Reviewerly/exHarmony huggingface dataset:
ScratchGAN: Network representation learning for scratch with preference‐based generative adversarial nets

With the rapid increase of users, Scratch, as a popular online social and programming platform, has accumulated massive project resources and complex relations across its social and programming learning network. However, it is challenging to utilize the network information for providing Scratch users with personalized services, despite there are already some useful network representation learning models. In this paper, a network representation learning model with preference-based generative adversarial nets for Scratch (ScratchGAN) is proposed to resolve this problem. In ScratchGAN, we first design a node-vector initialization approach to preserve structure information and side information of Scratch network. Then, considering to learn the fine-grained user preference information of network, we propose a novel Scratch adversarial learning model which includes a Scratch generative adversarial net and a user preference difference constraint component. The former aims to capture user preferences through a new generating strategy based on the delivery nature of preference. The latter attempts to embed users' detailed preference differences according to their interaction behaviors. ScratchGAN can mine user preferences while preserving network structure information and side information. Extensive experiments on the Scratch network show that ScratchGAN outperforms other state-of-the-art models in link prediction and recommendation tasks.
Towards Optimized IoT-based Context-aware Video Content Analysis Framework

Despite the success of convolutional neural networks (CNNs) in the area of spatial analysis, and recurrent neural networks (RNNs) on sequence modeling and interpretation tasks, video analysis has only seen limited interest and progress. This is partially due to focusing on the natural humanlike translation from video space to natural language space to the detriment of informativeness. This paper is proposing an automated context-aware video analysis framework that is directed by the constrains of its application. This framework encorporates an encoder-decoder neural network trained on a closed-domain video-to-text dataset. The network architecture and the standardized language model present in the dataset are optimized for speed, to allow the system to be applied on IoT devices, and for informativeness, to extract information easily from the model output to the following stages of the anlaysis. The proposed framework provides a practical method to integrate the power of CNN and RNN combination in a directed way to extract the most from video content. A classroom monitoring system is discussed as an example of the capabilities and limitations of the proposed framework using NVIDIA’s Jetson nano board.
Statistical Validation and Power Modelling of Hourly Profiles for a Large-Scale Photovoltaic Plant Portfolio

In the last decades, the number of photovoltaic (PV) generators significantly increased over the world. An accurate analysis of the massive data gathered from the meters of the PV plants can improve the management of their intermittent generation. This paper presents a methodology to analyse the production profiles of a portfolio of thousands of PV plants installed in an Italian region. The procedure faces the problem of filtering poor and incomplete data, then uses a stratified sampling technique for the statistical validation of the remaining profiles. Finally, the checked production profiles are used to adjust the energy model to better match the measured data and calculate the whole PV portfolio production.
A Survey on Blockchain Application: a Data Collaboration Perspective

With the continuous innovation of blockchain technology in recent years, the concept of “blockchain+” has become popular in all fields, and more and more people are exploring the application of blockchain. This paper compares the current situations of blockchain applications with various blockchain systems, and focuses on the solutions to the two major issues of data storage and privacy during the application process. From the perspective of data collaboration, three cutting-edge application cases in three major areas are selected for analysis, and opinions on how to choose a suitable blockchain architecture for different scenarios. Finally, it summarizes the current situations of blockchain applications and puts forward the prospects for blockchain applications.
Blockchain Technology Based on Algorand Applied to Low-Power and Low-Cost IoT Devices

Thanks to the ever-increasing number of connected, innovative and smart objects that have been adopted since now in almost every sector, the Internet of Things is transforming people lives. Nonetheless, one of the major challenges encountered by practitioners and experts every day is related to cybersecurity and the need of protecting collected information at all the levels of IoT ecosystems. Unfortunately, also due to the growing number of connected devices, data security has become extremely complex. One of the revolutionary technologies that has the potential to address such needs and, also, support privacy and scalability is the Blockchain. Among the various existing Blockchains present in literature and in the market, Algorand is emerging as one of the most promising technologies for the IoT due to its intrinsic self-sustaining and decentralized nature. This paper discusses preliminary results obtained in the adoption of the Algorand Blockchain in two of the most spread low-powered IoT devices such as the Raspberry Pi 4 Model B and the STMicroelectronics STM32MP157A-DK1.
Beyond Relevance Ranking: A General Graph Matching Framework for Utility-Oriented Learning to Rank

