Reviewerly/exHarmony huggingface dataset:
Users Meet Clarifying Questions: Toward a Better Understanding of User Interactions for Search Clarification

The use of clarifying questions (CQs) is a fairly new and useful technique to aid systems in recognizing the intent, context, and preferences behind user queries. Yet, understanding the extent of the effect of CQs on user behavior and the ability to identify relevant information remains relatively unexplored. In this work, we conduct a large user study to understand the interaction of users with CQs in various quality categories, and the effect of CQ quality on user search performance in terms of finding relevant information, search behavior, and user satisfaction. Analysis of implicit interaction data and explicit user feedback demonstrates that high-quality CQs improve user performance and satisfaction. By contrast, low- and mid-quality CQs are harmful, and thus allowing the users to complete their tasks without CQ support may be preferred in this case. We also observe that user engagement, and therefore the need for CQ support, is affected by several factors, such as search result quality or perceived task difficulty. The findings of this study can help researchers and system designers realize why, when, and how users interact with CQs, leading to a better understanding and design of search clarification systems.
Using LinkedIn Endorsements to Reinforce an Ontology and Machine Learning-Based Recommender System to Improve Professional Skills

Nowadays, social networks have become highly relevant in the professional field, in terms of the possibility of sharing profiles, skills and jobs. LinkedIn has become the social network par excellence, owing to its content in professional and training information and where there are also endorsements, which are validations of the skills of users that can be taken into account in the recruitment process, as well as in the recommender system. In order to determine how endorsements influence Lifelong Learning course recommendations for professional skills development and enhancement, a new version of our Lifelong Learning course recommendation system is proposed. The recommender system is based on ontology, which allows modelling the data of knowledge areas and job performance sectors to represent professional skills of users obtained from social networks. Machine learning techniques are applied to group entities in the ontology and make predictions of new data. The recommender system has a semantic core, content-based filtering, and heuristics to perform the formative suggestion. In order to validate the data model and test the recommender system, information was obtained from web-based lifelong learning courses and information was collected from LinkedIn professional profiles, incorporating the skills endorsements into the user profile. All possible settings of the system were tested. The best result was obtained in the setting based on the spatial clustering algorithm based on the density of noisy applications. An accuracy of 94% and 80% recall was obtained.
Assigning diagnosis codes using medication history

Diagnosis assignment is the process of assigning disease codes to patients. Automatic diagnosis assignment has the potential to validate code assignments, correct erroneous codes, and register completion. Previous methods build on text-based techniques utilizing medical notes but are inapplicable in the absence of these notes. We propose using patients' medication data to assign diagnosis codes. We present a proof-of-concept study using medical data from an American dataset (MIMIC-III) and Danish nationwide registers to train a machine-learning-based model that predicts an extensive collection of diagnosis codes for multiple levels of aggregation over a disease hierarchy. We further suggest a specialized loss function designed to utilize the innate hierarchical nature of the disease hierarchy. We evaluate the proposed method on a subset of 567 disease codes. Moreover, we investigate the technique's generalizability and transferability by (1) training and testing models on the same subsets of disease codes over the two medical datasets and (2) training models on the American dataset while evaluating them on the Danish dataset, respectively. Results demonstrate the proposed method can correctly assign diagnosis codes on multiple levels of aggregation from the disease hierarchy over the American dataset with recall 70.0% and precision 69.48% for top-10 assigned codes; thereby being comparable to text-based techniques. Furthermore, the specialized loss function performs consistently better than the non-hierarchical state-of-the-art version. Moreover, results suggest the proposed method is language and dataset-agnostic, with initial indications of transferability over subsets of disease codes.
Foldable Spaces: An Overt Redirection Approach for Natural Walking in Virtual Reality

An overview of overt redirection techniques derived from Foldable Spaces (to be seen in conjunction with Fig. 2).
Toward an Ideal Particle Swarm Optimizer for Multidimensional Functions

The Particle Swarm Optimization (PSO) method is a global optimization technique based on the gradual evolution of a population of solutions called particles. The method evolves the particles based on both the best position of each of them in the past and the best position of the whole. Due to its simplicity, the method has found application in many scientific areas, and for this reason, during the last few years, many modifications have been presented. This paper introduces three modifications to the method that aim to reduce the required number of function calls while maintaining the accuracy of the method in locating the global minimum. These modifications affect important components of the method, such as how fast the particles change or even how the method is terminated. The above modifications were tested on a number of known universal optimization problems from the relevant literature, and the results were compared with similar techniques.
Image super-resolution reconstruction algorithm based on significant network connection-collaborative migration structure

