Reviewerly/exHarmony huggingface dataset:
Issues and Experiences in Building Heterogeneous Co-Editing Systems

Most past research efforts in co-editing focused on homogeneous co-editing, which allows multiple users to use the same editor to edit shared documents in the same session, and nearly all real-world co-editors, such as Google Docs, are homogeneous co-editors. In this work, we explore issues and solutions in building heterogeneous co-editing systems, which allow multiple users to use different editors to edit shared documents in the same session. To drive our exploration, we built a prototype heterogeneous co-editing system, named CoVim+CoEmacs, which allows multiple users to use full functionalities and UI features of two comprehensive and rivalry text editors, Vim and Emacs, in the same co-editing session. In this paper, we focus on technical issues in designing and implementing heterogeneous co-editors in general and CoVim+CoEmacs in particular. We have motivated this work by potential usage benefits of heterogeneous co-editing systems and used working scenarios under the CoVim+CoEmacs prototype to illustrate some novel usages and inner workings of such systems, but left systematic user studies on heterogeneous co-editing to future work. We hope the insights and experiences drawn from this work can not only contribute to advancing state-of-the-art collaborative system design and implementation, but also provide inspiration to future heterogeneous collaborative application system designers.
The Blockchain–Sustainability Nexus: Can This New Technology Enhance Social, Environmental and Economic Sustainability?

With the rise and fall of the prominence of Bitcoin, blockchain technology, which provides public online ledgers used for the verification and recording of transactions, has started to become the center of attention for diverse parties in the global financial system. This chapter explores the nature of blockchain and discusses how it may contribute to, or obstruct, sustainability. To this end, first, blockchain technology is introduced. Next, a short discussion on sustainability is presented, including how it is defined, measured, reported, and understood in theoretical frameworks. After that, the 2015 United Nations Sustainable Development Goals are briefly explained. This is followed by a systematic literature review, which highlights the scarcity of literature linking blockchain to sustainability. Finally, the author offers her own reflections on the potential of blockchain to revolutionize the financial services industry and weighs up the pros and cons vis-a-vis sustainable development.
Multiple Hypothesis Video Relation Detection

Video relation in the form of triplet〈subject, predicate, object〉plays a vital role in video content understanding. Existing works on video relation detection are limited to associating short-term relations into long-term relations throughout the video, because of the inaccurate and missing problem of short-term proposals. To alleviate the weakness of existing video relation detection methods, this work proposes a novel approach called Multi-Hypothesis Relational Association (MHRA), that can generate multiple hypotheses for video relation instances for more robust long-term relation prediction. Experiments on the benchmark dataset show that MHRA is able to outperform the state-of-the-art methods.
Facial Expression Recognition of Nonlinear Facial Variations Using Deep Locality De-Expression Residue Learning in the Wild

Automatic facial expression recognition is an emerging field. Moreover, the interest has been increased with the transition from laboratory-controlled conditions to in the wild scenarios. Most of the research has been done over nonoccluded faces under the constrained environment, while automatic facial expression is less understood/implemented for partial occlusion in the real world conditions. Apart from that, our research aims to tackle the issues of overfitting (caused by the shortage of adequate training data) and to alleviate the expression-unrelated/intraclass/nonlinear facial variations, such as head pose estimation, eye gaze estimation, intensity and microexpressions. In our research, we control the magnitude of each Action Unit (AU) and combine several of the Action Unit combinations to leverage learning from the generative and discriminative representations for automatic FER. We have also addressed the problem of diversification of expressions from lab controlled to real-world scenarios from our cross-database study and proposed a model for enhancement of the discriminative power of deep features while increasing the interclass scatters, by preserving the locality closeness. Furthermore, facial expression consists of an expressive component as well as neutral component, so we proposed a generative model which is capable of generating neutral expression from an input image using cGAN. The expressive component is filtered and passed to the intermediate layers and the process is called De-expression Residue Learning. The residue in the intermediate/middle layers is very important for learning through expressive components. Finally, we validate the effectiveness of our method (DLP-DeRL) through qualitative and quantitative experimental results using four databases. Our method is more accurate and robust, and outperforms all the existing methods (hand crafted features and deep learning) while dealing the images in the wild.
A Method for Constructing Supervised Topic Model Based on Term Frequency-Inverse Topic Frequency

