Reviewerly/exHarmony huggingface dataset:
Designing a Data Corpus of Collaborative Group Tasks with the Members from Unbalanced Cultural Backgrounds

According to current estimates, the population of Japan will continuously decrease to be less than 100 million from currently 126.4 million by 2053 [1]. In order to supplement the decreasing labor force, the Japanese government has stated to relax the regulations on introducing foreignworkers. Thus, an increase of foreignerworkers in Japanese society can be expected in the near future. In such a situation, foreigner workers are the minority and have to collaboratively work with Japanese colleagues who are the majority. Our project is aiming at developing an environment for both foreigner and Japanese people to practice collaborative work in an unbalanced situation, that is, Japanese people are the majority and the task itself is Japanese favored. This paper describes our design of an experiment to acquire multimodal sensory data in collaborative tasks with unbalanced composition of cultural backgrounds. These data are supposed to be used for developing foreigner / Japanese behavior generation / detection models in a training environment with virtual agents. From our knowledge, there is no such dataset available and we believe the data collected can provide valuable resources for developing tools in supporting unbalanced groups.
New Modes of Using Block Ciphers: Error Correction and Pseudo-probabilistic Encryption

There are proposed two novel modes for using block ciphers that provides to perform error correction in the case of sending data via a noisy channel. In one of the modes, data encryption is combined with error correction coding. At the first stage of this mode, it is performed block encryption and then at the second stage, the intermediate ciphertext block is converted into the expanded block of the output ciphertext depending on the encryption key. In the second proposed mode, pseudo-probabilistic block encryption is combined with error correction coding in the single cryptoscheme. For providing the possibility to correct errors in specified redundancy, labels are concatenated to the intermediate ciphertext blocks. Then the labels and the intermediate ciphertext blocks are simultaneously transformed into the final ciphertext blocks that are computed as solution of the system of the linear equations and of the system of the linear congruencies.
Tweet Segmentation—A Novel Mechanism

In order to process tweet segmentation, it requires a sophisticated framework. Through a specific process the tweets are segmented based on several aspects. Some of them are the linguistics or context knowledge based and this is so protective as well as effective. They are separated by the downstream applications. It finds the best segmentation of a tweet by increasing the sum of the viscousness scores of its candidate segments. For testing planned system, we tend to thought-about two tweet phase datasets, the experimental results by the planned system considerably improved in terms of global and local contexts.
Design and Implementation of the Context-Based Adaptive Filtering System for Sensitive Words

Semantics for language application differ among different contexts. Sensitive words are also affected by application scenarios and application timeliness. The sensitivity of words will also change in different scenarios and timeliness. This study sets the context as political context, agricultural context and common context. The path research of acquiring method of sensitive words is studied and a set of sensitive word library is established based on self-learning, through the implementation of the system. Moreover, the context-based detecting method of sensitive words is established.
Methods of organization of extracurricular activities of students of 5–8th grades using robotic equipment and programming environments

The article describes the components of the model of extracurricular activities using robotic equipment and programming environments: aim, content, methodological and control and evaluation. This model can be used in the organization of extracurricular activities with students of 5–8th grades.
Methodology for Developing Secure Apps in the Clouds. (MDSAC) for IEEECS Confererences

The objective of this paper is to give a methodology that allows develop and maintain the security of applications in services of cloud, in a market where the migration of applications from private servers to cloud services continues to climb, the lack of methodologies for doing that, and the Clouds companies leave the security of applications in the hands of customers in a business model called shared responsibility of IT security, it is necessary then have a working methodology for developing and maintain the security applications that become one of the three most popular targets for cyber attacks.
Cube distinguisher extraction using division property in block ciphers

Cube attack, a simplified type of algebraic attack, is widely utilised to cryptanalyse ciphers. However, since the cube attack works without considering the cipher structure, it is highly complex. In 2017, division property, a successful approach to finding integral distinguishers, was used to extract cube distinguishers in a non-blackbox manner for stream ciphers, which led to a significant improvement of the previous results. This is the first paper employing division property for cube distinguisher extraction in block ciphers. To do this, first, an approach relying on Boolean satisfiability problem (SAT) is presented to evaluate the propagation of division property. Indeed, extraction of zero-sum distinguisher is mapped on a SAT problem and SAT solvers are used to finding division trails efficiently and automatically. Then, this approach is extended and adapted to extract cube distinguishers in block ciphers. However, there are similarities between our contribution and others but the different structure of block and stream ciphers lead to disparity in applying division property to extract cube distinguisher for block ciphers. To prove the efficiency of the presented approach, it is applied to the lightweight block cipher Katan and the cube distinguishers are extended to a higher round in comparison with previous results.
High Efficient Haar Wavelets for Medical Image Compression

