Reviewerly/exHarmony huggingface dataset:
A New Data Hiding Method for Protecting Bigger Secret Data

Security plays a vital role in business. To improve security, hiding secret data in an image can be employed when sending it in an electronic form. Some images are sensitive to modification, such as military and medical images. Thus, reversible data hiding needs to be utilized to prevent permanent distortion. We employ difference expansion, modulus function, and reduced difference expansion to conceal secret data into an image. Unlike previous methods, our scheme can conceal data in any block with any difference pixel value as long as they do not cause underflow or overflow. The result shows that this proposed method can increase the performance, especially the capacity of the payload. As the trade-off, the quality decreases, depending on the cover being used.
Exploring the Design of Availability Status in Mobile IM Messaging with User Enactments

Current mobile instant messaging (IM) applications offer limited information on the availability status of IM users, particularly, their availability for reading and responding to IM messages. Research suggests a gap between what IM recipients want to disclose and what IM senders want to see to determine when to initiate a conversation. The advancement of IM users' receptivity prediction makes it possible to present IM users' predicted availability status. In this research, we conducted user enactment, a design approach for researchers to let participants experience and reflect on possible designs of future technologies, to explore designs of IM availability status in 72 IM conversation scenarios. We explore how IM users interpret different presentations of an uncertain IM status from both the senders' and recipients' perspectives, and what they need and will act upon these presentations.
Adaptive Feature Selection and Classification Using Optimization Technique

Today, the dimensions of the datasets are becoming more and more. From the existing or collected features, some are useful and some are not useful. In order to analyze the dataset, a feature subset can be selected by applying feature selection method. To select best features in lesser time, optimization algorithms like Particle Swarm Optimization (PSO) can be implemented. There is a possibility of redundant features getting selected based on the correlation among features. Pearson correlation coefficient is being employed to reduce the redundancy. Further, PSO is implemented for classification. Datasets analyzed in this research work are, namely, Australian credit, German credit, Iris, Thyroid, Vehicle, WBC, and Wine. It is observed that convincing results were obtained from the proposed method.
Research on Chinese Chess Detection and Recognition Based on Convolutional Neural Network

A deep learning method based on convolutional neural network is introduced into the human–machine game of Chinese chess to detect and recognite pieces, which uses the faster R-CNN target detection architecture and the GoogLeNet convolutional neural network. Based on the shape features of pieces, an anchor mechanism is designed to detect pieces. The method of extracting eight-direction features and rotating invariant HOG features are used to train the ResNet convolutional neural network, which can improve the problem of fewer types of training-intensive chess pieces, single rotating pictures, etc. Experimental results show that the proposed convolutional neural network for chess detection and recognition can greatly enhance the detection of environmental adaptability, robustness, and improve the detection speed and the recognition accuracy.
Investigation of an alleged PLC hacking on sea vessels

Abstract This paper presents a case of alleged PLC (Programmable Logic controller) hacking aboard a commercial ship, and the forensic investigation of PLC components. It presents the lessons drawn from this case and the particular difficulty of investigating PLC and SCADA systems onboard. Whereas hacking is often seen as taking control of a ship, or part of a ship, it is also related to the alteration of the sensors, the PLCs, the data logger or the SCADA systems. This alteration can be done from a hacking group but in the investigated case, it is more likely an action triggered either by the ship owner, or the ship manufacturer himself. This paper also advocates for an addition of a section concerning cybersecurity in the SOLAS - Safety Of Life At Sea - convention or one of other IMO (International Maritime Organization) conventions.
Exploring Shape Deformation in 2D Images for Facial Expression Recognition

Facial expression recognition (FER) using 2D images has been rapidly developed in the past decade. However, existing 2D-based FER methods seldom consider the impact of identity factors, and do not utilize shape features which have been proven to be effective complement to texture features. Built upon latest 3D face reconstruction methods, this paper proposes to generate expression-induced shape deformation map (ESDM) from the 3D face reconstructed from the input 2D face image, and then extract shape feature from ESDM by using a deep network. The shape feature is then combined with the texture feature on the input 2D face image, resulting in a fused feature, based on which the expression of the input 2D face image is recognized by using a softmax classifier. Evaluation experiments on BU-3DFE, MMI and CK+ databases show that our proposed shape feature effectively improves the 2D-based FER accuracy, and our method using the fused feature achieves state-of-the-art accuracy.
A weighted recommendation algorithm based on multiview clustering of user

