Reviewerly/exHarmony huggingface dataset:
Multi-Step Critiquing User Interface for Recommender Systems

Recommendations with personalized explanations have been shown to increase user trust and perceived quality and help users make better decisions. Moreover, such explanations allow users to provide feedback by critiquing them. Several algorithms for recommender systems with multi-step critiquing have therefore been developed. However, providing a user-friendly interface based on personalized explanations and critiquing has not been addressed in the last decade. In this paper, we introduce four different web interfaces (available under https://lia.epfl.ch/critiquing/) helping users making decisions and finding their ideal item. We have chosen the hotel recommendation domain as a use case even though our approach is trivially adaptable for other domains. Moreover, our system is model-agnostic (for both recommender systems and critiquing models) allowing a great flexibility and further extensions. Our interfaces are above all a useful tool to help research in recommendation with critiquing. They allow to test such systems on a real use case and also to highlight some limitations of these approaches to find solutions to overcome them.
Sequence-Aware Graph Neural Network for Session-based Recommendation

Session-based recommendation (SBR) nowadays plays a vital role in many online services, aiming to predict users' next action based on anonymous sessions. Recent research of GNNs-based methods models a session as a graph via investigating complex transitions of items in a session. However, these methods do not consider sequential information of the session when aggregating item embeddings to form a session-level embedding. Most methods consider not all previous but the last one item as the interest of a user, which restricts the performance of the model. To address this problem, we propose a model named Sequence-Aware Graph Neural Network (SA-GNN) for session-based recommendation. In SA-GNN, we design a sequence-aware attention to adaptively weigh the previous items to generate a session-level embedding, which greatly improves the representation ability of the model. Also, to improve the representation ability of the item embeddings, SA-GNN harnesses the power of self-attention within the GNN layer to capture both transitions between adjacent items and long-range dependencies among all items in a session. In empirical evaluations on three public recommendation datasets, our method consistently outperforms an extensive of state-of-the-art session-based recommendation methods.
Context-Based Geodesic Dissimilarity Measure for Clustering Categorical Data

Measuring the dissimilarity between two observations is the basis of many data mining and machine learning algorithms, and its effectiveness has a significant impact on learning outcomes. The dissimilarity or distance computation has been a manageable problem for continuous data because many numerical operations can be successfully applied. However, unlike continuous data, defining a dissimilarity between pairs of observations with categorical variables is not straightforward. This study proposes a new method to measure the dissimilarity between two categorical observations, called a context-based geodesic dissimilarity measure, for the categorical data clustering problem. The proposed method considers the relationships between categorical variables and discovers the implicit topological structures in categorical data. In other words, it can effectively reflect the nonlinear patterns of arbitrarily shaped categorical data clusters. Our experimental results confirm that the proposed measure that considers both nonlinear data patterns and relationships among the categorical variables yields better clustering performance than other distance measures.
Functionalities and Approaches of Multi-cloud Environment

Cloud computing is a paradigm that envisions fast access to any resources from anywhere at any time with the most significant advantages of enabling an intuitive environment that offers plethora of services to mankind. The exponential increase in technological advances has been an advantage for the growth of cloud computing. The advancement in technology has enabled the shift in organizations from using a single cloud toward multi-cloud strategy. Multi-cloud uses multiple services from various cloud vendors which has been advantageous in several ways. Multi-cloud strategy has been designed to enable a hybrid mode for the organizations with ample security and savings in cost. This chapter gives an overview of multi-cloud computing and the security issues with respect to cloud computing.
Generating Layered Enterprise Architectures with Conceptual Structures

Enterprise Architecture (EA) uses metamodels to document and align organisations' business, information, and technology domains. This structure then enables these domains to work in harmony. Layered Enterprise Architecture Development (LEAD) builds upon EA by introducing layered metaobjects connected by semantic relations that make up LEAD's layered metamodel. Previously, an algorithm was developed to elicit active semantic relations to achieve a view highlighting the metaobject dependencies. Subsequently, CG-FCA (Conceptual Graph and Formal Concept Analysis) and a LEAD case study were used to develop an enhanced algorithm that also generates the LEAD layers. The resulting layered FCA lattice shows a way to discover the hitherto hidden insights in LEAD, including the relationship between business and information technology.
A Multipath Routing Approach for Tile-based Virtual Reality Video Streaming Based on SDN

