Reviewerly/exHarmony huggingface dataset:
Understanding behavioural design: barriers and enablers

Behavioural design has emerged as an important domain of design research and practice. However, there is a need to better distinguish behavioural design and subsequently map its unique features and challenges. In answer to this need, this work examines the evolving role of behaviour in design and contrasts this with eleven in-depth behavioural design cases. This has resulted in our identification of behavioural design as a new paradigm of design, with a number of unique characteristics. Based on this, we propose a model of behavioural design. Furthermore, we identify three critical barriers to behavioural design in practice, and suggest ten mitigating enablers. Together these findings provide implications for further development of theory, practice, and education in behavioural design.
CL-MAPF: Multi-Agent Path Finding for Car-Like robots with kinematic and spatiotemporal constraints

Multi-Agent Path Finding has been widely studied in the past few years due to its broad application in the field of robotics and AI. However, previous solvers rely on several simplifying assumptions. They limit their applicability in numerous real-world domains that adopt nonholonomic car-like agents rather than holonomic ones. In this paper, we give a mathematical formalization of Multi-Agent Path Finding for Car-Like robots (CL-MAPF) problem. For the first time, we propose a novel hierarchical search-based solver called Car-like Conflict-Based Search to address this problem. It applies a body conflict tree to address collisions considering shapes of the agents. We introduce a new algorithm called Spatiotemporal Hybrid-State A* as the single-agent path planner to generate path satisfying both kinematic and spatiotemporal constraints. We also present a sequential planning version of our method for the sake of efficiency. We compare our method with two baseline algorithms on a dedicated benchmark containing 3000 instances and validate it in real-world scenarios. The experiment results give clear evidence that our algorithm scales well to a large number of agents and is able to produce solutions that can be directly applied to car-like robots in the real world. The benchmark and source code are released in https://github.com/APRIL-ZJU/CL-CBS.
Quantum-Soft QUBO Suppression for Accurate Object Detection

Non-maximum suppression (NMS) has been adopted by default for removing redundant object detections for decades. It eliminates false positives by only keeping the image \(\mathcal {M}\) with highest detection score and images whose overlap ratio with \(\mathcal {M}\) is less than a predefined threshold. However, this greedy algorithm may not work well for object detection under occlusion scenario where true positives with lower detection scores are possibly suppressed. In this paper, we first map the task of removing redundant detections into Quadratic Unconstrained Binary Optimization (QUBO) framework that consists of detection score from each bounding box and overlap ratio between pair of bounding boxes. Next, we solve the QUBO problem using the proposed Quantum-soft QUBO Suppression (QSQS) algorithm for fast and accurate detection by exploiting quantum computing advantages. Experiments indicate that QSQS improves mean average precision from 74.20% to 75.11% for PASCAL VOC 2007. It consistently outperforms NMS and soft-NMS for Reasonable subset of benchmark pedestrian detection CityPersons.
Understanding Hypotheses Engineering in Software Startups through a Gray Literature Review

The higher availability of software usage data and the influence of the Lean Startup led to the rise of experimentation in software engineering, a new approach for development based on experiments to understand the user needs. In the models proposed to guide this approach, the first step is generally to identify, prioritize, and specify the hypotheses that will be tested through experimentation. However, although practitioners have proposed several techniques to handle hypotheses, the scientific literature is still scarce. The goal of this study is to understand what activities, as proposed in industry, are entailed to handle hypotheses, facilitating the comparison, creation, and evaluation of relevant techniques. We performed a gray literature review (GLR) on the practices proposed by practitioners to handle hypotheses in the context of software startups. We analyzed the identified documents using thematic synthesis. The analysis revealed that techniques proposed for software startups in practice compress five different activities: elicitation, prioritization, specification, analysis, and management. It also showed that practitioners often classify hypotheses in types and which qualities they aim for these statements. Our results represent the first description for hypotheses engineering grounded in practice data. This mapping of the state-of-practice indicates how research could go forward in investigating hypotheses for experimentation in the context of software startups. For practitioners, they represent a catalog of available practices to be used in this context.
Knowledge graph embedding by translating in time domain space for link prediction