In this paper, we proposed an improved high efficient Haar wavelets (HEHW) algorithm to improve the quality of image compression ratio (CR) rate and peak signal to noise ratio for medical imaging. The proposed algorithm starts by partitioning the original image into 2 * 2 submatrices. Then the wavelets transform coefficients obtained by working on the submatrices instead of the rows and on the columns in the original image. We re-compute the resulting coefficients for sub-matrices to obtain the approximation and the sub details of the original image. Then, we calculate statistical thresholds on the details subbands to complete the compression process. The proposed algorithm is applied to five different medical image structures to prove its efficiency using the evaluation factors like CR, peak signal to noise ratio, mean square error and transform time. The comparison between the proposed and the well-known modified Haar method also compared to the results of existing wavelets techniques: like Coiflet, Daubechies, Biorthogonal, Dmeyer, and Symlets to prove the proposed algorithm efficiency.
Study on the Method of Blood Microscopic Multi-field Image Stitching

The following topics are dealt with: diseases; medical image processing; feature extraction; medical computing; cellular biophysics; biology computing; patient treatment; neurophysiology; biomedical MRI; molecular biophysics.
ID3-Based Classification of College Students’ Physical Fitness Data

This paper uses the ID3 algorithm to analyze and extract the classification rules hidden in the original data of the physical fitness test of junior college students in mobile app platform database of “Running Shida”. These classification rules are highly consistent with the actual data in the database and are highly consistent with the results of individual survey of students. The forecasting conclusion of these classification rules is of great significance for quickly and scientifically determining students’ physique, putting forward reasonable suggestions for sports training and promoting the reform of “integration in-and-outside class” teaching mode of physical education in colleges and universities.
Design and evaluation on the immersive Virtual Reality system in learning archeology

Abstract This research aims to design and evaluate the three-dimensional Virtual Reality game at showroom 1 and 2 of the Sangiran Museum. This application is designed for the media campaign and increase the appeal of the public to educational tourism contained in Sragen regency, the location of the museum. The game genre is quiz with scoring and it allows the user to guess the fossil name that is displayed on the screen. Twenty visitors were participated to evaluate the application after they finished exploring both showrooms. Questionnaire is used to measure the use of the application. The result shows good acceptance in term of usability. It is expected that visitors can add educational value to their knowledge on the post-visit to the museum.
Analysis of Substitutive Fragile Watermarking Techniques for 3D Model Authentication

Due to the importance of multimedia data and the urgent need to use it in many fields such as industry, medicine and entertainment, protecting them becomes an important issue. Digital watermarking is considered an efficient solution for multimedia security as it preserves the original media content as it is. For 3D graphical models, 3D fragile watermarking aims to detect any attacks to the models and protect the copyright and the ownership of the models. In this paper, we present a comparative analysis between two substitutive 3D fragile watermarking algorithms. The first algorithm is based on adaptive watermark generation technique using Hamming code, while the other algorithm uses Chaos sequence for 3D models fragile watermarking in the spatial domain. The study uses different assessment measures to show the points of strength and weaknesses of both methods.
Organizational Level Cloud Diagnostics System of Complex Electromechanical System Based on SOA

Abstract In order to better solve the problem of insufficient support ability of high-tech electromechanical system, the cloud computing model was introduced into fault diagnostics system of electromechanical system. An open, loose-coupled architecture of Integrated Diagnostic System (IDS) for electromechanical system is put forward basing on Service- Oriented Cloud Computing Architecture (SOCCA). Test and diagnosis resources of repair shop at all levels and total life cycle information resources of electromechanical system were integrated by Portable Maintenance Aid (PMA) with cloud diagnostics platform. According to different demands, Test and diagnosis resources can be divided into data, software, platform and infrastructure, which can be provided on demand to maintainer of all levels as a service. This allows more complex maintenance issues to be accomplished at Forward Operating Locations (FOLs) by better utilizing the most relevant diagnostic information to enable direct, real-time, and engineering assistance when needed. Organizational Level (O-Level) IDS can effectively improve O-level maintainer support ability of complex electromechanical system.
Synonym Multi-keyword Search over Encrypted Data Using Hierarchical Bloom Filters Index

