Reviewerly/exHarmony huggingface dataset:
Seq-SG2SL: Inferring Semantic Layout From Scene Graph Through Sequence to Sequence Learning

Generating semantic layout from scene graph is a crucial intermediate task connecting text to image. We present a conceptually simple, flexible and general framework using sequence to sequence (seq-to-seq) learning for this task. The framework, called Seq-SG2SL, derives sequence proxies for the two modality and a Transformer-based seq-to-seq model learns to transduce one into the other. A scene graph is decomposed into a sequence of semantic fragments (SF), one for each relationship. A semantic layout is represented as the consequence from a series of brick-action code segments (BACS), dictating the position and scale of each object bounding box in the layout. Viewing the two building blocks, SF and BACS, as corresponding terms in two different vocabularies, a seq-to-seq model is fittingly used to translate. A new metric, semantic layout evaluation understudy (SLEU), is devised to evaluate the task of semantic layout prediction inspired by BLEU. SLEU defines relationships within a layout as unigrams and looks at the spatial distribution for n-grams. Unlike the binary precision of BLEU, SLEU allows for some tolerances spatially through thresholding the Jaccard Index and is consequently more adapted to the task. Experimental results on the challenging Visual Genome dataset show improvement over a non-sequential approach based on graph convolution.
Rich Interactions in Virtual Reality Exposure Therapy: A Pilot-Study evaluating a System for Presentation Training

Public Speaking Anxiety (PSA) or discomfort while speaking in public is a wide spread cognitive disorder. Exposure therapy offers the opportunity to treat patients suffering PSA by exposing them to the phobic stimulus. To plan and organize an in-vivo exposure takes a lot of effort in recruiting people for an audience and orchestrating their behavior. Virtual Reality (VR) offers the possibility to generate audiences that can be controlled by an orchestrator according the patient's individual needs. This paper explores a system that enables the therapists to richly interact verbally and non-verbally with immersed presenters. For evaluation, we conducted a study with 24 healthy participants in two groups (12 participants each). Our results indicate that the direct verbal interaction between an orchestrator outside the VR and an immersed presenter enhances the presenter's experience and increases the efficiency of therapy process. The non-verbal dimension enables an orchestrator to take-over an avatar using a motion-tracking camera that controls the avatar's movements. Our results indicate that the transition of animations and movements do not impacting the experience negatively.
Optical flow-based segmentation of moving objects for mobile robot navigation using pre-trained deep learning models

The segmentation of moving objects from video sequences is an intensely researched topic. The two leading application domains are video surveillance systems and self-driving vehicles. Many of the current approaches use some deep learning architecture to accomplish the task. For the training of such models, an extensive collection of input data is necessary. However, it is hard to find an openly accessible, properly annotated dataset for this task in the field of mobile robotics, so one has to create their own. Deep learning models of high complexity easily overfit small datasets if they are trained from scratch, therefore readily available pre-trained networks should be better used. The pretrained deep learning models usually expect RGB images as their input, meanwhile most of the state-of-the-art approaches for moving object segmentation incorporate optical flow data in the prediction process as well. In this paper, we propose a moving object segmentation deep neural network for avoiding dynamic obstacles in the case of indoor mobile robot navigation. The network was built on the top of a deep learning model that was pre-trained for RGB image recognition. We encode the optical flow data and the video frames together into a format that can be processed by the pre-trained network. We introduce a compound loss and training strategy for the model. The empirical evaluation results show that our network predicts satisfactory segmentation masks on the test data for the task of dynamic obstacle avoidance in indoor mobile robot navigation while operating in real-time ($\sim 30$ fps) and being robust to camera movement and the appearance of moving objects.
Enhancing Trust of Supply Chain Using Blockchain Platform with Robust Data Model and Verification Mechanisms

As supply chain has become extremely complicated, traditional supply chain management schemes reveal limitations in keeping track of resources for efficient risk management. Recently, blockchain-based solutions for supply chain have shown possibility to overcome those limits. However, numerous practical problems in this research domain are not fully explored up to date. Thus, we delve into one of the most significant problems among them: verification of business logic in transactions. In this paper, we propose a novel distributed ledger system for supply chain, enabled by anomaly detection framework that verifies semantic correctness of transactions based on business context data. Our blockchain data model is tailored to accurately represent events occurred in supply chain based on real-world business standards. In order to facilitate more efficient tracking of provenance, we leverage graph data model to represent supply chain network. On top of the data model, we present smart contract-based anomaly detection framework that verifies whether a transaction is anomalous. Generic rule-based and graph-based detection methods are devised. The feasibility of our proposed model is shown by the system implemented using Hyperledger Sawtooth. We show how our anomaly detection layer can be plugged into the system: how it interacts with other system components, and how overall system flow works with this new function. We evaluate our system with scenario-based simulations. For a number of use cases we synthesized, the correctness and effectiveness of our system are demonstrated.
Comparative Study of Ontologies Based ISO 27000 Series Security Standards

