Reviewerly/exHarmony huggingface dataset:
Short Zero-Knowledge Proof of Knowledge for Lattice-Based Commitment

Commitment scheme, together with zero-knowledge proof, is a fundamental tool for cryptographic design. Recently, Baum et al. proposed a commitment scheme (BDLOP), which is by far the most efficient lattice-based one and has been applied on several latest constructions of zero-knowledge proofs. In this paper, we propose a more efficient zero-knowledge proof of knowledge for BDLOP commitment opening with a shorter proof. There are a few technical challenges, and we develop some new techniques: First, we make an adaption of BDLOP commitment by evaluating the opening with the singular value rather than $$\ell _2$$ norm in order to get compact parameters. Then, we try to use the bimodal Gaussian technique to minimize the size of the proof. Finally, utilizing a modulus-switch technique, we can retain the size of the commitment.
MGGR: MultiModal-Guided Gaze Redirection with Coarse-to-Fine Learning

Gaze redirection aims at manipulating a given eye gaze to a desirable direction according to a reference angle and it can be applied to many real life scenarios, such as video-conferencing or taking groups. However, the previous works suffer from two limitations: (1) low-quality generation and (2) low redirection precision. To this end, we propose an innovative MultiModal-Guided Gaze Redirection~(MGGR) framework that fully exploits eye-map images and target angles to adjust a given eye appearance through a designed coarse-to-fine learning. Our contribution is combining the flow-learning and adversarial learning for coarse-to-fine generation. More specifically, the role of the proposed coarse branch with flow field is to rapidly learn the spatial transformation for attaining the warped result with the desired gaze. The proposed fine-grained branch consists of a generator network with conditional residual image learning and a multi-task discriminator to reduce the gap between the warped image and the ground-truth image for recovering the finer texture details. Moreover, we propose leveraging the gazemap for desired angles as an extra guide to further improve the precision of gaze redirection. Extensive experiments on a benchmark dataset show that the proposed method outperforms the state-of-the-art methods in terms of image quality and redirection precision. Further evaluations demonstrate the effectiveness of the proposed coarse-to-fine and gazemap modules.
Understanding Rural Parents’ Behavioral Intention to Allow Their Children to Use Mobile Learning

Faced with many challenges resulting in learners’ poor performance at the matriculation level, emphasis on Science, Technology, Engineering and Mathematics (STEM) education is in its infancy in South African’s high schools. However, studies have shown that mobile learning (m-learning) can be used to mitigate the challenges of STEM education. Despite, the benefits of mobile learning to rural STEM learners, its full potential has not been realized because the adoption of m-learning depends on users’ acceptance. Prior studies focused on teachers’ and learners’ acceptance of mobile learning. However, little is known about parents’ acceptance of m-learning, especially in rural areas. This study explores the acceptance of m-learning by parents of rural high school STEM learners. The study proposes the parents’ acceptance of m-learning model, which extends the technology acceptance model by introducing perceived social influence and perceived resources. Stratified random sampling was used to select 200 parents in the survey. Partial least squares structural equation modeling (PSL-SEM) was used to analyze data from 129 valid questionnaires. The proposed model explained 41% of the variance in parents’ acceptance of mobile learning. Attitude towards the use was found to be the best predictor and the only factor that have a direct effect on behavioral intention to use mobile learning. However, all other factors have an indirect influence on behavioral intention. The findings revealed that for mobile learning to be successfully implemented in rural areas, resources need to be provided.
Development of Projo Bale Model Based on Android Application in History and Local Wisdom Studies of Malang

Lack of research development on history and local wisdom studies of Malang and its integration in learning acitivities is the background of the development of Projo Bale model. Local history is full of relevant local wisdom values to be implanted in learners. The history department as the institution that produce history teachers should teach students with knowledge about local history at least the area of Malang. This Model was compiled in response to the problem. The collaboration between Outdoor Learning and Project Based Learning models with Android app based is a creative effort to leverage technology in historical learning innovations. The stages in this development include 5 phases ranging from analysis, development, design, implementation and evaluation. In the final stage of the evaluation of the model post implementation reached an average figure of 82.75% indicating this model is viable and effective as an innovative model in the study of history.
Abductive Causal Reasoning for Internet of Things

The Internet of Things (IoT) is defined as a "global infrastructure for the information society, enabling advanced services by interconnecting (physical and virtual) things based on existing and evolving interoperable information and communication technologies [1]." This network of heterogeneous devices that are connected can interact and share data as well as aid in decision making. Abductive causal reasoning (ACR) offers a new perspective to learn from data with the capability to support decisions by explaining the context or environment in which the choices are made. In this paper, we propose to use ACR in an IoT environment to infer sensible hypotheses from IoT data and explain how decisions can be made or improved. Examples are given to illustrate how ACR can support decisions from the available data.
The Hydrophily of Some Lacquers for Electrical Use

