Reviewerly/exHarmony huggingface dataset:
Social Network Polluting Contents Detection through Deep Learning Techniques

Nowadays social networks are widespread used not only to enable users to share comments with other users but also as tool from which is possible to extract knowledge. As a matter of fact, social networks are increasingly considered to understand the opinion trend about a politician or related to a certain event that occurred: in general social networks have been proved useful to understand the public opinion from both governments and companies. In addition, also from the end users point of view it is difficult to identify real contents. This is the reason why in last years we are witnessing a growing interest in tools for analyzing big data gathered from social networks in order to find common opinions. In this context, content polluters on social networks make the opinion mining process difficult to browse valuable contents. In this paper we propose a method aimed to discriminate between pollute and real information from a semantic point of view. We exploit a combination of word embedding and deep learning techniques to categorize semantic similarities between (pollute and real) linguistic sentences. We experiment the proposed method on a dataset composed of real-world sentences gathered from the Twitter social network obtaining interesting results in terms of precision and recall.
Using r-indiscernibility Relations to Hide the Presence of Information for the Least Significant Bit Steganography Technique

The paper presents some preliminary results concerning modification of the least significant bit steganography method. Using r - indiscernibility relations one can embed some information in the container in a semi-random way, whereas it can be reconstructed in a deterministic way. The combination of bits of RGB Bytes allows the bits of information to be hidden by means of a fixed mask. Each Byte of a pixel that contains a fixed combination of bits indiscernible in the fixed ratio with the mask may store the bit of information in the least significant bit. The bits indiscernible in other ratios form random gaps that leads to hiding the presence of information. In the paper we have implemented some steganography systems to test our approach by hiding the image in a cover picture.
Ensemble quantile classifier

Both the median-based classifier and the quantile-based classifier are useful for discriminating high-dimensional data with heavy-tailed or skewed inputs. But these methods are restricted as they assign equal weight to each variable in an unregularized way. The ensemble quantile classifier is a more flexible regularized classifier that provides better performance with high-dimensional data, asymmetric data or when there are many irrelevant extraneous inputs. The improved performance is demonstrated by a simulation study as well as an application to text categorization. It is proven that the estimated parameters of the ensemble quantile classifier consistently estimate the minimal population loss under suitable general model assumptions. It is also shown that the ensemble quantile classifier is Bayes optimal under suitable assumptions with asymmetric Laplace distribution inputs.
Visual Relationship Attention for Image Captioning

Visual attention mechanisms have been broadly used by image captioning models to attend to related visual information dynamically, allowing fine-grained image understanding and reasoning. However, they are only designed to discover the region-level alignment between visual features and the language feature. The exploration of higher-level visual relationship information between image regions, which is rarely researched in recent works, is beyond their capabilities. To fill this gap, we propose a novel visual relationship attention model based on the parallel attention mechanism under the learnt spatial constraints. It can extract relationship information from visual regions and language and then achieve the relationship-level alignment between them. Using combined visual relationship attention and visual region attention to attend to related visual relationships and regions respectively, our image captioning model can achieve state-of-the-art performances on the MSCOCO dataset. Both quantitative analysis and qualitative analysis demonstrate that our novel visual relationship attention model can capture related visual relationship and further improve the caption quality.
Knowledge graph-based entity importance learning for multi-stream regression on Australian fuel price forecasting

A knowledge graph (KG) represents a collection of interlinked descriptions of entities. It has become a key focus for organising and utilising this type of data for applications. Many graph embedding techniques have been proposed to simplify the manipulation while preserving the inherent structure of the KG. However, scant attention has been given to the investigation of the importance of the entities (the nodes of KGs). In this paper, we propose a novel entities importance learning framework that investigates how to weight the entities and use them as a prior knowledge for solving multi-stream regression problems. The framework consists of KG feature extraction, multi-stream correlation analysis, and entity importance learning. To evaluate the proposed method, we implemented the framework based on Wikidata and applied it to Australian retail fuel price forecasting. The experiment results indicate that the proposed method reduces prediction error, which supports the weighted knowledge graph information as a means for improving machine learning model accuracy.
Analyzing Multi-Channel Networks for Gesture Recognition