Security management standards as ISO/IEC 27000 series provide guidelines, which enable to evaluate the security in the company on a continuous basis. Security ontology technology is the most recommended to make links between security concepts and related standards. This paper presents on a review of ontologies based ISO/IEC 27000 series security standards and provides recommendations for professionals and researchers who need to understand or incorporate one of ISO/IEC 27000 standards features to cover their business security needs. We select and examine in details six main ontologies focusing on the usage of ISO/IEC 27000 series security standards. For each security ontology, we review and then describe it in terms of aim, security concepts and ISO 27000 features. Based on this analysis, we propose a comparison between these ontologies considering several factors to pick out their benefits and limits in order to give a set of recommendations to security decision makers helping them to select an ontology regarding their security requirements.
Neural Sentiment Analysis of User Reviews to Predict User Ratings

The significance of user satisfaction is increasing in the competitive open source software (OSS) market. Application stores let users send their feedbacks for applications, which are in the form of user reviews or ratings. Developers are informed about bugs or any additional requirements with the help of this feedback and use it to increase the quality of the software. Moreover, potential users rely on this information as a success indicator to decide downloading the applications. Since it is usually costly to read all the reviews and evaluate their content, the ratings are taken as the base for the assessment. This makes the consistency of the contents with the ratings of the reviews important for healthy evaluation of the applications. In this study, we use recurrent neural networks to analyze the reviews automatically, and thereby predict the user ratings based on the reviews. We apply transfer learning from a huge volume, gold dataset of Amazon Customer Reviews. We evaluate the performance of our model on three mobile OSS applications in the Google Play Store and compare the predicted ratings and the original ratings of the users. Eventually, the predicted ratings have an accuracy of 87.61% compared to the original ratings of the users, which seems promising to obtain the ratings from the reviews especially if the former is absent or its consistency with the reviews is weak.
Semi-supervised Learning Using Siamese Networks

Neural networks have been successfully used as classification models yielding state-of-the-art results when trained on a large number of labeled samples. These models, however, are more difficult to train successfully for semi-supervised problems where small amounts of labeled instances are available along with a large number of unlabeled instances. This work explores a new training method for semi-supervised learning that is based on similarity function learning using a Siamese network to obtain a suitable embedding. The learned representations are discriminative in Euclidean space, and hence can be used for labeling unlabeled instances using a nearest-neighbor classifier. Confident predictions of unlabeled instances are used as true labels for retraining the Siamese network on the expanded training set. This process is applied iteratively. We perform an empirical study of this iterative self-training algorithm. For improving unlabeled predictions, local learning with global consistency [22] is also evaluated.
A Secure Multi-User Database-as-a-Service Approach for Cloud Computing Privacy

Database-as-a-Service (DBaaS) is a new trend that allows industries and organizations outsource their databases and computations to external parties. However, despite the many advantages provided by this service in terms of cost reduction and efficiency, DBaaS raises many security issues regarding data privacy. The protection of privacy has been addressed by several research contributions proposing efficient solutions such as encrypted databases and blind queries over encrypted records. However, access control techniques still suffer from a lack of efficiency especially when dealing with encrypted databases. In this latter context, almost all proposed schemes consider an architecture of a single user (the data owner) that queries his encrypted database that he is the only one capable of decrypting. From a practical perspective, a database system is set up to support not only a single user but multiple users initiating multiple queries. However, managing multiple accesses to an encrypted database introduces several challenges like key sharing, key revocation, and data re-encryption. In this paper, we propose a simple and efficient proved protocol that allows multiple users to query the same database and decrypt the retrieved results without getting access to the secret key. In this protocol, the data owner is not risking the data privacy since he does not need to share the secret key to enable a multi-party collaboration.
An Efficient Model for Driving Focus of Attention Prediction using Deep Learning

In this work, we aim to build a real-time prediction model that can predict the driver's Focus of Attention (FoA) based on the image and motion information (RGB and Optical Flow) of the driving environment. A Y-shape-structured Fully Convolutional Neural Network (Y-FCNN) is proposed to learn and predict the FoA. This network first applies convolution and max-pool layers on RGB and Optical Flow respectively to get the low-level feature maps, and then merges the two encoded low-level feature maps together. After that, the Dilated Convolution, which can get a larger receptive field while still keeping high-resolution information, is applied to make the final prediction. The model is trained and tested using the Dr(eye)ve dataset. Experiment results show that our model can predict the FoA with high accuracy at a speed of about 122 frames per second, which outperforms some previous works for FoA prediction.
Do Cross Modal Systems Leverage Semantic Relationships?