Through gravimetric determinations, volume resistivity, dielectric spectroscopy, and comparative thermal analysis (TG, DTA and DTG), the interactions between the distilled water and three different types of alkyd-epoxy-melamine, epoxy and polyurethane lacquers were studied. From the experimental determinations it was found that after 700 h of immersion in water at 20 � 2�C the alkyd-epoxy-melamine based lacquer has a maximum water uptake, respectively 1.76%, followed by the epoxy lacquer 1.4% and polyurethane 0.93%. The thermal analysis sugests that because the water retained by the investigated polymers does not change the TG diagrams in the temperature range up to 150 �C, which suggests that the weight increase of the samples during the immersion could be due to some chemical processes between the water and polymer by which the chemistry structure of the polymer changes. Through electrical measurementes one can observe that after the immersion in water (over 700 hours), dielectric loss increases and the volume resistivity (measured in DC) of the investigated lakes decreases, which is explained by the increasing of polar groups (-OH) in the polymer structure. A comparative analysis of the experimental data reveals that in electrical applications the lacquer LS (polyurethane) is superior to the lacquers L-528 (alkyd-epoxy-melamine) and LG (epoxy), because it has no mass losses (structural changes) up to 280�C it has a volume resistivity of about 21 % higher than L-G, and about 300 % higher than L-528, and has water uptake and dielectric loss substantially lower comparing to L-528 and L-G.
From the Body with the Body: Performing with a Genome-Based Musical Instrument

INTRODUCTION:In this paper we present Silico, a new Digital Musical Instrument which ideally represents the performer itself.This instrument is composed by two parts: an interface (a sensor glove), which relies on the movements of the performer's hand, and a computational engine (a set of patches developed in Max 7), which generates sound events based on the genomic data of the performer.OBJECTIVES: We want to propose a new reflection on the relation between the body and musical instruments.Moreover, we aim to investigate the voluntary and involuntary aspects of our body, intended as a starting point for a musical performance.As a metaphor of these two layers, we used here the hand and the genome of the performer.METHODS: We have investigated our objectives through the whole design process of a Digital Musical Instrument, using a practice-based approach.RESULTS: Our system is a multilayered composed instrument which maps its computational part and its interface on the performer's body.Silico can be used as a standalone musical instrument to generate music in real time.CONCLUSION: Our works shows a new path about the use of genomic data in a musical way, as a new perspective of human-computer interaction in a performative contexts.
Designing the Structured Search Experience: Rethinking the Query-Builder Paradigm

Knowledge workers such as healthcare information professionals, legal researchers, and librarians need to create and execute search strategies that are comprehensive, transparent, and reproducible. The traditional solution is to use command-line query builders offered by proprietary database vendors. However, these are based on a paradigm that dates from the days when users could access databases only via text-based terminals and command-line syntax. In this paper, we present a new approach in which users express concepts as objects on a visual canvas and manipulate them to articulate relationships. This offers a more intuitive user experience (UX) that eliminates many sources of error, makes the query semantics more transparent, and offers new ways to collaborate, share, and optimize search strategies and best practices
Efficient Steganography on Video File using Discrete Cosine Transform Method (DCTM)

The desire to keep sensitive information and communications secret for many decades, and potential generations have been paramount.In this era of digital media and internet communications, the needs seem more pressing as lots of information and people's conversations are been tracked, stolen, manipulated and pirated.Steganography is the art of hiding a message, a covert substance (stego) so that only the sender and the intended recipient know that there is a message contained in the substance or the stego.It can also be stated to be the fine art of hiding information.This means that hiding message in a carrier file enables the deniability of the existence of that message.This work provides an overview of steganography, designs software to illustrate specific steganography methods and recent developments in the field.The methodology used to develop this work was objectoriented analysis and design methodology (OOAD), and the programming language used is Java programming language which is based on classes and objects as it took care of compatibility over multiple operating systems.The work was done by embedding text file in a video frame (video file) in such a manner that the video file does not lose its functionality using Discrete Cosine Transform Method (DCTM).This method strives for high data security to a hacker's inability to detect the existence of a hidden message or information.
Bridging boundaries between systemic functional linguistics and translation studies

