Reviewerly/exHarmony huggingface dataset:
Are Social Networks Watermarking Us or Are We (Unawarely) Watermarking Ourself?

In the last decade, Social Networks (SNs) have deeply changed many aspects of society, and one of the most widespread behaviours is the sharing of pictures. However, malicious users often exploit shared pictures to create fake profiles, leading to the growth of cybercrime. Thus, keeping in mind this scenario, authorship attribution and verification through image watermarking techniques are becoming more and more important. In this paper, we firstly investigate how thirteen of the most popular SNs treat uploaded pictures in order to identify a possible implementation of image watermarking techniques by respective SNs. Second, we test the robustness of several image watermarking algorithms on these thirteen SNs. Finally, we verify whether a method based on the Photo-Response Non-Uniformity (PRNU) technique, which is usually used in digital forensic or image forgery detection activities, can be successfully used as a watermarking approach for authorship attribution and verification of pictures on SNs. The proposed method is sufficiently robust, in spite of the fact that pictures are often downgraded during the process of uploading to the SNs. Moreover, in comparison to conventional watermarking methods the proposed method can successfully pass through different SNs, solving related problems such as profile linking and fake profile detection. The results of our analysis on a real dataset of 8400 pictures show that the proposed method is more effective than other watermarking techniques and can help to address serious questions about privacy and security on SNs. Moreover, the proposed method paves the way for the definition of multi-factor online authentication mechanisms based on robust digital features.
Taxonomy for Complexity Estimation in Agile Methodologies: A Systematic Literature Review

Currently, software development teams use different methods to estimate their projects, in Scrum, there are different methods to estimate the value and complexity of a user story. However, these methods are not accurate and most of them depend on many attributes that sometimes are not contemplated or are unknown by the development team. Although Planning Poker has many benefits, it has been found that this method is efficient mostly for experience teams, since the estimation will always depend on the observation of an expert and his experience. On the other hand, without a standard set of attributes for describing complexity, subjectivity problems remain when estimating user stories. This study presents a systematic literature review in which identifies the attributes that influence the complexity estimation in user stories.
Simplified Ciphertext Policy Attribute Based Encryption for Multimedia Applications

In this modern age we generate data from various sophisticated systems and technologies for our personal use, the security and privacy of the data becomes a major concern while sharing it over the cloud platform. The conventional CP-ABE scheme is used to secure different types of data.The CP-ABE uses the access policy in the form of access tree for generating keys to encrypt and decrypt data. However using an explicit access structure with several nodes requires more computational requirements and consumes significant amount of time to process every node of access structure and to encrypt the data. In this paper, to overcome these challenges, we proposed a simplified Ciphertext Policy Attribute Based Encryption for multimedia applications scheme without an access structure, which is more practical to secure the data with less overhead. In this proposed work, the owner of the data carefully frames the attribute string in a prescribed manner that can be used for encryption and decryption process. The performance measurement of the proposed scheme demonstrate that our system has high level of security with less computational requirements.
Communication in the formation of the institutional environment in the tourism industry

The survey study conducted in this article is aimed at identifying the communicative tendencies of the institutional environment of entrepreneurship in the sphere of tourism in St. Petersburg. In the process of research: the structure of tourism institutional environment and the process of its formation and factors, that make its identification harder was analyzed; legislative changes in the tourism and recreation sphere were considered; a survey for tourism agencies representatives in St.Petersburg was conducted. Having considered the theoretical approaches to the definition of the institutional environment of tourist and recreational services, authors came to the conclusion that studies do not account for changes in the institutional environment of tourism in the context of new information and communication technologies such as the internet - reservation services, tourist information centers, etc. Communication part of institutional business environment in tourism combines information communication technologies, such as: automatic information system "Unified Federal Register of tour operators", "Safety treats information for tourists", official website of Federal Tourism agency, online booking systems etc. As a result of the analysis, it was revealed that the rapid development of communication technologies, due to the multiplier effect of the tourism sector, contributes to the growth of: mobile services that simplify access to travel services at all levels; collaborations, including for advertising purposes (bloggers, online events, etc.), number of tourist information centers; the level of quality, speed and safety of tourist services.
Dew Computing and Asymmetric Security Framework for Big Data File Sharing

Due to the advancement of technology, devices generate huge working files at a rapid rate. These data, which are of considerable scale and are created very fast, can be called big data. Keeping such files in one storage device is impossible. Therefore, a large file size is recommended for storage in a cloud storage service. Although this concept is a solution to solve the storage problem, it still faces challenges in terms of reliability and security. The main issues are the unreliability of single cloud storage when its service is down, and the risk of insider attack from the storage service. Therefore, this paper proposes a file sharing scheme that increases both the reliability of the file fragments using a multi-cloud storage system and decreases the risk from insider attack. The dew computing concept is used as a contributor to the file-sharing scheme. The working file is split into fragments. Each fragment is deployed to cloud storage services, one fragment per one cloud provider manner. The dew server controls users’ access and monitors the availability of fragments. Finally, we verify the proposed scheme in aspects of the downloading performance, and security.
Classification of Soft Biometric Traits When Matching Near-Infrared Long-Range Face Images Against Their Visible Counterparts