Current cross modal retrieval systems are evaluated using R@K measure which does not leverage semantic relationships rather strictly follows the manually marked image text query pairs. Therefore, current systems do not generalize well for the unseen data in the wild. To handle this, we propose a new measure SemanticMap to evaluate the performance of cross modal systems. Our proposed measure evaluates the semantic similarity between the image and text representations in the latent embedding space. We also propose a novel cross modal retrieval system using a single stream network for bidirectional retrieval. The proposed system is based on a deep neural network trained using extended center loss, minimizing the distance of image and text descriptions in the latent space from the class centers. In our system, the text descriptions are also encoded as images which enabled us to use single stream network for both text and images. To the best of our knowledge, our work is the first of its kind in terms of employing a single stream network for cross modal retrieval systems. The proposed system is evaluated on two publicly available datasets including MSCOCO and Flickr30K and has shown comparable results to the current state-of-the-art methods.
Predicting Hospital Readmission of Diabetics using Deep Forest

Diabetes can cause a variety of complications, which also leads to a high rate of repeated admission of patients with diabetes, which greatly increases the pain and financial burden of patients. Higher readmission rates also reduce hospital evaluation and operational efficiency. Therefore, it is urgent to screen out high-risk readmission patients in advance and introduce adjuvant treatment to reduce the probability of readmission. In this study, we propose a deep learning model combining wavelet transform and deep forest to hospital readmission of the diabetic. The proposed model has been tested with real clinical records and compared with several prevalent approaches to patient prediction. The experimental results show that the feature representation transformed by wavelet transform may well represent the original features and the deep forest is able to outperform the state-of-the-art approaches to classify diabetics.
Modelling and Visualizing Holographic 3D Geographical Scenes with Timely Data Based on the HoloLens

Commonly, a three-dimensional (3D) geographic information system (GIS) is based on a two-dimensional (2D) visualization platform, hindering the understanding and expression of the real world in 3D space that further limits user cognition and understanding of 3D geographic information. Mixed reality (MR) adopts 3D display technology, which enables users to recognize and understand a computer-generated world from the perspective of 3D glasses and solves the problem that users are restricted to the perspective of a 2D screen, with a broad application foreground. However, there is a gap, especially dynamically, in modelling and visualizing a holographic 3D geographical Scene with GIS data/information under the development mechanism of a mixed reality system (e.g., the Microsoft HoloLens). This paper attempts to propose a design architecture (HoloDym3DGeoSce) to model and visualize holographic 3D geographical scenes with timely data based on mixed reality technology and the Microsoft HoloLens. The HoloDym3DGeoSce includes two modules, 3D geographic scene modelling with timely data and HoloDym3DGeoSce interactive design. 3D geographic scene modelling with timely data dynamically creates 3D geographic scenes based on Web services, providing materials and content for the HoloDym3DGeoSce system. The HoloDym3DGeoSce interaction module includes two methods: Human–computer physical interaction and human–computer virtual–real interaction. The human–computer physical interaction method provides an interface for users to interact with virtual geographic scenes. The human–computer virtual–real interaction method maps virtual geographic scenes to physical space to achieve virtual and real fusion. According to the proposed architecture design scheme, OpenStreetMap data and the BingMap Server are used as experimental data to realize the application of mixed reality technology to the modelling, rendering, and interacting of 3D geographic scenes, providing users with a stronger and more realistic 3D geographic information experience, and more natural human–computer GIS interactions. The experimental results show that the feasibility and practicability of the scheme have good prospects for further development.
Application and evaluation of payment channel in hybrid decentralized ethereum token exchange

Traditional centralized token exchange (CEX) has been suffering from hacking due to the centralized management of users’ tokens. In contrast, decentralized token exchange (DEX) maintains users’ assets by smart contracts in a decentralized manner, but introduces additional overhead in terms of gas fee and transaction confirmation latency. Hybrid decentralized token exchange (HEX) has been proposed to combine the benefits of CEX and DEX. However, existing HEX is criticized for two issues. First, trading transactions are time-consuming and expensive for frequent token traders. Second, excessive simultaneous transactions might cause the pending transaction congestion in the Ethereum network. In this paper, we propose a payment channel based HEX, which extends existing solutions by adding a new payment channel layer to benefit frequent traders and alleviate the pending transaction congestion. Besides, we propose the very first gas-price vs. transaction-confirmation-latency function to guide Ethereum transaction issuers to choose an optimal gas price that minimizes the overall cost. Extensive simulations are conducted to compare the cost in the proposed HEX with that in the conventional HEX. The results demonstrate the effectiveness of our proposed mechanism in terms of reducing gas fees and transaction confirmation latency for frequent traders as well as the pending transaction congestion in Ethereum.
Identity based blind signature scheme over NTRU lattices

