Reviewerly/exHarmony huggingface dataset:
Fitted Value Iteration in Continuous MDPs With State Dependent Action Sets

In this letter, we establish the convergence of fitted value iteration and fitted Q-value iteration for continuous-state continuous-action Markov decision problems (MDPs) with state-dependent action sets. We further extend the algorithm and the convergence result to the case of monotone MDPs.
A Service-oriented Perspective on the Summarization of Recommendations: Preliminary Experiment

The explanation and justification of recommender systems’ results are challenging research tasks. On the one hand, a model-based description that clarifies the reasoning approach behind the suggestions might be difficult to understand, or it might fail to convince the user, if (s)he does not agree on the applied inference mechanism. On the other hand, an aspect-based justification based on few characteristics might provide a partial view of items or, if more detailed, it might overload the user with too much information.
Evaluation of a Low-Cost Virtual Reality Surround-Screen Projection System

Two of the most popular mediums for virtual reality are head-mounted displays and surround-screen projection systems, such as CAVE Automatic Virtual Environments. In recent years, HMDs suffered a significant reduction in cost and have become widespread consumer products. In contrast, CAVEs are still expensive and remain accessible to a limited number of researchers. This study aims to evaluate both objective and subjective characteristics of a CAVE-like monoscopic low-cost virtual reality surround-screen projection system compared to advanced setups and HMDs. For objective results, we measured the head position estimation accuracy and precision of a low-cost active infrared (IR) based tracking system, used in the proposed low-cost CAVE, relatively to an infrared marker-based tracking system, used in a laboratory-grade CAVE system. For subjective characteristics, we investigated the sense of presence and cybersickness elicited in users during a visual search task outside personal space, beyond arms reach, where the importance of stereo vision is diminished. Thirty participants rated their sense of presence and cybersickness after performing the VR search task with our CAVE-like system and a modern HMD. The tracking showed an accuracy error of 1.66 cm and .4 mm of precision jitter. The system was reported to elicit presence but at a lower level than the HMD, while causing significant lower cybersickness. Our results were compared to a previous study performed with a laboratory-grade CAVE and support that a VR system implemented with low-cost devices could be a viable alternative to laboratory-grade CAVEs for visual search tasks outside the user's personal space.
Densely connected layer to improve VGGnet-based CRNN for Arabic handwriting text line recognition

In recent years, Deep neural networks (DNNs) have achieved great success in sequence modeling. Several deep models have been used for enhancing Handwriting Text Recognition (HTR). Among these models, Convolutional Neural Networks (CNNs) and Recurrent Neural network especially Long-Short-Term-Memory (LSTM) networks achieve state-of-the-art recognition accuracy. The recognition methods for Arabic text lines have been widely applied in many specific tasks. However, there are still some potential challenges as the lack of available and large Arabic text recognition dataset and the characteristics of Arabic script. In order to address these challenges, we propose an end-to-end recognition method based on convolutional recurrent neural networks (CRNNs), which adds feature reuse network component on the basis of a CRNN. The model is trained and tested on two Arabic text recognition datasets named KHATT and AHTID/MW. The experimental results demonstrate that the proposed method achieves better performance than other methods in the literature.
Structure-Adaptive Neighborhood Preserving Hashing for Scalable Video Search

In this paper, we propose a Structure-adaptive Neighborhood Preserving Hashing (SNPH) method for unsupervised scalable video search. Unlike most existing hashing methods which equally encode an entire video into a binary feature vector, we propose a neighborhood attention mechanism which encodes the neighborhood-relevant content of a video to better preserve the neighborhood relationships among videos. Motivated by the fact that a video usually contains multiple shots and each shot depicts a different activity, we further develop a structure-adaptive encoder to model the hierarchical structure of the video. Specifically, the encoder adaptively divides each video into multiple segments via detecting temporal boundaries across frames and encodes these segments as a compact binary vector to capture rich structural information. We integrate the neighborhood attention mechanism into the structure-adaptive encoder to learn hash functions that jointly preserve the neighborhood relationships among videos and exploit the hierarchical structure in a video. Experimental results on three widely used benchmark datasets show that our proposed method consistently outperforms state-of-the-art unsupervised video hashing methods.
Multi-transformation Consistency Regularization for Semi-supervised Medical Image Segmentation