Erich Steiner, as a leading scholar in systemic functional linguistics (SFL), has been involved in various important strands of research on SFL and translation. In this interview, he discusses his motivation of studying linguistics, and introduces his works in different areas, including machine translation in the 1980s, corpora, register, explicitation, grammatical metaphor, integration of product- and process-based researches, as well as language description and comparison.
Exposing Digital Image Forgeries by Detecting Contextual Abnormality Using Convolutional Neural Networks

Traditionally, digital image forensics mainly focused on the low-level features of an image, such as edges and texture, because these features include traces of the image’s modification history. However, previous methods that employed low-level features are highly vulnerable, even to frequently used image processing techniques such as JPEG and resizing, because these techniques add noise to the low-level features. In this paper, we propose a framework that uses deep neural networks to detect image manipulation based on contextual abnormality. The proposed method first detects the class and location of objects using a well-known object detector such as a region-based convolutional neural network (R-CNN) and evaluates the contextual scores according to the combination of objects, the spatial context of objects and the position of objects. Thus, the proposed forensics can detect image forgery based on contextual abnormality as long as the object can be identified even if noise is applied to the image, contrary to methods that employ low-level features, which are vulnerable to noise. Our experiments showed that our method is able to effectively detect contextual abnormality in an image.
Unsupervised Feature Selection Based on Low-Rank Regularized Self-Representation

Feature selection aims to find a set of features that are concise and have good generalization capabilities by removing redundant, uncorrelated, and noisy features.Recently, the regularized self-representation (RSR) method was proposed for unsupervised feature selection by minimizing the L 2,1 norm of residual matrix and self-representation coefficient matrix.In this paper, we find that minimizing the L 2,1 norm of the self-representation coefficient matrix cannot effectively extract the features with strong correlation.Therefore, by adding the minimum constraint on the kernel norm of the self-representation coefficient matrix, a new unsupervised feature selection method named low-rank regularized self-representation (LRRSR) is proposed, which can effectively discover the overall structure of the data.Experiments show that the proposed algorithm has better performance on clustering tasks than RSR and other related algorithms.
3. Inventorizing, situating, transforming : Social semiotics and data visualization

This chapter is an overview of social semiotics as a productive framework for research on data visualization. It provides conceptual instruments that can be used to explore the relationship between the formal properties of data visualization and the meanings and practices that these may promote or hinder among users. In particular, the chapter argues that a social semiotic framework can be used to inventorize, situate, and transform visualization resources. Overall, it links descriptive, interpretive, and critical objectives to generate a framework aimed at understanding how data visualization ‘works’ from a formal standpoint, what meanings are consistently associated with particular semiotic resources, and how both key semiotic ‘rules’ and dominant meanings may be questioned and changed.
DIGITAL CYBER FORENSIC EMAIL ANALYSIS AND DETECTION BASED ON INTELLIGENT TECHNIQUESINVESTIGATION

The Internet has become open, public and widely used as a source of data transmission and exchanging messages between criminals, terrorists and those who have illegal motivations. Moreover, it can be used for exchanging important data between various military and financial institutions, or even ordinary citizens. One of the important means of exchanging information widely used on the Internet medium is the e-mail. Email messages are digital evidence that has been become one of the important means to adopt by courts in many countries and societies as evidence relied upon in condemnation, that prompts the researchers to work continuously to develop email analysis tool using the latest technologies to find digital evidence from email messages to assist the forensic expertise into to analyze email groups .This work presents a distinct technique for analyzing and classifying emails based on data processing and extraction, trimming, and refinement, clustering, then using the SWARM algorithm to improve the performance and then adapting support vector machine algorithm to classify these emails to obtain practical and accurate results. This framework, also proposes a hybrid English lexical Dictionary (SentiWordNet 3.0) for email forensic analysis, it contains all the sentiwords such as positive and negative and can deal with the Machine Learning algorithm. The proposed system is capable of learning in an environment with large and variable data. To test the proposed system will be select available data which is Enron Data set. A high accuracy rate is 92% was obtained in best case. The experiment is conducted the Enron email dataset corpus (May 7, 2015 Version of the dataset).
Effect-Cause Analysis and Prediction Convergence of Random Failure Gate in a Probabilistic Competitive Environment with Case Study on Quality Control Process

The world of competition is not always deterministic.Probabilistic approach is more relevant and practical in any competitive environments.The effect of random failure gate which usually comes in the way of anything that promotes the low quality and at the same time denies high quality to come up in a competition and having a probability distribution for an instant of time is discussed in the present article.For this purpose the article introduces a novel methodology using the outcome probability of each rank along with the newly added concept of position ratios with a view to study the possibility of making proper predictions in the competitive environment discussed.The study also extends to infinite rank model cases as well.Moreover, a case study has been conducted to predict the risk of appropriate forecasting of different quality level using the model developed.The article emphasized the impact of number of failure gate and their respective influence in the area of ascertaining production quality making use of the concept of position ratio along with the outcome probabilities, which in turn improves the decision making to foresee the possible product quality variations in any manufacturing system, after a specific tenure of production.The methodology developed here will definitely find its application in the area of designing some powerful Decision Support Systems (DSS), where competition is fundamentally concerned with.
Automatic Image Matting of Synthetic Aperture Radar Target Chips

