Reviewerly/exHarmony huggingface dataset:
Intrusion Detection: Spider Content Analysis to Identify Image-Based Bogus URL Navigation

Every user wants to access their expected contents from legitimate web pages on the Internet by entering key phrases into search engines. Fortunately, crawler or spider (also known as web robots) retrieves the precise contents along with more number of vulnerable data, due to the crawler’s page navigations system. Search engines are simply bringing their responsive web pages based on assigned indexing values for all the visited links, without identifying hidden intrusion contents and web links on all the malicious pages. In the previous attacking era, spammers used (Spam) text for their attacks; later, many text-based filtering tools came into the picture to analyze spam text. Since images have very complicated features to extract its contents, after spam text identification, hackers nurtured their attacking methods based on the images, and they started to embed their spam contents into the image and spread it on victim’s web pages or email id. These images are called spam images. Images are scanned for its text extraction by Optical Character Recognition (OCR) system, and then, the mined contents are matched with spam text databases for spam text identifications. Based on threshold values of matched contents, spam images are identified and applied to the remedial actions. Subsequent failures of said techniques, hackers started to attack targeted victim’s data and system with the help of non-spam images. In which, they simply imbed their malicious web links and erroneous contents into the images, and those images are placed at the legitimate web pages in the form of some advertisements or stimulating user’s desires. Navigating to these intruded sites causes to DoS attacks, data and security breaching of a victim’s system. This paper is going to discuss web robots and its architecture, web content analysis and identification of intrusive substances of bogus images on web page contents.
A joint neuro-fuzzy malaria diagnosis system

Abstract Diagnosis takes a definitive role in the course of determining about clarifying patients as either having or not having the disorder. This method is relatively sluggish and tedious. Various fact-finding and data-mining methods are part of the approach of this article. In the development of the Collaborative Neuro-Fuzzy Expert System diagnosis platform, Neural Networks and Fuzzy Logic, which are artificial intelligence methods, have been merged together. Oral interviews were conducted with medical professionals whose experience was caught in the Expertise Developed Fuzzy Proficient Scheme. With Microsoft Visual C # (C Sharp) Programming Language and Microsoft SQL (Structured Query Language) Server 2012 to handle the database, the Neuro-Fuzzy Expert Framework diagnostic software was introduced. To capture the predominant signs, questionnaires were administered to the patients and filled out by the doctors on behalf of the patients.
WITHDRAWN: An ensemble framework based outlier detection system in high dimensional data using Tree Technique

Machine learning based outlier detection methods are widely used in various domains. However, an ensemble of such detection methods could leverage detection performance. The existing ensemble methods made up of multiple unsupervised learning algorithms lack in ideal strategy for choosing right candidates as constituent detectors. It resulted in mediocrity in model stability and accuracy. To overcome this problem, in this paper, we propose an ensemble framework based outlier detection system in high dimensional data. It has ideal mechanism for effectively choosing base outlier detectors. Out of many candidate outlier detectors, the ones that yield highest performance are combined. An algorithm named Damped Average Selection and Ensemble of Candidates for Outlier Detection (DASEC-OD). Many real world datasets are used for empirical study. The results of experiments revealed that the proposed framework outperforms many existing methods.
Introducing Bidirectional Ordinal Classifier Cascades Based on a Pain Intensity Recognition Scenario

Ordinal classifier cascades (OCCs) are popular machine learning tools in the area of ordinal classification. OCCs constitute specific classification ensemble schemes that work in sequential manner. Each of the ensemble’s members either provides the architecture’s final prediction, or moves the current input to the next ensemble member. In the current study, we first confirm the fact that the direction of OCCs can have a high impact on the distribution of its predictions. Subsequently, we introduce and analyse our proposed bidirectional combination of OCCs. More precisely, based on a person-independent pain intensity scenario, we provide an ablation study, including the evaluation of different OCCs, as well as different popular error correcting output codes (ECOC) models. The provided outcomes show that our proposed straightforward approach significantly outperforms common OCCs, with respect to the accuracy and mean absolute error performance measures. Moreover, our results indicate that, while our proposed bidirectional OCCs are less complex in general, they are able to compete with and even outperform most of the analysed ECOC models.
A Blockchain based model for Curbing Doctors Shopping and Ensuring Provenance Management