Learning to rank from logged user feedback, such as clicks or purchases, is a central component of many real-world information systems. Different from human-annotated relevance labels, the user feedback is always noisy and biased. Many existing learning to rank methods infer the underlying relevance of query–item pairs based on different assumptions of examination, and still optimize a relevance based objective. Such methods rely heavily on the correct estimation of examination, which is often difficult to achieve in practice. In this work, we propose a general framework U-rank+ for learning to rank with logged user feedback from the perspective of graph matching. We systematically analyze the biases in user feedback, including examination bias and selection bias. Then, we take both biases into consideration for unbiased utility estimation that directly based on user feedback, instead of relevance. In order to maximize the estimated utility in an efficient manner, we design two different solvers based on Sinkhorn and LambdaLoss for U-rank+ . The former is based on a standard graph matching algorithm, and the latter is inspired by the traditional method of learning to rank. Both of the algorithms have good theoretical properties to optimize the unbiased utility objective while the latter is proved to be empirically more effective and efficient in practice. Our framework U-rank+ can deal with a general utility function and can be used in a widespread of applications including web search, recommendation, and online advertising. Semi-synthetic experiments on three benchmark learning to rank datasets demonstrate the effectiveness of U-rank+ . Furthermore, our proposed framework has been deployed on two different scenarios of a mainstream App store, where the online A/B testing shows that U-rank+ achieves an average improvement of 19.2% on click-through rate and 20.8% improvement on conversion rate in recommendation scenario, and 5.12% on platform revenue in online advertising scenario over the production baselines.
Robust Federated Learning in Wireless Channels with Transmission Outage and Quantization Errors

Federated learning (FL) has been recognized as a viable distributed learning paradigm which trains a machine learning model collaboratively with massive mobile devices in the wireless edge while protecting user privacy. Although various communication schemes have been proposed to reduce the training latency in order to expedite the FL learning process, most of them have assumed that the wireless channels are ideal which provide reliable and lossless communication links between the server and mobile clients. Unfortunately, the performance of FL highly depends on the quality of wireless channels. In this paper, we consider FL in non-ideal wireless channels with transmission outage (TO) and quantization errors (QE). We analytically show that both TO and QE have significant impacts on the FL and can lead the FL algorithm to a biased solution if the clients have heterogeneous TO probabilities. In view of this, we propose a robust FL scheme which performs joint bandwidth and quantization bit allocation across clients to achieve robust FL performance in such non-ideal wireless channels. Extensive experimental results are presented to show the superior performance of the proposed scheme for a deep learning-based classification task with training latency constraints.
Measuring collaborative problem solving: research agenda and assessment instrument

In this paper, we present an agenda for the research directions we recommend in addressing the issues of realizing and evaluating communication in CPS instruments. We outline our ideas on potential ways to improve (1) generalizability in Human–Human assessment tools and ecological validity in Human–Agent ones; (2) flexible and convenient use of restricted communication options; and (3) an evaluation system of both Human–Human and Human-Agent instruments. Furthermore, in order to demonstrate possible routes for realizing some of our suggestions, we provide examples through an introduction of the features of our own CPS instrument. It is a Human–Human pre-version of a future Human–Agent instrument and a promising diagnostic and research tool in its own right, as well as the first example of transforming the so-called MicroDYN approach so that it is suitable for Human–Human collaboration. We offer new alternatives for communication in addition to pre-defined messages within the test, which are also suitable for automated coding. For example, participants can send or request visual information in addition to verbal messages. As regards evaluation as a hybrid solution, not only are the pre-defined messages proposed as indicators of different CPS skills, but so are a number of behavioural patterns.
Free Software at School and the Educational Process: Current State, Systematization of Typical Problems

The article presents the genesis of the problem of using free software in a Russian general education school, reveals a range of problems that educational institutions face when using (choosing) free software. The authors describe the general set of problems characteristic of the migration process to free software, and also consider in detail the relevance of each of them in relation to the field of education in the Russian Federation over the past 12 years, starting in 2008, when the first attempt was made to translate the educational process general education Russian school for free software. The article also provides possible solutions to some of the identified problems and examples of their implementation .
Modeling an Adaptive Resident-System Interaction for Cognitive Assistance in Ambient Assisted Living