Multi-channel architectures are becoming increasingly common, setting the state-of-the-art for performance in gesture recognition challenges. Unfortunately, we lack a clear explanation of why multi-channel architectures outperform single channel ones. This paper considers two hypotheses. The Bagging hypothesis says that multi-channel architectures succeed because they average the result of multiple unbiased weak estimators in the form of different channels. The Society of Experts (SoE) hypothesis suggests that multi-channel architectures succeed because the channels differentiate themselves, developing expertise with regard to different aspects of the data.(/p)(/p)To distinguish between these hypotheses, this paper reports on two experiments. The first measures the drop in individual channel performance when the input is degraded by removing high frequency, color, or motion information. The second looks at fusion weights relative to gesture properties. Both experiments support the SoE hypothesis, suggesting multi-channel architectures succeed because of channel specialization.
Seq-DNC-seq: Context Aware Dialog Generation System Through External Memory

Most of the conventional Seq2seq based chit-chat models can analyze and process one or two sentences at a time and are trained to answer specific patterns. However, in a real chit-chat conversation, one sentence is interpreted in various ways according to the preceding context. It is often difficult to make a different response for the same input with the existing Seq2seq based chit-chat models. This makes it difficult for Seq2seq models to understand the previous context in a multi-turn chit-chat conversation. To overcome this problem, the dialogue generation models should have an external memory to store the contextual information related to a conversation. In this paper, we propose a new dialogue generation model, which uses differentiable neural computer (DNC) in the conventional Seq2seq model named Seq-DNC-seq. The proposed Seq-DNC-seq model incorporates external memory into the conventional Seq2seq structure to generate an appropriate dialogue based on the memory of previous conversations. Experimental results show that the proposed Seq-DNC-seq model successfully generates multi-turn chit-chat and the output sentences differ depending on the previous sentence even with the same input text. This not only helps the agent to overcome the existing limitations of chit-chat conversation but also understand the context in longer conversation.
Blockchain in Supply Chain Management: Australian Manufacturer Case Study

The recent explosion of interest around Blockchain and capabilities of this technology to track all types of transaction more transparently and securely motivate us to explore the possibilities Blockchain offers across the supply chain. This paper examines whether Blockchain makes a good fit for use in an Australian manufacturer supply chain. To address this, the research uses Technology Acceptance Model (TAM) as a framework from the literature. Blockchain allows us to have permissioned or permission-less distributed ledgers where stakeholders can interact with each other. It details how Blockchain works and the mechanism of hash algorithms which allows for greater security of information. It also focuses on the supply chain management and looks at the intricacies of a manufacturers supply chain. We present a review of the processes in place of an electrical manufacturer and the problems faced in the supply chain. A model is proposed in using public and private Blockchains to overcome these issues. The proposed solution has the potential to bring greater transparency, validity across the supply chain, and improvement of communication between stakeholders involved. We also point out some potential issues that should be considered if adopting Blockchain.
Nested Hardware Architecture for Self-Organizing Map

This paper proposes a hardware SOM architecture with nested structure. The proposed hardware SOM is made of a single module, and the module is made of four smaller modules, then the smaller module consists of four further smaller modules. With this simple recursive structure, size of SOM can be easily increased. In addition, the proposed SOM requires only one clock cycle to process a single input vector, and hence very high throughput operation is provided. Experimental results show that that the proposed SOM architecture provides he scalability without degrading the performance, and the proposed SOM achieved 33024 MCUPS of high throughput.
From Traditional Neural Networks to Deep Learning: Towards Mathematical Foundations of Empirical Successes

How do we make computers think? To make machines that fly, it is reasonable to look at the creatures that know how to fly: the birds. To make computers think, it is reasonable to analyze how we think—this is the main origin of neural networks. At first, one of the main motivations was speed—since even with slow biological neurons, we often process information fast. The need for speed motivated traditional 3-layer neural networks. At present, computer speed is rarely a problem, but accuracy is—this motivated deep learning. In this paper, we concentrate on the need to provide mathematical foundations for the empirical success of deep learning.
Correction Learning for Medical Image Segmentation

Breast tumor segmentation is useful to diagnose breast cancer. However, challenges, such as intensity inhomogeneity and shadowing artifacts arise in this task. To address these two issues, this paper proposes a robust ultrasound image segmentation method based on correction learning. At first, a novel idea of correction learning is introduced. In contrast to traditional methods that develop the complex models to obtain accurate segmentation results, correction learning aims to detect the erroneous segmentation in advance and correct this automatically by only using simple method. The proposed correction learning method mainly involves two steps: coarse segmentation and correction learning. First, an active contour model is firstly constructed for coarse segmentation by introducing assumption of bias field and local intensity clustering property. Then, correction learning is developed to address the erroneous segmentation and to improve the segmentation performance. In this paper, correction learning mainly contains Internal Tumor Block Correction (ITBC) and Boundary Block Correction(BBC). In order to correct the erroneous segmentation caused by intensity inhomogeneity, the internal tumor blocks detection model based on Simple Linear Iterative Clustering(SLIC) and Support Vector Machine (SVM) is learned to detect these segmented blocks. Based on the detection result, the incorrect segmented blocks can be corrected. In addition, BBC is proposed to correct the erroneous segmentation of boundary blocks which are caused by shadowing artifacts. Our experiment results on the constructed database demonstrate the effectiveness and robustness of the proposed method.
Visual Analytics for Cyber Security Domain: State-of-the-Art and Challenges