Search on encrypted data refers to the capability to identify and retrieve a set of objects from an encrypted collection that suit the query without decrypting the data. Users probably search not only exact or fuzzy keyword due to their lack of data content. Therefore, they might be search the same meaning of stored word but different in structure, so, this paper presents synonym multi-keyword search over encrypted data with secure index represented by hierarchical bloom filters structure. The hierarchical index structure improves the search process, and it can be efficiently maintained and constructed. Extensive analysis acquired through controlled experiments and observations on selected data, show that the proposed scheme is efficient, accurate and secure.
Feature Extraction Using Semantic Similarity

Recently, opinion mining has become a focus in the field of natural language processing and web mining. Due to the massive amount of users’ reviews on the web about some entities or services, opinion mining is appeared to track users’ emotions and feelings. Sentiment analysis is a synonym to opinion mining. Feature extraction is an important task in the sentiment analysis process. So, in this paper, a novel model is proposed to extract the most related features to a product from customer reviews using semantic similarity. Wordnet taxonomy and Stanford Part of Speech (POS) tagger are used in the feature extraction process. The extracted features are very important to generate a meaningful feature based product reviews summery which helps the customers to make a decision. The experiments are performed on three different datasets. The proposed model achieves promising results in terms of Precision, Recall and F-measure performance measures.
A Mobile Robot Generating Video Summaries of Seniors' Indoor Activities

We develop a system which generates summaries from seniors' indoor-activity videos captured by a social robot to help remote family members know their seniors' daily activities at home. Unlike the traditional video summarization datasets, indoor videos captured from a moving robot poses additional challenges, namely, (i) the video sequences are very long (ii) a significant number of videoframes contain no-subject or with subjects at ill-posed locations and scales (iii) most of the well-posed frames contain highly redundant information. To address this problem, we propose to exploit pose estimation for detecting people in frames. This guides the robot to follow the user and capture effective videos. We use person identification to distinguish a target senior from other people. We also make use of action recognition to analyze seniors' major activities at different moments, and develop a video summarization method to select diverse and representative keyframes as summaries.
Efficient and Scalable Exploration via Estimation-Error

Exploring efficiently in complex environments is still a challenging problem in reinforcement learning. Recent exploration algorithms based on "optimism in the face of uncertainty" or intrinsic motivation achieved promising performance in sparse reward settings, but they often rely on additional structures which are hard to build in large scale problems. It renders them impractical and hinders the process of combining with reinforcement learning algorithms. Hence, the most state-of-the-art RL algorithms still use the naive action space noise as exploration strategy. In this paper, we model the uncertainty about environment through agent's ability to estimate the value across state and action space. Then, we parameterize the uncertainty by a neural network and regard it as a reward bonus signal to reward uncertain states. In this way, we generate an end-to-end bonus which can scale to complex environments with less computational cost. In order to prove the effectiveness of our method, we evaluate it on the challenging Atari 2600 games. We observed that our method achieves superior or comparable exploratory performance compared to action space noise in all environments, including environments whose rewards are sparse. The results demonstrate that our exploration method can motivate agent to explore effectively even in complex environments and it generally outperforms the naive action space noise.
Modified Value-and-Criterion Filters for Speckle Noise Reduction in SAR Images

Speckle noise disturbance is the most essential factor that affects the quality and the visual appearance of the synthetic aperture radar (SAR) coherent images. For remote sensing systems, the initial step always involves a suitable method to reduce the effect of speckle noise. Several non-adaptive and adaptive filters have been proposed to enhance the noisy SAR images. In this paper, two proposed non-adaptive filters have been introduced. These proposed filters utilize traditional mean, median, root-mean square (RMS) values, and large size filter kernels to improve the SAR image appearance while maintaining image information. The performance of the proposed filters is compared with a number of non-adaptive filters to assess their ability to reduce speckle noise. For quantitative measurements, four metrics have been used to evaluate the performance of the proposed filters. From the experimental results, the proposed filters have achieved promising results for significantly suppressing speckle noise and preserving image information compared with other well-known filters.
Microsoft Word Forensic Artifacts in Windows 10 Registry

In the era of digitalization, most of the computer users use Microsoft Word as a primary tool for creating, accessing and modifying associated digital documents. With the rapid increase in the usage of digital devices, Digital Investigations became essential while dealing with deleted, hidden, modified, tempered or stolen documents either in case of an incident or a digital crime. In windows, registry is the core component that keeps track of almost all applications. In this paper, a novel study is carried out specifically on Microsoft Word to figure out its forensics artifacts in Windows 10 registry. The registry locations identified as a result provide clues about a user's interaction with Microsoft Word. These clues not only provide information about documents that currently exist but most importantly about deleted documents as well.
A Deep Bidirectional Highway Long Short-Term Memory Network Approach to Chinese Semantic Role Labeling