Blockchain technology has been increasingly being recognized nowadays by healthcare industry as it guarantees precision with its secure cryptology framework and safeguards against fraud and forgery. The healthcare field is facing a major problem of Prescription Drug Abuse or Doctors shopping that entails drug misuse and leading to fatality of a large number of people worldwide. Painkillers like Oxycodone and Vicodin which are over prescribed by doctors are among the most abused legal drugs alongside sleeping pills and anxiety medication. For this reason there is an imminent need to devise a system that identifies and monitors prescription abuse. Blockchain technology's decentralisation and auditability offers a promising solution to drug tracking that not only makes prescriptions safer but also guarantees a reliable transaction history of medical records. Blockchain is one of the best ways to ensure transparency, integrity and authenticity of pharmacy or doctor's office distribution of drugs. In this research, we propose a novel drug supply chain integrity management by employing Hyperledger Fabric as the underlying blockchain platform and InterPlanetary File System (IPFS) as the decentralised file system to prevent prescription abuse. Through this approach, it is possible to recognize and track doctor's shopping and pharmacy hopping patients in an attempt to misuse drugs. The proposed system viz HealthChain solves this problem by performing drug tracking transactions by employing smart contract functionality on a blockchain to create a smart health care ecosystem. HealthChain seeks to improve the way opioid and prescriptions are administered and distributed by creating a cryptographically secure and reliable framework for physicians, pharmacists and patients.
Inductive Generalized Zero-Shot Learning with Adversarial Relation Network

We consider the inductive Generalized Zero Shot Learning (GZSL) problem where test information is assumed unavailable during training. In lack of training samples and attributes for unseen classes, most existing GZSL methods tend to classify target samples as seen classes. To alleviate such problem, we design an adversarial Relation Network that favors target samples towards unseen classes while enjoying robust recognition for seen classes. Specifically, through the adversarial framework, we can attain a robust recognizer where a small gradient adjustment to the instance will not affect too much the classification of seen classes but substantially increase the classification accuracy on unseen classes. We conduct a series of experiments extensively on four benchmarks i.e., AwA1, AwA2, aPY, and CUB. Experimental results show that our proposed method can attain encouraging performance, which is higher than the best of state-of-the-art models by 10.8%, 14.0%, 6.9%, and 1.9% on the four benchmark datasets, respectively in the inductive GZSL scenario. (The code is available on https://github.com/ygyvsys/AdvRN-with-SR)
Machine Translation System Using Deep Learning for Punjabi to English

Machine Translation (MT) is ongoing research from the last decades. Research started with a simple word to word replacement from source (e.g. English) to target language (e.g. Hindi). Then research moved to statistical-based machine translation (SBMT) which is based on the parallel corpus. Now from the last few years, deep learning is used to develop an MT system. In this paper, an artificial neural network-based machine translation (ANMT) system is trained and tested for Punjabi to the English language. To train the proposed system a parallel corpus of Punjabi-English language is prepared and based on this corpus three models for Punjabi to English NMT system have been developed. In this work, the BLEU score is used to evaluate the performance of the system. The proposed system had shown a BLEU score of 36.98 for smaller sentences, 34.38 for medium sentences and 24.51 for large sentences using model 1, BLEU score of 36.62 for smaller sentences, 35.51 for medium sentences and 26.61 for large sentences using model 2, BLEU score of 60.68 for smaller sentences, 39.22 for medium sentences and 26.38 for large sentences using model 3.
Boosting Single Image Super-Resolution Learnt From Implicit Multi-Image Prior

Learning-based single image super-resolution (SISR) aims to learn a versatile mapping from low resolution (LR) image to its high resolution (HR) version. The critical challenge is to bias the network training towards continuous and sharp edges. For the first time in this work, we propose an implicit boundary prior learnt from multi-view observations to significantly mitigate the challenge in SISR we outline. Specifically, the multi-image prior that encodes both disparity information and boundary structure of the scene supervise a SISR network for edge-preserving. For simplicity, in the training procedure of our framework, light field (LF) serves as an effective multi-image prior, and a hybrid loss function jointly considers the content, structure, variance as well as disparity information from 4D LF data. Consequently, for inference, such a general training scheme boosts the performance of various SISR networks, especially for the regions along edges. Extensive experiments on representative backbone SISR architectures constantly show the effectiveness of the proposed method, leading to around 0.6 dB gain without modifying the network architecture.
Complete Quality Preserving Data Hiding in Animated GIF with Reversibility and Scalable Capacity Functionalities