With the increasing demand of virtual reality (VR) video applications, it is necessary to adopt corresponding strategies to deal with the challenges they bring to the network. Multipath routing is proposed to address the VR video bandwidth problem by splitting a large flow into multiple subflows and routing them separately. In addition, Software Defined Networking (SDN) is used to manage these subflows so that they are assigned to the appropriate paths. This paper presents a MCTS-based VR video multipath transmission approach (MVRMPT), which allocates better paths to the VR video tiles that have greater impacts on the user’s Quality of Experience (QoE). More specifically, the Monte Carlo tree search (MCTS) algorithm is modified to find multiple disjoint paths with the minimum delay between node pairs. Then the paths are sorted by the predicted QoE. Finally, the VR video is spatially divided into different zones, and these zones are assigned to different paths according to their impacts on the user’s QoE. The proposed algorithm is implemented in the SDN controller, and the evaluation results show that our method achieves higher QoE and network throughput.
An Augmented Reality-Based Solution for Monitoring Patients Vitals in Surgical Procedures

In this work, an augmented reality (AR) system is proposed to monitor in real time the patient’s vital parameters during surgical procedures. This system is characterised metrologically in terms of transmission error rates and latency. These specifications are relevant for ensuring real-time response. The proposed system automatically collects data from the equipment in the operating room (OR), and displays them in AR. The system was designed, implemented and validated through experimental tests carried out using a set of Epson Moverio BT-350 AR glasses to monitor the output of a respiratory ventilator and a patient monitor in the OR.
Integrated Actor-Critic for Deep Reinforcement Learning

We propose a new deep deterministic actor-critic algorithm with an integrated network architecture and an integrated objective function. We address stabilization of the learning procedure via a novel adaptive objective that roughly ensures keeping the actor unchanged while the critic makes large errors. We reduce the number of network parameters and propose an improved exploration strategy over bounded action spaces. Moreover, we incorporate some recent advances in deep learning to our algorithm. Experiments illustrate that our algorithm speeds up the learning process and reduces the sample complexity considerably over the state-of-the-art algorithms including TD3, SAC, PPO, and A2C in continuous control tasks.
Convolutional Recurrent neural network with attention mechanism based improved skip-gram algorithm for text sentiment classification

With the development of technology, increasing number of online data saved by texts has came into our life. Traditional machine learning classifier, such as SVM and naive Bayes, works well for many text classification tasks. However, these classifiers have sparsity problems and do not have long-term dependencies due to lack of text in short text and the limitations of convolutional and pooling layers. In this paper, we has designed a convolutional recurrent neural network based on improved skip-gram algorithm. We use L2 regularization method to adversarial training for skip-gram algorithm. It can not only improve the robustness and generalization ability of this model but also enhance the performance ability in text sentiment classification tasks. We used Bidirectional Long Short-Term Memory network that includes attention mechanisms to get whole text information and reduce the effects of non-keywords. Finally, we realize text sentiment classification by CNNS. This model and algorithm were proved to be more effective and accuracy than other classifiers in the IMDB dataset.
Grab the Frog: Comparing Intuitive Use and User Experience of a Smartphone-only, AR-only, and Tangible AR Learning Environment

The integration of Augmented Reality (AR) in teaching concepts allows for the visualization of complex learning contents and can simultaneously enhance the learning motivation. By providing Tangible Augmented Reality (TAR), an AR learning environment receives a haptic aspect and allows for a direct manipulation of augmented learning materials. However, manipulating tangible objects while using handheld AR might reduce the intuitive use and hence user experience. Users need to simultaneously control the application and manipulate the tangible object. Therefore, we compare the differences in intuitive use and user experience evoked by varied technologies of knowledge presentation in an educational context. In particular, we compare a TAR learning environment targeting the learning of the anatomy of vertebrates to its smartphone-only and AR-only versions. The three versions of the learning environment only differ in their method of knowledge presentation. The three versions show a similar perceived intuitive use. The TAR version, however, yielded a significantly higher attractiveness and stimulation than AR-only and smartphone-only. This suggests a positive effect of TAR learning environments on the overall learning experience.
Can Humans and Machines Classify Photographs as Depicting Negation?

