Reviewerly/exHarmony huggingface dataset:
Automated Segmentation of Breast Tissue and Pectoral Muscle in Digital Mammography

Breast tissue and pectoral muscle segmentation are an important preliminary processing step in automated analysis of breast tumors from digital mammography. Without proper pectoral muscle exclusion, textural analysis of the breast tissue imagery would be hindered. This paper presents an automated segmentation of breast tissue and pectoral muscle in digital mammography based on simple histogram operation and k-means classification of the mammogram pixel intensity. We tested our algorithm in 25 digital medio-lateral oblique (MLO) mammograms with confirmed microcalcifications provided by the mini MIAS public database. Our results show that the proposed algorithm can successfully delineate the boundary of pectoral muscle next to the adjacent breast tissue. The best accuracy is achieved in mammograms of fatty-glandular dominant breast tissue and the worst accuracy is achieved in dense-glandular breast mammograms. Due to its simplicity and independence to mammogram intensity range, the algorithm may have potentials to be implemented in the pipeline of mammographic computer-aided diagnosis to assist large-scale screening of breast cancer.
SMORe

In the Age of Big Data, graph embedding has received increasing attention for its ability to accommodate the explosion in data volume and diversity, which challenge the foundation of modern recommender systems. Respectively, graph facilitates fusing complex systems of interactions into a unified structure and distributed embedding enables efficient retrieval of entities, as in the case of approximate nearest neighbor (ANN) search. When combined, graph embedding captures relational information beyond entity interaction and towards a problem's underlying structure, as epitomized by struct2vec [20] and PinSage [26]. This session will start by brushing up on the basics about graphs and embedding methods and discussing their merits. We then quickly dive into using the mathematical formulation of graph embedding to derive the modular framework: Sampler-Mapper-Optimizer for Recommendation, or SMORe. We demonstrate existing models used for recommendation, such as MF and BPR, can all be assembled using three basic components: sampler, mapper, and optimizer. The tutorial is accompanied by a hands-on session, where we show how graph embedding can model complex systems through the multi-task learning and the cross-platform data sparsity alleviation tasks.
Genetic Algorithm With Hill Climbing for Correspondences Discovery in Ontology Mapping

Meta-heuristics are used as a tool for ontology mapping process in order to improve their performance in mapping quality and computational time. In this article, ontology mapping is resolved as an optimization problem. It aims at optimizing correspondences discovery between similar concepts of source and target ontologies. For better guiding and accelerating the concepts correspondences discovery, the article proposes a meta-heuristic hybridization which incorporates the Hill Climbing method within the mutation operator in the genetic algorithm. For test concerns, syntactic and lexical similarities are used to validate correspondences in candidate mappings. The obtained results show the effectiveness of the proposition for improving mapping performances in quality and computational time even for large OAEI ontologies.
Characterization of depolarizing channels using two-photon interference

Abstract Depolarization is one of the most important sources of error in a quantum communication link that can be introduced by the quantum channel. Even though standard quantum process tomography can, in theory, be applied to characterize this effect, in most real-world implementations depolarization cannot be distinguished from time-varying unitary transformations, especially when the timescales are much shorter than the detectors response time. In this paper, we introduce a method for distinguishing true depolarization from fast polarization rotations by employing Hong–Ou–Mandel interference. It is shown that the results are independent of the timing resolutions of the photodetectors.
Face Image Super-Resolution Using Inception Residual Network and GAN Framework

Single Image Super-Resolution (SISR) is an image reconstruction technique that aims to generate a high-resolution image from a low-resolution image. One of the SISR implementations is to reconstruct face images in order to gain more facial information from a low-resolution face images. In this paper, we propose a method to reconstruct face images using a Generative Adversarial Network (GAN) framework that able to generate plausible high-resolution images. Inside the GAN framework, we use inception residual network to improve the generated image quality and stabilize the training. Experimental results demonstrated that our proposed method was able to generate visually pleasant face images with the highest PSNR score of 26.615 and SSIM score of 0.8461.
No Story without a Backstory

The research presented below is an early-stage research project regarding the role and importance of backstory in an augmented reality application for cultural heritage. Literature and desk research show little attention for backstory when developing a storyline for extended reality applications in the cultural heritage field. However when looking at cinematic productions, theme parks and games for example, it can be stated that backstory plays an important role as a storytelling element to create a cohesive and immersive narrative. The hypothesis presented by Scan4Stories is that adding a backstory to the applications designed for cultural heritage sites, could help enhance the immersivity of the experience. To test the hypothesis presented above, we will develop a prototype, examining how the backstory of characters can be presented to the audience in an extended reality application on a cultural heritage site. By eventually combining the results from literature research and the development of a prototype we aim to formulate preliminary results concerning the use of backstory in cultural heritage extended reality applications.
Quality Requirements and the Requirements Quality

