Reviewerly/exHarmony huggingface dataset:
Saliency Heat-Map as Visual Attention for Autonomous Driving Using Generative Adversarial Network (GAN)

The ability to sense and understanding the driving environment is a key technology for ADAS and autonomous driving. Human drivers have to pay more visual attention to important or target elements and ignore unnecessary ones present in their field of sight. A model that computes this visual attention of targets in a specific driving environment is essential and useful in supporting autonomous driving, object-specific tracking & detection, driving training, car collision warning, traffic sign detection, etc. In this paper, we propose a new framework of visual attention that can predict important objects in the driving scene using a conditional generative adversarial network. A large scale Visual Attention Driving Database (VADD) of saliency heat-maps is built from existing driving datasets using a saliency mechanism. The proposed framework model takes its strength from these saliency heat-maps as conditioning label variables. The results show that the proposed approach makes us able to predict heat-maps of most important objects in a driving environment.
An Attention-Enhanced Multi-Scale and Dual Sign Language Recognition Network Based on a Graph Convolution Network

Sign language is the most important way of communication for hearing-impaired people. Research on sign language recognition can help normal people understand sign language. We reviewed the classic methods of sign language recognition, and the recognition accuracy is not high enough because of redundant information, human finger occlusion, motion blurring, the diversified signing styles of different people, and so on. To overcome these shortcomings, we propose a multi-scale and dual sign language recognition Network (SLR-Net) based on a graph convolutional network (GCN). The original input data was RGB videos. We first extracted the skeleton data from them and then used the skeleton data for sign language recognition. SLR-Net is mainly composed of three sub-modules: multi-scale attention network (MSA), multi-scale spatiotemporal attention network (MSSTA) and attention enhanced temporal convolution network (ATCN). MSA allows the GCN to learn the dependencies between long-distance vertices; MSSTA can directly learn the spatiotemporal features; ATCN allows the GCN network to better learn the long temporal dependencies. The three different attention mechanisms, multi-scale attention mechanism, spatiotemporal attention mechanism, and temporal attention mechanism, are proposed to further improve the robustness and accuracy. Besides, a keyframe extraction algorithm is proposed, which can greatly improve efficiency by sacrificing a little accuracy. Experimental results showed that our method can reach 98.08% accuracy rate in the CSL-500 dataset with a 500-word vocabulary. Even on the challenging dataset DEVISIGN-L with a 2000-word vocabulary, it also reached a 64.57% accuracy rate, outperforming other state-of-the-art sign language recognition methods.
Much more than a mere technology: A systematic review of Wikidata in libraries

Wikidata is gaining popularity in libraries as an open and collaborative global platform for sharing and exchanging library metadata. Based on a systematic review of the Library and Information Studies (LIS) literature, this study explores how and why Wikidata is being used in libraries as well as what are some of the benefits and barriers that have surfaced as a result of the early experimentation of Wikidata during its first decade of existence. The review revealed that Wikidata in libraries is generally described as an open and reusable knowledgebase of structured data capable of linking local metadata with a network of global metadata. Libraries have started experimenting with Wikidata to improve the global reach and access of their unique and prominent collections and scholars. While Wikidata holds great potential to become the repository choice for authority data disambiguation and linking, its sustainable integration into library operations remains a challenge. Much more than a technology, Wikidata provides a way for librarians to embrace social justice and practice librarianship on a global scale for the public good.
The Blockchain Manifesto

The process of implementing education globally basically cannot keep up with current developments. The education sector is currently facing various problems; in this case, the issue of verification and the existence of a third party in handling certificates are the micro and macro results of a learning process. A lighthouse about Blockchain technology is often cited as one of the revolutionary solutions to educational polemics so far. The combination of educational technology with blockchain technology will create revolutionary technology in the education industry. There are various kinds of insight from this problem, but this cannot explain the blockchain manifesto in detail from education for learning based on scientific studies. This blockchain manifesto in the future will be useful as a literature platform for researchers who will begin basic and applied research leading to blockchain for education. Besides, in terms of cost, storage space, and blockchain technology speed, a solution will be proposed in the form of a framework. As a result, this research can also become a new blockchain platform and manifesto as the blockchain technology-based government's embryo.
Influence of Chirp, Jitter, and Relaxation Oscillations on Probabilistic Properties of Laser Pulse Interference