How logical concepts such as negation can be visually represented is of central importance in the study of diagrammatic reasoning. To explore various ways in which negation can be visually represented, this study focuses on photographs as instances of purely visual representations. We use real-world photographic image data and study how well humans can classify those images as depicting negation. We also compare the human performance with a state-of-the-art machine (deep) learning model on this classification task. The present paper gives some preliminary results on our data-driven analyses.
Bolstering Adversarial Robustness with Latent Disparity Regularization

Recent research has revealed that neural networks and other machine learning models are vulnerable to adversarial attacks that aim to subvert their predictions' integrity or privacy by adding a small calculated perturbation to inputs. Further, the adversary can significantly degrade the performance of the model. The number and severity of attacks continues to grow. However, a dearth of techniques robustly defends machine learning models in a computationally inexpensive way. Against this background, we propose an adversarially robust training procedure and objective function for arbitrary neural network architectures. Robustness of neural networks against adversarial attacks on integrity is achieved by augmentation of a novel regularization term. This regularizer penalizes the discrepancy between the representations induced in hidden layers by benign and adversarial data. We benchmark our regularization approach on the Fashion-Mnist and Cifar-10 datasets. Our model is benchmarked against three state-of-the-art defense methods, namely: (i) regularization to the largest eigenvalue in the Fisher information matrix of the activity of the terminal layer, (ii) a higher-level representation guided denoising autoencoder (trained with adversarial examples), and (iii) training an otherwise undefended model on data distorted by additive white Gaussian noise. Our experiments show that the proposed regularizer provides significant improvements in adversarial robustness over both an undefended baseline model as well as the same model defended with other techniques. This result is observed over several adversarial budgets with only a small (but seemingly unavoidable) decline in benign test accuracy.
Region-Based Distance Analysis of Keyphrases: A New Unsupervised Method for Extracting Keyphrases Feature from Articles

Due to the exponential growth of information’s and web sources, Automatic keyphrase extraction is still a challenging issue in the current research area. Keyphrases are very helpful for several tasks in natural language processing (NLP) and information retrieval (IR) systems. Feature extractions for those keyphrases execute a vital role in extracting the top-quality keyphrases and summarising the documents at a superior level. This paper proposes a new region-based distance analysis of keyphrases (RDAK) unsupervised technique for feature extraction of keyphrases from articles. The proposed method comprises six phases: data acquisition and preprocessing, data processing, distance calculation, average distance, curve plotting, and curve fitting. At first, the system inputs the collected different datasets to the preprocessing step by employing some text preprocessing techniques. Afterwards, the preprocessed data is applied to the data processing phase, and then after distance calculation, it is passed to the region-based average calculation process, then curve plotting analysis, and afterwards, the curve fitting technique is utilized. Finally, the proposed system has tested and evaluated the performance through implementing them on benchmark datasets. The proposed system will significantly improve the performance of existing keyphrase extraction techniques.
Magnitude and Interevent Time Statistics of Strombolian Activity of Villarrica Volcano and Inference Regarding the Flow Regime

Abstract The different flow regimes of two‐phase gas‐in‐liquid flow—as it occurs in the shallow conduit of basaltic volcanoes—are characterized by distinct frequency‐size distributions of the liquid and gaseous slugs. Assuming that the ascent of gaseous bubbles is indicated by seismic events, we explore the possibility of inferring the flow regime from the frequency distributions of magnitudes and interevent times. Our data set consists of 20,000 volcanic seismic events recorded in early March 2012 at Villarrica Volcano (Chile), which are commonly attributed to Strombolian activity. One crucial factor is the completeness of the catalog in terms of detectable amplitudes, which we assess using a stochastic simulation of the network output based on statistical properties of the ambient seismicity. Magnitudes, at which we consider the catalog complete, show an exponential occurrence. Yet, the simulation approach indicates that low magnitude events occur indeed more sparsely than expected for an exponential distribution, and that the magnitude distribution does not obey the Gutenberg‐Richter law. Interevent times are log‐normally distributed, which implies a preferred recurrence interval. The distributions are consistent with a slug flow regime. They correlate weakly with the preceding magnitude, suggesting that slugs coalesce while ascending.
Edge-Cloud-Assisted Multiuser Forward Secure Searchable Encryption (EMFSSE) Scheme in the P2P Networking Environment

P2P network enables users to share resources effectively. However, with the advent of the big data era, the sensitive data of users in P2P network are also increasing dramatically. In order to solve the contradiction between the huge amount of sensitive data and the limited local storage space, an increasing number of users choose to encrypt their sensitive data and store them in the cloud server. For the problem of the secure storage and flexible access of large amounts of user data in P2P networks, an edge-cloud-assisted multiuser forward secure searchable encryption scheme is proposed. The scheme uses the proxy reencryption technique to optimize the multiuser searchable encryption and prevent the decryption key from being directly transmitted between users. By introducing an edge-cloud architecture, the system achieves efficient communication and timely response capabilities. The security analysis proves that our scheme achieves the CPA (chosen-plaintext attack) security based on DBDH assumption and the forward privacy. Finally, the theoretical and experimental comparisons between this scheme and other schemes show that our scheme has high efficiency in the process of data update, search, and trapdoor generation. In addition, due to the use of edge-cloud architecture, our scheme reduces about 90% and 75% of the user’s consumption in the encryption and token generation process.
SEEN: Few-Shot Classification with SElf-ENsemble

