Reviewerly/exHarmony huggingface dataset:
Blockchain-Based Data Market (BCBDM) Framework for Security and Privacy

In the internet era, incalculable data is generated every day. In the process of data sharing, complex issues such as data privacy and ownership are emerging. Blockchain is a decentralized distributed data storage technology. The introduction of blockchain can eliminate the disadvantages of the centralized data market, but at the same time, distributed data markets have created security and privacy issues. It summarizes the industry status and research progress of the domestic and foreign big data trading markets and refines the nature of the blockchain-based big data sharing and circulation platform. Based on these properties, a blockchain-based data market (BCBDM) framework is proposed, and the security and privacy issues as well as corresponding solutions in this framework are analyzed and discussed. Based on this framework, a data market testing system was implemented, and the feasibility and security of the framework were confirmed.
Planning of Service Mobile Robot Based on Convolutional LSTM Network

Abstract This paper proposes method that can complete motion planning in one step using convolutional Long Short-Term Memory (LSTM) network. Service mobile robot movement from the starting position to the target position includes three main tasks: mapping, positioning, motion planning. The convolutional LSTM network mainly is used to complete motion planning. The input of the network is a GRB picture with obstacles, target position, starting position. The outputs of the network are linear velocity and angular velocity of service mobile robot. The convolution layer of the network is to mark obstacles, target position and starting position. LSTM layer describes the time characteristics of movement and full connected layer is used to smoothly fit linear velocity and angular velocity of service mobile robot. The convolutional LSTM network can complete tasks of path finding and control, that is, mapping pictures with obstacles, target position, starting position to linear velocity and angular velocity of service mobile robot. Compared with traditional separate solutions for motion tasks, this method has obvious advantages such as good fault tolerance and complete motion tasks planning in real time. In experiment, a mobile robot “Turtlebot” based on ROS systems was used to verify the effectiveness and convenience of the method for motion planning.
ColorfulFeedback: Enhancing Interest Prediction Performance through Multi-dimensional Labeled Feedback from Users

Recommendation systems help to predict user demand and improve the quality of services offered. While the performance of a recommendation system depends on the quality and quantity of feedback from users, the two major approaches to feedback sacrifice quality for quantity or vice versa; implicit feedback is more abundant but less reliable, while explicit feedback is more credible but harder to collect. Although a hybrid approach has the potential to combine the strengths of both kinds of feedback, the existing approaches using explicit feedback are not suitable for such a combination. In this study, we design a novel feedback suitable for the hybrid approach and use it improve the performance of a recommendation system. The system enables us to collect more varied and less biased feedback from users. It improves performance without requiring major changes to the inference model. It also provides a unique and rich source of information of the model itself. We demonstrate an application of Colorful Feedback showing how it can improve an existing recommendation model.
Multilingual and Multimodal Hate Speech Analysis in Twitter

Automatic hate speech detection has become a crucial task nowadays, due the increase of hate on the Internet and its negative consequences. Therefore, in our PhD we propose the design and implementation of methods for the automatic processing of hate messages. The study is focused on the hate messages on Twitter. The hypothesis on which the research is based is that the prediction of hate speech, considering textual content, can be improved by the combination of features such as the activity and communities of users, as well as the images that can be shared with the tweets. In this way, we intend to develop strategies for the automatic detection of hate with multimodal and also multilingual (both in English and Spanish) approaches. Furthermore, our research includes the study of counter-narrative as an alternative to mitigate the effects of hate speech. To address the problem, we employ deep learning techniques, deepening the study of approaches based on representation with graphs.
Smaller World Models for Reinforcement Learning

Abstract Model-based reinforcement learning algorithms try to learn an agent by training a model that simulates the environment. However, the size of such models tends to be quite large which could be a burden as well. In this paper, we address the question, how we could design a model with fewer parameters than previous model-based approaches while achieving the same performance in the 100 K-interactions regime. For this purpose, we create a world model that combines a vector quantized-variational autoencoder to encode observations and a convolutional long short-term memory to model the dynamics. This is connected to a model-free proximal policy optimization agent to train purely on simulated experience from this world model. Detailed experiments on the Atari environments show that it is possible to reach comparable performance to the SimPLe method with a significantly smaller world model. A series of ablation studies justify our design choices and give additional insights.
A benchmarking procedure for quantum networks

