Reviewerly/exHarmony huggingface dataset:
Distributed phase estimation and networked quantum sensors with <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>W</mml:mi></mml:math> -type quantum probes

Quantum sensors, offering novel sensing and imaging technologies beyond the capabilities of any classical device, enable harnessing new technologies from atomic clocks and nanoscale microscopy to gravitational wave detection and delicate biological measurements. We consider the performance of $W$-type quantum states as the probe for the networked quantum sensors. Providing an analytic result on the metrological performance of networked quantum sensors, we attain a criterion for the enhancement of the distributed phase estimations in networked quantum sensors. This insight enables us to compare the metrological power of certain quantum states. We also consider schemes that enable the generation and utility of such entangled quantum probes in practical settings.
Improving Event Representation via Simultaneous Weakly Supervised Contrastive Learning and Clustering

Representations of events described in text are important for various tasks.In this work, we present SWCC: a Simultaneous Weakly supervised Contrastive learning and Clustering framework for event representation learning.SWCC learns event representations by making better use of co-occurrence information of events.Specifically, we introduce a weakly supervised contrastive learning method that allows us to consider multiple positives and multiple negatives, and a prototype-based clustering method that avoids semantically related events being pulled apart.For model training, SWCC learns representations by simultaneously performing weakly supervised contrastive learning and prototypebased clustering.Experimental results show that SWCC outperforms other baselines on Hard Similarity and Transitive Sentence Similarity tasks.In addition, a thorough analysis of the prototypebased clustering method demonstrates that the learned prototype vectors are able to implicitly capture various relations between events.Our code will be available at https://github.com/gaojun4ever/SWCC4Event.
Benchmarking missing-values approaches for predictive models on health databases

As databases grow larger, it becomes harder to fully control their collection, and they frequently come with missing values. These large databases are well suited to train machine learning models, e.g., for forecasting or to extract biomarkers in biomedical settings. Such predictive approaches can use discriminative-rather than generative-modeling and thus open the door to new missing-values strategies. Yet existing empirical evaluations of strategies to handle missing values have focused on inferential statistics.Here we conduct a systematic benchmark of missing-values strategies in predictive models with a focus on large health databases: 4 electronic health record datasets, 1 population brain imaging database, 1 health survey, and 2 intensive care surveys. Using gradient-boosted trees, we compare native support for missing values with simple and state-of-the-art imputation prior to learning. We investigate prediction accuracy and computational time. For prediction after imputation, we find that adding an indicator to express which values have been imputed is important, suggesting that the data are missing not at random. Elaborate missing-values imputation can improve prediction compared to simple strategies but requires longer computational time on large data. Learning trees that model missing values-with missing incorporated attribute-leads to robust, fast, and well-performing predictive modeling.Native support for missing values in supervised machine learning predicts better than state-of-the-art imputation with much less computational cost. When using imputation, it is important to add indicator columns expressing which values have been imputed.
Exploring Edge Disentanglement for Node Classification

Edges in real-world graphs are typically formed by a variety of factors and carry diverse relation semantics. For example, connections in a social network could indicate friendship, being colleagues, or living in the same neighborhood. However, these latent factors are usually concealed behind mere edge existence due to the data collection and graph formation processes. Despite rapid developments in graph learning over these years, most models take a holistic approach and treat all edges as equal. One major difficulty in disentangling edges is the lack of explicit supervisions. In this work, with close examination of edge patterns, we propose three heuristics and design three corresponding pretext tasks to guide the automatic edge disentanglement. Concretely, these self-supervision tasks are enforced on a designed edge disentanglement module to be trained jointly with the downstream node classification task to encourage automatic edge disentanglement. Channels of the disentanglement module are expected to capture distinguishable relations and neighborhood interactions, and outputs from them are aggregated as node representations. The proposed DisGNN is easy to be incorporated with various neural architectures, and we conduct experiments on $6$ real-world datasets. Empirical results show that it can achieve significant performance gains.
Triangle Attack: A Query-Efficient Decision-Based Adversarial Attack