In this chapter, we investigate the advantages and limitations of the heterogeneous problem of matching Near-Infrared (NIR) long-range, night time, face images against their visible counterparts. Image quality degradation can result due to a variety of factors including low illumination, variable standoff distance, and is responsible for performance degradation of conventional face recognition (FR) systems. In addition to intra-spectral matching (i.e. NIR vs. NIR face images), cross-spectral matching (i.e. matching NIR face images against their visible counterparts) is a challenging matching scenario that increases system complexity. In this work, we propose the usage of a set of FR algorithms when working with operational-based face matching scenarios, namely, where the face images used are collected by a night vision, long range (from 30 to 120 m), NIR-based face imaging system. First, we establish a system identification baseline using a set of commercial and academic face matchers. To improve baseline performance, we propose a scenario dependent convolutional neural network (CNN) to, first, categorize the face images of our challenging face dataset, in terms of gender, ethnicity, and facial hair. For each of the aforementioned generated categories, we apply our proposed algorithmic pipeline including, image restoration and a multi-feature based fusion scheme. Then, a set of FR algorithms are used before and after image restoration and data categorization. Based on the experimental results, we conclude that our proposed image restoration and fusion schemes, as well as the usage of demographic-based face categories, result in improved identification performance. For example, for the 30 m vs. 30 m NIR face matching scenario, the rank-1 identification rate is improved from 48% (all vs. all) using a commercial face matching system to 73% (all vs. all) and to 82% (if we use only the male with beards face data category). Experimental results suggest that our proposed methodological approach can improve system performance (i.e. efficiently identifying the subject of interest) on various cross-spectral face matching scenarios.
ExemPoser

It is important for beginners to imitate poses of experts in various sports; especially in sport climbing, performance depends greatly on the pose that should be taken for given holds. However, it is difficult for beginners to learn the proper poses for all patterns from experts since climbing holds are completely different for each course. Therefore, we propose a system that predict a pose of experts from the positions of the hands and feet of the climber--the positions of holds used by the climber--using a neural network. In other words, our system simulates what pose experts take for the holds the climber is now using. The positions of hands and feet are calculated from a image of the climber captured from behind. To allow users to check what pose is ideal in real time during practice, we have adopted a simple and lightweight network structure with little computational delay. We asked experts to compare the poses predicted by our system with the poses of beginners, and we confirmed that the poses predicted by our system were in most cases better than or as good as those of beginners.
Study on The Impact of Artificial Intelligence on Government E-service in Dubai

AI has become a phenomenon of interest in the current world of technological advancement. There are many applications of this technology, including its usage to facilitate government activities. Dubai has taken note of multiple application of AI and thus integrated it into its e-services sector. Accordingly, the purpose of this study is to investigate the impact of AI on government e-service in Dubai. The findings of the study indicated that AI is highly beneficial at both individual and organizational level, government and the private sector are responsible for its adoption. This study also found that there are both positive and negative response to the use of AI in the government services. Users are impressed by the positive impact, while others fear that AI might take over, resulting in job loss. There are limitations associated with the use of AI. However, they can be overcome by developing effective strategies. The key recommendations provided in this study include use of national values in the course of developing an AI system, effective address of societal problems, and provision of assistance to people to adapt to the transformation caused by AI.
Multi-Path Planning for Autonomous Navigation of Multiple Robots in a Shared Workspace with Humans

Path finding for multiple robots is one of most important problems in robotics when to find a way to move robots from their starting positions to reach their respective goals without collisions. However, in the case of a complex environment with the presence of humans and other unpredictable moving objects, fixing a single path to the goal may lead to a situation where there are a lot of obstacles on the planned path and the robots may fail to realise the moving plan. To address this issue, a new approach of using multiple path planning where each robot has different options to choose its path to the goal is introduced in this paper. The information about planned moving paths are shared among the robots in the working domain, combined with obstacle avoidance constraints in local ranges, and formulated as an optimisation problem. Solution of the problem leads to the optimal moving plans of robots. The effectiveness of the proposed approach is demonstrated by experimental results.
Mobile Translation Apps. and Second Language Teaching; What do Student's think?