Blind signatures are very crucial and important part of digital signatures. Zhu et al. proposed an identity based blind signature scheme based on NTRU (nth degree Truncated polynomials Ring Unit) lattices. They claimed that their scheme is secure in the random oracle model. In this paper, we underlined a security flaw in that scheme and proposed an alternative identity based blind signature scheme over NTRU lattices.
Absolute and relative knowledge of ordinal position on implied lists.

Does serial learning result in specific associations between pairs of items, or does it result in a cognitive map based on relations of all items? In 2 experiments, we trained human participants to learn various lists of photographic images. We then tested the participants on new lists of photographic images. These new lists were constructed by selecting only 1 image from each list learned during training. In Experiment 1, participants were trained to choose the earlier (experimenter defined) item when presented with adjacent pairs of items on each of 5 different 5-item lists. Participants were then tested on derived lists, in which each item retained its original ordinal position, even though each of the presented pairs was novel. Participants performed above chance on all of the derived lists. In Experiment 2, a different group of participants received the same training as those of Experiment 1, but the ordinal positions of items were systematically changed on each derived list. The response accuracy for Experiment 2 varied inversely with the degree to which an item's original ordinal position was changed. These results can be explained by a model in which participants learned to make both positional inferences about the absolute rank of each stimulus, and transitive inferences about the relative ranks of pairs of stimuli. These inferences enhanced response accuracy when ordinal position was maintained, but not when it was changed. Our results demonstrate quantitatively that, in addition to item-item associations that participants acquire while learning a list of arbitrary items, they form a cognitive map that represents both experienced and inferred relationships. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
Reputation-Based Maintenance in Case-Based Reasoning

Abstract Case Base Maintenance algorithms update the contents of a case base in order to improve case-based reasoner performance. In this paper, we introduce a new case base maintenance method called Reputation-Based Maintenance (RBM) with the aim of increasing the classification accuracy of a Case-Based Reasoning system while reducing the size of its case base. The proposed RBM algorithm calculates a case property called Reputation for each member of the case base, the value of which reflects the competence of the related case. Based on this case property, several removal policies and maintenance methods have been designed, each focusing on different aspects of the case base maintenance. The performance of the RBM method was compared with well-known state-of-the-art algorithms. The tests were performed on 30 datasets selected from the UCI repository. The results show that the RBM method in all its variations achieves greater accuracy than a baseline CBR, while some variations significantly outperform the state-of-the-art methods. We particularly highlight the RBM_ACBR algorithm, which achieves the highest accuracy among the methods in the comparison to a statistically significant degree, and the R B M c r algorithm, which increases the baseline accuracy while removing, on average, over half of the case base.
Internet of Things (IoT)-Enabled Solar Micro Inverter Using Blockchain Technology

Performance of the conventional photovoltaic (PV) systems is limited by the on-board embedded systems in the solar inverters. Moreover, an expensive supervisory control and data acquisition (SCADA) system or phase management units (PMUs) are still required for intelligent maintenance of the PV systems. It is expected that the conventional solar micro inverters can be advanced as a result of further investigation of the emerging Internet of Things (IoT) technologies and cloud/edge computing services. This paper proposes an internet of things (IoT)-enabled solar inverter using blockchain technology, which improving connectivity, observability, cyber security, and decentralized control toward a smarter PV system in smart grid environments. The IoT device incorporating blockchain technology enables the solar micro inverter to securely: 1) aggregate and send data to a main server for PV system health monitoring; 2) sharing data with other devices for decentralized optimal controls; and 3) execute PV inverter-level energy transaction and record. The concept of the proposed IoT-enabled solar micro inverter is validated by experimental studies.
Virtual customer service

Nowadays, there is a significant evolution in the simulation of conversations with humans through chats. These chatbots provide an improvement in organizational efficiency and enhance the performance in supporting customers and partners. That is why they are increasingly a trend on the web. There are several solutions to conversational virtual agents and chatbots at this time. Many of these solutions have built-in artificial intelligence, natural language processing, and understanding systems. This research aims to build a roadmap for those who want to develop a virtual assistant using a framework or a pre-built solution. This work compares the several products related to chatbots providing information that helps one to find the best solution for each case.