Systems quality requirements are defined by ISO/IEC 25000 series. In specifying these requirements, using Natural Language, it is possible that there are symptoms of low quality, Requirements Smells (RSs). The present work has the objective of confirming and analyzing the presence of Requirements Smells in specifications of quality requirements classified by ISO/IEC 25010. The specifications of 26 systems of a large public financial organization were analyzed. Content analysis and Nvivo software were used and 870 quality requirements were categorized and analyzed. As a result, it was verified that 44% of the analyzed requirements present Requirements Smells which signals the importance of the inspection of the requirements with this bias. It was also identified that the most representative RSs are related to Subjective Language (34.6%), Incomplete Reference (22%) and Non verifiable terms (16%). The RSs less found in the specifications are of the Superlative, Loopholes and Comparative categories.
The synergistic benefits of systematic and random interleaving for second language grammar learning.

Repeatedly switching between a series of to-be-learned topics, also called interleaved practice, can improve learning over traditional, one-topic-at-a-time blocked practice. We investigated whether interleaving's benefits for second language learning are facilitated by random schedules, wherein training trials follow unpredictable patterns, or by systematically alternating schedules, wherein trials are predictably sorted. Students learned to conjugate Spanish verbs in the preterite and imperfect tenses and then took a 48-hr delayed verb conjugation test. A consistently random (Experiment 1) or systematically alternating schedule (Experiment 2) did not improve learning versus blocking. However, the combination of both types–systematic alternation for study trials and randomization for practice trials–enhanced learning (Experiments 3–4). Thus, neither interleaving schedule alone appears to be sufficient; for verb conjugation skills and likely other materials involving study and problem-solving practice, both are needed. Interleaving's benefits are therefore impacted by the alignment between training schedule and task type.
The impact of sentiment on content post popularity through emoji and text on social platforms

A model of sentiment-based content post popularity is proposed and empirically tested on Facebook emojis set using SentiStrength and Linguistic Inquiry and Word Count (LIWC) software. This study analysed 16,316 and 54,099 comments obtained from the 218 (Clinton campaign) and 148 (Trump campaign) content posts. The results showed Hillary Clinton had higher score of popularity posts concerning elections than Donald Trump. We infer that the background of Hillary Clinton may be the reason behind attracting followers to support the posts on the election issues. Our model (individual and joint topic) helps reveal popularity of a post as well as the emotional direction. The outcomes showed emoticons have positive relations with the number of shares; particularly, Like and Love have highest impact on the relation. Our model proves the positive influence of emoticons on a post in social media.
Note on “On the extension of nullnorms and uninorms to fuzzy truth values” [Fuzzy Sets Syst. 352 (2018) 92-118]

Xie recently extended nullnorms and uninorms to fuzzy truth values and investigated whether the extended nullnorms and extended uninorms form respectively type-2 nullnorms and type-2 uninorms on the algebra of fuzzy truth values. In this note, we show by counterexamples that there exist some flaws in a previous paper by Xie [Fuzzy Sets Syst. 352 (2018) 92-118], such as Corollaries 2.1, 3.1, 4.1 and 4.2, Lemmas 3.3, 3.4, 3.5, 3.6, 3.7 and 3.8, Propositions 3.4, 3.5, 3.10, 3.11, 4.1 and 4.4, Theorems 3.1, 3.3, 4.1 and 4.3, and then we provide the correct versions. In addition, Proposition 3.7 in that paper is correct, but its proof contains a flaw. We point out the flaw and give a more general result than Proposition 3.7 in that paper.
Through-Wall Human Motion Representation via Autoencoder-Self Organized Mapping Network

This paper addresses a problem of human motion representation for through wall radar exploiting an autoencoder-self organized mapping network. Motivated by the natural language processing, we interpret the echoes of the through-wall human motions into a simple integer sequence with motion “semantics” by regarding each range profile as a “word” and treating the multi-temporal-frame hidden human motion range profile data as a “sentence”, simplifying and maintaining the effectiveness of the through-wall human motion representations. Specifically, we firstly use an auto encoder network (AEN) with three dense layers to reduce the dimension and extract the features of each range profile. Then, a self organized mapping (SOM) network is employed to concatenate the trained AEN to establish the word-to-index mapping, transferring the temporal sequential range-profile data of through-wall human motions into an integer sequence which implies the “semantic” information of the motion. Finally, experimental data including four through-wall human motion types validate the proposed framework.
Analysing Censorship Circumvention with VPNs Via DNS Cache Snooping