Knowledge graph embedding, which aims to learn distributed representations of entities and relations, has been proven to be an effective method for predicting missing links in knowledge graphs. Existing knowledge graph embedding models mainly consider that the space where head and tail entities are located has the same properties. However head and tail entities can be different types of objects, and they should not be located in the vector space with the same properties. In this paper, we propose a novel knowledge graph embedding model called TimE, which presents each entry of the head (or tail) entity embeddings as a point in time domain space, and the corresponding entry of tail (or head) entity embeddings as a point in frequency domain space. Specifically, TimE defines each relation as a composition relation, which consists of a translation between entities and a diagonal projection matrix that projects the entities into the time domain space. In addition, we propose a cross-operation to model inverse and symmetric relations. Experimental results show that TimE not only outperforms existing state-of-the-art models on several large-scale benchmark datasets for the link prediction task, but also can better capture diversity distribution of entity embeddings for different relation patterns effectively, and can also model all relation patterns (including symmetry/antisymmetry, inversion, and composition).
Human action recognition using distance transform and entropy based features

Human action recognition based on silhouette images has wide applications in computer vision, human computer interaction and intelligent surveillance. It is a challenging task due to the complex actions in nature. In this paper, a human action recognition method is proposed which is based on the distance transform and entropy features of human silhouettes. In the first stage, background subtraction is performed by applying correlation coefficient based frame difference technique to extract silhouette images. In the second stage, distance transform based features and entropy features are extracted from the silhouette images. The distance transform based features and entropy features provide the shape and local variation information. These features are given as input to neural networks to recognize various human actions. The proposed method is tested on three different datasets viz., Weizmann, KTH and UCF50. The proposed method obtains an accuracy of 92.5%, 91.4% and 80% for Weizmann, KTH and UCF50 datasets respectively. The experimental results show that the proposed method for human action recognition is comparable to other state-of-the-art human action recognition methods.
Few-Shot Guided Mix for DNN Repairing

Although deep neural networks (DNNs) achieve rather high performance in many cutting-edge applications (e.g., autonomous driving, medical diagnose), their trustworthiness on real-world scenarios still posts concerns, where some specific failure examples are often encountered during the real-world operational environment. With the limited failure examples collected during the practical operation, how to effectively leverage such failure cases to repair and enhance DNN so as to generalize to more potentially suspicious samples is challenging, but of great importance. In this paper, we formulate the failure-data-driven DNN repairing as a data augmentation problem, and design a novel augmentation-based repairing method, which to the best extent leverages limited failure cases. To realize the DNN repairing effects that generalize to specific failure examples, we originally propose few-shot guided mix (FSGMix) that augments training data with the guidance of failure examples. As a result, our method is able to achieve high generalization to the collected failure examples and other similar suspicious data. The preliminary evaluation on CIFAR-10 dataset demonstrates the potential of our proposed technique, which automatically learns to resolve the potential failure patterns in the DNN operational environment.
An Approach To Enhance Fall Detection Using Machine Learning Classifier

Environmental protection is a concept that uses information and communication technology(ICT) for enhancing people's daily lives. Human fall detection is an effective environmental support sub-area. One of the critical issues in elderly people is the fall detection. In this work, we have proposed the fall detection algorithms using machine learning which later uses a fog computing approach to send information to the caregiver in real-time. One class method based on a support vector machine is used to build fall detection and a Smartphone accelerometer is used for the data collection. We have considered five features from the Smartphone accelerometer for the building of the fall detection model. Fog computing approach is used to intimate the caregiver about the fall in real-time through the cloud connection even in the absence of fog node. The innovation is that we have used the multiplication of the kernel matrix to enforce a one-class classification. During the detection of human fall, the model has achieved 100% sensitivity and 98.8% specificity.
Reliable CRC-Based Error Detection Constructions for Finite Field Multipliers With Applications in Cryptography

Finite-field multiplication has received prominent attention in the literature with applications in cryptography and error-detecting codes. For many cryptographic algorithms, this arithmetic operation is a complex, costly, and time-consuming task that may require millions of gates. In this work, we propose efficient hardware architectures based on cyclic redundancy check (CRC) as error-detection schemes for postquantum cryptography (PQC) with case studies for the Luov cryptographic algorithm. Luov was submitted for the National Institute of Standards and Technology (NIST) PQC standardization competition and was advanced to the second round. The CRC polynomials selected are in-line with the required error-detection capabilities and with the field sizes as well. We have developed verification codes through which software implementations of the proposed schemes are performed to verify the derivations of the formulations. Additionally, hardware implementations of the original multipliers with the proposed error-detection schemes are performed over a Xilinx field-programmable gate array (FPGA), verifying that the proposed schemes achieve high error coverage with acceptable overhead.
Atrous cGAN for SAR to Optical Image Translation