Visual Analytics is a complex sub-field of data analytics that concentrates on the use of the information visualization methods for facilitating effective analysis of data by employing visual and graphical representation. In cyber security domain, Effective visualization of the data allows to infer valuable insights that enable domain analysts to construct successful strategies to mitigate cyber attacks and provide decision support. We perform a survey of the state-of-the-art in the cyber security domain, analyze main challenges and discuss future trends. We summarize a large number of cyber security and digital forensics visualization works using the Five Question Method of Five W's and How (Why, Who, What, How, When, and Where) approach as a methodological background. We perform analysis of the works using J. Bertin's Semiotic Theory of Graphics, and VIS4ML ontology as a theoretical foundation of visual analytics. As a result, we formulate the main challenges for the development of this area of research in the future.
Beyond mean rating: Probabilistic aggregation of star ratings based on helpfulness

Abstract The star‐rating mechanism of customer reviews is used universally by the online population to compare and select merchants, movies, products, and services. The consensus opinion from aggregation of star ratings is used as a proxy for item quality. Online reviews are noisy and effective aggregation of star ratings to accurately reflect the “true quality” of products and services is challenging. The mean‐rating aggregation model is widely used and other aggregation models are also proposed. These existing aggregation models rely on a large number of reviews to tolerate noise. However, many products rarely have reviews. We propose probabilistic aggregation models for review ratings based on the Dirichlet distribution to combat data sparsity in reviews. We further propose to exploit the “helpfulness” social information and time to filter noisy reviews and effectively aggregate ratings to compute the consensus opinion. Our experiments on an Amazon data set show that our probabilistic aggregation models based on “helpfulness” achieve better performance than the statistical and heuristic baseline approaches.
Disfluencies in spontaneous speech in easy and adverse communicative situations: the effect of age

Disfluencies are a pervasive feature of speech communication.Their function in communication is still widely discussed with some proposing that their usage might aid understanding.Accordingly, talkers may produce more disfluencies when conversing in adverse communicative situations, e.g. in background noise.Moreover, increasing age may have an effect on disfluency use as older adults report particular difficulties when communicating in adverse conditions.In this study, we elicited spontaneous speech via a problem-solving task from four different age groups (19-76 years old) to investigate the effect of energetic and informational maskers on the use of filled pauses (FPs), and its interaction with age.Measures of disfluency rates, effort ratings, and communication efficiency were obtained.Results show that, against our predictions, FP usage may decrease in adverse conditions.Moreover, age does not play a great role in adults with normal hearing.The results indicate that individuals differ greatly in their disfluency adaptations, utilising different strategies to overcome challenging communicative situations
A Cost-Sensitive Feature Selection Method for High-Dimensional Data

With the increase of data dimension in many application fields, feature selection, as an essential step to avoid the curse of dimensionality and enhanced the generalization of the model, is attracting more and more research attention. However, most existing feature selection methods always assume the features have the same cost. These research efforts mainly focus on features' relevance to learning performance while neglecting the cost to obtain them. Feature cost is a crucial factor need to be considered in feature selection problem especially for the real world applications. For example, in the process of medical diagnosis, each feature may have a very different testing cost. To select low-cost subsets of informative features, in this paper, we propose a stratified random forest-based cost-sensitive feature selection method. Unlike commonly used two-step cost-sensitive feature selection approaches, in our model, the cost of features is incorporated into the construction process of the base decision tree, that is, the cost and the performance of each feature are optimized simultaneously. Moreover, we adopt a stratified sample method to enhance the performance of the feature subset for high-dimensional data. A series of experimental results show that compared with the state-of-the-art methods, the proposed approach can lower the cost of the selected feature subset while maintaining comparable learning performance.
GUI-Based Percentage Analysis for Curing Breast Cancer Survivors

The modeling approach is increasing the intensity of research in all the domains. The present paper deals with predictive modeling and probabilities. Data analysis is a technique used to transform, reconstruct, and revise some information that is an essential step to achieve the goal or the end result. The present study involves the usage of logistic regression technique for data analysis. Various domain-specific methods pertaining to science, business, etc., are available for data analysis which plays a key role in decision-making and model building. The significance of this analysis is to get the percentage of the survival of patients with advanced breast cancer.
Large-Margin Multiple Kernel Learning for Discriminative Features Selection and Representation Learning