Anecdotal evidence suggests an increasing number of people are turning to VPN services for the properties of privacy, anonymity and free communication over the internet. Despite this, there is little research into what these services are actually being used for. We use DNS cache snooping to determine what domains people are accessing through VPNs. This technique is used to discover whether certain queries have been made against a particular DNS server. Some VPNs operate their own DNS servers, ensuring that any cached queries were made by users of the VPN. We explore 3 methods of DNS cache snooping and briefly discuss their strengths and limitations. Using the most reliable of the methods, we perform a DNS cache snooping scan against the DNS servers of several major VPN providers. With this we discover which domains are actually accessed through VPNs. We run this technique against popular domains, as well as those known to be censored in certain countries; China, Indonesia, Iran, and Turkey. Our work gives a glimpse into what users use VPNs for, and provides a technique for discovering the frequency with which domain records are accessed on a DNS server.
Persistent Fault Analysis of OCB, DEOXYS and COLM

Persistent Fault Analysis (PFA) was introduced as a new approach to attack block ciphers at CHES 2018. Since then, it has been proven to be a powerful attack with an easy to achieve fault model which relies on the persistent alternation of constants e.g. S-Boxes. One of the main benefits, when working with PFA, comes from the perspective of an attacker: there is no need to conduct fault injections at runtime. As authenticated encryption is gaining more and more attraction from the research community e.g. the CAESAR competition, we opted to apply the principals of PFA to authenticated encryption schemes. Therefore, we decided to attack a subset of the AES based CAESAR finalists. In this work, we present a PFA of Deoxys-II, OCB and COLM. We show how to extend the original PFA to fit the needs of authenticated encryption schemes and what makes them vulnerable to PFA. Finally, we demonstrate the efficiency of the attacks by means of simulation.
Comparison of Feature Selection for Imbalance Text Datasets

The numbers of documents are increasing rapidly in a web format. Therefore, automatic document classification is needed to help human to classify the documents. Text classification is one of the common tasks in text mining problems. In order to build a model that is able to classify a document, the words are the main source as a feature to create a model. Because there are so many words in a corpus, we need to be selective which features that are significant to the labels. Feature selection has been introduced to improve the classification task. Moreover, it could be used to reduce the high dimensionality feature space. Feature selection becomes one of the most familiar solutions to high dimensionality problem of document classification. In text classification, selection of good features plays an important role. Feature selection is an approach that can be used to increase model classification accuracy and computational efficiency. This paper presents an empirical study of the most widely used feature selection methods. Term Frequency (TF), Mutual Information (MI), and Chi-square (X2) with 2 distinct classifiers Support Vector Machine (SVM) and Multinomial Naïve Bayes (MNB). The experimentations are tested out on commonly used benchmark datasets such as 20-Newsgroups, Reuters and our dataset. Because there are some parameters on how many features that we should take given that documents, we use the best 10 percent until 20 percent of features to test it out. The obtained results show that the six experiments that has been conducted Chi-squared is out as the best performance for text classification.
Geochemical Fingerprinting of Conflict Minerals Using Handheld XRF: An Example for Coltan, Cassiterite, and Wolframite Ores from Democratic Republic of the Congo, Africa

Conflict minerals are those mined in politically unstable regions of the world and are then sold to finance war or other illegal activities. Industrial manufacturers are required to show that minerals used in their applications are not derived from conflict areas. Several geochemical and geochronological methods have been suggested to fingerprint conflict minerals; however, all these methods require sophisticated and extensive laboratory procedures. Portable X-ray fluorescence data of 108 samples from various location in Democratic Republic of the Congo shows that cassiterite and wolframite ores from all studied regions can be fingerprinted using various discrimination diagrams. Coltan ore samples from several regions can also be discriminated using major and trace elements of these samples. In addition, patterns in chondrite-normalized spider diagrams for each region are unique and can be used as fingerprinting tools.
Feasibility of a Keystroke Timing Attack on Search Engines with Autocomplete

Many websites induce the browser to send network traffic in response to user input events. This includes websites with autocomplete, a popular feature on search engines that anticipates the user's query while they are typing. Websites with this functionality require HTTP requests to be made as the query input field changes, such as when the user presses a key. The browser responds to input events by generating network traffic to retrieve the search predictions. The traffic emitted by the client can expose the timings of keyboard input events which may lead to a keylogging side channel attack whereby the query is revealed through packet inter-arrival times. We investigate the feasibility of such an attack on several popular search engines by characterizing the behavior of each website and measuring information leakage at the network level. Three out of the five search engines we measure preserve the mutual information between keystrokes and timings to within 1% of what it is on the host. We describe the ways in which two search engines mitigate this vulnerability with minimal effects on usability.
How quickly do we learn conceptual models?