Decision-based attack poses a severe threat to real-world applications since it regards the target model as a black box and only accesses the hard prediction label. Great efforts have been made recently to decrease the number of queries; however, existing decision-based attacks still require thousands of queries in order to generate good quality adversarial examples. In this work, we find that a benign sample, the current and the next adversarial examples can naturally construct a triangle in a subspace for any iterative attacks. Based on the law of sines, we propose a novel Triangle Attack (TA) to optimize the perturbation by utilizing the geometric information that the longer side is always opposite the larger angle in any triangle. However, directly applying such information on the input image is ineffective because it cannot thoroughly explore the neighborhood of the input sample in the high dimensional space. To address this issue, TA optimizes the perturbation in the low frequency space for effective dimensionality reduction owing to the generality of such geometric property. Extensive evaluations on ImageNet dataset show that TA achieves a much higher attack success rate within 1,000 queries and needs a much less number of queries to achieve the same attack success rate under various perturbation budgets than existing decision-based attacks. With such high efficiency, we further validate the applicability of TA on real-world API, i.e., Tencent Cloud API.
A Predicate Transformer for Choreographies

Abstract Construction and analysis of distributed systems is difficult; choreographic programming is a deadlock-freedom-by-construction approach to simplify it. In this paper, we present a new theory of choreographic programming. It supports for the first time: construction of distributed systems that require decentralised decision making (i.e., if/while-statements with multiparty conditions); analysis of distributed systems to provide not only deadlock freedom but also functional correctness (i.e., pre/postcondition reasoning). Both contributions are enabled by a single new technique, namely a predicate transformer for choreographies.
ALP: Data Augmentation Using Lexicalized PCFGs for Few-Shot Text Classification

Data augmentation has been an important ingredient for boosting performances of learned models. Prior data augmentation methods for few-shot text classification have led to great performance boosts. However, they have not been designed to capture the intricate compositional structure of natural language. As a result, they fail to generate samples with plausible and diverse sentence structures. Motivated by this, we present the data Augmentation using Lexicalized Probabilistic context-free grammars (ALP) that generates augmented samples with diverse syntactic structures with plausible grammar. The lexicalized PCFG parse trees consider both the constituents and dependencies to produce a syntactic frame that maximizes a variety of word choices in a syntactically preservable manner without specific domain experts. Experiments on few-shot text classification tasks demonstrate that ALP enhances many state-of-the-art classification methods. As a second contribution, we delve into the train-val splitting methodologies when a data augmentation method comes into play. We argue empirically that the traditional splitting of training and validation sets is sub-optimal compared to our novel augmentation-based splitting strategies that further expand the training split with the same number of labeled data. Taken together, our contributions on the data augmentation strategies yield a strong training recipe for few-shot text classification tasks.
Models and algorithms of the positioning and trajectory stabilisation system with elements of structural analysis for robotic applications

The efficiency of mobile robotic systems in different applications including wearable robotics, underwater and even spacecraft application, and therefore the efficiency of solving the tasks assigned to them, is significantly determined by the functionality of these control systems, their technical and operational features. Biomechatronic systems with microactuators possess the most extensive functional capabilities, but their essential disadvantage is the consumption of the energy, which limits the useful operation time of the whole system. To solve the research problem aspect and number of similar tasks aimed at increasing the energy efficiency trajectory planning tasks for mechatronic systems, it is necessary to assess the degree of impact of various factors on the operation of these systems including positioning system functionality. The simulation includes an assessment of the reliability of calculating the Earth's magnetic field parameters, as well as an assessment of the quality of object stabilisation by resetting the total momentum with the aid of the system under review.
Chinese text classification based on character-level CNN and SVM

Aiming at the problems of curse of dimensionality, sparse data and long computation time in traditional SVM classification algorithm based on term frequency-inverse document frequency (TF-IDF), we propose a novel hybrid system for Chinese text classification: CSVM, which is independent of the hand-designed features and domain knowledge. Firstly, the encoding words are done by constructing a text vocabulary of size m for the input language, and then quantise each word using 1-of-m encoding. Secondly, we exploit the convolutional neural network (CNN) to extract the morphological features of character vectors from each word, and then through large scale text material training the semantic feature of each word vectors are be obtained the semantic feature of each word vectors. Finally, the text classification is carried out with the SVM multiple classifier. The experimental results show that the CSVM algorithm is more effective than other traditional Chinese text classification algorithm.
A profile based data segmentation for in-home activity recognition