Although deep learning is widely used in the field of image super-resolution reconstruction, the number of network layers and the complexity of deep learning models continue to increase, and the speed at which the difficulty of model training increases has exceeded the speed of network performance improvement. This situation prevents deep learning frameworks from fully utilizing their generalization ability in image super-resolution reconstruction. Moreover, the existing image reconstruction methods have the problem of partial information loss. Therefore, this paper first proposes a structure that simulates the feature extraction function in the visual attention mechanism in the convolutional neural network. We call this a significant network connection. The feature information extracted by the network architecture is more significant, while other information is less significant. This setup has a reduced impact on the resulting image reconstruction effect. Then, a network architecture focusing on collaborative information migration is proposed. This architecture can obtain the implicit domain of the intermediate state of the image domain to be reconstructed, and it can make the two networks use the learned hidden domain during the reconstruction process. The dual networks trained in this way are more symmetrical. This approach can better maintain the common feature information of the reconstructed image and effectively solve the problem of partial feature information loss in the image. The experimental results show that the texture, artificial effects and noise of the reconstructed image obtained by the method proposed in this paper are significantly improved over those of the images produced by other mainstream methods. In addition, the method proposed in this paper exhibits a certain degree of improvement over other deep learning methods in terms of model training speed and feature information retention.
EUPHORIA: A neural multi-view approach to combine content and behavioral features in review spam detection

Nowadays, online reviews are the main source to customer opinions. They are especially important in the realm of e-commerce, where reviews regarding products and services influence the purchase decisions of customers, as well as the reputation of the commerce websites. Unfortunately, not all the online reviews are truthful and trustworthy. Therefore, it is crucial to develop machine learning techniques to detect review spam. This study describes EUPHORIA — a novel classification approach to distinguish spam from truthful reviews. This approach couples multi-view learning to deep learning, in order to gain accuracy by accounting for the variety of information possibly associated with both the reviews’ content and the reviewers’ behavior. Experiments carried out on two real review datasets from Yelp.com – Hotel and Restaurant – show that the use of multi-view learning can improve the performance of a deep learning classifier trained for review spam detection. In particular, the proposed approach achieves AUC-ROC equal to 0.813 and 0.708 in Hotel and Restaurant, respectively.
Deep Variational Embedding Representation on Neural Collaborative Filtering Recommender Systems

Visual representation of user and item relations is an important issue in recommender systems. This is a big data task that helps to understand the underlying structure of the information, and it can be used by company managers and technical staff. Current collaborative filtering machine learning models are designed to improve prediction accuracy, not to provide suitable visual representations of data. This paper proposes a deep learning model specifically designed to display the existing relations among users, items, and both users and items. Making use of representative datasets, we show that by setting small embedding sizes of users and items, the recommender system accuracy remains nearly unchanged; it opens the door to the use of bidimensional and three-dimensional representations of users and items. The proposed neural model incorporates variational embedding stages to “unpack” (extend) embedding representations, which facilitates identifying individual samples. It also replaces the join layers in current models with a Lambda Euclidean layer that better catches the space representation of samples. The results show numerical and visual improvements when the proposed model is used compared to the baselines. The proposed model can be used to explain recommendations and to represent demographic features (gender, age, etc.) of samples.
Comparative Analysis of High-Performance Computing Systems and Machine Learning in Enhancing Cyber Infrastructure: A Multiple Regression Analysis Approach

The invention of advanced digital technologies like machine learning, big data and other tools has disrupting the computing landscape in the current decades. The data corporations like Amazon, Google, Microsoft and others are focusing in tapping new opportunities through better information technology infrastructure, support in delivering higher performance for many companies, individuals and government. The traditional method of sing computing technologies to enhance coyer infrastructure is inadequate with the current development of Industry 5.0, hence the companies are implementing radical approach to understand the current and future requirement of the business and create better cyber infrastructure using high performance computing systems and machine learning. The continuous quest in understanding the overall pattern which shift through high quality data set has disrupted technological platform, the combination of machine learning approaches, robotics, high performance computing systems has boosted the overall performance in information technologies and companies are adopting these novel measures to use high computing methods, deep learning techniques and others for enhancing the cyber infrastructure. Since Machine learning (ML) and High-performance Computing (HPC) are continuously evolving, companies can now able to process the data with higher volume, understand the criticality of business operation and reduce the time to take quick action for sustainable development. Hence this study&#x0027;s is involved in understanding the criticality of implementing ML and HPC measures for supporting the cyber infrastructure requirements of organization in an effective manner.
Characterisation of Intelligent Autonomous Agents Inspired by Biological Theory in Cognitive Environment