In the last decade, advances in the field of ambient assisted living (AAL) have changed the way services can be provided in smart homes. New possibilities are now offered for addressing complex interaction problems between the technology and users with special needs. Within this context, this study addresses human-computer interactions for cognitive assistance to people with Traumatic Brain Injury (TBI). Interdisciplinary research combining computer science and occupational therapy was conducted to model the interaction between an AAL system (AALS) and a person with cognitive impairments due to TBI residing in an AAL environment. Cognitive assistance is modelled as an interactive exchange where an AALS spreads assistance cues that should induce appropriate behaviours from an assisted person as responses. After an assistance cue is delivered, the AALS should evaluate the user reaction and, stop the interaction if the intended reaction is observed or resume by adapting the assistance. To do so, evidence-based cognitive rehabilitation and speech acts are used to model the interaction content i.e., assistive cues and user feedback, while an ontology formalizes the semantics of this knowledge in a computer readable format. A behavioural model based on behaviour trees informed by the ontological model is then used to enable the cognitive assistant to plan the sequence of cues to be delivered adaptively depending on the circumstances, the assistance goal and the user's behaviour. To show that the proposed interaction model can help an AALS to provide adaptive assistance to people with cognitive impairments, it is exemplified on a cooking safety assistant designed for people with TBI.
MinerRepu: A Reputation Model for Miners in Blockchain Networks

Blockchain technology holds several promises for many application areas; however, it is not without its limitations. One of the most significant weaknesses of blockchain technology is its substantial energy consumption. Many researchers have proposed solutions to reduce the energy demands of this technology - such as the use of alternative consensus algorithms and the use of renewable energy. However, the use of alternative trust and reputation models to improve sustainability (by, for instance, selecting miners based on these trust or reputation values) has not been widely investigated. In this paper, we propose a reputation model that quantifies and compares the trustworthiness of miners based on their behaviours within a blockchain network. The model is evaluated analytically and compared to other trust and reputation models for miners. The evaluation shows that our model fulfils several desirable properties that should always be satisfied by reputation models, whereas other models do not always meet these requirements. In addition, we perform experimental evaluations to represent the performance of our model and its accuracy in detecting malicious miners. We also report the effectiveness of using the model in reducing the energy consumption of blockchain-based systems.
An Ontology-Based Expert System for Rice Disease Identification and Control Recommendation

A great deal of information related to rice cultivation has been published on the web. Conventionally, this information is studied by end-users to identify pests, and to prevent production losses from rice diseases. Despite its benefits, such information has not yet been encoded in a machine-processable form. This research closes the gap by modeling the knowledge-bases using ontologies and semantic technologies. Our modeled ontologies are externalized from existing reliable sources only, and offer axioms that describe abnormal appearances in rice diseases (and insects) and the corresponding controls. In addition, we developed an expert system called RiceMan, based on our ontologies, to support technical and non-technical users for diagnosing problems from observed abnormalities. We also introduce a composition procedure that aggregates users’ observation data with others for realizing spreadable diseases. This procedure, together with ontology reasoning, lies at the heart of our methodology. Finally, we evaluate our methodology practically with four groups of stakeholders in Thailand: senior agronomists, junior agronomists, agricultural students, and ontology specialists. Both ontologies and RiceMan are evaluated to verify their correctness, usefulness, and usability in various aspects. Our experimental results show that ontology reasoning is a promising approach for this domain problem.
Task-Oriented High-Order Context Graph Networks for Few-Shot Human-Object Interaction Recognition

Few-shot human-object interaction (FS-HOI) recognition aims at inferring new interactions between human actions and surrounding objects merely with a few available instances. It is beneficial to alleviate the long-tail and combinatorial explosion problems in human-object interaction (HOI). Nevertheless, the existing FS-HOI methods only focus on modeling the relationships between labeled samples and unlabeled samples in the Euclidean domain, which neglects the rich relational structures of the visual information among labeled samples and between human actions and objects. Accordingly, we tackle the few-shot HOI task in the non-Euclidean domain and present a graph-based model, namely, task-oriented high-order context graph network (THCG-Net). It contains a task attention module (TA-Module) and a high-order context graph module (HG-Module). In TA-Module, an attention mechanism is designed by utilizing task information to build a task-oriented space, in which the discriminative information for the current task (episode) is captured by embedding the visual features into the task-oriented space. The HG-Module is proposed to construct a task-level graph and takes the context information as high-order knowledge, which provides discriminative guidance for propagating visual information. It captures the discriminability among different categories while highlights the commonality of related categories adaptively, which effectively transfers knowledge to related categories. Extensive experimental results on two benchmark datasets, HICO-FS and TUHOI-FS, are provided. It demonstrates that our THCG-Net significantly outperforms the state-of-the-art approaches, which proves its impressive effectiveness in recognizing various human actions and surrounding objects in few-shot scenarios.
Improving Neural Models for the Retrieval of Relevant Passages to Geographical Queries

