Reviewerly/exHarmony huggingface dataset:
Analysis on Integrating Machine Learning with Blockchain to Ensure Data Privacy

These days data is gathered with next to no particular reason, each movement of a machine or a person is recorded, if required later on then the data will be breaking down. Be that as it may, here the topic of trust emerges as the data will go through many stages for the examination by various gatherings. The data might contain some touchy or private data that can be miss used by the associations engaged with the investigation stages. So it is required for the hour to consider the data protection issues truly. Data protection alludes to how to control the utilization of a piece of data by its relative significance. For instance, somebody won't spare a moment to say his/her name to outsiders however won't share versatile no. address until the outsider gets comfortable. In this computerized age, the individual utilization of data security by and large applied to basic individual data. According to a business perspective, data security works from a more extensive perspective past the basic data of representatives and clients. There is an overall end that AI and Machine Learning driven advancements are deterred by data protection issues as the ML utilizes enormous data collections to prepare and test. However, would the impediment be able to obstruct convert to a venturing stone? Indeed, It can be. Envision a situation where nobody is trust-capable, so all things considered, Blockchain enters the scene. Blockchain utilizes the data yet in an obscurity way. So, in this paper, we are proposing a way, how to consolidate Machine Learning and Blockchain to guarantee data security.
The Dow Jones Knowledge Graph

Dow Jones is a leading provider of market, industry and portfolio intelligence serving a wide range of financial applications including asset management, trading, analysis and bankruptcy/restructuring. The information needed to provide such intelligence comes from a variety of heterogeneous data sources. Integrating this information and answering complex queries over it presents both conceptual and computational challenges. In order to address these challenges Dow Jones have used the RDFox system to integrate the various sources in a large RDF knowledge graph. The knowledge graph is being used to power an expanding range of internal processes and market intelligence products.
Research on JPS Algorithm Route Planning Based on RNP

Navigation and positioning system, as the important part of battlefield environment perception, is the premise and basic guarantee to ensure the safety and accurate completion of route planning tasks of unmanned air vehicle (UAV). In the application of UAV route planning, due to navigation system error and flight technology error, the real-time position of UAV deviates from the scheduled flight path, which may cause the collision between the UAV and obstacles, into the dangerous area and other dangerous situations. In order to ensure the flight safety of UAV, this paper evaluates and monitors the navigation performance of UAV according to the UAV Navigation Monitoring Requirement specification. On this basis, the route of UAV is optimized, and the route planning model of jump point search based on Required Navigation Performance is established. The experimental results show that this method can reduce the protected area of UAV, reduce route planning path length, improve the efficiency of route planning, and provide support for the flight safety of UAV in complex environments.
Augmenting Graph Inductive Learning Model with Topographical Features

Knowledge Graph (KG) completion aims to find the missing entities or relationships in a knowledge graph. Although many approaches have been proposed to construct complete KGs, graph embedding methods have recently gained massive attention. These methods performed well in transductive settings, where the entire collection of entities must be known during training. However, it is still unclear how effectively the embedding methods capture the relational semantics when new entities are added to KGs over time. This paper proposes a method, AGIL, for learning relational semantics in knowledge graphs to address this issue. Given a pair of nodes in a knowledge graph, our proposed method extracts a subgraph that contains common neighbors of the two nodes. The subgraph nodes are then labeled based on their distance from the two input nodes. Some heuristic features are computed and given along with the adjacency matrix of the subgraph as input to a graph neural network. The GNN predicts the likelihood of a relationship between the two nodes. We conducted experiments on five real datasets to demonstrate the effectiveness of the proposed framework. The AGIL in relation prediction outperforms the baselines both in the inductive and transductive setting.
Time Series Attention Based Transformer Neural Turing Machines for Diachronic Graph Embedding in Cyber Threat Intelligence

The cyber threats are often found to threaten individuals, organizations and countries at different levels and evolve continuously over time. Cyber Threat Intelligence (CTI) is an effective approach to solve cyber security problems. However, existing processes are considered inherent responses to known threats. CTI experts recommend proactively checking for emerging threats in existing knowledge. In addition, most researches focus on static snapshots of the CTI knowledge graph, while ignoring the temporal dynamics. To this end, we create a novel framework TSA-TNTM (Time Series Attention based Transformer Neural Turing Machines) for diachronic graph embedding framework, which uses time series self-attention mechanism to capture the non-linearly evolving entity representations over time. We demonstrate significantly improved performance over various approaches. A series of benchmark experiments illustrate that TSA-TNTM could generate higher quality than the state-of-the-art word embedding models in tasks pertaining to semantic analogy, clustering, threat classification and proactively identify emerging threats in CTI fields.
Usability Insights from Establishing TLS Connections

