Reviewerly/exHarmony huggingface dataset:
Development and Implementation of Mobile Platform-Oriented Interactive English Grammar Learning System

Based on the characteristics of mobile learning, an English grammar mobile learning system on Android platform is designed. Firstly, the background and advantages of mobile terminal and Android system are analyzed. Then, the development mode, database creation and communication mode are discussed. In the system implementation process, we design and implement data transmission based on Tomcat Web server and HTTP protocol, and text information exchange and voice chat based on Openfire instant messaging server and XMPP protocol. At the same time, using online learning algorithm combined with interactive system, as well as C-SVC and v-svc data analysis, the modular design and overall test of the system are realized. The results show that the system is accurate in search, comprehensive in content, conducive to English grammar learning, and it has strong practicability.
Scalable Translation Validation of Unverified Legacy OS Code

Formally verifying functional and security properties of a large-scale production operating system is highly desirable. However, it is challenging as such OSes are often written in multiple source languages that have no formal semantics - a prerequisite for formal reasoning. To avoid expensive formalization of the semantics of multiple high-level source languages, we present a lightweight and rigorous verification toolchain that verifies OS code at the binary level, targeting ARM machines. To reason about ARM instructions, we first translate the ARM Specification Language that describes the semantics of the ARMv8 ISA into the PVS7 theorem prover and verify the translation. We leverage the radare2 reverse engineering tool to decode ARM binaries into PVS7 and verify the translation. Our translation verification methodology is a lightweight formal validation technique that generates large-scale instruction emulation test lemmas whose proof obligations are automatically discharged. To demonstrate our verification methodology, we apply the technique on two OSes: Google's Zircon and a subset of Linux. We extract a set of 370 functions from these OSes, translate them into PVS7, and verify the correctness of the translation by automatically discharging hundreds of thousands of proof obligations and tests. This took 27.5 person-months to develop.
A Day's Work on Facebook and Other Collaborative Trends in Animal Welfare

Animal shelters generate an enormous amount of data and require extensive information and communication management, however very little focus has been given to this field through the CSCW lens. The aim of this autoethnographic study is to portray the different events and resulting artifacts from daily shelter tasks using a combination of workflow modeling and customer journey mapping techniques. The Shelter Touchpoint Workflow sheds light on unique trends in the collaborative environment of animal welfare. Thus, demonstrating room for the CSCW community to help develop solutions to alleviate shortcomings of legacy technology and other tools that have been adapted to fit into their daily tasks. The two specific insights we are examining are the data fragmentation and siloing of information about an animal, as well as the contribution of social media to compassion fatigue.
Diagnostic Analysis: Directional Relation Graph

System administrators can employ various diagnostic tests to identify failures in high performance computing systems, but manual analysis of the results can be time-consuming. Moreover, the execution of these tests can occupy system resources and individual diagnostic results only represent the instantaneous state of the system. In this paper, we propose the use of a directional relation graph to summarize and visualize diagnostic results over time. The graph is a visual representation of the frequency of different test failures and relations among failures in a specific time range. We demonstrate the directional relation graph using diagnostic results obtained during the execution of synthetic anomalies. Furthermore, we discuss how graph analysis of relations among failures can narrow the suite of tests to reduce overall test time.
High Data Rate Audio Steganography

Steganography is an art to hide information withoutRoy, Sangita affectingKapoor, Vivek perceptual transparency of digital media files. Media files include audio, video, image, text, software and so on. Hiding information is required for secure transmission of data. In general, media files are known as cover file which wrap the data to be transmitted. The media file must be imperceptible before and after embedding of data. But when we embed data, we do some modification at different bit plane that introduces error or noisy media. Motivation of this work is based on the property of human auditory system (HAS) which is discernible towards any kind of modification or distortion in audio file. Keeping in mind this HAS property, in this work, we concentrate on hiding a large amount of data in a small size cover file considering reduced noise and minimized bit error rate.
Automatic Meta-Feature Engineering for CNN Fusion in Aerial Scene Classification Task