Recommender systems are widely used to provide users with items they may be interested in without explicitly searching. However, they suffer from low accuracy and scalability problems. Although existing clustering techniques have been incorporated to solve these inherent problems, most of them fail to achieve further improvement in recommendation accuracy because of ignoring the correlations between items and the different effects of item attributes on recommendation results. In this article, we propose a novel recommendation algorithm to alleviate these issues to a large extent. First of all, users and items are clustered into multiple cluster subsets based on user-item rating matrix and item attribute deriving from domain experts, respectively. Then we use a selection method relying on item attribute to mine candidate items and only their predictions will be calculated in the next step, which can save the computation time greatly. Furthermore, by weighting the predictions with TF-IDF (Term Frequency-Inverse Document Frequency) weights, the top-N recommendations are generated to the target user for return. Finally, comparative experiments on two real datasets demonstrate that this algorithm provides superior recommendation accuracy in terms of MAE (Mean Absolute Error) and RMSE (Root Mean Square Error).
Batch Mode Active Learning with Nonlocal Self-Similarity Prior for Semantic Segmentation

Semantic segmentation is a task that heavily relies on the annotated data. The image annotation cost is very expensive. While active learning aims to select the most valuable samples by an iterative procedure. It can reduce the annotation cost and improve the performance of classification. In semantic segmentation, its more common to select a batch of instances instead of a single instance at each iteration. In this paper, we propose a novel batch mode active learning algorithm for semantic segmentation. Different from the previous active learning algorithms for the image classification, we first introduce a new selecting criterion : the image prior of nonlocal self-similarity. It can measure the interaction between the pixels of the image. We combine the informativeness and representativeness with the criterion of nonlocal self-similarity to complete the selection of images at each iteration. In addition, we also use the model uncertainty to measure the information of samples. The model uncertainty is captured by using Monte Carlo Dropout in the semantic segmentation model. In this work, we evaluate our method on the CamVid and PASCAL VOC 2012 datasets. The importance of the nonlocal self-similarity is also assessed. The experiments demonstrate that our algorithm outperforms current state-of-the-art active learning methods over the segmentation performance.
WearAuth: Wristwear-Assisted User Authentication for Smartphones Using Wavelet-Based Multi-Resolution Analysis

Zero-effort bilateral authentication was introduced recently to use a trusted wristwear to continuously authenticate a smartphone user. A user is allowed to use the smartphone if both wristwear and smartphone are determined to be held by the same person by comparing the wristwear's motion with the smartphone's input or motion, depending on the grip — which hand holds the smartphone and which hand provides the input. Unfortunately, the scheme has several shortcomings. First, it may work improperly when the user is walking since the gait can conceal the wrist's motions of making touches. Second, it continuously compares the motions of the two devices, which incurs a heavy communication burden. Third, the acceleration-based grip inference, which assumes that the smartphone is horizontal with the ground is inapplicable in practice. To address these shortcomings, we propose WearAuth, wristwear-assisted user authentication for smartphones in this paper. WearAuth applies wavelet-based multi-resolution analysis to extract the desired touch-specific movements regardless of whether the user is stationary or moving; uses discrete Fourier transform-based approximate correlation to reduce the communication overhead; and takes a new approach to directly compute the relative device orientation without using acceleration to infer the grip more precisely. In two experiments with 50 subjects, WearAuth produced false negative rates of 3.6% or less and false positive rates of 1.69% or less. We conclude that WearAuth operates properly under various usage cases and is robust to sophisticated attacks.
Designing with Data. Anticipating the Impact of Personal Data Usage on Individuals and Society

In a world where digital technologies are becoming human symbionts, in the form of personal companions, wearable devices, and connected environments, digital services can rely more and more on the massive collection of personal data to provide extremely tailored experiences. [1] It becomes necessary to explore the consequences of data harvesting and use during the design of new systems and services, especially before such solutions are fully deployed. In this paper, we propose the Impact Anticipation Method, which collects knowledge on potential issues related to the use of personal data in the design of new physical-digital solutions and we exemplify its application through a use case. The goal is to provide designers of data-rich digital solutions with a tool that analyzes the issues and the perturbations their designs could have on people and society, ultimately supporting the formulation of guidelines to refine the designed solutions.
User Power Behavior Similarity Clustering Based on Unsupervised Extreme Learning Machine Algorithm