TLS is crucial to network security, but TLS-related APIs have been repeatedly shown to be misused. While existing usable security research focuses on cryptographic primitives, the specifics of TLS interfaces seem to be under-researched. We thus set out to investigate the usability of TLS-related APIs in multiple libraries with a focus on identifying the specifics of TLS. We conducted a three-fold exploratory study with altogether 60 graduate students comparing the APIs of three popular security libraries in establishing TLS connections: OpenSSL, GnuTLS, and mbed TLS. We qualitatively analyzed submitted reports commenting on API usability and tested created source code. User satisfaction emerged as an interesting, potentially under-researched theme as all APIs received both positive and negative reviews. Abstraction level, error handling, entity naming, and documentation emerged as the most salient usability themes. Regarding functionality, checking for revoked certificates was especially complicated and other basic security checks seemed not easy as well. In summary, although there were conflicting opinions on both the interface and documentation of the libraries, several usability issues were shared among participants, forming a target for closer inspection and subsequent improvement.
Performance Evaluation of Isotropic and Anisotropic Empirical Models for Estimation of Solar Irradiance Over Inclined Surfaces at Different Geographical Locations

The aim of this effort is to evaluate the performance of various empirical relationships available for computation of solar radiation over tilted surface. In this work we have considered four isotropic models and four anisotropic models for evaluation of radiation over titled surface. The angle of inclination was fixed equivalent to the geographical latitude of the area under study. Two different locations have been considered for computation purpose, New Delhi, the national capital of India, situated in the sub-tropical belt of globe and Bhopal, a city located in the central part of India, near the Tropic of Cancer. All the computations have been carried out in the MATLAB environment. It has been deduced that Badescu model has exhibited the lowest amount of error and has predicted the value of radiation nearest to the measured data. This is an isotropic model. While Klucher’s model has shown the highest error for the considered geographic locations. Thus, Badescu’s empirical relationship can be realized for calculation of solar irradiance over inclined planes in this geographical belt and can hence provide support for solar PV applications.
Non-destructive Interaction. Embracing Mistakes as an Activity Theoretical UX-Design Approach

With increasing technological complexity, especially within the field of computer mediated interaction, possibilities for user mistakes, and security risks, follows. This paper presents an interaction design pattern for anticipating mistakes in a computer mediated context by letting the users store their actions and current state of ‘object fulfilment’ as a temporary action status. This paper presents parts of an empirical material from a doctoral research project, and a case study of designing a planning software in a shipping company. The interaction design approach involved facilitating a “safe place” for errors in order to anticipate and use the mistakes as learning points. By developing a function for temporary storing current activity states, the users could work on [multiple] preliminary scenarios. Based on the literature and the empirical findings, the paper suggests that this interaction design pattern of “iterative interaction” for anticipating and embracing user-errors adds a layer of security, and an experience of mastery and an emerging sense of development and might contribute to a perceived good user experience.
Cyber-Defense Mechanism Considering Incomplete Information Using POMDP

This article focuses on developing a cyber-defense system in which the defender has incomplete information about the state of the network under the threat of cyber-attack. It involves deriving optimal defense strategies for a layered network with uncertain state of security. The strategies are derived using the Partially Observable Markov Decision Process (POMDP), modelling the uncertainty of the state space. The defender observes its environment via noisy alarms triggered by the Intrusion Detection System (IDS). Based on its belief of the current state of the network, the defender chooses the action, which changes the network’s accessibility, thus blocking the adversary by decreasing its probability of reaching its goal (s). To circumvent the state-space explosion problem in traditional Markov models, an algorithm is developed which enumerates all the reachable paths an attacker can take, considering the possible sequential progress of the attacker within the structure of the network. This prunes the unreachable set of states out of the universal set, resulting in reduced computation time. Two scenarios based on the capability of the attacker are simulated, and the responses from the defender are recorded. It has been shown that the POMDP-based defence strategy is both economic and effective to mitigate cyber-attacks.
DCWEB-SOBA: Deep Contextual Word Embeddings-Based Semi-automatic Ontology Building for Aspect-Based Sentiment Classification