The aerial scene-classification task is a challenging problem to remote sensing area with important applicability to civil and military affairs. A technique that has achieved excellent results in this task is the convolutional neural network (CNN). CNNs are powerful semantic-level feature-extraction techniques successfully applied to many application domains. Nevertheless, many works in the literature have shown that a single CNN cannot solve all kinds of application domains properly. Hence, an alternative solution might be the joining of CNN architectures as an ensemble of classifiers. In this sense, this letter proposes a new strategy of deep feature-based classifier fusion through a meta-feature engineering approach based on the Kaizen programming (KP) technique for the aerial scene-classification task. KP is a technique that continuously improves partial solutions and combines them into a complete solution. In the context, a partial solution is a meta-feature, and a complete solution is an ensemble of classifiers. In our experiments on three different public data sets, we show that KP can automatically engineer meta-features that significantly improve the performance of a stacked classifier while reducing the number of total meta-features.
Measuring Similarity to Observe Learners’ Syntactic Awareness in Web-Based Writing Environments

Writing in a foreign language is a struggle for learners and revising their writings is time consuming for teachers as well. For this reason, writing support systems have been widely proposed and one of its main functions is to automatically detect and revise errors in learners’ writings. However, the detection technologies are a work in progress and the effectiveness of error revision feedback is arguable. Meanwhile, numerous efforts have been made to enhance learners’ writing proficiency and reduce errors. Reading is considered as one of the important strategies. However, few studies have reported the linguistic knowledge that learners pay attention to and how they use the knowledge of web-based learning in their writings. In this paper, we performed a reading-to-write experiment in a web-based writing environment and analyzed reading materials and learners’ writings to explore how to observe learners’ awareness of syntactic structures in materials. Sentence patterns, proposed in our previous studies, have been introduced to categorize sentences, and the syntactic similarities between reading materials and learners’ writings have been calculated. The experimental results revealed that students showed higher comprehension of content but displayed poor attention towards syntactic structures in reading activities, if the structures were not significantly salient. It is assumed that the similarity measure is effective in observing students’ awareness of syntactic structures in materials, and further studies are needed to automatically observe the awareness.
Identifying and Correcting Mislabeled Satellite Image Data by Iterative Ordering of Ensemble Margins

The accuracy of a supervised classifier is directly influenced by the quality of the training data used. However, real-world data often suffers from mislabelling issues. To handle the mislabeling problem, we propose an ensemble margin-based mislabeled training data identification, elimination and correction approach based on data ordering. A powerful ensemble method, random forest, is at the core of our algorithms design. The effectiveness of our methods is demonstrated in performing mapping of land covers. A comparative analysis is conducted with respect to the majority vote filter, a popular ensemble-based mislabeled data filter.
Load curves partitioning with the application of soft clustering algorithms

Load profiling refers to a procedure which leads to the formulation of daily load curve and consumer categories regarding the similarity of their curves shapes. This procedure incorporates a set of pattern recognition algorithms. While many crisp clustering algorithms have been purposed for grouping load curves into classes, only one soft clustering algorithm is utilized for the aforementioned purpose, namely the Fuzzy C-Means (FCM). Since the benefits of the soft clustering is demonstrated in a variety of applications, we examine the potential of introducing soft clustering algorithms in the electricity demand patterns segmentation. This paper introduces in the load profiling studies, two soft clustering algorithms which have been already used in other clustering problems, namely the Possibilistic C-Means (PCM) and the Gustafson-Kessel Fuzzy C-Means (GKFCM). A detailed comparison takes places between the algorithms and their performance is checked by a set of adequacy measures that have been proposed in the load profiling related literature and by a set of traditional fuzzy clustering validity measures.
Efficient Conversion of Handwritten Text to Braille Text for Visually Challenged People

The World Health Organization has submitted a report in 2017 which declared that a population of nearly 36 million people are blind out of 253 million visually impaired people. Braille books in the tactile format are very helpful for the visually impaired people to gain knowledge but the availability of these resources is limited. With the improvement of electronic innovation, Braille ended up being appropriate to PC helped generation in light of its coded structures. Programming based content to-Braille interpretation has been ended up being a fruitful arrangement in Assistive Innovation. Various research studies and papers describe the methods for obtaining machine readable document from textual image. In future, character recognition might serve as a key role to create a paperless environment that helps the visually impaired people to gain enormous amount of educational materials. In current world the innovations and advancements in modern scientific systems has expanded the boundaries of human effort like Optical Character Recognition. In the field of machine learning and pattern matching Handwritten recognition has gained lot of attention. A novel method is proposed to convert Handwritten text into Braille text due to which huge resources of Handwritten text materials will be available in Braille text that helps the visually impaired and low vision people to expand their knowledge. The novelty of this system has gained a better accuracy.
An Aspect of Internet of Things Security: Analysis of Digital Fingerprinting of Generic Twitter Sessions by Using Forensic Tool