In organisations, conceptual models are used for understanding domain concepts. Learning the domain from models is crucial for the analysis and design of information systems that are intended to support the domain. Past research has proposed theories to structure conceptual models in order to improve learning. It has, however, never been investigated how quickly domain knowledge is acquired when using theory-guided conceptual models. Based on theoretical arguments, we hypothesise that theory-guided conceptual models expedite the initial stages of learning. Using the REA ontology pattern as an example of theoretical guidance, we show in a laboratory experiment how an eye-tracking procedure can be used to investigate the effect of using theory-guided models on the speed of learning. Whereas our experiment shows positive effects on both outcome and speed of learning in the initial stages of learning, the real contribution of our paper is methodological, i.e. an eye-tracking procedure to observe the process of learning from conceptual models.
Human body reshaping and its application using multiple RGB-D sensors

Automatic reshaping of human bodies is a computer vision and graphics technique with many applications. It manipulates various shape attributes of the visual appearance of a person without any manual editing. Keeping coherent reshaping results across many video frames is more challenging. In this paper, we present a novel pipeline to reshape the human body using noisy depth data from multiple RGB-D sensors. Compared with a single view, the data from multiple RGB-D sensors provide more constraints and lead to more consistent results. However, there exist a number of challenges in estimating the pose and shape of human in RGB-D data due to self-occlusion and motion complexity. To cope with the time-varying articulated human shape, we propose a new approach that combines a Gaussian Mixture Model (GMM) based fitting approach with a morphable model learned from range scans. Without any user input, this approach can automatically account for the variations in pose and shape, and enable different types of reshaping by changing body attributes such as height, weight or other physical features. Experimental results are provided to demonstrate the effectiveness of our system in manipulation of human body shapes.
Development of EMG Controlled Electric Wheelchair Using SVM and kNN Classifier for SCI Patients

Spinal cord injury (SCI) is a devastating condition which can lead to quadriplegia or paraplegia depending upon the level at which injury has occurred resulting in restricted mobility and reduced quality of life. Electromyogram (EMG) signal based human machine interface (HMI) is a system that can be used as an assistive technology to enhance the life of SCI patients. The present paper aims to provide an assistive device (electric wheelchair) for patients suffering from SCI at lower cervical level. EMG signal is used to control the movement of the electric wheelchair, for which time domain feature are used to train support vector machine (SVM) and k-nearest neighbour (kNN) classifier in python 3.5 software to categorize the 5 different movement controls. Raspberry pi 3 is used to acquire signals from MyoWare sensor and processes these signals to provide control pulses to DC motor drive, which finally executes by the electric wheelchair.
An Improved Text to Speech Technique for Tamil Language Using Hidden Markov Model

Application of digital signal processing in speech processing plays a major part in our everyday life. Text to speech conversion (TTS) can apply to many applications such as automation, audio recording and audio-based assistance system. Text to speech conversion can be applied for various multinational languages as well as for a number of local languages. An efficient text to speech conversion for Tamil language with extreme accuracy is proposed in this work. Multi feature, with a Hidden Markov Model (HMM) predictor is used to convert text to speech efficiently. By using the proposed method, the precision of the framework is enhanced by a factor of 6% when contrasted with the traditional system.
Defense-Net: Defend Against a Wide Range of Adversarial Attacks through Adversarial Detector

Recent studies have demonstrated that Deep Neural Networks(DNNs) are vulnerable to adversarial input perturbations: meticulously engineered slight perturbations can result in inappropriate categorization of valid images. Adversarial Training has been one of the successful defense approaches in recent times. In this work, we propose an alternative to adversarial training by training a separate model with adversarial examples instead of the original classifier. We train an adversarial detector network known as 'Defense-Net' with strong adversary while training the original classifier with only clean training data. We propose a new adversarial cross entropy loss function to train Defense-Net appropriately differentiate between different adversarial examples. Defense-Net solves three major concerns regarding the development of a successful adversarial defense method. First, our defense does not have clean data accuracy degradation in contrast to traditional adversarial training based defenses. Second, we demonstrate this resiliency with experiments on the MNIST and CIFAR-10 data sets, and show that the state-of-the-art accuracy under the most powerful known white-box attack was increased from 94.02 % to 99.2 % on MNIST, and 47 % to 94.79 % on CIFAR-10. Finally, unlike most recent defenses, our approach does not suffer from obfuscated gradient and can successfully defend strong BPDA, PGD, FGSM and C & W attacks.