People often ask questions about places, and this is reflected on the frequency of geo-spatial queries made to information retrieval and question answering systems. Recent developments associated to these two types of systems rely on deep neural networks, specifically on methods for passage retrieval based on Transformer models, trained on large datasets like MS-MARCO. Despite significant progress in approaches for retrieving (or re-ranking) passages from a document collection according to their relevance to an input query, few studies have specifically looked at geo-spatial queries (i.e., where-questions directly concerning locations, and also questions covering other informational needs relating to places, their types, and affordances). In this work, we explore neural retrieval models in the context of geo-spatial queries, using a subset of MS-MARCO with questions and passages containing place-names. After characterizing the subset of MS-MARCO, we analyzed a re-ranking strategy based on geographic distance, which we argue to be useful for selecting hard negative examples for model training. Then, we fine-tuned neural ranking models, following bi-encoder or cross-encoder strategies, using the MS-MARCO subset together with a geographically-aware negative sampling procedure. Experimental results show that the fine-tuned models can indeed achieve a superior performance. We also describe a simple knowledge distillation procedure to further improve the computationally more efficient bi-encoder models, using the results of the cross-encoder.
Feature Selection Based on a Modified Adaptive Human Learning Optimization Algorithm

This paper proposes a new wrapper feature selection method based on the modified adaptive Human Learning Optimization (MAHLO) algorithm and Support Vector Machine (SVM). To achieve better results, the initialization and random learning strategies are modified in MAHLO to solve the feature selection problem more efficiently and the adaptive strategies are used to enhance the performance and relieve the effort of parameter setting. Besides, a two-stage evaluation function is adopted to eliminate the useless and redundant features, which is easier to operate in applications. The simulation results indicate that MAHLO can solve high-dimensional feature selection problems more efficiently, and the classification results on the UCI benchmark problems further demonstrate the efficiency and the advantage of the proposed MAHLO-based wrapper feature selection method.
Low-frequency fluorescence spectrum of a laser driven polar quantum emitter damped by squeezed vacuum with finite bandwidth

Abstract A two-level quantum emitter with broken inversion symmetry driven by external semiclassical monochromatic high-frequency electromagnetic (e.g., laser) field and damped by squeezed vacuum reservoir with finite bandwidth is presented. The squeezed vacuum source is assumed to be either degenerate parametric oscillator (DPO) or a non-degenerate parametric oscillator (NDPO). It is shown that the shape of low-frequency fluorescence spectrum of the emitter can be effectively alternated by controlling the degree of the squeezed vacuum source degeneration and phase of the squeezing.
Transfer Learning Capabilities of Untrained Neural Networks for MIMO CSI Recreation

Machine learning (ML) applications for wireless communications have gained momentum on the standardization discussions for 5G advanced and beyond. One of the biggest challenges for real world ML deployment is the need for labeled signals and big measurement campaigns. To overcome those problems, we propose the use of untrained neural networks (UNNs) for MIMO channel recreation/estimation and low over-head reporting. The UNNs learn the propagation environment by fitting a few channel measurements and we exploit their learned prior to provide higher channel estimation gains. Moreover, we present a UNN for simultaneous channel recreation for multiple users, or multiple user equipment (UE) positions, in which we have a trade-off between the estimated channel gain and the number of parameters. Our results show that transfer learning techniques are effective in accessing the learned prior on the environment structure as they provide higher channel gain for neighbouring users. Moreover, the proposed UNN channel state information (CSI) estimators are under-parameterized and can further enable low-overhead CSI reporting.
Integrating and Validating Maritime Transport Security Services: Initial results from the CS4EU demonstrator

Maritime transport is a characteristic example of a collaborative and complex cyber-physical environment, involving various stakeholders and actors, with different goals and requirements. Securing such a complex ecosystem is a challenging task and has recently attracted various research efforts in different areas including, threat management, system hardening, trust management and communication security. However, the integration and validation of such targeted maritime transport security services is a complex task that has its own challenges. In this paper we present the preliminary results of the maritime transport security services demonstrator, developed under the CyberSecurityForEurope (CS4EU) pilot project. We have set up a demonstrator to integrate, extend and validate four maritime-specific security services, covering risk and threat management, system hardening, trust management and secure communications. Our goal is to enhance the provisioning of these services and to identify possible research and implementation gaps.