Supervised topic modeling has been successfully applied in the fields of document classification and tag recommendation in recent years. However, most existing models neglect the fact that topic terms have the ability to distinguish topics. In this paper, we propose a term frequency-inverse topic frequency (TF-ITF) method for constructing a supervised topic model, in which the weight of each topic term indicates the ability to distinguish topics. We conduct a series of experiments with not only the symmetric Dirichlet prior parameters but also the asymmetric Dirichlet prior parameters. Experimental results demonstrate that the result of introducing TF-ITF into a supervised topic model outperforms several state-of-the-art supervised topic models.
Clinical Decision Support for Ovarian Carcinoma Subtype Classification: A Pilot Observer Study With Pathology Trainees

Context.— Clinical decision support (CDS) systems could assist less experienced pathologists with certain diagnostic tasks for which subspecialty training or extensive experience is typically needed. The effect of decision support on pathologist performance for such diagnostic tasks has not been examined. Objective.— To examine the impact of a CDS tool for the classification of ovarian carcinoma subtypes by pathology trainees in a pilot observer study using digital pathology. Design.— Histologic review on 90 whole slide images from 75 ovarian cancer patients was conducted by 6 pathology residents using: (1) unaided review of whole slide images, and (2) aided review, where in addition to whole slide images observers used a CDS tool that provided information about the presence of 8 histologic features important for subtype classification that were identified previously by an expert in gynecologic pathology. The reference standard of ovarian subtype consisted of majority consensus from a panel of 3 gynecologic pathology experts. Results.— Aided review improved pairwise concordance with the reference standard for 5 of 6 observers by 3.3% to 17.8% (for 2 observers, increase was statistically significant) and mean interobserver agreement by 9.2% (not statistically significant). Observers benefited the most when the CDS tool prompted them to look for missed histologic features that were definitive for a certain subtype. Observer performance varied widely across cases with unanimous and nonunanimous reference classification, supporting the need for balancing data sets in terms of case difficulty. Conclusions.— Findings showed the potential of CDS systems to close the knowledge gap between pathologists for complex diagnostic tasks.
Research on Traceability Algorithm of Logistics Service Transaction Based on Blockchain

The traditional logistics transaction lacks a perfect traceability mechanism, and the data information's integrity and safety are not guaranteed in the existing traceability system. In order to solve the problem of main body responsibility caused by the participation of many stakeholders and the uncompleted supervision system in the process of logistics service transaction, This paper proposes a traceability algorithm for logistics service transactions based on blockchain. Based on the logistics service supply chain and alliance chain, the paper firstly investigates the traditional logistics service supply chain, analyzes the existing problems, and combines the structural characteristics of the blockchain to propose a decentralized new logistics service supply chain concept model based on blockchain. Then, using Globe sandara 1 to standardize the physical products and data circulating in the new logistics service supply chain, form unified and standard traceable data, and propose a multi-dimensional traceable data model based on logistics service supply chain. Based on the proposed model, combined with the business process of the logistics service supply chain and asymmetric encryption, a blockchain-based logistics service transaction traceability algorithm is designed. Finally, the simulation results show that the algorithm realizes the end-to-end traceability of the logistics service supply chain, and the service transaction is transparent while ensuring the integrity and security of the data.
Geochemical And Hyperspectral Data Fusion For Drill-Core Mineral Mapping

Hyperspectral imaging is increasingly being used in the mining industry for the investigation of drill-core samples. It provides the means to analyze a large amount of cores considerably faster than traditional methods and in a non-invasive and non-destructive manner. Traditional approaches used to analyse drill-core hyperspectral data are mainly based on visual observations and need significant human interactions. Thus, they are time-consuming and subjective. In this paper, we explore the use of supervised machine learning techniques for mineral mapping in drill-core hyperspectral data. For this purpose, we suggest to use geochemical data for generating a training set. The main contribution of this work is to fuse geochemical and hyperspectral data within a machine learning framework. Moreover, for a more complete mineral mapping task, we integrate visible near-infrared (VNIR), short-wave infrared (SWIR) and long-wave infrared (LWIR) hyperspectral data. For the extraction of input features, the traditional Principal Component Analysis (PCA) is implemented. For classification, we propose to use Random Forest (RF) because of its significant performance in hyperspectral data classification when there are few training samples available. Experimental results show that the proposed method provides comprehensive mineral maps in which the distribution and patterns of different minerals are well characterised.
Pedestrian Detection Based on Spatial Attention Module for Outdoor Video Surveillance