Existing approaches to Chinese semantic role labeling (SRL) mainly adopt deep long short-term memory (LSTM) neural networks to address the long-term dependencies problem. However, deep LSTM networks cannot address the vanishing gradient problem properly. In addition, the complexity of the Chinese language, as a hieroglyphic language, decreases the performance of traditional SRL approaches to Chinese SRL. To address these problems, this paper proposes a new approach with a deep bidirectional highway LSTM network. The performance of the proposed approach is further improved by introducing the conditional random fields (CRFs) constraints and part-of-speech (POS) feature since POS tags are the classes of formal equivalents of words in linguistics. The experimental results on the commonly used Chinese Proposition Bank dataset show that the proposed approach outperforms existing approaches. With an easily acquired and reliable POS feature for practical applications, the proposed approach substantially improves Chinese SRL.
Improving Time-Aware Recommendations in Open Source Packages

Collaborative filtering techniques have been studied extensively during the last decade. Many open source packages (Apache Mahout, LensKit, MyMediaLite, rrecsys etc.) have implemented them, but typically the top-N recommendation lists are only based on a highest predicted ratings approach. However, exploiting frequencies in the user/item neighborhood for the formation of the top-N recommendation lists has been shown to provide superior accuracy results in offline simulations. In addition, most open source packages use a time-independent evaluation protocol to test the quality of recommendations, which may result to misleading conclusions since it cannot simulate well the real-life systems, which are strongly related to the time dimension. In this paper, we have therefore implemented the time-aware evaluation protocol to the open source recommendation package for the R language — denoted rrecsys — and compare its performance across open source packages for reasons of replicability. Our experimental results clearly demonstrate that using the most frequent items in neighborhood approach significantly outperforms the highest predicted rating approach on three public datasets. Moreover, the time-aware evaluation protocol has been shown to be more adequate for capturing the life-time effectiveness of recommender systems.
Transformative Power of Smart Technologies Enabled by Advances in AI

The internet has transformed the landscape in the field of marketing and consumer behaviour in the last two decades, enabling unprecedented reach to the consumers for marketers, inducing low costs in general, providing opportunities to analyse interactions and facilitating the development of novel strategies in digital marketing. As the information age is maturing, it is entering a new era of the fourth industrial revolution where internet's reach is coupled with the smart technologies powered by AI, cloud-based scalable infrastructure, sensor infusion harvesting an ever greater amount of data. The consumer demands and preferences are ever more sophisticated. In this context, the digital marketing field will go through a period of tremendous change, where established norms and practices will no longer serve the purpose of attracting and engaging consumers. A thought experiment is discussed using a persona for the effectiveness of existing digital marketing models. Further, a concept of swarm marketing using AI has been also discussed.
Interactive-Predictive Neural Multimodal Systems

Despite the advances achieved by neural models in sequence to sequence learning, exploited in a variety of tasks, they still make errors. In many use cases, these are corrected by a human expert in a posterior revision process. The interactive-predictive framework aims to minimize the human effort spent on this process by considering partial corrections for iteratively refining the hypothesis. In this work, we generalize the interactive-predictive approach, typically applied in to machine translation field, to tackle other multimodal problems namely, image and video captioning. We study the application of this framework to multimodal neural sequence to sequence models. We show that, following this framework, we approximately halve the effort spent for correcting the outputs generated by the automatic systems. Moreover, we deploy our systems in a publicly accessible demonstration, that allows to better understand the behavior of the interactive-predictive framework.
Building an Extractive Arabic Text Summarization Using a Hybrid Approach

Nowadays, textual information in numerical format is hugely produced, which requires the development of tools such as Automatic Text Summarization to produce a condensed and relevant representation, thus helping the reader to decide whether the source document contains the information they are looking for or not. The Text Summarization has known these last decades an enormous progress, in particular works of Arabic language. The objective of this paper is to contribute more to these advances by proposing an Arabic text summarization tool (SumSAT), which adopts an extraction approach using hybridization between three techniques which are: Contextual exploration, indicative expression, and the graph method. Experimental results showed that the proposed approach achieved competitive and promising in the optic of generating a small and coherent summary.