Segmentation algorithms based on supervised learning have achieved good results, but supervised learning algorithms require a large amount of labeled data for training. The scarcity of labeled data has greatly limited the development of medical image segmentation. In this paper, we propose a novel semi-supervised segmentation method, where a multi-transformation consistency regularization model is established for medical image segmentation. The model is trained with abundant unlabeled data of medical images. Our semi-supervised framework consists of two modules: supervised training and transformation consistency training. A variety of complex spatial and non-spatial transformations and perturbations are used for consistency regularization, including affine transformation, Euclidean transformation, similarity transformation, color transformation and virtual adversarial training. Multiple transformations are carried on unlabeled data randomly during the training process, and the network is trained by optimizing the supervised loss of labeled data and the multi-transformation consistency regularization loss of unlabeled data. Experimental results on two public datasets show that our method improves the performance in terms of segmentation accuracy to a great extent and achieves state-of-the-art results.
Leveraging Temporal and Topological Selectivities in Temporal-clique Subgraph Query Processing

We study the problem of temporal-clique subgraph pattern matching. In such patterns, edges are required to jointly overlap in time within a given temporal window in addition to forming a topological sub-structure. This problem arises in many application domains, e.g., in social networks, life sciences, smart cities, telecommunications, and others. State-of-the-art subgraph matching techniques, however, are shown to be limited and inefficient in processing queries with both temporal and topological constraints. We propose an approach that takes full advantage of both topological and temporal selectivities during the processing of temporal-clique subgraph queries. Additionally, we investigate a number of optimizations that can be introduced into our approach to improve its efficiency. Our experimental results demonstrate that our approach outperforms the existing methods by a wide margin at a small additional storage cost.
A two-valued semantics for infectious logics

In this article, we introduce a new two-valued semantics for infectious logics. The main motivation for this semantics stems from the question if conjunctions and disjunctions of infectious logics can be rightfully called conjunction and disjunction. Building on our new semantics, we will happily conclude that this is the case. We will also show that our semantics can capture various expansions of infectious logics.
Opportunities and Challenges for Long-Term Tracking

As self-tracking has evolved from a niche practice to a mass-market phenomenon, it has become possible to track a broad range of activities and vital parameters over years and decades. This creates both new opportunities for long term research and also illustrates some challenges associated with longitudinal research. We establish characteristics of very long-term tracking, based on previous work from diverse areas of Ubicomp, HCI, and health informatics. We identify differences between long- and short-term tracking, and discuss consequences on the tracking process. A model for long-term tracking integrates the specific characteristics and facilitates identifying viewpoints of tracking. Finally, a research agenda suggests major topics for future work, including respecting gaps in data and incorporating secondary data sources.
Arabic Optical Character Recognition Using Attention Based Encoder-Decoder Architecture

Optical character recognition (OCR) systems are used to convert scanned documents into text. Arabic OCR is an active area of research where high accuracy is demanding. This paper focuses on building a model for converting images that contain Arabic text into their corresponding text using a deep learning approach. This model does not require any knowledge of the underlying language and it is simply trained end-to-end on the KAFD dataset. It combines several standard neural components from vision and natural language processing. Features are extracted from images using Convolutional Neural Networks (CNNs) where the features are arranged in a grid. Each row is then encoded using a Recurrent Neural Networks (RNNs). An RNN decoder with a visual attention mechanism is used to generate the output text. Our preliminary experiments show that the presented approach is effective. The overall obtained accuracy is 89.82%. However, the individual results for some fonts are higher than this score.
Enhancement of the e-Invoicing Systems by Increasing the Efficiency of Workflows via Disruptive Technologies

E-invoicing is a rapidly growing e-service in Europe as well as in the world. It is identified as a substantially significant element in progressing towards the goals of ‘Digital Economy’ in the European Union. This paper focuses on identifying inefficiencies in e-invoicing systems currently in use and the opportunities to apply emerging technologies such as artificial intelligence and robotic process automation, in order to increase efficiency and level of automatization. The study incorporates expert opinions and users’ perceptions in e-invoicing systems on the status quo and the necessities for higher automation. We focus on e-invoicing systems in the Baltic region consisting of the countries Estonia, Latvia and Lithuania. Based on the conducted research, the drawbacks in e-invoicing systems were identified related to operational, technological and information security-related. Furthermore, the automation opportunities and general requirements for automation were identified. The functionalities that can be improved are discovered as well discussed in this paper and the advantages of using emerging technologies in the context are explained. Based on research outcomes we propose a conceptual e-invoicing ecosystem and present recommendations for its application along the future work needed in that field.
Global Feature Fusion Attention Network For Single Image Dehazing