Conditional (cGAN)-based methods proposed so far for synthetic aperture radar (SAR)-to-optical image synthesis tend to produce noisy and unsharp optical outcomes. In this work, we propose the atrous-cGAN, a novel cGAN architecture that improves the SAR-to-optical image translation. The proposed generator and discriminator networks rely on atrous convolutions and incorporate an atrous spatial pyramid pooling (ASPP) module to enhance fine details in the generated optical image by exploiting spatial context at multiple scales. This letter reports experiments carried out to assess the performance of atrous-cGAN for the synthesis of Landsat-8 images from Sentinel-1A data based on three public data sets. The experimental analysis indicated that the atrous-cGAN consistently outperformed the classical pix2pix counterpart in terms of visual quality, similar to the true optical image, and as a feature learning tool for semantic segmentation.
Supervised Learning Achieves Human-Level Performance in MOBA Games: A Case Study of Honor of Kings

We present JueWu-SL, the first supervised-learning-based artificial intelligence (AI) program that achieves human-level performance in playing multiplayer online battle arena (MOBA) games. Unlike prior attempts, we integrate the macro-strategy and the micromanagement of MOBA-game-playing into neural networks in a supervised and end-to-end manner. Tested on Honor of Kings, the most popular MOBA at present, our AI performs competitively at the level of High King players in standard 5v5 games.
A Survey of Location Privacy Preservation in Social Internet of Vehicles

Social Internet of Vehicles (SIoV) is an emerging complex network where the features of Social Networks are applied to the SIoV system.User location data forms the basis for the implementation of SIoV functions.However, this type of data contains a large amount of user personal information, which may cause privacy leakage if it is stolen.Protecting the privacy of the user location can eliminate concerns about the leakage of user personal privacy data, increase users' viscosity, and help to contribute to the improvement of the SIoV system.This paper systematically analyzes the location privacy protection technology utilized in recent years in the field of SIoV, proposes three types of user data location privacy protection technology, and evaluates the performance of these technologies.We further present some potential future research directions for location privacy protection technology through the analysis and summary of existing work.
An Enhanced Certificateless Aggregate Signature Without Pairings for E-Healthcare System

Recently, Gayathri et al. introduced an efficient certificateless aggregate signature (CLAS) for e-healthcare system and claimed that it can achieve efficient aggregation to different signatures on different messages from different medical sensors. In this article, we analyze it and find that the CLAS scheme and the underlying certificateless signature (CLS) scheme in it are not secure. Anyone can forge a valid aggregate/individual signature replacing the legitimate medical sensor only based on the public parameters set in Gayathri et al.'s CLAS scheme. Moreover, the malicious medical sensors can deny a valid aggregate signature (AS) by offering false individual signatures aggregated into this AS. We also present a secure CLAS scheme by aggregating an existing lightweight CLS scheme. The aggregation algorithm adopted in our new scheme is proven sound against inside attacks.
Synthetic geochemical well logs generation using ensemble machine learning techniques for the Brazilian pre-salt reservoirs

Geochemical logs are an essential tool for hydrocarbon reservoir characterization. The rock composition given by these logs is useful for porosity calculation, stratigraphic modeling, diagenesis estimation, and well acidification. In reduced logging operations performed in carbonate reservoirs of the Brazilian pre-salt, the geochemical tool is no longer acquired, aiming at optimization and cost reduction. This research aims the development of synthetic geochemical logs using machine learning algorithms. The database includes 22 wells with complete logging acquisition, and the input logs are natural gamma-ray, gamma-ray spectroscopy, density, photoelectric factor, neutron porosity, nuclear magnetic resonance, and sonic. The chemical elements chosen as output are Al, Ca, Fe, Mg, Na, Si, S, and Ti. Five models based on machine learning are trained using 19 wells: Support-vector machine, Multilayer perceptron, Random Forest, AdaBoost, and XGBoost. AdaBoost represents the best algorithm because, in addition to showing the best results, it allowed for a more simplified preprocessing and hyperparameter tuning. The evaluation of the models applies R2 and root mean squared error (RMSE) in validation data and cross-validation. Robust models are acquired for Al, Ca, Fe, Mg, Si, S, and Ti, with R2 above 0.80. Na shows R2 slightly above 0.70. All the models have RMSE between 10-2 to 10-4. The most important logs during training are density, photoelectric factor, K, Th, U, and neutron porosity. Synthetic geochemical logs created for three test wells show a good agreement with acquired logs, being able to reproduce general trends of the pre-salt formations. The machine learning model is capable of substituting the acquisition of geochemical logs with high confidence, representing cost reduction, and supplying engineers and geoscientists with quality data to be used in formation evaluation.
iContractML