A technique is put forward to hide data into an animated GIF by exploiting the transparent pixels. Specifically, a new frame is crafted based on the data to be embedded. The newly crafted frame is inserted between 2 existing frames, and the delay time of the affected frames are adjusted accordingly to achieve complete imperceptibility. To the best of our knowledge, this is the first attempt to hide data into an animated GIF by exploiting the transparent pixel. Irregardless of the characteristics of the animated GIF image, the proposed method can completely preserve the quality of the image before and after hiding data. The hiding capacity achieved by the proposed method is scalable, where more information can be embedded by introducing more frames into the animated GIF. While file size expansion is inevitable, reverse zero run length is adopted to suppress the expansion. The proposed method is reversible, i.e., the original image can be recovered.
Statistical Randomness Tests of Long Sequences by Dynamic Partitioning

Random numbers have a wide usage in the area of cryptography. In practice, pseudo random number generators are used in place of true random number generators, as regeneration of them may be required. Therefore because of generation methods of pseudo random number sequences, statistical randomness tests have a vital importance. In this paper, a randomness test suite is specified for long binary sequences. In literature, there are many randomness tests and test suites. However, in most of them, to apply randomness test, long sequences are partitioned into a certain fixed length and the collection of short sequences obtained is evaluated instead. In this paper, instead of partitioning a long sequence into fixed length subsequences, a concept of dynamic partitioning is introduced in accordance with the random variable in consideration. Then statistical methods are applied. The suggested suite, containing four statistical tests: Collision Tests, Weight Test, Linear Complexity Test and Index Coincidence Test, all of them work with the idea of dynamic partitioning. Besides the adaptation of this approach to randomness tests, the index coincidence test is another contribution of this work. The distribution function and the application of all tests are given in the paper.
Ternary Sentiment Classification of Airline Passengers’ Twitter Text Based on BERT

Abstract Traditional word vector generation model cannot solve the polysemy problem of word representation in Twitter text, so, a method is presented here, firstly, by using Bidirectional Encoder Representations from Transformers (BERT), the semantic feature vector of the text can be obtained, and then, the feature vector is inputted into the Softmax classifier to implement the sentiment classification of Twitter text. The experimental datasets are source from passengers’ Twitter comments of USA six airlines, the sentiment classification model based on Embeddings from Language Model (ELMo) as the experimental control group. The experimental results indicate that the proposed model is advantage over the experimental control group by using F1-score as the evaluation index.
Deep Learning Algorithm and Its Applications to IoT and Computer Vision

Deep learning, which is the next phase of machine learning in recent decades, has significantly changed the way in which computer systems interpret human-centric content such as images, video, speech and audio. Different models have been introduced based on learning techniques such as supervised, unsupervised, reinforcement, and it is expected to accelerate and create even more innovative models in the coming years. With the rise of the Internet of Things (IoT), many real-time applications collect data about people and their environment using IoT sensors and feed them into deep learning models to enhance the intelligence and the capabilities of an application, for offering better recommendations and service. Experimental results of most of these applications looks promising when compared with traditional machine learning approaches. So, the main objective of this chapter is to make a self-contained review of Deep Learning (DL) models, starting with the Convolutioanl Neural Network (CNN), Recurrent Neural Network (RNN), Long-Term Short Memory (LSTM), AutoEncoder (AE), Generative Adversarial Network (GAN) and Deep Reinforcement Learning (DRL). Additionally, for providing better understanding of the models and their efficiency, we have added recently developed DL tools or framework and their applications.
CanvasEmb: Learning Layout Representation with Large-scale Pre-training for Graphic Design

Layout representation, which models visual elements and their inter-relations in a canvas, plays a crucial role in graphic design intelligence. With a large variety of layout designs and the unique characteristic of layouts that visual elements are defined as a list of categorical (e.g., type) and numerical (e.g., position and size) properties, it is challenging to learn general and compact representations with limited data. Inspired by the recent success of self-supervised pre-training techniques in various natural language processing tasks, in this paper, we propose CanvasEmb (Canvas Embedding), which pre-trains deep representations from unlabeled graphic designs by jointly conditioning on all the context elements in a canvas, with a multi-dimensional feature encoder and a multi-task learning objective. The pre-trained CanvasEmb model can be fine-tuned with just one additional output layer and with a small size of training data to create models for a wide range of downstream tasks. We verify our approach with presentation slides data. We construct a large-scale dataset with more than one million slides and propose two layout understanding tasks with human-labeled sets, namely element role labeling and image captioning. Evaluation results on these two tasks show that our model with fine-tuning achieves state-of-the-art performance. Furthermore, we conduct a deep analysis aiming to understand the modeling mechanism of CanvasEmb and demonstrate its great potential with two extended applications: layout auto completion and layout retrieval.
Feature Selection With Multi-Source Transfer