A matting technique to extract the targets from synthetic aperture radar (SAR) images is presented.Binary segmentation is performed initially for rough identification of target boundaries.Trimap is then estimated by combining the boundary structures of the input and segmented images using guided filter.In order to improve the accuracy of estimated trimap, super-pixels based segmentation is performed.A propagation based matting algorithm is then applied to separate the target from non-target region.Simulations conducted on different SAR images from MSTAR database show significance of proposed technique.
A Research on The Method of Chinese Sentiment Analysis Based on Transfer Learning

Sentiment analysis has great commercial value, which is widely used in product analysis and product recommendation in the industry. This paper presents the potential application of transfer learning in Chinese text categorization and have a binary classification of the automobile comment dataset, demonstrating that the model presented in this paper is effective and the classification accuracy is improved to 96.35%.
Extracting Polarity Shifting Patterns from Any Corpus Based on Natural Annotation

In recent years, online sentiment texts are generated by users in various domains and in different languages. Binary polarity classification (positive or negative) on business sentiment texts can help both companies and customers to evaluate products or services. Sometimes, the polarity of sentiment texts can be modified, making the polarity classification difficult. In sentiment analysis, such modification of polarity is termed as polarity shifting , which shifts the polarity of a sentiment clue (emotion, evaluation, etc.). It is well known that detection of polarity shifting can help improve sentiment analysis in texts. However, to detect polarity shifting in corpora is challenging: (1) polarity shifting is normally sparse in texts, making human annotation difficult; (2) corpora with dense polarity shifting are few; we may need polarity shifting patterns from various corpora. In this article, an approach is presented to extract polarity shifting patterns from any text corpus. For the first time, we proposed to select texts rich in polarity shifting by the idea of natural annotation , which is used to replace human annotation. With a sequence mining algorithm, the selected texts are used to generate polarity shifting pattern candidates, and then we rank them by C-value before human annotation. The approach is tested on different corpora and different languages. The results show that our approach can capture various types of polarity shifting patterns, and some patterns are unique to specific corpora. Therefore, for better performance, it is reasonable to construct polarity shifting patterns directly from the given corpus.
Teaching Vocabulary Using Blended Learning Method

This is a case study aimed at describing teaching vocabulary based blended learning method. There were 30 students involved in this study. The data were taken using mixed methods by giving an interview and observation. The findings of this study were analyzed using triangulation analysis. The results of this study showed that: 1) vocabulary teaching model based blended learning method create an interactive teaching and learning atmosphere, 2) the students are not familiar with the learning application, so they found difficulties using smartphone in accessing learning application, 3) the internet connection is unstable, so the teacher should prepare another activity, 4) the selection of learning applications that suitable with students need to make the teaching and learning activities more fun, easy to understand, and keep following the development of teaching and learning methods.
Fuzzy Logic based Real Time Go to Goal Controller for Mobile Robot

For any mobile device, the ability to move smoothly in its environment is of the ultimate importance, which rationalizes the persistent work of researchers to find new technologies to achieve this target.In this work, we briefly describe the tough work done for designing a fuzzy logic controller (FLC) for the reacting behaviour in a mobile robot, namely "go-to goal" problem.This new technology allows optimal planning of movement in terms of path length and travel time; it is intended to achieve the shortest path followed by a mobile robot.The efficiency of the proposed motion control unit is checked against the results of other smart methods; its features make it an effective alternative way to solve the go-to goal problem for the mobile robot.
MODEL OF HUMAN WEIGHT CORRECTION BASED ON INTERVAL DATA ANALYSIS

Scientists have been engaged in research on issues of excessive person’s body weight, which causes the obesity problem, for a long time. At the beginning of the 20th century the obesity problem was admitted as a medical illness. Therefore, the study of this problem and the search for solutions to it are an urgent task. At the same time, overweight people are reluctant to see doctors-dietitians due to many circumstances. They usually look for easy ways to solve their problems, and nowadays, one of these ways is the global network Internet. However, in the Internet one can only find websites with information about the caloric content of products, obesity problems, different kinds of diet, etc. The development of special software product that would allow us to simulate the process of weight loss and on its basis to automatically develop the “individual” diet is an urgent task.