Reviewerly/exHarmony huggingface dataset:
Real Estate Recommendation Approach for Solving the Item Cold-Start Problem

The item cold-start problem occurs when a recommendation system cannot recommend new items owing to record deficiencies and new listing omissions. When searching for real estate, users can register a concurrent interest in recent and prior projects. Thus, an approach to recommend cold-start and warm-start items simultaneously must be determined. Furthermore, unrequired membership and stop-by behavior cause real estate recommendations to have many cold-start and new users. This characteristic encourages the use of a content-based approach and a session-based recommendation system. Herein, we propose a real estate recommendation approach for solving the item cold-start problem with acceptable warm-start item recommendations in the many-cold-start-users scenario. We modify a session-based recommendation system and employ existing mechanisms to efficiently deal with sequential and context information for the next-interacted item's encoded attribute prediction. Subsequently, we use the nearest-neighbors approach using weighted cosine similarity to determine conforming candidates. We use Recall@K and MRR@K with the top-n recommendation to evaluate warm-start and cold-start item recommendations among different applied mechanisms and against the baselines. The results demonstrate the effectiveness of efficiently integrating the information and the difficulty in performing well in warm-start and cold-start item recommendations simultaneously. Our proposed approach illustrates the capability of solving the item cold-start problem while yielding promising results in both recommendations although neither result is the best. We believe that our approach provides a suitable compromise between both recommendations and that it will benefit recommendation tasks focusing on both recommendations.
Social competence of student youth

The relevance of the scientific investigation involves understanding the importance of the social competence concept in the students’ environment in terms of distance learning, which has arisen based on quarantine restrictions. The purpose of the scientific investigation is to identify the formation level of social competence of student youth in the educational environment within the conditions of pandemic in the process of distance learning. Methods of pedagogical research (remote interaction), sociological (survey in Google-forms) and statistical methods have been used to form a methodological base. It has been revealed that in the conditions of distance learning at HEI during a pandemic, social competence undergoes significant changes, considering that the results of a survey of students are significant, indicating a decrease in the level of socialization. The practical significance of the results of the scientific investigation is aimed at improving the social development of the individual, outlining the main transitions between levels of socialization in terms of quarantine restrictions and distance learning.
Research on multi decision making security performance of IoT identity resolution server based on AHP

<abstract> The application scenarios of IoT (Internet of Things) are complex and diverse. Failure of security defense in any part of IoT can lead to huge information leakage and incalculable losses. IoT security issues are affecting and limiting its application prospects, and have become one of the hotspots in the field of IoT. Identity resolution security of IoT has become a core issue in solving the security problem of IoT. The aim of this paper is to apply AHP, a well-known decision making method, to IoT identity resolution security. Selecting 6 indicators, several pairwise comparison matrices are constructed based on scores from experts and lab researchers. The AHP method is used to calculate malicious resolution value as a quantitative basis for judging the security performance of each resolution server. An experimental case is used to verify the validity and correctness of the AHP-based IoT identity resolution security evaluation model. </abstract>
VISITRON: Visual Semantics-Aligned Interactively Trained Object-Navigator

Interactive robots navigating photo-realistic environments need to be trained to effectively leverage and handle the dynamic nature of dialogue in addition to the challenges underlying vision-and-language navigation (VLN).In this paper, we present VISITRON, a multi-modal Transformer-based navigator better suited to the interactive regime inherent to Cooperative Vision-and-Dialog Navigation (CVDN).VIS-ITRON is trained to: i) identify and associate object-level concepts and semantics between the environment and dialogue history, ii) identify when to interact vs. navigate via imitation learning of a binary classification head.We perform extensive pre-training and fine-tuning ablations with VISITRON to gain empirical insights and improve performance on CVDN.VISITRON's ability to identify when to interact leads to a natural generalization of the gameplay mode introduced by Roman et al. ( 2020) for enabling the use of such models in different environments.VISITRON is competitive with models on the static CVDN leaderboard and attains state-of-the-art performance on the Success weighted by Path Length (SPL) metric.
Translation quality assessment: a brief survey on manual and automatic methods