Pedestrian detection remains challenging because of hard instances, such as illumination change, various occlusion, and special appearance, etc. The current methods to detect these hard examples depend on complicate manual designs or additional annotations. We observe that the spatial information of pedestrians can be obtained through motion information, which enlightens us to utilize this spatial information to guide effective training of detectors. In this paper, we introduce the Spatial Attention Module, which guides Convolutional Neural Networks (CNNs) to focus on potential pedestrian positions indicated by hierarchical unsupervised guidance information, including motion information and static information. The experimental results on two datasets demonstrate that the proposed method outperforms the state-of-the-art and can capture hard examples, which are missed by the baseline.
A Design for a Cryptographically Secure Pseudo Random Number Generator

We proposes the design of a cryptographically secure pseudo random number generator (CSPRNG) that involves the permutation of the internal state of the PRNG. The design of the CSPRNG is described and Dieharder test comparisons are made against AES. The results of these tests are described and a discussion of future work concludes the paper.
Projection-Based Continuous 3D Printing Process With the Grayscale Display Method

Abstract The printing resolution and scale of projection-based 3D printing are affected by the pixel size and projected light power. An effective and versatile method to print complex constructs with high resolution and large area is still required since light distribution in printing systems is generally non-uniform. Here, we propose a projection-based continuous 3D printing with the grayscale display method to serve as an effective and precise way to improve printing resolution and area. The light characterization results demonstrated that the power density presented a non-uniform distribution, and the power values are linear to the excitation power. After modifying the masks into grayscale according to the duty cycle of the digital micro-mirror device (DMD) display, projected light exhibited improved uniformity along with expected light power and uniform ratio. To validate this developed printing process, the grayscale continuous printing of mesh and hexahedron frame constructs enabled a remarkable increase in the printing area and alleviation of under/over curing. This work reveals significant progress in printing of constructs at larger area and higher resolution in projection-based continuous 3D printing under non-uniform light.
Don't Worry, Be Happy – Exploring Users' Emotions During App Usage for Requirements Engineering

User feedback and usage data are a valuable source of knowledge for requirements engineering (RE) and software evolution. Recent research in RE has focused on the acquisition and analysis of such user input. However, we argue that important information complementing existing data has been neglected, namely users' emotions shown during app usage, which provide insight into how users experience the app. We are aware that tracking users' emotions at large-scale might be challenging. So far, emotion tracking is often applied in lab and test settings only. We envision an unobtrusive emotion tracker running on the users' smartphones in their regular usage context. However, we do not know whether such an approach would be accepted by the app users and which requirements such an emotion-tracking solution would need to fulfill. In this paper, we present the first results from our exploratory interview study with eight mobile app users regarding their preferences and concerns with respect to emotion tracking. Early results are encouraging and suggest that app users tend to accept emotion tracking during app usage, having only minor privacy concerns. In this paper, we discuss our ongoing research, including the long-term direction and prospects of considering users' emotions for RE.
An Empirical Study on Expectation of Relationship Between Human and Smart Devices—With Smart Speaker as an Example

Artificial smart devices, represented by smart speakers, are entering Chinese families increasingly. AI technology allows people to interact with them in a natural way, such as spontaneous dialogue, and also makes people generate anthropomorphic associations with them. This is an important feature that distinguishes AI from PC and the mobile age. And what role and relationship do the smart devices fit into people's daily life has become an issue to be discussed. We combined questionnaires and focus groups to investigate the mainstream smart speakers on the market in China. We found that the users' expectative relationships with smart devices are assistants (51.3%) and friends (48.7%). And there is still a gap between actual perceptions and expectations of smart speakers. This article also discusses the ways to bridge the gap.
Fuzzy entropy clustering by searching local border points for the analysis of gene expression data

Clustering data by identifying a subset of representative border points is essential for detecting dataset structures and processing complicated messages in data. Such border points can be found by exactly choosing an initial subset of data points, and then iteratively refining it with fuzzy membership values; however, this only works well if the fuzzy rule choice is close to a good solution. We propose a clustering algorithm, known as affine fuzzy neuron (AFN), which measures the relations of linear memberships between border points and the cluster centers of points within a subset. Fuzzy memberships of border points are calculated until their corresponding clusters gradually emerge. AFN exploits a typical feature of real-life biological gene clusters, like gene expression measurements, from microarrays or single-cell RNA-Seq. That is, genes sharing the same expression profile belong to different clusters with diverse functions. We hypothesize that these genes only appear at cluster boundaries and a subset of genes comprises points with similar expression profiles but entirely different functions. We use AFN to cluster several test cases and analyze various gene expression datasets. The proposed clustering algorithm is a flexible modeling framework that increases accuracy and combines border regions across multiple clusters while remaining computationally feasible.
Six types of audio that DEFY reality!