In this paper, we propose the use of deep contextualised word embeddings to semi-automatically build a domain sentiment ontology. Compared to previous research, we use deep contextualised word embeddings to better cope with various meanings of words. A state-of-the-art hybrid method is used for aspect-based sentiment analysis, called HAABSA++, to evaluate our obtained ontology on the SemEval-2016 restaurant dataset. We achieve a prediction accuracy of 81.85% for the hybrid model with our ontology, which outperforms the hybrid model with other considered ontologies. Furthermore, we find that the ontology obtained from our proposed domain sentiment ontology builder, called DCWEB-SOBA, on itself improves the accuracy for the conclusive cases from 83.04% to 84.52% compared to the ontology builder based on non-contextual word embeddings, WEB-SOBA.
SPCS: Strong Privacy-Preserving-Constrained Shortest Distance Queries on Encrypted Graphs

A constrained shortest distance (CSD) query calculates the shortest distance between two vertices of a graph and places constraints on certain factors so as not to exceed corresponding thresholds. With the development of cloud computing, outsourcing graph data storage and the computation of CSD queries to a cloud platform (CP) are an attractive choice for graph owners; however, this choice is accompanied by well-known privacy issues. Certain privacy-preserving schemes have been proposed to support CSD queries on encrypted graphs, but all such schemes consider only single-CSD queries, even though, in practical applications, users may need to set two or more constraints when executing the shortest distance query. Additionally, existing schemes disclose too much private information to the CP. To address these considerable problems, we propose a strong privacy-preserving CSD query scheme called SPCS, which realizes accurate double-CSD queries without disclosing any critical private information. We prove that SPCS does not reveal any private information to the CP except for the number of vertices and conduct numerous experiments on real-world data sets to test this scheme’s efficiency. The experimental results show that SPCS is practical, especially in its computational efficiency in the graph-encryption phase, which is higher than that of available, state-of-the-art schemes.
Multi-Scale U-Net Via Joint Spatial Domains for Rectal Image

Medical image segmentation is very important for the diagnosis of related diseases. To reduce the labeling work of related medical images, numerous models based on U-Net have been proposed to achieve automatic segmentation of target regions. However, most of these models are only trained in one coordinate system, ignoring the joint effects of different spatial coordinate systems. In addition, most of the encoding modules in these models do not pay attention to multi-scale spatial information. For these problems, we propose a multi-scale U-Net segmentation model based on the joint spatial domains, to achieve a staged segmentation of the rectal images. The model includes a self-designed multi-layer dilated convolution encoding module named AIR (Atrous Inception Residual Block), to achieve a multi-scale content fusion. Besides this, it utilizes the center point and polar coordinates to realize the attention mechanism and rotation invariance. Furthermore, retraining the output of the polar coordinate network using the Cartesian coordinate system realizes the translation invariance of segmentation.Â Compared with the commonly used medical segmentation models, the dice coefficient of our model is improved by about 2% on our in-house rectal dataset.
Providing Syntactic Awareness to Neural Machine Translation by Graph-Based Transformer

Graph Neural Networks (GNNs) are a powerful tool to mine graph-structural information. To utilize GNNs in Neural Machine Translation (NMT) tasks, previous research applies a semantic parser to transform a sentence into a semantic graph, while this heuristic approach can be regarded as the data augmentation technique. Following this idea, this manuscript investigates the Dependency Parsing graph via GNNs to improve the current performance of the Transformer in NMT tasks. Briefly, we employ a Graph Transformer layer with graph-based input to probe graph-level information, then integrate the learned information into an encoder side. The experimental results on IWSLT15 English-Vietnamese datasets yield significant improvements over the scaled parameter Transformer by +0.46 and +0.47 BLEU, while the parameters are reduced by 53.2%. Further analyses reveal that the proposed is more robust in long and complex sentences than the Transformer, thus implying GNNs can capture non-trivial information in such groups to benefit the overall performance. Overall, the proposed method outperforms the Transformer, meanwhile cutting down the cost of training.
A Robust and Secure Watermarking Algorithm Based on Dwt and Svd in the Fractional Order Fourier Transform Domain

The present paper aims to develop and validate a robust and secure watermarking algorithm. Firstly, the algorithm transforms the cover image and the watermark image separately by FRFT to obtain the amplitudes of both the cover image and watermark image, and then the two-level DWT transformation only carries out for the amplitude of the cover image. Secondly, the algorithm applies the SVD to the low-frequency sub-band of the second level DWT of the cover image and the amplitude of the watermark image. Thirdly, a new matrix is constructed based on the singular values from both the cover and watermark images to embed watermark information. Lastly, the algorithm takes the preliminary numerical calculation to determine an appropriate transformation order of FRFT for the cover image and to provide an outstanding balance between the imperceptibility and robustness in the proposed watermarking scheme. Moreover, it guarantees the security of watermarking scheme by the transformation order of FRFT. Experimental results demonstrate that the proposed watermarking scheme provides better performance in imperceptibility and resistance against traditional signal processing and geometric attacks, especially in image rotation, image cropping, average filtering, median filtering, and Gaussian filtering in comparison with the existing methods in the literature.
Sentence Continuation Inference of Urdu Text by BERT Technique

