot collaborative navigation framework with two different batches of robots is proposed to explore the environment and achieve the obtained targets, respectively. The first batch of robots act as detection robots, which are driven by a proposed informative-based directed coverage path planning (DCPP) through a multi-robot minimum spanning tree algorithm. It refines and optimizes the coverage path based on the information gained from the environment. The second batch of robot reaches the multiple targets by guidance from a hub-based multi-target routing (HMTR) scheme, which is applicable to row-based environments. The feasibility and effectiveness of the proposed methods are validated by simulation and comparison studies.
Linguistic Steganalysis Merging Semantic and Statistical Features

With the rapid development of Natural Language Processing (NLP), more and more linguistic steganography methods have appeared in recent years, which may bring great challenges to the protection of cyberspace security. Due to the powerful feature extraction capabilities of Deep neural networks (DNN) to learn semantic features of large volumes of text, traditional steganalysis methods using manual features have gradually evolved into DNN-based methods. However, whether these DNN-based steganalysis methods can extract enough carrier features to achieve efficient steganalysis so that they can completely replace traditional methods based on handcrafted features remains an open question. To explore the answer, in this paper, we propose a new steganalysis method to integrate semantic and statistical features. We use BERT to extract semantic features and TF-IDF with AutoEncoder to obtain statistical features of the input text. Finally, we design a fusion mechanism to combine these two features. The experimental results show that due to the addition of statistical features, the proposed model can significantly improve the detection performance over current DNN-based linguistic steganalysis models.
Generative Adversarial Network for Future Hand Segmentation from Egocentric Video

We introduce the novel problem of anticipating a time series of future hand masks from egocentric video. A key challenge is to model the stochasticity of future head motions, which globally impact the head-worn camera video analysis. To this end, we propose a novel deep generative model – EgoGAN. Our model first utilizes a 3D Fully Convolutional Network to learn a spatio-temporal video representation for pixel-wise visual anticipation. It then generates future head motion using the Generative Adversarial Network (GAN), and predicts the future hand masks based on both the encoded video representation and the generated future head motion. We evaluate our method on both the EPIC-Kitchens and the EGTEA Gaze+ datasets. We conduct detailed ablation studies to validate the design choices of our approach. Furthermore, we compare our method with previous state-of-the-art methods on future image segmentation and provide extensive analysis to show that our method can more accurately predict future hand masks. Project page: https://vjwq.github.io/EgoGAN/ .
A Platform for Autonomous Swarms of UAVs

The usage of aerial drones has become more popular as they also become more accessible, both in economic and usability terms. Nowadays, these vehicles can present reduced dimensions and a good cost-benefit ratio, which makes it possible for several services and applications supported by aerial drone networks to emerge. Taking into account the enormous diversity of use cases, many of the existing solutions for autonomous control focus on specific scenarios. Generic mission planning platforms also exist, but most of them only allow missions consisting of linear waypoints to be traversed. These situations translate into a mission support that is not very flexible. This paper proposes a modular infrastructure that can be used in various scenarios, enabling the autonomous control and monitoring of a fleet of aerial drones in a mission context. The platform allows the communication with the flight controller so that it can collect telemetry data and send movement instructions to the drone, and to monitor this data and send the commands remotely, also enabling robust mission planning with multiple drones, and enabling the interaction with internal and external sensors. The real tests performed through the platform show that the planned missions are executed exactly as they are planned in the platform.
Sports Video Analysis on Large-Scale Data

This paper investigates the modeling of automated machine description on sports video, which has seen much progress recently. Nevertheless, state-of-the-art approaches fall quite short of capturing how human experts analyze sports scenes. There are several major reasons: (1) The used dataset is collected from non-official providers, which naturally creates a gap between models trained on those datasets and real-world applications; (2) previously proposed methods require extensive annotation efforts (i.e., player and ball segmentation at pixel level) on localizing useful visual features to yield acceptable results; (3) very few public datasets are available. In this paper, we propose a novel large-scale NBA dataset for Sports Video Analysis (NSVA) with a focus on captioning, to address the above challenges. We also design a unified approach to process raw videos into a stack of meaningful features with minimum labelling efforts, showing that cross modeling on such features using a transformer architecture leads to strong performance. In addition, we demonstrate the broad application of NSVA by addressing two additional tasks, namely fine-grained sports action recognition and salient player identification. Code and dataset are available at https://github.com/jackwu502/NSVA .
Path planning and collision avoidance methods for distributed multi-robot systems in complex dynamic environments