The paper investigates a naturally motivated meaning of wise shrewd self-ruling specialists of humans. Knowledge is identified with whether the conduct of a framework adds to its self-upkeep. Conduct turns out to be clearer (or adapts to more biological issue factors) when it is able to make and utilize portrayals. The thought of portrayal ought not to be confined to formal articulations with neuro hypothetical semantics. The element at different degrees of canny frameworks assumes a fundamental part in shaping portrayals. The paper investigates an organically roused meaning of shrewd self-ruling specialists of humans. Insight is identified with whether the conduct of a framework adds to its self-upkeep. Conduct turns out to be keener when it is proficient to make and utilize portrayals. The idea of portrayal also focused on formal articulations with sentimental hypothetical semantics.
The reliability of photovoltaic power generation scheduling in seventeen European countries

As photovoltaic (PV) electricity generation is becoming ubiquitous, the ability to forecast solar power becomes crucial for such aspects as economical dispatch, optimal unit commitment and the stability of the grid. Our research investigated the discrepancies between day-ahead and intraday country-specific PV power generation forecasts and the real generation data in the member states of the European Network of Transmission System Operators for Electricity for the year 2021. The goal of the research was, firstly, to offer an insight into the schedule accuracy achieved in the examined countries, secondly, to present the potentials for improvement in each of them (plausible even by the practical application of the methods available in 2021), and thirdly, to show the amount of regulation need resulting from PV electricity production forecasting error, the knowledge of which can help with the estimation of the size of the necessary storage capacities. This is the first work that analyzes the schedule accuracy from a practical perspective by examining the accuracy of the day-ahead and intraday PV forecasts of seventeen European countries, following the latest recommendations for verification. The results found significant differences between the forecast accuracy of different countries, which indicates a huge potential for imbalance reduction in most countries by improving their own algorithms or creating a uniform European PV power generation forecasting system. Overall, the intraday forecasts are less skillful than the day-ahead forecasts in all but one of the countries, which highlights the significance of further application-related studies in the intraday horizon. The aggregate regulatory need resulting from the discrepancies between the forecasted and actually produced amounts of energy of all studied countries is 4 TWh in the positive and 3 TWh in the negative direction, which calls for a large amount of energy storage capacity to provide carbon-free balancing power and reduce the regulation challenges of the increasing PV penetration.
A Smart Noise Jamming Suppression Method Based on Atomic Dictionary Parameter Optimization Decomposition

Smart noise jamming is a new jamming style against radar, which plays an important role in modern radar electronic warfare. In order to solve the complex problem of convolutional smart noise jamming suppression with different time delay and jamming frequency modulation, a novel jamming suppression method based on the optimized atomic dictionary decomposition by estimating significant jamming parameters is proposed in this paper. First, the dual channel jamming signal information is obtained by designing twinning waveforms, then the independent estimation of time delay and jamming frequency parameters are established based on the spatial position sequences of the false targets. After that, the atomic dictionary is optimized by utilizing the obtained jamming parameters to achieve more effective decomposition. The performance of the new jamming suppression method is analyzed and verified by experimental simulations. The results show that this method can feasibly and practicably suppress the smart noise jamming with a deceptive effect, and the atomic decomposition efficiency is highly improved.
Complete coverage problem of multiple robots with different velocities