To facilitate effective translation modeling and translation studies, one of the crucial questions to address is how to assess translation quality. From the perspectives of accuracy, reliability, repeatability and cost, translation quality assessment (TQA) itself is a rich and challenging task. In this work, we present a high-level and concise survey of TQA methods, including both manual judgement criteria and automated evaluation metrics, which we classify into further detailed sub-categories. We hope that this work will be an asset for both translation model researchers and quality assessment researchers. In addition, we hope that it will enable practitioners to quickly develop a better understanding of the conventional TQA field, and to find corresponding closely relevant evaluation solutions for their own needs. This work may also serve inspire further development of quality assessment and evaluation methodologies for other natural language processing (NLP) tasks in addition to machine translation (MT), such as automatic text summarization (ATS), natural language understanding (NLU) and natural language generation (NLG).
Functional Outlier Detection by Means of h-Mode Depth and Dynamic Time Warping

Finding outliers in functional infinite-dimensional vector spaces is widely present in the industry for data that may originate from physical measurements or numerical simulations. An automatic and unsupervised process of outlier identification can help ensure the quality of a dataset (trimming), validate the results of industrial simulation codes, or detect specific phenomena or anomalies. This paper focuses on data originating from expensive simulation codes to take into account the realistic case where only a limited quantity of information about the studied process is available. A detection methodology based on different features, such as h-mode depth or the dynamic time warping, is proposed to evaluate the outlyingness both in the magnitude and shape senses. Theoretical examples are used to identify pertinent feature combinations and showcase the quality of the detection method with respect to state-of-the-art methodologies of detection. Finally, we show the practical interest of the method in an industrial context thanks to a nuclear thermal-hydraulic use case and how it can serve as a tool to perform sensitivity analysis on functional data.
Federated Continual Learning with Weighted Inter-client Transfer

There has been a surge of interest in continual learning and federated learning, both of which are important in deep neural networks in real-world scenarios. Yet little research has been done regarding the scenario where each client learns on a sequence of tasks from a private local data stream. This problem of federated continual learning poses new challenges to continual learning, such as utilizing knowledge from other clients, while preventing interference from irrelevant knowledge. To resolve these issues, we propose a novel federated continual learning framework, Federated Weighted Inter-client Transfer (FedWeIT), which decomposes the network weights into global federated parameters and sparse task-specific parameters, and each client receives selective knowledge from other clients by taking a weighted combination of their task-specific parameters.FedWeITminimizes interference between incompatible tasks, and also allows positive knowledge transfer across clients during learning. We validate ourFedWeITagainst existing federated learning and continual learning methods under varying degrees of task similarity across clients, and our model significantly outperforms them with a large reduction in the communication cost.
Non-Negative Bregman Divergence Minimization for Deep Direct Density Ratio Estimation

The estimation of the ratio of two probability densities has garnered attention as the density ratio is useful in various machine learning tasks, such as anomaly detection and domain adaptation. To estimate the density ratio, methods collectively known as direct density ratio estimation (DRE) have been explored. These methods are based on the minimization of the Bregman (BR) divergence between a density ratio model and the true density ratio. However, existing direct DRE suffers from serious overfitting when using flexible models such as neural networks. In this paper, we introduce a non-negative correction for empirical risk using only the prior knowledge of the upper bound of the density ratio. This correction makes a DRE method more robust against overfitting and enables the use of flexible models. In the theoretical analysis, we discuss the consistency of the empirical risk. In our experiments, the proposed estimators show favorable performance in inlier-based outlier detection and covariate shift adaptation.
A Machine Learning Approach to Detect Image Blurring

The advent of smart mobile phones and cameras has unprecedently increased the number of photographs captured by people. Despite the hype, image blur remains as one of the distortions or quality degradation parameters, which can be raised by various parameters during the image capturing to processing phase. In the proposed paper, a machine learning-based detection technique is suggested to detect the image blur region and classify them into two categories. Image pre-processing has been applied to improve the textual information. Further, a pre-trained convolutional neural network model is used to classify the image blur. A fully automatic approach for blur detection as well classification is suggested in this paper. Three different image datasets are used to check the performance of the proposed method. Various parameters are estimated to demonstrate the results.
Classification with Rejection Based on Cost-sensitive Classification

The goal of classification with rejection is to avoid risky misclassification in error-critical applications such as medical diagnosis and product inspection. In this paper, based on the relationship between classification with rejection and cost-sensitive classification, we propose a novel method of classification with rejection by learning an ensemble of cost-sensitive classifiers, which satisfies all the following properties: (i) it can avoid estimating class-posterior probabilities, resulting in improved classification accuracy, (ii) it allows a flexible choice of losses including non-convex ones, (iii) it does not require complicated modifications when using different losses, (iv) it is applicable to both binary and multiclass cases, and (v) it is theoretically justifiable for any classification-calibrated loss. Experimental results demonstrate the usefulness of our proposed approach in clean-labeled, noisy-labeled, and positive-unlabeled classification.
Fast Image Encryption Framework for Medical Images