Interference of laser pulses is an essential ingredient of quantum randomness; therefore, probabilistic properties of laser pulses gains new relevance. Here, we consider in detail the combined influence of the three effects – chirp, jitter and relaxation oscillations – on the probability density function of the interference of pulses from a gain-switched semiconductor laser. We develop a rigorous model based on laser rate equations and demonstrate that only consideration of all these three effects together allows describing the interference statistics properly. We supplement our theoretical calculations with corresponding measurements at various pump currents. Experimental results demonstrate perfect agreement with predictions of the model and are well reproduced by Monte-Carlo simulations.
Adversarial Image Composition with Auxiliary Illumination

Dealing with the inconsistency between a foreground object and a background image is a challenging task in high-fidelity image composition. State-of-the-art methods strive to harmonize the composed image by adapting the style of foreground objects to be compatible with the background image, whereas the potential shadow of foreground objects within the composed image which is critical to the composition realism is largely neglected. In this paper, we propose an Adversarial Image Composition Net (AIC-Net) that achieves realistic image composition by considering potential shadows that the foreground object projects in the composed image. A novel branched generation mechanism is proposed, which disentangles the generation of shadows and the transfer of foreground styles for optimal accomplishment of the two tasks simultaneously. A differentiable spatial transformation module is designed which bridges the local harmonization and the global harmonization to achieve their joint optimization effectively. Extensive experiments on pedestrian and car composition tasks show that the proposed AIC-Net achieves superior composition performance qualitatively and quantitatively.
How blockchain will transform the healthcare ecosystem

Blockchain has the potential to fundamentally transform the healthcare ecosystem, impacting all healthcare participants. In this article, we study the potential impact of blockchains on patient-centered care delivery by looking at the ecosystem of blockchain-based solutions being developed for key stakeholders: patients, pharmaceutical firms, hospital systems, and insurance providers. Building from our interviews with senior managers, our actual experience in the healthcare industry, and our academic research, we outline how blockchain will transform healthcare, and we provide guidance as to how those in healthcare need to start thinking about engaging with blockchain technology.
Big Data and Personalisation for Non-Intrusive Smart Home Automation

With the advent of the Internet of Things (IoT), many different smart home technologies are commercially available. However, the adoption of such technologies is slow as many of them are not cost-effective and focus on specific functions such as energy efficiency. Recently, IoT devices and sensors have been designed to enhance the quality of personal life by having the capability to generate continuous data streams that can be used to monitor and make inferences by the user. While smart home devices connect to the home Wi-Fi network, there are still compatibility issues between devices from different manufacturers. Smart devices get even smarter when they can communicate with and control each other. The information collected by one device can be shared with others for achieving an enhanced automation of their operations. This paper proposes a non-intrusive approach of integrating and collecting data from open standard IoT devices for personalised smart home automation using big data analytics and machine learning. We demonstrate the implementation of our proposed novel technology instantiation approach for achieving non-intrusive IoT based big data analytics with a use case of a smart home environment. We employ open-source frameworks such as Apache Spark, Apache NiFi and FB-Prophet along with popular vendor tech-stacks such as Azure and DataBricks.
Image Co-Saliency Detection and Instance Co-Segmentation Using Attention Graph Clustering Based Graph Convolutional Network

Co-Saliency Detection (CSD) is to explore the concurrent patterns and salient objects from a group of relevant images, while Instance Co-Segmentation (ICS) aims to identify and segment out all of these co-salient instances, generating corresponding mask for each instance. To simultaneously tackle these two tasks, we present a novel adaptive graph convolutional network with attention graph clustering (GCAGC) for CSD and ICS, termed as GCAGC-CSD and GCAGC-ICS, respectively. The GCAGC-CSD contains three key model designs: first, we develop a graph convolutional network architecture to extract multi-scale representations to characterize the intra- and inter-image consistency. Second, we propose an attention graph clustering algorithm to distinguish the salient foreground objects from common areas in an unsupervised manner. Third, we present a unified framework with encoder-decoder structure to jointly train and optimize the graph convolutional network, attention graph cluster, and CSD decoder in an end-to-end fashion. Afterwards, we design a salient instance segmentation network for GCAGC-ICS, and combine the outputs of GCAGC-CSD and the instance segmentation branch to obtain instance-aware co-segmentation masks. The proposed GCAGC-CSD and GCAGC-ICS are extensively evaluated on four CSD benchmark datasets (iCoseg, Cosal2015, COCO-SEG and CoSOD3k) and five ICS benchmark datasets (CoSOD3k, COCO-NONVOC, COCO-VOC, VOC12 and SOC), and achieve superior performance over state-of-the-arts on both tasks.
A 3D Informational Database for Automatic Archiving of Archaeological Pottery Finds