In this paper we examine how the term 'Audio Augmented Reality' (AAR) is used in the literature, and how the concept is used in practice. In particular, AAR seems to refer to a variety of closely related concepts. In order to gain a deeper understanding of disparate work surrounding AAR, we present a taxonomy of these concepts and highlight both canonical examples in each category, as well as edge cases that help define the category boundaries.
Video Steganography by Neural Networks Using Hash Function

Video Steganography is an extension of image steganography where any kind of file in any extension is hidden into a digital video. The video content is dynamic in nature and this makes the detection of hidden data difficult than other steganographic techniques. The main motive of using video steganography is that the videos can store large amount of data in it. This paper focuses on security using the combination of hybrid neural networks and hash function for determining the best bits in the cover video to embed the secret data. For the embedding process, the cover video and the data to be hidden is uploaded. Then the hash algorithm and neural networks are applied to form the stego video. For the extraction process, the reverse process is applied and the secret data is obtained. All experiments are done using MatLab2016a software.
A Fast Denoising Algorithm for X-Ray Images with Variance Stabilizing Transform

We propose a fast denoising algorithm for X-Ray images with variance stabilizing transformations. The variance stabilizing transformations are used to transform Poisson noisy images to Gaussian noisy images. Therefore, we can utilize advantages of the fast denoising algorithm based on the alternative direction method of multipliers. In experiments, we evaluate denoising quality by the Peak signal-to-noise ratio and the Structure Similarity metrics. Comparing results show that our method outperforms other similar denoising methods.
The Application of the Internal Restructuring Method of the Information Resource Data According to the Sign of the Number of Series of Units to Improve the Statistical Coding Efficiency

A fundamentally new approach to restructuring information resource data is considered - internal restructuring on a quantitative sign for a more favorable presentation of the encoded data. A technology has been developed for clustering information resource data based on the sign of number of series of units. The analysis of the application of the method of internal restructuring of information resource data on the sign of the number of series of units to improve the statistical coding efficiency from the position of reducing the length of the presentation of information.
Sentiment Analysis Of Indonesian Homosexual Tweets Using Support Vector Machine Method

Sentiment analysis is Natural Language Processing in the form of processing sentiments of words and sentences that are used to determine the polarity of the word or sentence. Twitter became social media with the highest spread of pornographic content. This condition makes Twitter as a suitable object for the process of analyzing the sentiments of tweets of homosexuals, one of which is the negative sentiment that has pornographic content. This research is expected to contribute to the government to be able to assist homosexuals and also provide information for the public so that they did not expose to harmful content about homosexuality that can found on social media twitter.
ObjectMap

In recent years there is a surge of serialization-based vulnerabilities in web applications which have led to serious incidents, exposing private data of millions of individuals. Although there have been some efforts in addressing this problem, there is still no unified solution that is able to detect implementation-agnostic vulnerabilities. We aim to fill this gap by proposing ObjectMap, an extendable tool for the detection of deserialization and object injection vulnerabilities in Java and PHP based web applications. Furthermore, we also introduce the first deserialization test environment which can be used to test deserialization vulnerability detection tools and for educational purposes. Both of these tools are easily extendable and the first to implement this combination of features to the best of our knowledge and they bring together a synthesis of cross-complementing functionalities that are able to ignite further research in the field and help in the development of more feature-rich solutions.
An Efficient Image Watermarking Technique Based on IWT-DCT-SVD

Confidentiality of multimedia data such as image must be protected from illicit users. Thus, a novel image watermarking technique using Integer Wavelet Transform (IWT), Discrete Cosine Transform (DCT), and Singular Value Decomposition (SVD) is proposed in this paper. LL (low-low) subband is selected after applying one-level IWT on host image, which is further processed with DCT. Embedding of watermark is performed directly into Singular Values (SVs) of transformed image. Robustness of proposed technique is comprehensively determined under various set of attacks such as scaling and histogram equalization. The comparative study is also done among proposed technique and other prevailing techniques and experimental results clearly indicate effectiveness of this proposed method.