As a common low-level visual task, image dehazing is widely used in various multimedia fields. However, it faces great challenges due to its ill-posed property, especially the problem with dense haze removal. In this paper, we propose an efficient end-to-end global feature fusion attention network for single image dehazing based on an encoder-decoder structure. We design a Multi-scale Global Context Fusion (MGCF) block to capture the global context feature and integrates them into the network to assist in the recovery of the dense haze area. After the global feature is aggregated in the context modeling module, a two-stream structure is used to combine two feature transformation modes and two feature fusion modes to obtain more comprehensive and accurate features. We simplify the pixel attention block and combine it with the MGCF block to form a Global Feature Fusion Attention (GFFA) module. Based on the local residual learning and the GFFA module, the Feature Enhancement (FE) module is proposed as the basic module of the encoder-decoder, allowing the network to focus on enhancing more useful feature. Experimental results show that the proposed method surpasses state-of-the-art dehazing methods, both quantitatively and qualitatively.
Augmented Reality Teaching Resources and Its Implementation in the Teaching-Learning Process

This work presents a revision of possible applications of the augmented reality in the scholar context, such as the utilization of augmented reality to improve reading comprehension or its application in geometry learning and many others. At the same time, as a contribution to the research field, there is a proposal about the design and development of didactical resources that count with augmented reality objects, images, or illustrative graphs, as well as multimedia videos to improve the teaching-learning process of various academic topics.
EMVE-DeCK: A Theory-Based Framework for Designing and Tailoring Persuasive Technology

Although the importance of tailoring persuasive technologies (PTs) has been discussed extensively in the literature, there is insufficient theory-driven guidance on how to employ social psychology theories in the design of PT interventions. In this paper, we provide an overview of the key frameworks in the extant literature for designing information systems, in general, and persuasive systems, in particular. Specifically, we identify their limitations, and propose a new framework called ”EMVE-DeCK Framework” based on the synthesis of the strengths of the existing frameworks. The EMVE-DeCK Framework, which is grounded in Bandura’s Triad of Reciprocal Determinism, comprises seven steps, which include: (1) Explain: Employ “Theory” to explain the target “Behavior” by uncovering the relationship between the “Behavioral Determinants” and the target “Behavior”; (2) Map: Map the significant “Behavioral Determinants” in the “Theory” domain to “Persuasive Strategies” in the “Technology” domain; (3) Validate: Validate the target users’ receptiveness to the “Persuasive Strategies” in the “Technology” domain; (4) Explicate: Employ “Theory” to explicate (explain) the adoption of the proposed persuasive “Technology” by uncovering the relationship between the user experience (UX) “Design Attributes” and the persuasive “Technology Adoption”; (5) Design: Design and implement theory-driven, tailored persuasive “Technology”; (6) Change: Deploy the persuasive “Technology” to change “Behavior” in the field; and (7) Knowledge: Contribute “Findings” to Knowledge. We discuss the framework in the context of PT interventions.
A Survey on Solution of Imbalanced Data Classification Problem Using SMOTE and Extreme Learning Machine

Imbalanced data are a common classification problem. Since it occurs in most real fields, this trend is increasingly important. It is of particular concern for highly imbalanced datasets (when the class ratio is high). Different techniques have been developed to deal with supervised learning sets. SMOTE is a well-known method for over-sampling that discusses imbalances at the level of the data. In the area, unequal data are widely distributed, and ensemble learning algorithms are a more efficient classifier in classifying imbalances. SMOTE synthetically contrasts two closely connected vectors. The learning algorithm itself, however, is not designed for imbalanced results. The simple ensemble idea, as well as the SMOTE algorithm, works with imbalanced data. There are detailed studies about imbalanced data problems and resolving this problem through several approaches. There are various approaches to overcome this problem, but we mainly focused on SMOTE and extreme learning machine algorithms.
Exploring dropout discriminator for domain adaptation

Adaptation of a classifier to new domains is one of the challenging problems in machine learning. This has been addressed using many deep and non-deep learning based methods. Among the methodologies used, that of adversarial learning is widely applied to solve many deep learning problems along with domain adaptation. These methods are based on a discriminator that ensures source and target distributions are close. However, here we suggest that rather than using a point estimate obtaining by a single discriminator, it would be useful if a distribution based on ensembles of discriminators could be used to bridge this gap. This could be achieved using multiple classifiers or using traditional ensemble methods. In contrast, we suggest that a Monte Carlo dropout based ensemble discriminator could suffice to obtain the distribution based discriminator. Specifically, we propose a curriculum based dropout discriminator that gradually increases the variance of the sample based distribution and the corresponding reverse gradients are used to align the source and target feature representations. An ensemble of discriminators helps the model to learn the data distribution efficiently. It also provides a better gradient estimates to train the feature extractor. The detailed results and thorough ablation analysis show that our model outperforms state-of-the-art results.
Logics of intuitionistic Kripke-Platek set theory

We investigate the logical structure of intuitionistic Kripke-Platek set theory IKP, and show that the first-order logic of IKP is intuitionistic first-order logic IQC.
An Evaluation of Tweet Sentiment Classification Methods