IoT is an emerging technology nowadays, which integrates personal computers (PC), Laptops, smart phones and other electronic gadgets used in our routine life communication. This communication can be for leisure purposes or for any official task. Twitter is the most popular Instant Message (IM) toolkit in today's world. The digital remains left after using twitter can be of great source of evidence when an IoT is involved in a court of law. These evidences can be helpful in finding the culprit. For this purpose, digital forensics of IM can be practiced in order to find the evidences and from those evidences the law and order maintaining officer and the officials responsible for security of information can find some heinous cybercrime syndicates. This research has targeted on the usage of Twitter on the desktop PC under windows 10 operating system for the purpose of presenting supportive digital evidences for information security concerns by collecting and analyzing volatile digital bread crumbs. This analysis is done by using forensic tools such as belkasoft Ramcapturer, Wireshark and ProDiscover basic.
THE LIMITS OF LIBERAL INCLUSIVITY: HOW DEFINING ISLAMOPHOBIA NORMALIZES ANTI-MUSLIM RACISM

Abstract Responding to recent calls made within the UK Parliament for a government-backed definition of Islamophobia, this article considers the unanticipated consequences of such proposals. I argue that, considered in the context of related efforts to regulate hate speech, the formulation and implementation of a government-sponsored definition will generate unforeseen harms for the Muslim community. To the extent that such a definition will fail to address the government's role in propagating Islamophobia through ill-considered legislation that conflates Islamist discourse with hate speech, the concept of a government-backed definition of Islamophobia appears hypocritical and untenable. Alongside opposing government attempts to define Islamophobia (and Islam), I argue that advocacy efforts should instead focus on disambiguating government counterterrorism initiatives from the government management of controversies within Islam. Instead of repeating the mistakes of the governmental adoption of the International Holocaust Remembrance Alliance (IHRA)'s definition of antisemitism by promoting a new definition of Islamophobia, we ought to learn from the errors that were made. We should resist the gratuitous securitization of Muslim communities, rather than use such definitions to normalize compliance with the surveillance state.
The role of superior image composition in children's analogical reasoning

Summary Analogical reasoning, as a higher cognitive ability, can help children make inferences about a novel situation. It is vital to help children's analogical reasoning development. However, the traditional intervention methods are simple and the effects cannot maintain. Aiming at this problem, the present study was the first to use computer technology especially image composition technique to promote children's analogical reasoning from an interdisciplinary perspective. Specifically, one minimum region entropy based composition model was proposed. On the one hand, sparse coding model and spatial pyramid matching model were used for searching semantically matching images. On the other hand, minimum region entropy model could contribute to composite the candidate region into an ideal position. Furthermore, we set up a database using massive images and adequate experiments based on it to verify the model's effectiveness and robustness. What's more important, we applied the improved image composition to analogical reasoning task. The results showed that the performance of intervention group was obviously better than control group during intervention stages and posttest stage. In general, the present study not only demonstrated the advantages of the improved image composition but also revealed composition's remarkable contribution for getting analogical relationship by children.
Learning Interpretable Prefix-Based Patterns from Demographic Sequences

There are many different methods for computing relevant patterns in sequential data and interpreting the results. In this paper, we compute emerging patterns (EP) in demographic sequences using sequence-based pattern structures, along with different algorithmic solutions. The purpose of this method is to meet the following domain requirement: the obtained patterns must be (closed) frequent contiguous prefixes of the input sequences. This is required in order for demographers to fully understand and interpret the results.
Generic interactive pixel‐level image editing

Abstract Several image editing methods have been proposed in the past decades, achieving brilliant results. The most sophisticated of them, however, require additional information per‐pixel. For instance, dehazing requires a specific transmittance value per pixel, or depth of field blurring requires depth or disparity values per pixel. This additional per‐pixel value is obtained either through elaborated heuristics or through additional control over the capture hardware, which is very often tailored for the specific editing application. In contrast, however, we propose a generic editing paradigm that can become the base of several different applications. This paradigm generates both the needed per‐pixel values and the resulting edit at interactive rates, with minimal user input that can be iteratively refined. Our key insight for getting per‐pixel values at such speed is to cluster them into superpixels, but, instead of a constant value per superpixel (which yields accuracy problems), we have a mathematical expression for pixel values at each superpixel: in our case, an order two multinomial per superpixel. This leads to a linear least‐squares system, effectively enabling specific per‐pixel values at fast speeds. We illustrate this approach in three applications: depth of field blurring (from depth values), dehazing (from transmittance values) and tone mapping (from brightness and contrast local values), and our approach proves both favorably interactive and accurate in all three. Our technique is also evaluated with a common dataset and compared favorably.
Using Hyperledger Fabric Blockchain to Maintain the Integrity of Digital Evidence in a Containerised Cloud Ecosystem