A major problem in smart-home activity recognition is the ambiguity of interpreting the actions that formulate activities. It resulted from the redundancy of irrelevant actions and the concurrent interleaving among activities themselves. In this paper, we present a framework to minimise the effect of such ambiguity using profile based data segmentation and actions refinement. The proposed methodology relies on defining a profile for each sensor in the environment for enriching existing features with semantic ones. Furthermore, according to these profiles, irrelevant actions within data segments are removed. Moreover, the proposed methodology addresses the interleaving among different activities. Experiments have been conducted to measure the performance of the proposed framework on different datasets. We evaluated our methodology using three different classifiers. The results indicated that the proposed framework achieved statistically significant improvements. Such enhancements resulted from minimising the effect of irrelevant actions and resolving the concurrent interleaving among activities within datasets.
METORY: Development of a Demand-Driven Blockchain-Based Dynamic Consent Platform Tailored for Clinical Trials

The recent advent of the dynamic consent concept intensified the data integrity issue in clinical trials. Incorporating blockchain technology into a dynamic consent platform can be a feasible solution. Due to various clinical trial settings, a demand-driven development strategy is required. We developed a blockchain-based dynamic consent platform named METORY tailored for clinical trials. The platform consisted of three parts: web and mobile application user interface, study management platform, and blockchain platform. Hyperledger Fabric, an enterprise-grade private blockchain framework, was used to integrate blockchain into the study consent platform. We conducted user acceptance tests and applied feedback to the improvement of the platform. Identity and role-based access control was constructed by combining mobile-application-based certificate system and access control functionalities in Hyperledger fabric. Data were encrypted using SHA-256 prior to transmission to blockchain server and TLS protocol was used for in-transit encryption. File-system level encryption was separated implemented within the security measures from Amazon RDS. Users' experience in the clinical trial was acceptable in the ease and usefulness of the platform.
A Global Path Planning Method for Unmanned Ground Vehicles in Off-Road Environments Based on Mobility Prediction

In a complex off-road environment, due to the low bearing capacity of the soil and the uneven features of the terrain, generating a safe and effective global route for unmanned ground vehicles (UGVs) is critical for the success of their motion and mission. Most traditional global path planning methods simply take the shortest path length as the optimization objective, which makes it difficult to plan a feasible and safe route in complex off-road environments. To address this problem, this research proposes a global path planning method, which considers the influence of terrain factors and soil mechanics on UGV mobility. First, we established a high-resolution 3D terrain model with remote sensing elevation terrain data, land use and soil type distribution data, based on a geostatistical method. Second, we analyzed the vehicle mobility by the terramechanical method (i.e., vehicle cone index and Bakker’s theory), and then calculated the mobility cost based on a fuzzy inference method. Finally, based on the calculated mobility cost, the probabilistic roadmap method was used to establish the connected matrix and the multi-dimensional traffic cost evaluation matrix among the sampling nodes, and then an improved A* algorithm was proposed to generate the global route.
GRU with Dual Attentions for Sensor-Based Human Activity Recognition

Human Activity Recognition (HAR) is nowadays widely used in intelligent perception and medical detection, and the use of traditional neural networks and deep learning methods has made great progress in this field in recent years. However, most of the existing methods assume that the data has independent identical distribution (I.I.D.) and ignore the data variability of different individual volunteers. In addition, most deep learning models are characterized by many parameters and high resources consumption, making it difficult to run in real time on embedded devices. To address these problems, this paper proposes a Gate Recurrent Units (GRU) network fusing the channel attention and the temporal attention for human activity recognition method without I.I.D. By using channel attention to mitigate sensor data bias, GRU and the temporal attention are used to capture important motion moments and aggregate temporal features to reduce model parameters. Experimental results show that our model outperforms existing methods in terms of classification accuracy on datasets without I.I.D., and reduces the number of model parameters and resources consumption, which can be easily used in low-resource embedded devices.
A Brief Review on Internet of Things, Industry 4.0 and Cybersecurity

The advance of industrialization regarding the optimization of production to obtain greater productivity and consequently generate more profits has led to the emergence of Industry 4.0, which aims to create an environment called smart manufacturing. On the other hand, the Internet of Things is a global network of interrelated physical devices, such as sensors, actuators, intelligent applications, computers, mechanical machines, objects, and people, becoming an essential part of the Internet. These devices are data sources that provide abundant information on manufacturing processes in an industrial environment. A concern of this type of system is processing large sets of data and generating knowledge. These challenges often raise concerns about security, more specifically cybersecurity. Good cybersecurity practices make it possible to avoid damage to production lines and information. With the growing increase in threats in terms of security, this paper aims to carry out a review of existing technologies about cybersecurity in intelligent manufacturing and an introduction to the architecture of the IoT and smart manufacturing.
Event-Based Supply Chain Network Modeling: Blockchain for Good Coffee