In this paper, we present the result of our research in predicting sentiment from Twitter data derived from a Kaggle competition. Our goal was to determine the efficacy of different supervised classification methods to predict Twitter sentiment to be Positive, Neutral or Negative. We evaluated four different classification statistical models: 1. Logistic Regression (LR), 2. Linear Support Vector Machine (LSV), 3. Multinomial Naïve Bayesian (NB), and 4. Random Forest (RF). We also evaluated two different tokenization methods 1. Document Term Matrix (DTM) and 2. Term Frequency–Inverse Document Frequency (TF-IDF). We combined this with three text extraction methods 1. Original Tweet Text, 2. Rapid Automatic Keyword Extraction (RAKE) and 3. Hand curated "Selected Text". Furthermore, various Neural Networks were applied to the Tweet Text and BERT extracted data that reduced the original 1000 features to be 768 that were applied to different models. Our experiment shows RF and LR gives the best results and there is little difference between DTM and TF-IDF. Fully connected neural network (FCNN) performed the best for the BERT extracted data with a test score of 0.75.
Learning to improve persona consistency in conversation generation with information augmentation

In an open-domain conversation system, maintaining consistent persona is a key factor to earn trust from users and engage users in the conversation. Existing methods suffer from the issue that only sparse persona-relevant signals are available in the target responses, leading to the generation of responses with inconsistent persona. To address the issue, in this paper, we propose two methods to augment persona learning signals for persona preservation. At the sentence level, we develop a dual variational learning model based on the bidirectional encoder representations from transformers (i.e., BERT), which enriches persona signals with relevant persona sentences, in addition to target responses. Therefore, both the encoder part and the latent variable can be guided to learn consistent persona features through back-propagation of losses, which will drive response decoding towards consistent persona expression. At the word level, we propose a persona-based calibration network, which is used to amplify the influence of persona-relevant words in target responses. The experimental results show that our developed model outperforms the strong baseline algorithms by large margins and effectively promotes persona consistency in conversation generation.
OmniLayout: Room Layout Reconstruction from Indoor Spherical Panoramas

Given a single RGB panorama, the goal of 3D layout reconstruction is to estimate the room layout by predicting the corners, floor boundary, and ceiling boundary. A common approach has been to use standard convolutional networks to predict the corners and boundaries, followed by post-processing to generate the 3D layout. However, the space-varying distortions in panoramic images are not compatible with the translational equivariance property of standard convolutions, thus degrading performance. Instead, we propose to use spherical convolutions. The resulting network, which we call OmniLayout performs convolutions directly on the sphere surface, sampling according to inverse equirectangular projection and hence invariant to equirectangular distortions. Using a new evaluation metric, we show that our network reduces the error in the heavily distorted regions (near the poles) by ≈ 25% when compared to standard convolutional networks. Experimental results show that OmniLayout outperforms the state-of-the-art by ≈4% on two different benchmark datasets (PanoContext and Stanford 2D-3D). Code is available at https://github.com/rshivansh/OmniLayout.
User and Event Behavior Analytics on Differentially Private Data for Anomaly Detection

In today's world of digitization, anomaly detection has become one of the most important issues in our lives. User and Entity Behavior Analytics (UEBA) is a security solution for anomaly detection. UEBA minimizes the impact of attacks on data and decreases the risk of privacy breeches by keeping track of normal user and entity behaviors. Organizations need to deploy security control through UEBA in order to achieve security for their data by detecting attacks in their early stages, but the cost of such security control is often out of reach for many small and medium enterprises. One possible solution for such organizations is to outsource UEBA to third parties. However, such an approach can result in disclosure of private data. In this paper, we propose a novel scheme that integrates differential privacy into UEBA. We will show that by introducing noise to the data before publishing it to the third party for anomaly detection, we can ensure that the privacy of the data remains intact, while allowing the third party to preform accurate UEBA analysis. Our experimental results and security analysis will show the validity of our approach.
The challenges and Issues with Virtualization in Cloud Computing

Cloud computing is efficient way to improving storing and accessing of data over the internet or cloud in this case. This is done with the help of virtualization process. Virtualization creates an abstraction layer i.e., cloned hardware over the real hardware. Hypervisor plays important role in this. It is a software that creates and runs Virtual machines (VM) allowing the host computer to support multi guest VM by virtually sharing its resources i.e., memory, storage, processor, and network, simultaneously. For security purpose, we need a well-equippedway to isolate every V from each other. Hypervisor is also known as VM manager as it prevents the VMs from interfering each other. In this paper, many security challenges have been discussed in consideration of Cloud Computing and Virtualization.