From archaeological excavations, huge quantities of material are recovered, usually in the form of fragments. Their correct interpretation and classification are laborious and time-consuming and requires measurement, analysis and comparison of several items. Basing these activities on quantitative methods that process 3D digital data from experimental measurements allows optimizing the entire restoration process, making it faster, more accurate and cheaper. The 3D point clouds, captured by the scanning process, are raw data that must be properly processed to be used in automatic systems for the analysis of archeological finds. This paper focuses on the integration of a shape feature recognizer, able to support the semantic decomposition of the ancient artifact into archaeological features, with a structured database, able to query the large amount of information extracted. Through the automatic measurement of the dimensional attributes of the various features, it is possible to facilitate the comparative analyses between archaeological artifacts and the inferences of the archaeologist and to reduce the routine work. Here, a dedicated database has been proposed, able to store the information extracted from huge quantities of archaeological material using a specific shape feature recognizer. This information is useful for making comparisons but also to improve the archaeological knowledge. The database has been implemented and used for the identification of pottery fragments and the reconstruction of archaeological vessels. Reconstruction, in particular, often requires the solution of complex problems, especially when it involves types of potsherds that cannot be treated with traditional methods.
Learning-Based Extended Object Tracking Using Hierarchical Truncation Measurement Model With Automotive Radar

This paper presents a data-driven measurement model for extended object tracking (EOT) with automotive radar. Specifically, the spatial distribution of automotive radar measurements is modeled as a hierarchical truncated Gaussian (HTG) with structural geometry parameters that can be learned from the training data. The HTG measurement model provides an adequate resemblance to the spatial distribution of real-world automotive radar measurements. Moreover, large-scale radar datasets can be leveraged to learn the geometry-related model parameters and offload the computationally demanding model parameter estimation from the state update step. The learned HTG measurement model is further incorporated into a random matrix based EOT approach with two (multi-sensor) measurement updates: one is based on a factorized Gaussian inverse-Wishart density representation and the other is based on a Rao-Blackwellized particle density representation. The effectiveness of the proposed approaches is verified on both synthetic data and real-world nuScenes dataset over 300 trajectories.
Continual Learning of Knowledge Graph Embeddings

In recent years, there has been a resurgence in methods that use distributed (neural) representations to represent and reason about semantic knowledge for robotics applications.However, while robots often observe previously unknown concepts, these representations typically assume that all concepts are known a priori, and incorporating new information requires all concepts to be learned afresh.Our work relaxes this limiting assumption of existing representations and tackles the incremental knowledge graph embedding problem by leveraging the principles of a range of continual learning methods.Through an experimental evaluation with several knowledge graphs and embedding representations, we provide insights about trade-offs for practitioners to match a semanticsdriven robotics applications to a suitable continual knowledge graph embedding method.
Addressing Imbalance in Multi-Label Classification Using Weighted Cross Entropy Loss Function

Training a model and network on an imbalanced dataset always has been a challenging problem in the machine learning field that has been discussed by researchers. In fact, available machine learning algorithms are designed moderately imbalanced datasets and mainly do not consider the dataset's imbalanced problem. In the machine learning algorithm, the imbalance problem appears when the number of one class samples are significantly minor than another class. In order to solve the imbalance problem of a dataset, multiple algorithms are proposed in the field of machine learning and especially in deep learning. In this study, we have benefited from weighted binary cross-entropy in the learning process as a loss function instead of ordinary cross-entropy (binary cross-entropy). This model allocates more penalty to minority class samples during the learning process, and it makes that minority class samples are detected more accurately. Finally, we could improve Recall with preserving Accuracy. In fact, results show that using weighted binary cross-entropy recall increases about 10%, and precision does not decrease more than 3% in comparison to binary cross-entropy.
A joint global and local path planning optimization for UAV task scheduling towards crowd air monitoring

Large-scale crowd management systems are used to monitor and manage crowds in various industries aspects by utilizing relevant innovative technologies. In order to overcome the shortcomings of traditional CCTV equipment in shooting angle and deployment, some scholars propose to use unmanned ariel vehicle (UAV) carried appropriate optical sensory equipment to perform aerial scene surveillance. However, UAV flight missions have problems such as poor adaptability of single-mode path planning to site conditions space and complex cluster scheduling systems. Therefore, we combine the improved particle swarm optimization(PSO) algorithm, the optimized artificial potential algorithm, path exploration switching mode and energy-based task scheduling mechanism to propose a joint global and local path planning optimization for UAV task scheduling towards crowd air monitoring (JGLPP-UTS). In this model, the PSO algorithm is improved based on mutation mechanism and iterative number dependent adaptive inertia weight, we add a path smoothing mechanism. Then, we optimize the artificial potential algorithm for the problem of the target point unreachable and the local minimum. The proposed model switches the path planning mode according to the global and local obstacle environment. Finally, our model comprehensively considers the information of the site to realize the surveillance task scheduling of the UAV. Experiments show that our proposed algorithm can effectively improve the ability of global and local path planning. Compared with the standard PSO path length, the global path is reduced by 8.92%, and the adaptive value is reduced by 82.9%. After the smoothing operation, we also report that the path length can be further reduced. Moreover, the task scheduling strategy can realize the effective use of airborne resources.
Empirical Investigation About the Factors Affecting the Cost Estimation in Global Software Development Context