Abstract We propose network benchmarking: a procedure to efficiently benchmark the quality of a quantum network link connecting quantum processors in a quantum network. This procedure is based on the standard randomized benchmarking protocol and provides an estimate for the fidelity of a quantum network link. We provide statistical analysis of the protocol as well as a simulated implementation inspired by nitrogen-vacancy center systems using Netsquid, a special purpose simulator for noisy quantum networks.
CoEdit: A novel error correction mechanism in compilers using spelling correction algorithms

Modern compilers can be useful, not only in detecting programming errors but also by suggesting several repairs and solutions for those errors using error repair techniques. Error repair refers to the process of finding a repair for an error that happens as a result of compiling a piece of code written by computer programmers. However, a repair is not always consistent with the purpose of programmers. This means a compiler suggests a repair which is different from what the programmer wanted to write. Many compilers fail to suggest the correct repair when programming errors occur as a result of misspelling errors. The aim of this paper is to enhance the error repair process in compilers using spelling correction algorithms. A typical compiler does not provide solutions for the most common syntax programming errors which occur as a result of misspelling. Such errors are easy to detect by compilers but difficult to suggest a fix that is the correct form. Therefore, a novel error correction mechanism which is called CoEdit approach is developed to help compilers to suggest the most suitable repair for programming errors occurred as a result of mistyping errors. Four-Way and Editex algorithms are well known spelling correction algorithms are also extended and employed to be compatible to work with programming languages. CoEdit approach employs these algorithms in order to find repairs to misspelling errors. CoEdit is a generic approach to enhance error repair in any compilers for any language because it targets misspelling compiler errors. This paper concludes that using the Editex algorithm with CoEdit is the best choice in the case of finding repairs to programming errors that occur as a result of spelling errors.
Wasserstein Divergence GAN With Cross-Age Identity Expert and Attribute Retainer for Facial Age Transformation

We propose the Wasserstein Divergence GAN with an identity expert and an attribute retainer for facial age transformation. The Wasserstein Divergence GAN (WGAN-div) can better stabilize the training and lead to better target image generation. The identity expert aims to preserve the input identity at output, and the attribute retainer aims to preserve the input attribute at output. Unlike the previous works which take a specific model for identity and attribute preservation without giving a reason, both the identity expert and the attribute retainer in our proposed model are determined from a comprehensive comparison study on the state-of-the-art pretrained models. The candidate networks considered for identity preservation include the VGG-Face, VGG-Face2, LightCNN and ArcFace. The candidate backbones for making the attribute retainer are the VGG-Face, VGG-Object and DEX networks. This study offers a guidebook for choosing the appropriate modules for identity and attribute preservation. The interactions between the identity expert and attribute retainer are also extensively studied and experimented. To further enhance the performance, we augment the data by the 3DMM and explore the advantages of the additional training on cross-age datasets. The additional cross-age training is validated to make the identity expert capable of handling cross-age face recognition. The performance of our approach is justified by the desired age transformation with identity well preserved. Experiments on benchmark databases show that the proposed approach is highly competitive to state-of-the-art methods.
Conditional particle filters with diffuse initial distributions

Conditional particle filters (CPFs) are powerful smoothing algorithms for general nonlinear/non-Gaussian hidden Markov models. However, CPFs can be inefficient or difficult to apply with diffuse initial distributions, which are common in statistical applications. We propose a simple but generally applicable auxiliary variable method, which can be used together with the CPF in order to perform efficient inference with diffuse initial distributions. The method only requires simulatable Markov transitions that are reversible with respect to the initial distribution, which can be improper. We focus in particular on random walk type transitions which are reversible with respect to a uniform initial distribution (on some domain), and autoregressive kernels for Gaussian initial distributions. We propose to use online adaptations within the methods. In the case of random walk transition, our adaptations use the estimated covariance and acceptance rate adaptation, and we detail their theoretical validity. We tested our methods with a linear Gaussian random walk model, a stochastic volatility model, and a stochastic epidemic compartment model with time-varying transmission rate. The experimental findings demonstrate that our method works reliably with little user specification and can be substantially better mixing than a direct particle Gibbs algorithm that treats initial states as parameters.The online version contains supplementary material available at 10.1007/s11222-020-09975-1.
Energy-saving solution based on the Internet of Things electronic boundary pile

Abstract As an important tool of scope management, boundary pile is also an important basis for judging boundary violations and an important means for the supervision of industry authorities. However, the external environment where many boundaries are located is so bad that it is difficult to measure the manpower and material resources wasted in manual inspection and maintenance management every year. In view of this situation, a new type of boundary pile is designed in this paper, which can accurately locate the specific location of the problem by collecting GNSS (Global Navigation Satellite System) positioning information, and can also collect the surrounding state by carrying sensors, so as to save a lot of manpower and material resources for inspection.
Input Bias in Rectified Gradients and Modified Saliency Maps