The role of Information and Communication Technology (ICT) in healthcare has increased tremendously. The era of Artificial Intelligence, the Internet of Health Things (IoHT), and networked technologies have revolutionized the paradigm of healthcare. In the healthcare domain, digital images are a means by which sensitive information about the patients can be transferred wirelessly. Maintaining the confidentiality of these images requires designing a cryptosystem that is fast and sturdy. In this work, we present a fast chaos-based encryption scheme for medical images. The scheme employs a Logistic map, Chebyshev map, and Piecewise Linear Chaotic Map (PWLCM) for confusion and diffusion of the medical image. Initially, the image is circularly shifted, and subsequently, bit plane slicing is performed. The Most Significant Bit (MSB) plane is replaced with a plane formed by the XOR operation between the MSB and 7 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">th</sup> ISB plane. The resultant image is scrambled using the Pseudo Random Number (PRN) generated by the Logistic map. The scheme is adaptive and calculates the image parameter like sum or mean to determine the initial condition for the PWLCM chaotic map. The PWLCM is used to generate a key image that is XORed with the scrambled image. Finally, a Chebyshev map is iterated and a PRN sequence is generated to permute the image pixels and obtain the final encrypted image. The cryptosystem has been evaluated for multiple types and sizes of medical images and on natural images as well. The average encryption time for an image of size 256×256 is 0.3287 seconds. The entropy is more than 99.70% and the correlation coefficient is close to zero for all the images. Simulation results validate that the cryptosystem has comparable performance with state-of-the-art schemes.
WITHDRAWN: Ground water: A study of its importance, its sources, and the causes of its pollution

The study of groundwater, its sources and the causes of its pollution has received great interest from researchers and those interested in studying the environment and pollution, given its great importance in several areas. It is the life under the surface of the earth formed by the gathering of rainwater, rivers, etc., and it has many uses, especially in desert areas where surface water and rain are less. The research addressed several important topics, including defining the concept of groundwater and its movement in the ground, its importance as one of the most important sources of life, and the research referred to the components of groundwater from salts, and the most important sources such as rainwater, geological water, crystallization water, and others. The research discussed the most prominent advantages of using groundwater, the reasons for its contamination with chemicals resulting from the use of pesticides and fertilizers, sewage water, and various types of waste, including industrial and agricultural, liquid and solid, as well as its pollution with nitrates. The research shed light on the most important methods of treating groundwater contaminated with nitrates, and the most important means to reduce its pollution.
A Probabilistic Framework for Modeling the Variability Across Federated Datasets

We propose a novel federated learning paradigm to model data variability among heterogeneous clients in multi-centric studies. Our method is expressed through a hierarchical Bayesian latent variable model, where client-specific parameters are assumed to be realization from a global distribution at the master level, which is in turn estimated to account for data bias and variability across clients. We show that our framework can be effectively optimized through expectation maximization over latent master’s distribution and clients’ parameters. We tested our method on the analysis of multi-modal medical imaging data and clinical scores from distributed clinical datasets of patients affected by Alzheimer’s disease. We demonstrate that our method is robust when data is distributed either in iid and non-iid manners: it allows to quantify the variability of data, views and centers, while guaranteeing high-quality data reconstruction as compared to the state-of-the-art autoencoding models and federated learning schemes.
Lesson Study in initial training: an interdisciplinary academic experience. A case study in Spain

Purpose This paper focuses on analysis of the incorporation of a Lesson Study cycle within the university training proposal, as a teaching strategy in initial training in the Infant Education Degree. Design/methodology/approach Qualitative research was conducted through a case study methodology. Findings Proposing training strategies based on this methodology encourages both the reconstruction of future teachers' practical knowledge and also adequate professional development from initial training. Research limitations/implications The main constraint in the study was the cost-benefit ratio, since this experience has required significant practical and emotional dedication by the people involved, while the expected results have only been partially evident in the students. This would seem to imply that the strategy requires continuity over time in order for future education professionals to take it fully on board. Practical implications More continuity in such experiences would be required in order to fully analyse their actual value. To this end, the experiences need to be more closely related to the university curriculum, and there must be greater coordination between the subjects in order to ensure a holistic approach to LS. Originality/value This article sets out an LS experience as a collaborative action-research strategy that promotes the reconstruction of students' practical knowledge and their professional development in initial training.
Automatic Generation of Informative Video Thumbnail