Software organization always aims at developing a quality software product using the estimated development resources, effort, and time.Global Software Development (GSD) has emerged as an essential tool to ensure optimal utilization of resources, which is performed in globally distributed settings in various geographical locations.Global software engineering focuses on reducing the cost, increasing the development speed, and accessing skilled developers worldwide.Estimating the required amount of resources and effort in the distributed development environment remains a challenging task.Thus, there is a need to focus on cost estimation models in the GSD context.We nevertheless acknowledge that several cost estimation techniques have been reported.However, to the best of our knowledge, the existing cost estimation techniques/models lack considering the additional cost drivers required to compute the accurate cost estimation in the GSD context.Motivated by this, the current work aims at identifying the other cost drivers that affect the cost estimation in the context of GSD.To achieve the targeted objectives, current stateof-the-art related to existing cost estimation techniques of GSD is reported.We adopted SLR and Empirical approach to address the formulated research questions.The current study also identifies the missing factors that would help the practitioners improve the cost estimation models.The results indicate that previously conducted work ignores the additional elements necessary for the cost estimation in the GSD context.Moreover, the current work proposes a conceptual cost estimation model tailored to fit the GSD context.
Identity-based encryption with equality test and datestamp-based authorization mechanism

Due to the advancement of cloud computing, public key encryption with equality test has become a hot topic in the cryptography community. The notion was extended to the identity-based setting to obtain a new primitive, called identity-based encryption with equality test (IBEET). To the best of our knowledge, there are two categories of authorization mechanisms for IBEET: ciphertext-specified authorization and user-specified authorization. However, these authorizations are not applicable for some scenarios. Motivated by this, we propose in this paper a new primitive, called identity-based encryption with equality test and datestamp-based authorization mechanism (IBEET-DBA). In the primitive, the data owner can control the validity of trapdoor by embedding datestamp into it. The cloud server can only obtain the correct equivalence results on the ciphertexts which are generated within the valid period of trapdoor. In this paper, we formally define this primitive and its security notion. Moreover, we propose the first IBEET-DBA scheme and prove its security.
Enabling low-voltage, peer-to-peer, quasi-real-time electricity markets through consortium blockchains

Variable renewable energy sources are continuously increasing their share in the world energy mix. However, their uncertainty has a deep impact in the electric grid safe operation. One of the most promising solutions to tackle such challenge at a distribution grid level consists of quasi-real-time, peer-to-peer electricity markets. These can use the blockchain to be successfully implemented in a secure and privacy preserving way, making effective use of the increasing intelligence of Internet of Things appliances. Nevertheless, providing blockchain-based solutions compatible with the energy sector is not an easy task. In particular, in order to be compliant with typical electricity costs of the order of some €cents/kWh, blockchain transactions are required to be very cheap. Unfortunately, none of the existing research works took the issue of transaction costs into account. As a result, real-world feasibility of existing solutions is rather to be investigated. In this work, we propose an ad-hoc Ethereum-based consortium blockchain explicitly addressing this issue. Leveraging on this platform, we design a blockchain-based electricity market. The market consists of two-steps. The first (day-ahead) step settles the amounts and prices of the energy traded among peers. The second step is instead quasi-real-time, thus enabling the unexpected excess or lack of electricity to be directly negotiated on a peer-to-peer basis. Negotiations occurs without the need of any intermediary, except for the check of grid physical limits, performed by the distribution system operator. To demonstrate the functionality of the solution, a proof-of-concept has been implemented at the University of Pisa (Italy).
A comprehensive survey on cultural algorithms

Cultural Algorithms (CAs) are evolutionary algorithms (EAs) inspired by the conceptual models of the human cultural evolution process. In contrast to the conventional EAs, which work only based on the population space, CAs employ an additional space, called belief space, to collect the information about the behaviour of individuals in the search space. Since the emergence of CAs, they have been successfully extended to solve a wide variety of problems in different branches of science and technology. In this paper, a comprehensive survey on the recent advances in CAs is presented. Literature survey reveals some interesting challenges and future research directions.