Interpretation and improvement of deep neural networks relies on better understanding of their underlying mechanisms. In particular, gradients of classes or concepts with respect to the input features (e.g., pixels in images) are often used as importance scores or estimators, which are visualized in saliency maps. Thus, a family of saliency methods provide an intuitive way to identify input features with substantial influences on classifications or latent concepts. Several modifications to conventional saliency maps, such as Rectified Gradients [1] and Layer-wise Relevance Propagation (LRP) [2], have been introduced to allegedly denoise and improve interpretability. While visually coherent in certain cases, Rectified Gradients and other modified saliency maps introduce a strong input bias (e.g., brightness in the RGB space) because of inappropriate uses of the input features. We demonstrate that dark areas of an input image are not highlighted by a saliency map using Rectified Gradients, even if it is relevant for the class or concept. Even in the scaled images, the input bias exists around an artificial point in color spectrum. Our modification, which simply eliminates multiplication with input features, removes this bias. This showcases how a visual criteria may not align with true explainability of deep learning models.
Impact of using UI Design Patterns on the Workload of Rapid Prototyping of Smartphone Applications: An Experimental Study

UI design pattern-driven prototyping introduces the idea of utilizing UI design patterns to create rapid prototypes. In this paper, we investigated the impact of using UI design pattern-driven approach on the subjective workload of rapid prototyping. We conducted a between-group workload study (NASA-TLX) for the pattern-driven and traditional approach of rapid prototyping smartphone applications to compare the subjective workload, physical demand, mental demand, temporal demand, performance, effort, and frustration experienced by UI/UX designers. Within the pattern-driven approach, we explored the impact of using pattern libraries with and without pattern standard. Our results indicate that the subjective workload, physical demand, and effort experienced by UI/UX designers using the pattern-driven approach is significantly less than the traditional approach of rapid prototyping. However, there is no significant difference in subjective workload experienced while using UI design pattern libraries with and without pattern standard for documenting UI design patterns.
Optimization of Computer Network Database Security Management Technology in the Era of Big Data

With the deepening of the application of information technology in enterprise management, computer information security has been paid more and more attention. In particular, some secret departments put forward higher and higher requirements for information security. This paper studies the optimization of computer network data security management technology in the era of big data. Through the design, analysis and test of the transmission speed and response speed of the system, it is concluded that the system can meet the needs of enterprise data security management.
Supporting the Discovery and Reuse of Digital Content in Creative Industries using Linked Data

Creative industries play an important role in today's economy, being sources of growth that boost future development. Their products have a huge potential to be re-used and re-purposed, while at the same time they can enrich and improve the design process and create newly exploitable content. This short paper presents V4Link, a semantic annotation and linking framework of multimodal annotations deriving from visual and textual analysis of digital content. The framework reuses the Web Annotation Data Model to capture the annotations, while it semantically enriches the generated knowledge graphs by defining links to Linked Datasets, offering to users advanced query capabilities beyond the ones that can be supported on top of the initial multimodal knowledge base. We illustrate the capabilities of the framework presenting a number of use cases that inspire and support the design, architecture, as well as the 3D and VR game creative industries.
A Self-attention Network Based Node Embedding Model

Despite several signs of progress have been made recently, limited research has been conducted for an inductive setting where embeddings are required for newly unseen nodes – a setting encountered commonly in practical applications of deep learning for graph networks. This significantly affects the performances of downstream tasks such as node classification, link prediction or community extraction. To this end, we propose SANNE – a novel unsupervised embedding model – whose central idea is to employ a transformer self-attention network to iteratively aggregate vector representations of nodes in random walks. Our SANNE aims to produce plausible embeddings not only for present nodes, but also for newly unseen nodes. Experimental results show that the proposed SANNE obtains state-of-the-art results for the node classification task on well-known benchmark datasets.
Chinese Semantic Role Labeling Integrating Global Information and Attention Mechanism