Thumbnail plays a vital role in boosting the discovery and viewership of online video. Although it can be easily obtained by simply selecting a certain image from the video sequence, most of popular videos in today's video sharing platforms come with elaborately designed custom thumbnails to better showcase the highlight within the video. Unfortunately, both the selection of salient content from thousands of frames and the creation of an eye-catching thumbnail are very time-consuming and require highly specialized skills. In this paper, we present a fully automatic approach for generating informative and eye-catching video thumbnails. The proposed pipeline first splits up a given video into shot units and then selects a set of keyframes using keyframe representativeness and quality assessment metric. After performing salient region detection, a synthetic thumbnail can be obtained by embedding as many as salient regions of keyframes into the carrier image. Experimental results demonstrate the feasibility and effectiveness of our method in informative thumbnail creation.
DELIA: Distributed Efficient Log Integrity Audit Based on Hierarchal Multi-Party State Channel

Audit log contains the trace of different activities in computing systems, which makes it critical for security management, censorship, and forensics. However, experienced attackers may delete or modify the audit log after their attacks, which makes the audit log unavailable in attack investigation. In this article, we focus on the log integrity audit in the same domain, in which a number of servers update audit logs for a single or several organizations as an alliance. We propose a distributed efficient log integrity audit framework, called DELIA, which employs the distributed ledger technique to protect audit information, and utilizes the idea of state channel to improve the throughput of distributed ledger. To generate stable state from the rapidly-updated logs in the domain, we propose a log state generation scheme, which not only generates state suitable for audit logs, but also enables mutual supervision within the domain. To overcome the high latency in existing state channel schemes, we propose a hierarchal multi-party state channel scheme, which makes the latency in our framework independent of the number of servers in the domain. We implement DELIA on Ethereum and evaluate its performance. The results show that our framework is efficient and secure in practice.
A Risk-Based IoT Decision-Making Framework Based on Literature Review with Human Activity Recognition Case Studies

The Internet of Things (IoT) is a key and growing technology for many critical real-life applications, where it can be used to improve decision making. The existence of several sources of uncertainty in the IoT infrastructure, however, can lead decision makers into taking inappropriate actions. The present work focuses on proposing a risk-based IoT decision-making framework in order to effectively manage uncertainties in addition to integrating domain knowledge in the decision-making process. A structured literature review of the risks and sources of uncertainty in IoT decision-making systems is the basis for the development of the framework and Human Activity Recognition (HAR) case studies. More specifically, as one of the main targeted challenges, the potential sources of uncertainties in an IoT framework, at different levels of abstraction, are firstly reviewed and then summarized. The modules included in the framework are detailed, with the main focus given to a novel risk-based analytics module, where an ensemble-based data analytic approach, called Calibrated Random Forest (CRF), is proposed to extract useful information while quantifying and managing the uncertainty associated with predictions, by using confidence scores. Its output is subsequently integrated with domain knowledge-based action rules to perform decision making in a cost-sensitive and rational manner. The proposed CRF method is firstly evaluated and demonstrated on a HAR scenario in a Smart Home environment in case study I and is further evaluated and illustrated with a remote health monitoring scenario for a diabetes use case in case study II. The experimental results indicate that using the framework's raw sensor data can be converted into meaningful actions despite several sources of uncertainty. The comparison of the proposed framework to existing approaches highlights the key metrics that make decision making more rational and transparent.
Synchronous Interactive Decoding for Multilingual Neural Machine Translation

To simultaneously translate a source language into multiple different target languages is one of the most common scenarios of multilingual translation. However, existing methods cannot make full use of translation model information during decoding, such as intra-lingual and inter-lingual future information, and therefore may suffer from some issues like the unbalanced outputs. In this paper, we present a new approach for synchronous interactive multilingual neural machine translation (SimNMT), which predicts each target language output simultaneously and interactively using historical and future information of all target languages. Specifically, we first propose a synchronous cross-interactive decoder in which generation of each target output does not only depend on its generated sequences, but also relies on its future information, as well as history and future contexts of other target languages. Then, we present a new interactive multilingual beam search algorithm that enables synchronous interactive decoding of all target languages in a single model. We take two target languages as an example to illustrate and evaluate the proposed SimNMT model on IWSLT datasets. The experimental results demonstrate that our method achieves significant improvements over several advanced NMT and MNMT models.