Multi-robot systems are experiencing increasing popularity in joint rescue, intelligent transportation, and other fields. However, path planning and navigation obstacle avoidance among multiple robots, as well as dynamic environments, raise significant challenges. We propose a distributed multi-mobile robot navigation and obstacle avoidance method in unknown environments. First, we propose a bidirectional alternating jump point search A* algorithm (BAJPSA*) to obtain the robot's global path in the prior environment and further improve the heuristic function to enhance efficiency. We construct a robot kinematic model based on the dynamic window approach (DWA), present an adaptive navigation strategy, and introduce a new path tracking evaluation function that improves path tracking accuracy and optimality. To strengthen the security of obstacle avoidance, we modify the decision rules and obstacle avoidance rules of the single robot and further improve the decision avoidance capability of multi-robot systems. Moreover, the mainstream prioritization method is used to coordinate the local dynamic path planning of our multi-robot systems to resolve collision conflicts, reducing the difficulty of obstacle avoidance and simplifying the algorithm. Experimental results show that this distributed multi-mobile robot motion planning method can provide better navigation and obstacle avoidance strategies in complex dynamic environments, which provides a technical reference in practical situations.
Blockchain-Based Network Hawkeye Function: Building the Trust Triangle in 6G

Clients, service providers (SPs), and manufacturers lay the foundation for 6G networking, and thus the direct relationships between them are abstracted as the trust triangle of telecommunications. As one component among three, manufacturers are facing considerable criticism from SPs and clients regarding cybersecurity and privacy, which compromises the whole trust triangle. Therefore, we aim to develop a block-chain-based framework to rebuild and maintain the trust triangle in this study. The introduction of blockchain sheds light on building a decentralized digital forensics model for the trust triangle. After analyzing the potential threats and vulnerabilities, we first design the Blockchain-based Network Hawkeye Function (BNHF) to provide an open and privacy-preserving way for clients watching SPs, SPs monitoring manufacturers, and manufacturers proving themselves trustworthy. We offer a practical BNHF solution compatible with 5G and envision its evolution for 6G. Furthermore, we propose two BNHF-aided mechanisms in clients' interests to help construct a complete trust triangle. Comprehensive test results under experimental networking setups illustrate the effectiveness of the proposed framework.
Graph Convolutional Networks based on manifold learning for semi-supervised image classification

Due to a huge volume of information in many domains, the need for classification methods is imperious. In spite of many advances, most of the approaches require a large amount of labeled data, which is often not available, due to costs and difficulties of manual labeling processes. In this scenario, unsupervised and semi-supervised approaches have been gaining increasing attention. The GCNs (Graph Convolutional Neural Networks) represent a promising solution since they encode the neighborhood information and have achieved state-of-the-art results on scenarios with limited labeled data. However, since GCNs require graph-structured data, their use for semi-supervised image classification is still scarce in the literature. In this work, we propose a novel approach, the Manifold-GCN, based on GCNs for semi-supervised image classification. The main hypothesis of this paper is that the use of manifold learning to model the graph structure can further improve the GCN classification. To the best of our knowledge, this is the first framework that allows the combination of GCNs with different types of manifold learning approaches for image classification. All manifold learning algorithms employed are completely unsupervised, which is especially useful for scenarios where the availability of labeled data is a concern. A broad experimental evaluation was conducted considering 5 GCN models, 3 manifold learning approaches, 3 image datasets, and 5 deep features. The results reveal that our approach presents better accuracy than traditional and recent state-of-the-art methods with very efficient run times for both training and testing.
The application of blockchain algorithms to the management of education certificates

Blockchain is a new application technology in many sectors and the same is true in the world of education. Therefore, there is an increasingly emerging need to research blockchain technology, as it is still taking its first steps in different sectors, such as education. This article presents a review of the state of the art of blockchain technology in the education sector, focusing on identifying the advantages, disadvantages, and challenges associated with the introduction of blockchain technology in the education sector. In addition, the implementation of a title certificate solution through blockchain technology through the BeCertify project is presented. In this solution, the development stages of the platform, the system architecture, and the operation of the API have been carried out, resulting in a platform that constitutes the first step towards a more transparent and technologically advanced way of managing the certifications of the students' qualifications.