Complete coverage, which is integral to many robotic applications, aims to cover an area as quickly as possible. In such tasks, employing multiple robots can reduce the overall coverage time by appropriate task allocation. Several multi-robot coverage approaches divide the environment into balanced subareas and minimize the maximum subarea of all robots. However, balanced coverage in many situations, such as in the cases of robots with different velocities and heterogeneous multi-robot systems, may have inefficient results. This study addresses the unbalanced complete coverage problem of multiple robots with different velocities for a known environment. First, we propose a novel credit model to transform the unbalanced coverage problem into a set of single-objective optimization problems, which can find a combinational optimal solution by optimizing each separate objective function of the single-objective optimization problem to alleviate the computational complexity. Then, we propose a credit-based algorithm composed of a cyclic region growth algorithm and a region fine-tuning algorithm. The cyclic region growth algorithm finds an initial solution to the single-objective optimization problems set by a regional growth strategy with multiple restricts, whereas the region fine-tuning algorithm reallocates the tasks of the partitions with too many tasks to the partitions with too few tasks by constructing a search tree, thereby converging the initial solution to the optimal solution. Simulation results indicate that compared with conventional multi-robot complete coverage problem algorithms, the credit-based algorithm can obtain the optimal solution with the increased number of robots and enlarged size of the mission environment.
Multigraph classification using learnable integration network with application to gender fingerprinting

Multigraphs with heterogeneous views present one of the most challenging obstacles to classification tasks due to their complexity. Several works based on feature selection have been recently proposed to disentangle the problem of multigraph heterogeneity. However, such techniques have major drawbacks. First, the bulk of such works lies in the vectorization and the flattening operations, failing to preserve and exploit the rich topological properties of the multigraph. Second, they learn the classification process in a dichotomized manner where the cascaded learning steps are pieced in together independently. Hence, such architectures are inherently agnostic to the cumulative estimation error from step to step. To overcome these drawbacks, we introduce MICNet (multigraph integration and classifier network), the first end-to-end graph neural network based model for multigraph classification. First, we learn a single-view graph representation of a heterogeneous multigraph using a GNN based integration model. The integration process in our model helps tease apart the heterogeneity across the different views of the multigraph by generating a subject-specific graph template while preserving its geometrical and topological properties conserving the node-wise information while reducing the size of the graph (i.e., number of views). Second, we classify each integrated template using a geometric deep learning block which enables us to grasp the salient graph features. We train, in end-to-end fashion, these two blocks using a single objective function to optimize the classification performance. We evaluate our MICNet in gender classification using brain multigraphs derived from different cortical measures. We demonstrate that our MICNet significantly outperformed its variants thereby showing its great potential in multigraph classification.
Accuracy and processing speed trade-offs in classical and quantum SVM classifier exploiting PRISMA hyperspectral imagery

Quantum machine learning (QML) focuses on machine learning models developed explicitly for quantum computers. Availability of the first quantum processor led to further research, particularly exploring possible practical applications of QML algorithms in the remote sensing field. The demand for extensive field data for remote sensing applications has started creating bottlenecks for classical machine learning algorithms. QML is becoming a potential solution to tackle big data problems as it can learn from fewer data. This paper presents a QML model based on a quantum support vector machine (QSVM) to classify Holm Oak trees using PRISMA hyperspectral Imagery. Implementation of quantum models was carried on a quantum simulator and a real-time superconducting quantum processor of IBM. The performance of the QML model is validated in terms of dataset size, overall accuracy, number of qubits, training and predicting speed. Results were indicative that (i) QSVM offered 5% higher accuracy than classical SVM (CSVM) with 50 samples and ≥12 qubits/feature dimensions whereas with 20 samples at 16 Qubits/feature dimension, (ii) training time for QSVM at maximum accuracy was 284 s with 50 samples and with 20 samples was 53.68 s and (iii) predicting time for 400 pixels using the QSVM model trained with 50 samples dataset was 5243 s whereas with 20 samples dataset was 2845 s. Results were indicative that QML offers better accuracy but lack training and predicting speed for hyperspectral data. Another observation is that predicting speed of QSVM depends on the number of samples used to train the model.
Ebublio: Edge Assisted Multi-user 360-Degree Video Streaming

Compared to traditional videos, streaming 360&#x00B0; videos is more difficult. We propose Ebublio, a novel intelligent edge caching framework consisting of a collaborative FoV prediction (CFP) module and a long-term tile caching optimization (LTO) module. The former integrates the features of video content, user trajectory, and other users&#x0027; records for combined prediction. The latter employs the Lyapunov framework and a subgradient optimization toward the optimal caching replacement policy. Our trace-driven evaluation further demonstrates the superiority of our framework, with about 42&#x0025; improvement in FoV prediction, and 36&#x0025; improvement in QoE under similar traffic consumption.