The transformer-based BERT pre-trained language model has helped to improve state-of-the-art performance on a number of natural language processing (NLP) tasks. The BERT for Arabic has been well researched and has shown to be effective. However, the BERT for Urdu has yet to be tested. In this study, we show how pre-trained BERT models may be used to recognise cursive text such as Urdu. We employed pre-trained models because a dataset for synthetic Urdu was not accessible for research, thus we provided a synthetic Urdu dataset of 53k characters and got good results. If we could provide the BERT with a large amount of data, it would deliver impressive results. Initial results for Urdu text show that if we provide more data to the BERT pre-trained model, accuracy will improve even further.
A Memetic Whale Particle Optimization Algorithm for Solving Optimization Problems

A metaheuristic algorithm is a novel optimization algorithm usually inspired by nature. In recent years, scholars have proposed various metaheuristic algorithms that are used to solve optimization problems such as the genetic algorithm (GA), artificial bee colony algorithm (ABC), particle swarm optimization algorithm (PSO), crow search algorithm (CSA), and whale optimization algorithm (WOA). Among these, PSO is the most common. However, different algorithms have different shortcomings. For example, the PSO is prone to premature convergence and falls into a local optimum, and the coding of the GA is difficult and uncertain. Therefore, if an algorithm that increasing the computing power and diversity of particles can be proposed, which can solve the problems of the existing algorithms. This study proposes a hybrid algorithm called the Whale Particle Optimization Algorithm (WPO). It combines the advantages of the WOA and PSO to increase the diversity of particles and has the ability to jump out of the local optimum. This study uses four optimization problems to evaluate its performance, specifically function evaluation, image segmentation, permutation flow shop scheduling, and data clustering. The selected test data were selected from real-life to evaluate the proposed algorithm. The results demonstrate that the proposed algorithm competes well against existing algorithms.
Player State and Diagnostics in Adaptive Serious Games. Suggestions for Modelling the Adversarial Contradiction

The premises of theories about Adaptive Serious Games are analyzed. Monistic approaches, which assume a determinacy of humans, and dualistic approaches, in which the ability of humans to self-determine is central, are contrasted. With reference to the concept of Digital Humanism, approaches that focus on the human's ability to self-determine are given preference. With this perspective, existing approaches to Adaptive Serious Games are discussed and suggestions for further development are made. The focus is on the adaptability of adaptive systems. This can be achieved in particular by suitable configuration options and an editor, with which players can determine the adaptivity of an adaptive serious game themselves.
Application of Machine Learning Techniques for Information Extraction from Airline Tweet Data

Sentimental data analysis is a way of determining the sentiments shared through text. Today, the social media is a powerful platform to share the sentiments which can be in the form of reviews, opinions, etc. Twitter is such a social media platform where we can share our opinions, thoughts, or any reviews about any product, events, or services. For commercial service providers, it is important to identify the attitudes in the sentiments to improve their services. For this purpose, where these tweets need to be analyzed, the sentimental data analysis has made this task easier. This paper throws light on two different classifiers used to build two different models to classify the airline sentiments into positive, negative, and neutral classes The process starts with data collection followed by the pre-processing the data to remove those words which are irrelevant to identify the tone of the sentiments. Pre-processing includes data segmentation, tokenization, stop word removal, lemmatization, and stemming. The next step is to apply classifiers to classify the tweets. We used two classifiers: Support Vector Machine (SVM) and Artificial Neural Network (ANN). Finally, using the confusion matrix obtained, accuracy, specificity, and sensitivity are calculated. These measures are used to compare the performance of the classifiers which proves the efficiency of the SVM-based classier over the ANN-based classifiers in classifying the tweets.
Mixing Zero-Shot Learning Up: Learning Unseen Classes from Mixed Features

Zero-Shot Learning (ZSL) objective is to classify instances of classes that were not seen during the training phase. ZSL methods take advantage of side information, i.e., class attributes, to leverage information between the seen and unseen classes. Lately, generative methods have been used to synthesize unseen features in order to train a classifier for the unseen classes. Although generative methods obtain high performance, the learned distribution may not properly represent the real distribution of the unseen classes. We propose an approach to alleviate this issue by creating a new set of mixed features. These mixed features provided a new distribution for the generative method to learn from. By using these mixed features we obtained an +2.2% improvement over tf-VAEGAN in the Oxford Flowers (FLO) dataset.