Background: Advanced Metering Infrastructure (AMI) for the smart grid is growing rapidly which results in the exponential growth of data collected and transmitted in the device. By clustering this data, it can give the electricity company a better understanding of the personalized and differentiated needs of the user. Objective: The existing clustering algorithms for processing data generally have some problems, such as insufficient data utilization, high computational complexity and low accuracy of behavior recognition. Methods: In order to improve the clustering accuracy, this paper proposes a new clustering method based on the electrical behavior of the user. Starting with the analysis of user load characteristics, the user electricity data samples were constructed. The daily load characteristic curve was extracted through improved extreme learning machine clustering algorithm and effective index criteria. Moreover, clustering analysis was carried out for different users from industrial areas, commercial areas and residential areas. The improved extreme learning machine algorithm, also called Unsupervised Extreme Learning Machine (US-ELM), is an extension and improvement of the original Extreme Learning Machine (ELM), which realizes the unsupervised clustering task on the basis of the original ELM. Results: Four different data sets have been experimented and compared with other commonly used clustering algorithms by MATLAB programming. The experimental results show that the US-ELM algorithm has higher accuracy in processing power data. Conclusion: The unsupervised ELM algorithm can greatly reduce the time consumption and improve the effectiveness of clustering.
Visual Saliency Detection Via Background Features and Object-Location Cues

In this work, we describe a simple visual saliency-detection model based on spatial position of salient objects and background cues. At first, discrete wavelet frame transform (DWDT) are selected to represent directionality characteristics for estimating the centoid of salient objects in the input image. Then, the colour contrast feature performed is to represent the physical characteristics of salient objects. Conversely, sparse dictionary learning is applied to obtain the background feature map. Finally, three typical cues of the directional feature, the colour contrast feature and the background feature are mixed to create a credible saliency map. Simulation experiments verify that the designed algorithm is useful and effective.
Scalable implementation of measuring distances in a Riemannian manifold based on the Fisher Information metric

This paper focuses on the scalability of the Fisher Information manifold by applying techniques of distributed computing. The main objective is to investigate methodologies to improve two bottlenecks associated with the measurement of distances in a Riemannian manifold formed by the Fisher Information metric. The first bottleneck is the quadratic increase in the number of pairwise distances. The second is the computation of global distances, approximated through a fully connected network of the observed pairwise distances, where the challenge is the computation of the all sources shortest path (ASSP). The scalable implementation for the pairwise distances is performed in Spark. The scalable global distance computations are addressed by applying the Dijkstra algorithm (SSSP) sequentially on two proposed networks based on prototypes that approximate the manifold. The proposed solutions are compared with a single-machine implementation in Matlab with experiments showing the first bottleneck solution is faster in Spark, but the distributed solutions for the second bottleneck is slower. Therefore, our conclusion is that the most appropriate method is a hybrid approach, where in terms of runtime and scalability a hybrid approach performs best; running the distributed method and the single-machine approach to solve bottleneck one then two, respectively.
Insecure at any bit rate: why Ralph Nader is the true OG of the software design industry

The software design industry lacks standards for both code quality and security; as a result, code vulnerability at the time of a product’s release is often compromised at subsequent, critical junctures in its consumer-use phase. Exacerbating this problem is the fact that developers typically waive all liability for code errors and place the burden of security on unqualified, non-expert users. Although certain legal remedies exist in the US – often in the form of US Federal Trade Commission (FTC) enforcement actions classifying inadequate data security as an ‘unfair trade practice’ – they are limited in nature, infrequently utilised and are generally incapable of meaningfully protecting consumers. History has shown that other major technological advances, including developments in the aircraft and automotive industries, have occurred in similar unregulated manners, often resulting in negative and potentially dangerous outcomes for the public. To ensure the security of today’s software environment, change from within the industry – not unlike the Ralph Nader-inspired industry-wide automotive safety improvements in the 1970s – is necessary to create a shared liability model for software which protects users from poor development practices. Such a model, when coupled with certification standards and education, will result in a more secure software design industry.
Using Resampling Techniques with Heterogeneous Stacking Ensemble for Mobile App Stores Reviews Analytics