Blockchain and distributed ledger technology (BC/DLT) provides distributed databases with decentralized governance, tamper-proof recording, high availability and non-copyable digital assets, which have made it a natural technological basis for supply chain management. In this paper, we introduce REALISTIC, a novel event-based modeling framework for supply chain networks (SCNs) that includes production processes . It extends McCarty’s Resources-Events-Agents (REA) accounting model with secure transformations , which, across the entire SCN, guarantee that certified output resources cannot be digitally produced ex nihilo , but require certified input resources of at least the same amount as what is produced. This generalizes the no-double-spend guarantee of current BC/DLT to (digital twins of) physical resources and their production. Authenticated human or robotic Internet of Things (IoT) actors digitally sign and cryptographically commit to the veracity of real-world events on an immutable database, without having to take responsibility for their aggregate consequences. User-specifiable interpretations, corresponding to queries and analytical functions in database systems, provide auditable aggregate information computed from recorded events across the entire SCN. This includes fine-grained and trustworthy tracing of final products through multiple stages of production processes, semi-finished products, quality certifications and transportation all the way back to their raw materials. We present a case study for an end-to-end coffee supply chain that tracks fine-grained and detailed information from a farmer’s coffee cherries to retail coffee bags, involving all its actors. Our model handles product provenance; auditable sustainability, quality and trade information; production processes from parchment via green to roasted coffee; product quality tests; farmer certifications; and transportation across the entire coffee supply chain. It is based on field work involving farmers, cooperatives, processors, traders, importers, and a major roasting company stretching from Colombia to Scandinavia. Its REALISTIC-based modeling is the foundation for the design of our prototype implementation, which includes Ethereum blockchain code, RDBMS-based server code and a web app client. Their source code is publicly available on GitHub.
Rushing and Strolling among Answer Sets – Navigation Made Easy

Answer set programming (ASP) is a popular declarative programming paradigm with a wide range of applications in artificial intelligence. Oftentimes, when modeling an AI problem with ASP, and in particular when we are interested beyond simple search for optimal solutions, an actual solution, differences between solutions, or number of solutions of the ASP program matter. For example, when a user aims to identify a specific answer set according to her needs, or requires the total number of diverging solutions to comprehend probabilistic applications such as reasoning in medical domains. Then, there are only certain problem specific and handcrafted encoding techniques available to navigate the solution space of ASP programs, which is oftentimes not enough. In this paper, we propose a formal and general framework for interactive navigation toward desired subsets of answer sets analogous to faceted browsing. Our approach enables the user to explore the solution space by consciously zooming in or out of sub-spaces of solutions at a certain configurable pace. We illustrate that weighted faceted navigation is computationally hard. Finally, we provide an implementation of our approach that demonstrates the feasibility of our framework for incomprehensible solution spaces.
SMINet: State-Aware Multi-Aspect Interests Representation Network for Cold-Start Users Recommendation

Online travel platforms (OTPs), e.g., bookings.com and Ctrip.com, deliver travel experiences to online users by providing travel-related products. Although much progress has been made, the state-of-the-arts for cold-start problems are largely sub-optimal for user representation, since they do not take into account the unique characteristics exhibited from user travel behaviors. In this work, we propose a State-aware Multi-aspect Interests representation Network (SMINet) for cold-start users recommendation at OTPs, which consists of a multi-aspect interests extractor, a co-attention layer, and a state-aware gating layer. The key component of the model is the multi-aspect interests extractor, which is able to extract representations for the user's multi-aspect interests. Furthermore, to learn the interactions between the user behaviors in the current session and the above multi-aspect interests, we carefully design a co-attention layer which allows the cross attentions between the two modules. Additionally, we propose a travel state-aware gating layer to attentively select the multi-aspect interests. The final user representation is obtained by fusing the three components. Comprehensive experiments conducted both offline and online demonstrate the superior performance of the proposed model at user representation, especially for cold-start users, compared with state-of-the-art methods.