Few-shot classification aims at learning new concepts with only a few labeled examples. In this paper, we focus on metric-based methods that have achieved state-of-the-art performance. However, they classify query examples based on embeddings extracted from only the last layer. These embeddings tend to be class-specific and may not generalize well to novel classes or domains. To alleviate this problem, we propose the SElf-ENsemble (SEEN) that leverages embeddings from multiple layers. Specifically, a base classifier is built for each of the last few layers, and the resultant base classifiers are then combined together. Experiments on various benchmark datasets demonstrate that the proposed SEEN method outperforms existing methods in both standard few-shot classification and cross-domain few-shot classification scenarios.
Etiquette Action Similarity Evaluation Based on Posture Recognition

At present, more and more vocational colleges set up the etiquette course. In traditional etiquette education, due to the shortage of teachers, it is not practicable to effectively manage students. In order to address this problem, this paper proposes a method based on posture recognition technology and using fuzzy comprehensive evaluation method to evaluate the similarity of etiquette action. This method can be used in action video teaching, used for individual teaching. The student can carry out real-time learning or evaluation of the etiquette action, and be given guidance, thus his resources can be accumulated in the process, so that the teacher can scientifically manage students’ knowledge. The research found that the proposed method can automatically match standard and test keyframe, and can calculate similarity for skeletons of different body sizes. By comparing the similarity of different postures, the consequence shows that the method is more feasible than the European distance. This method can be used not only for etiquette action teaching, but also for the other field of action teaching.
A study on priming methods for graphical passwords

Recent work suggests that a type of nudge or priming technique called the presentation effect may potentially improve the security of PassPoints-style graphical passwords. These nudges attempt to prime or non-intrusively bias user password choices (i.e., point selections) by gradually revealing a background image from a particular edge to another edge at password creation time. We conduct a large-scale user study (n=710) to develop further insights into the presence of this effect and to perform the first evaluations of its security impacts. We explore the usability impacts of this effect using the subset (n=100) of participants who returned for all three sessions. Our usability analyses indicate that these priming techniques do not harm usability. Our security analyses reveal that the priming techniques can measurably enhance the security of graphical passwords; however, this effect is dependent on the combination of both the image and priming techniques used.
Robot navigation using modified SLAM procedure based on depth image reconstruction

In modern mobile robots, technologies are used that make it possible to build the most optimal path for its movement. This uses simultaneous navigation and mapping techniques known as SLAM. A problem with all depth mapping methods is the presence of lost areas. This problem occurs due to poor lighting, mirrored surfaces of objects, or the fine-grained surface of materials, which makes it impossible to measure depth information. As a result, the effect of overlapping objects appears, which makes it impossible to distinguish one object from another, or an increase in the object's boundaries (obstacles) occurs. It is possible to solve this problem with the help of image reconstruction methods. This article presents an approach based on a modified algorithm for finding similar blocks using the autoencoder-learned local image descriptor for image inpainting. For this purpose, we learn the descriptors using a convolutional autoencoder network. The proposed algorithm also uses the concepts of a sparse representation of quaternions, which uses a new gradient to calculate the priority function by integrating the structure of quaternions with the saliency map. Compared with state-of-the-art techniques, the proposed algorithm provides plausible restoration of the depth map from multimodal images, making them a promising tool for robot navigation and defence applications. Analysis of the processing results shows that the proposed method allows you to correctly reconstruct the boundaries of objects in the image and the depth map, which is a prerequisite for increasing the accuracy of robot navigation.
A Novel Grayscale Image Steganography via Generative Adversarial Network

Steganography is an effective technique in the field of information hiding that typically involves embedding secret information into an image to resist steganalysis detection. In recent years, several works on image steganography based on deep learning have been presented, but these works still have issues with steganographic image and revealed image quality, invisibility, and security. In this paper, a novel grayscale image steganography via generative adversarial network is proposed. To boost the invisibility of the model, we construct an encoding network, which is comprised of a secret image feature extraction module and an integration module that conceals a grayscale secret image into another color cover image of the same size. Moreover, considering the security of the model, adversarial training between the encoding-decoding network and the steganalyzer is used. As compared to state-of-the-art steganography models, experimental results show that our proposed steganography scheme not only has higher peak signal-to-noise ratio and structural similarity index but also better invisibility.