Over the past few years, a boom in the popularity of mobile devices and mobile apps has appeared. More than 205 billion apps were downloaded in 2018. Developers directly distribute mobile apps to end users via a centralized platform like the “App Store” for iOS or the “Play Store” for Android. The Mobile app developers get continuous feedback from users’ reviews added to these stores. Tools like CLAP or AR-MINER were used to crawl reviews from the stores and try to classify them into lots of classifications like (Bug, required feature, usability issue, performance issue,) to facilitate the categorization of issues or features addition to the developer. Some machine learning techniques is used to get the most accurate data classification to help the developer to classify the reported reviews on the stores. This paper presents a machine learning model that uses the Resampling techniques for handling imbalanced classes in addition to ensemble learning and stacking. The model outperforms those tools and enhances the results applied on Mobile App Stores Reviews Analytics. In addition to that the paper provides experiments applied on different kinds of datasets and showed improvements in accuracy from 85% (previous model) to 90% (our model) and ROC from 96% to 98% especially on the Reviews dataset.
Evaluation of Interoperability Maturity Level: Case Study Indonesian Directorate General of Tax

Most of the e-Government projects facing problem in integration with external services, hence there is a lack of collaboration and exchange information among different public administration e-services. Directorate General of Tax (DGT) has developed many electronic services, but until today the interoperability maturity level of that services never been assessed. Interoperability Maturity Model (IMM) is a methodology to get insight and evaluate interoperability maturity of public service based on a set of defined attribute and maturity stage. We used IMM as the methodology for interoperability maturity evaluation in this research because it is the most recent, has a complete attribute for each interoperability layer, open resources and has been used in Europe’s interoperability improvement program. According to the assessment, DGT’s Interoperability maturity is in level 3 for overall areas. In other words, DGT electronic services are at an essential stage. The main component of interoperability best practice has implemented in DGT’s electronic services.
Script Independent Morphological Segmentation for Arabic Maghrebi Dialects: An Application to Machine Translation

This research deals with resources creation for under-resourced languages. We try to adapt existing resources for other resourced-languages to process less resourced ones. We focus on Arabic dialects of the Maghreb, namely Algerian, Moroccan and Tunisian. We first adapt a well known statistical word segmenter to segment Algerian dialect texts written in both Arabic and Latin scripts. We demonstrate that unsupervised morphological segmentation could be applied to Arabic dialects regardless of used script. Next, we use this kind of segmentation to improve statistical machine translation scores between the tree Maghrebi dialects and French. We use a parallel multidialectal corpus that includes six Arabic dialects in addition to MSA and French. We achieved interesting results. Regardsto word segmentation, the rate of correctly segmented words reached 70% for those written in Latin scriptand 79% for those written in Arabic script. For machine translation, the unsupervised morphological segmentation helped to decrease out of vocabulary words rates by a minimum of 35%.
Truth, Reflection, and Commitment

Ever since the discovery of the arithmetical incompleteness phenomena, proof-theoretic reflection principles have been intensively investigated in mathematical logic. But reflection principles also have philosophical significance. As articulations of trust in formal systems, they provide information about the relations between concepts such as provability, rational belief, truth, consistency. Against this background, this chapter discusses proof-theoretic reflection principles from a logico-philosophical perspective.
Towards a Unified Process Model for Comprehensive Software Metrics Suite: An Introduction

Literature has revealed proliferation in the fabrication of new software metrics and their automated representations. The number of software metrics have been on high increase with relatively unmonitored standardization in formalized definitions and documentation of software metrics. This has produced inconsistent user-defined metrics on the different tools that implement software metrics. The consequent draw-back is inconsistent metrics result across different tools implementing the same metrics. This problem forms the foundation to irregular standard and/or universal definition and interpretation of metrics output. There is therefore need for the establishment and/or reinforcement of standards and technology that monitors and track metrics evolution towards achieving homogeneity in software metrics definitions and the tools that implement them. This paper is introducing a yet comprehensive implementation of new metrics model that unifies definition, interpretation, archiving, automation and consequently, extensibility. These rippling chained processes form the unified process model for implementing software metrics.
A Study on the Development of an Online Platform for Mass Customization of Hanbok Design Using QFD Method (Ⅰ)

The purpose of this study is to derive the target specifications for mass customization of the Hanbok design platform to develop a platform where consumers can directly participate in and design Hanboks by reflecting the demands and trends of the times. The research method for this study was to first derive customer demand attributes and pursuit images through a consumer survey of mass customization Hanbok products, identify the importance and priority of mass customization Hanbok design properties using the QFD technique, and then set the target specifications of the mass customization Hanbok design platform based on this work. In addition, the target specifications of the design platform obtained from this study yielded the following five results. First, the details, patterns, trimming, length, and color of Hanbok design are important customization elements and need to be designed to be selectable and applied according to consumers