Smart contracts are immutable digital programs deployed onto blockchain platforms to codify agreements. They enable blockchain technology to play a vital role in many fields, such as finance, health care, and energy. An important aspect of modeling and deploying smart contracts is to define the business process and rules that govern the agreements under which the corresponding actions are executed. Unfortunately, these models use a mix of technical and business-centric terminologies that are different based on the underlying blockchain platform that the smart contract is targeting. To address this issue, in this paper, we followed a feature-oriented domain analysis approach to identify the commonalities and variations between three of the common blockchain platforms that are used to deploy smart contracts; namely IBM Hyperledger Composer, Azure Blockchain Workbench, and Ethereum. Accordingly, we propose a reference model for smart contracts. The reference model is then realized as a modeling framework that enables developers to model and generate the structural code required to deploy a smart contract onto multiple blockchain platforms. The coverage of the proposed reference model was shown through mapping the concepts of the reference models to its corresponding constructs within each blockchain platform. Moreover, we provide three use cases to show how the proposed framework can empower developers to generate the structural code of smart contracts for the target platform through model transformation.
Beyond Health Literacy: Navigating Boundaries and Relationships During High-risk Pregnancies

Few studies in HCI4D have examined the lived experiences of women with pregnancy complications. We conducted a qualitative study with 15 pregnant women to gain an in-depth understanding of the context in which pregnancy takes place and everyday experiences living with complications in rural North-West India. To complement our interviews, we conducted six focus groups with three pregnant women, three community health workers and three members of an NGO. Our study reveals insights about the challenges and experiences of the pregnant women with complications while navigating the physical, spatial, social and emotional aspects of antenatal care as part of complex and contradictory structures and settings of their everyday life. We argue that the design of digital health in support of pregnancy care for the Global South must center around supporting the navigational work done by the pregnant women and their families.
Weak form of self-testing

The concept of self-testing (or rigidity) refers to the fact that for certain Bell inequalities the maximal violation can be achieved in an essentially unique manner. In this work we present a family of Bell inequalities which are maximally violated by multiple inequivalent quantum realisations. We completely characterise the quantum realisations achieving the maximal violation and we show that each of them requires a maximally entangled state of two qubits. This implies the existence of a new, weak form of self-testing in which the maximal violation allows us to identify the state, but does not fully determine the measurements. From the geometric point of view the set of probability points that saturate the quantum bound is a line segment. We then focus on a particular member of the family and show that the self-testing statement is robust, i.e. that observing a non-maximal violation allows us to make a quantitative statement about the unknown state. To achieve this we present a new construction of extraction channels and analyse their performance. For completeness we provide two independent approaches: analytical and numerical. The noise robustness, i.e. the amount of white noise at which the bound becomes trivial, of the analytical bound is rather small (~0.06%), but the numerical method takes us into an experimentally-relevant regime (~5%). We conclude by investigating the amount of randomness that can be certified using these Bell violations. Perhaps surprisingly, we find that the qualitative behaviour resembles the behaviour of rigid inequalities like the Clauser-Horne-Shimony-Holt inequality. This shows that at least for some device-independent applications rigidity is not a necessary ingredient.
Discrete crow-inspired algorithms for traveling salesman problem

Crow search algorithm is one of bio-inspired optimization algorithms which is essentially derived for solving continuous based optimization problems. Although many main-frame discrete optimizers are available, they still have some performance challenges. This paper proposes three discrete crow inspired algorithms for enhancing the performance of the original crow search algorithm when it is applied for solving discrete traveling salesman problems. The proposed algorithms are derived based on modular arithmetic, basic operators and dissimilar solutions techniques. Each technique guarantees switching from continuous spaces into discrete spaces without losing information. Such algorithms are called Modular Arithmetic, Basic Operators, and Dissimilar Solutions algorithms. For evaluating their performance, the proposed algorithms are compared with the most state-of-the-art discrete optimizers for solving 111 instances of traveling salesman problems. Simulation results illustrate that, the performance of the proposed algorithms is much better than the performance of most state-of-the-art discrete optimizers in terms of the average optimal solutions accuracy, the average errors from the optimal solutions and the average of computational time.