Multiple kernel learning (MKL) algorithms combine different base kernels to obtain a more efficient representation in the feature space. Focusing on discriminative tasks, MKL has been used successfully for feature selection and finding the significant modalities of the data. In such applications, each base kernel represents one dimension of the data or is derived from one specific descriptor. Therefore, MKL finds an optimal weighting scheme for the given kernels to increase the classification accuracy. Nevertheless, the majority of the works in this area focus on only binary classification problems or aim for linear separation of the classes in the kernel space, which are not realistic assumptions for many real-world problems. In this paper, we propose a novel multi-class MKL framework which improves the state-of-the-art by enhancing the local separation of the classes in the feature space. Besides, by using a sparsity term, our large-margin multiple kernel algorithm (LMMK) performs discriminative feature selection by aiming to employ a small subset of the base kernels. Based on our empirical evaluations on different real-world datasets, LMMK provides a competitive classification accuracy compared with the state-of-the-art algorithms in MKL. Additionally, it learns a sparse set of non-zero kernel weights which leads to a more interpretable feature selection and representation learning.
A Kotas-Style Characterisation of Minimal Discussive Logic

In this paper, we discuss a version of discussive logic determined by a certain variant of Jaśkowski’s original model of discussion. The obtained system can be treated as the minimal discussive logic. It is determined by frames with serial accessibility relation. As the smallest one, this logic can be treated as a basis which could be extended to richer discussive logics that are obtained by varying accessibility relation and resulting in a lattice of discussive logics. One has to remember that while formulating discussive logics there is no one-to-one determination of discussive logics by modal logics. For example, it is proved that Jaśkowski’s logic D 2 can be expressed by other than S 5 modal logics. In this paper we consider a deductive system for the sketchily described minimal logic. While formulating the deductive system, we apply a method of Kotas that was used to axiomatize D 2 . The obtained system determines a logic D 0 as a set of theses that is contained in D 2 . Moreover, any discussive logic that would be expressed by means of the provided model of discussion would contain D 0 , so it is the smallest discussive logic.
Ontology Development in dot NET Platform: An Empirical Assessment

Ontology has been a subject of interest to Semantic Web developers in the past decade. However, only a few has used the Microsoft .NET platform for building ontologies despite its popularity in the software industry. A large part of ontology research in the field of Semantic Web has been done using open source platforms. This study applies Semantic Web technologies to develop ontology in SemWeb.NET, a Semantic Web library for Microsoft .NET. The experiments show that there is lack of tools in SemWeb.NET for a user-friendly editing of ontology as well as parsing and querying RDF graphs of ontologies stored in popular RDBMS such as MySQL, from C# applications. These findings suggest that further research be carried out to extend the SemWeb.NET library with APIs for graphical user interface and user-friendly editing of ontologies as well as suitable plug-ins for parsing and querying ontologies from relational databases.
The Luoshu Magic Square as Evidence of the Rational and Mathematical Orientation of the Chinese Style of Thinking

This article considers the meaning of the ancient Chinese magic square Luoshu. It is known that this square is the most ancient of this type of squares. The importance of the magic square in the philosophical tradition and in the whole culture of China is large. The ancient understanding of number differs from the modern one by its dual character, combining the features of philosophical symbolism and mathematical constructions. Unfortunately, modern interpretations of the Luoshu as well as other numerical constructions preserved by the Chinese tradition are too often limited to a superficial statement of their symbolic, “numerological” nature, ignoring the effects of the mathematical structure represented by them. At the same time, some scholars strongly emphasize the intuitive aspects of numerology, which do not presuppose accuracy. On the contrary, Leibniz saw in the Chinese binary system of thinking the beginning of an exact universal language. The author, appealing to the concept of A.A. Krushinskiy, analyzes the arithmetic laws of the structure of the square. The article shows that the magic square Luoshu is not a numbers game. Without any doubt, its main meaning lies in the application of arithmetic. Chinese magic square is an illustrative implementation of one of the main features of Chinese language and traditional thinking – its rational and mathematical basis. The Luoshu square is a vivid example of visual constructivism in the Chinese style of thinking. By means of the magic square’s scheme, the procedure of generalization that is a basis of concepts formation is carried out. This procedure uses a mathematical magic square algorithm based on the numerical codes of the five elements. The quinary spontaneous symbolism is rooted in the Chinese traditional culture of thinking and life order.