Feature selection aims at choosing a subset of features to represent the original feature space. In practice, however, it is hard to achieve desirable performance due to limited training data. To alleviate this issue, we propose a novel problem named feature selection with multi-source transfer where the privileged information from another data source or modality– only available during the training phase, is exploited to improve the performance of feature selection. To be exact, we propose a novel objective function that formulates the privileged information into feature selection. Moreover, an efficient optimization algorithm is introduced to solve the proposed problem of high dimension. Extensive experimental results demonstrate that the proposed algorithm significantly outperforms several popular algorithms, especially when the training data size and the selected feature size are small.
Blockchain Applications to combat the global trade of falsified drugs

The globalization of the pharmaceutical supply chain has lead to new challenges, the leading position among them is the fight against falsified and substandard pharmaceutical products. Such kind of products causes ineffective or harmful therapies all over the world. Traditional centralized technical tools can hardly satisfy the requirements of the changing industry. In this paper, we research the application of Blockchain solutions to modernize the drug supply chain and minimize the amount of the poor-quality medications.
Predicting Flake Mass: A View from Machine Learning

Estimating flake mass based on remaining attributes bears an important relationship for the interpretation of lithic assemblages. Previous works have pointed out the relationship between flake attributes and prediction of flake mass. This study builds on previous works by using data from an experimental collection of flakes. Estimated mass was arrived at by generating a multiple linear regression model that combines several predictive variables. Variable selection for model training was carried out by using best subset selection, which evaluates all possible combinations of variables. Evaluation of the model was performed by computing common machine learning statistics along with estimated percentage error. Results make it possible to determine the best variables and estimate their relationships with flake mass. On the other hand, results also show that although the model is slightly biased and performs adequately, it has a limited inferential ability, especially when compared with other methods/indexes employed to estimate reduction.
Accuracy of Time Duration Estimations in Virtual Reality

Virtual Reality Head Mounted Displays (VR HMDs) offer highly immersive experiences that may alter users’ awareness and perception of the “real-world”. This paper investigates users’ perception of short duration (&lt;5 minutes) time periods during simulated scenarios between two different simulation mediums. Fifty participants (Age: M=29.2 years; SD=10.7 years; 15 Females; 35 Males) were randomly assigned to one of two groups: (i) VR HMD or (ii) desktop computer configuration, where they performed identical searching tasks within a virtual environment three consecutive times. After each virtual session participants were asked to verbally report their perception of time duration. Results reveal that participants across both groups overestimated time duration in virtual environments by an average of 20.3%. As participants gained experience performing the task repeatedly across three consecutive attempts, their task completion time reduced (i.e. learning effect). However, participants’ accuracy of time perception continued to be consistently overestimated across both conditions.
BYOD - Bringing Your Own Device into Single-Surface Interaction Models

Interactive installations face several challenges when used in public or semi-public contexts, such as users not noticing interactivity, or refraining from interacting due to the fear of social embarrassment. Several interaction models proposing solutions to these issues have been established over the years. However, most of these models focus on single-surface installations, which have to be approached for interaction. When the users' own devices (ODs) are included in the installation, though, the interaction focus shifts. To determine the influence of the ODs on the applicability of existing interaction models, we developed a multi-device quiz game, the "Weisskunig Quiz", according to these models. The installation was deployed in a real museum exhibition and tested in two qualitative evaluations with random museum visitors and a school class. We describe our design approach, reflect on the found differences, and propose an extension for single-surface interaction models by incorporating the users' own devices.
Set-Membership Extrinsic Calibration of a 3D LiDAR and a Camera

To fuse information from a 3D Light Detection and Ranging (LiDAR) sensor and a camera, the extrinsic transformation between the sensor coordinate systems needs to be known. Therefore, an extrinsic calibration must be performed, which is usually based on features extracted from sensor data. Naturally, sensor errors can affect the feature extraction process, and thus distort the calibration result. Unlike previous works, which do not consider the uncertainties of the sensors, we propose a set-membership approach that takes all sensor errors into account. Since the actual error distribution of off- the-shelf sensors is often unknown, we assume to only know bounds (or intervals) enclosing the sensor errors and accordingly introduce novel error models for both sensors. Next, we introduce interval-based approaches to extract corresponding features from images and point clouds. Due to the unknown but bounded sensor errors, we cannot determine the features exactly, but compute intervals guaranteed to enclose them. Subsequently, these feature intervals enable us to formulate a Constraint Satisfaction Problem (CSP). Finally, the CSP is solved to find a set of solutions that is guaranteed to contain the true solution and simultaneously reflects the accuracy of the calibration. Experiments using simulated and real data validate our approach and show its advantages over existing methods.