With the rapid development of artificial intelligence and Chinese information processing technology, natural language processing related research has gradually deepened to the level of semantic understanding, and Chinese semantic role labeling is the core technology in the field of semantic understanding. In the field of Chinese information processing where statistical machine learning is still the mainstream, traditional labeling methods rely heavily on the parsing degree of sentence syntax and semantics, so the labeling accuracy is limited and cannot meet current needs. In response to the above problems, this paper proposes a CNN-BiLSTM-Attention-CRF fusion model for Chinese semantic role tagging, and at the same time the model performance optimization research. In the model training stage, convolution kernels of different sizes are used to capture the local features of the sentence, and then the different local features are spliced into a new feature vector through the average pooling technology, which is the global feature that integrates the semantic information of the entire sentence sequence, the same part of speech, sentence phrase Multi-level linguistic feature groups such as structure are fed into the model together. Through multiple sets of experimental demonstrations, the feature set integrated into the sequence global information can significantly improve the various indicators of the model.
Highly Contrasting Image Colorization with Deep Learning

In this propose technique for colorizing photographs, the shading variant of a given dim scale picture is taken from it. The technique does not depend upon human data, and is totally modified. It doesn't depend upon division, writing or complex picture taking care of strategies. It relies upon setting up a direct classifier applying back inducing over a preparation set of shading and looking at diminish scale pictures. The classifier predicts the shade of a pixel reliant on the diminish level of the pixels enveloping it. This little fix gets a close by surface. To save the space for the pointer little, the shades are reduced using Self Organizing Maps. This lessening conveys a little plan of Chroma regards with enough assortment as to make extraordinary approximations for all tones in the planning set.
Signal Properties under Multi-Carrier Chaos–Shift Keying

Abstract In recent years, increased demand for high-quality multimedia services requires increasing the wireless communications application data rates and be more reliable and secure. To meet this request, the researches related to Multi-Carrier (MC) transmission technologies have made considerable efforts in recent years because multi-carrier techniques can improve the data transmission rates. In this research, an overall study on performing Multi-Carrier Chaos Shift keying by using a technique Orthogonal Frequency Division Multiplexing (OFDM) is presented. A multi-carrier system simulation is designed because of the limited speed provided by Chaos Shift keying (CSK) due to the spreading factor. To increase security in this system; the chaotic sequence was generated by proposed to modify on one of the well-known chaos generators that is a tent map. The performance test and the security analysis were carried out using the Bit Error Rate (BER), Mean Squared Error (MSE), correlation test; the chaotic characteristics of the chaos generators sequences using are tested the Lyapunov Exponent. Results have showed the proposed tent map the autocorrelation is like a delta function, so that it appears as a noise that is difficult to discern the information carried by signal, it is much more sensitive to change the initial state and control parameters, where the proposed tent map provides better chaotic properties for encryption such as greater Lyapunov exponent, compared with well-known chaos maps (Logistic map1, Tent map, Quadratic map), it provides a lower Bit Error Rate (BER) performance than a tent map. In addition, the proposed method to multi-carrier CSK system with various Spreading Factors (SF) values outperforms the CSK system in improving the data transmission rate.
A Comparative Study of Machine Learning Approaches for Recommending University Faculty

Academic program selection is a vital decision as it plays a substantial role in enhancing opportunities in career paths. Numerous researches utilizing psychometrics have been conducted in recent years, and result reveals an evident association between academic fields and personality traits of students. To assist students in making an informed academic decision, this study analyzes the differences amongst the students of nine study programs in Bangladesh and envisages the appropriate academic choice among these nine fields for the prospective students by analyzing their academic grades, personality traits, and intelligence. For the experiment, we have collected data from 103 participants and used students' academic grades, Big-5 personality traits, and Factor B (Reasoning) of 16PF to develop classification models by utilizing two classification approaches. Results show that the Hierarchical classification approach using Random Forest Classifier outperformed the One-level Random Forest classifier approach by obtaining 96.1% accuracy for the 1st-level and 92.86%, 89.29%, and 94.74% respectively for 2nd-level. The findings of this study demonstrate that the proposed framework has great potential in assisting prospective students to make an informed decision to determine the suitable higher study options while unlocking human potential.
Age-Invariant Speaker Embedding for Diarization of Cognitive Assessments

This paper investigates an age-invariant speaker embedding approach to speaker diarization, which is an essential step towards the automatic cognitive assessments from speech. Studies have shown that incorporating speaker traits (e.g., age, gender, etc.) can improve speaker diarization performance. However, we found that age information in the speaker embeddings is detrimental to speaker diarization if there is a severe mismatch between the age distributions in the training data and test data. To minimize the detrimental effect of age mismatch, an adversarial training strategy is introduced to remove age variability from the utterance-level speaker embeddings. Evaluations on an interactive dialog dataset for Montreal cognitive assessments (MoCA) show that the adversarial training strategy can produce age-invariant embeddings and reduce diarization error rate (DER) by 4.33%. The approach also outperforms the conventional method even with less training data.