As Cloud technology continues to evolve with a vast amount of data being transmitted daily, it has added another form of complexity in a forensic investigation. In order to mitigate the complexity of evidence acquisition in the Cloud, we need a method that can forensically maintain digital accounting and trustworthiness with data confidentiality and integrity in the Cloud environment. The current systems have severe security vulnerabilities as it does not provide data confidentiality, integrity and accountability of users and system logs. Due to the complexity in the Cloud ecosystem, acquired evidence can easily be contaminated and cannot be admissible in a court of law. The geolocation, multi-tenancy and political issues have added to the complexity in forensic investigations. Blockchain will facilitate trust between the Cloud Service Providers (CSP) and their customers by heightening the trustworthiness of data and its integrity both in transit and storage. In this paper, we are proposing an innovative permissioned Hyperledger Fabric Blockchain Containerised Cloud ecosystem that will secure and preserve the integrity of digital evidence both in transit or storage. Then, design and implement a Dockerised private Blockchain Cloud ecosystem architecture that will mitigate the challenges faced by forensic investigators in the Cloud ecosystem by preserving evidence integrity in a multi-tenancy and private Cloud environment.
Viability of long-short term memory neural networks for seismic refraction first break detection – a preliminary study

SummarySeismic data processing and analysis focuses on identifying the arrival of seismic waves or ‘first-breaks’. The identification of the arrival of first breaks is complicated by the variance of recording quality typically found across the dataset. In an exploration setting, models need to be developed and refined multiple times. Picking these first breaks then becomes time consuming, limiting the interpreter to processing their dataset rather than considering the implications of their model. Machine Learning as a field continues to respond to many data centric issues within geoscience. However, the field as a whole continues to grapple with balancing the power of these new techniques against operator expertise and skill.This paper presents a methodology to identify the first break in seismic refraction data using a Long-Short Term Memory (LSTM) network, which is a recurrent network architecture. I propose one way to delineate between different groups of traces that the operator would intuitively pick differently, by using dynamic time warping to generate a distance matrix of the seismic traces for clustering. This clustering of trace types allows for a more targeted selection of training samples. I conclude with a proposed framework for the integration of operator skill with machine learning speed and repeatability.
Optical Character Recognition with Chinese and Korean Character Decomposition

We present our work on Optical Character Recognition on Chinese and Korean Characters for line level transcriptions. One challenge for recognizing Chinese and Korean is that there are thousands of characters for a system to recognize. In addition, many uncommon characters only appear a couple of times in training. We use character decomposition methods to break characters into smaller constituent graphemes. CangJie is used for Chinese character decomposition and Korean Jamo is used for Korean character decomposition. Character decomposition reduces the size of the Neural Network models and allows training examples to be shared across uncommon characters with the same graphemes. We report that a CNN-TDNN neural network model using character decomposition has significantly fewer parameters than the baseline while also improving character error rate.
Dealing with Clutter in Augmented Museum Environments

Augmented Reality (AR) can be used in museum and exhibition spaces to extend the available information space. However, AR scenes in such settings can become cluttered when exhibits are displayed close to one another. To investigate this problem, we have implemented and evaluated four AR headset interaction techniques for the Microsoft HoloLens that are based on the idea of Focus+Context (F+C) visualisation [Kalkofen et al. 2007]. These four techniques were made up of all combinations of interaction and response dimensions where the interaction was triggered by either “walk” (approaching an exhibit) or “gaze” (scanning/looking at an exhibit) and the AR holograms responded dynamically in either a “scale” or “frame” representation. We measured the efficiency and accuracy of these four techniques in a user study that examined their performance in an abstracted exhibition setting when undertaking two different tasks (“seeking” and “counting”). The results of this study indicated that the “scale” representation was more effective at reducing clutter than the “frame” representation, and that there was a user preference for the “gaze-scale” technique.