In the U.A.E. both local and international students use mobile translation applications because of the strong sociological imperatives to learn English and it is having a powerful impact on language learning. Students regularly make use of translation apps in the classroom. These apps are not teachers and cannot know learners' needs or focus attention on course outcomes. Equally, teaching is disconnected from mobile translation technology. In the future, as technology evolves, translation software may make language learning obsolete. Until then, it will be the educator's job to connect this popular means of learning with language teaching.
Persistent‐Homology‐Based Microstructural Optimization of Materials Using t‐Distributed Stochastic Neighbor Embedding

Abstract Microstructure optimization is a core issue to maximize the performance of materials. Due to the increasing demand for highly efficient materials, traditional trial‐and‐error‐based experimental methods have become insufficient for designing novel materials with useful properties. Based on the fact that materials with similar microstructural features exhibit similar properties, this work proposes a persistent‐homology‐based microstructure optimization approach performed with a machine learning algorithm of t‐distributed stochastic neighbor embedding to find optimal microstructures for specific properties. The method is applied to dual‐phase steels, where a microstructure with high‐fraction martensite is identified for achieving a maximum stress. The method proposed here is expected to provide new basis to understand the materials paradigm and thus accelerate the materials discovery process.
PDD: Predictive Diabetes Diagnosis using Datamining Algorithms

Data analytics is used to obtain the useful insights from small or large data set to conclude some useful information and also used for future recommendation and decision making. Predictive Analytics uses the data mining, machine learning techniques to make predictions about future. It involves analysis of available data. The predictive analytics in health care is primarily used to determine patients having initial stages of diabetes, asthma, heart disease and other critical lifetime disease. The proposed method PDD uses data mining algorithms to predict the type2 diabetes. The data mining algorithms used in the proposed system are K-Means Clustering and Random Forest. The predictive model, PDD provides better results in terms of accuracy when compared to hierarchical clustering and Bayesian network clustering with random forest prediction.
On Classification of Acceptable Images for Reliable Artificial Intelligence Systems: A Case Study on Pedestrian Detection

Images are essential data for many artificial intelligence (AI) systems such as pedestrian detection. However, image processing circuits or image storage devices may produce erroneous image data due to aging or radiation. In this paper we will show that there actually exists much tolerability in image errors. Moreover, for AI systems we find that the tolerability is even larger. This finding provides an attractive reliability enhancement solution by classifying and filtering acceptable images. This solution allows acceptable images to still go to the AI inference process, while unacceptable images are discarded, together with warning signals activated. In this work, we first evaluate and compare error tolerability of images from human and machine perspectives. Then a number of possible test methods to support machine based error-tolerance are discussed and compared in terms of their acceptability classification accuracy and computation cost. In particular, these methods should not need golden (error-free) images as the comparison basis. This greatly facilitates developing a low-cost on-line test architecture to enable a real-time reliability enhancement solution. Our experimental results show that when applying the suggested test method to pedestrian detection, 93.48% of the erroneous images can be correctly classified. The results also show that adopting machine-based error-tolerance can extend MTTF (Mean Time To Failure) of the pedestrian detection system up to additional 88.7%, while human vision based error-tolerance can extend only additional 35.1%.
Self-Managed Block Storage Scheduling for OpenStack-based Cloud

Software-defined storage (SDS) enables cloud providers to abstract the storage resources from underlying physical hardware by making storage resources programmable. It enhances the scalability and management of storage resources for better efficiency. Software-defined block storage component in OpenStack is referred to as cinder. The storage scheduling algorithm in OpenStack cinder is inefficient and one dimensional as it considers the capacity of storage disks as the only parameter to schedule the request. This compromises QoS to the customer. This paper presents the design of a self-managed block storage scheduling model. Proposed algorithms considers the performance attributes like read and write IOPS of storage hosts and classifies hosts by generating a quality status using machine learning classifiers. Further, this status is used in conjunction with a best -fit algorithm to make a final scheduling decision. Private cloud test-bed with 5 cinder hosts is set up to test scheduler performance. Results clearly show that the proposed model outperforms the default scheduler by evenly distributing the volume requests across the cinder nodes. The proposed model also maintains the QoS by providing the best available storage backend for every volume request.
Style Image Retrieval for Improving Material Translation Using Neural Style Transfer

In this paper, we propose a CNN-feature-based image retrieval method to find the ideal style image that better translates the material of an object. An ideal style image must share semantic information with the content image, while containing distinctive characteristics of the desired material. Therefore, we first refine the search by selecting the most discriminative images from the target material. Subsequently, our search process focuses on the object semantics by removing the style information using instance normalization whitening. Thus, the search is performed using the normalized CNN features. In order to translate materials to object regions, we combine semantic segmentation with neural style transfer. We segment objects from the content image by using a weakly supervised segmentation method, and transfer the material of the retrieved style image to the segmented areas. We demonstrate quantitatively and qualitatively that by using ideal style images, the results of the conventional neural style transfer are significantly improved, overcoming state-of-the-art approaches, such as WCT, MUNIT, and StarGAN.
Human Emotion Distribution Learning from Face Images using CNN and LBC Features

Human emotion recognition from facial expressions depicted in images is an active area of research particularly for medical, security and human-computer interaction applications. Since there is no pure emotion, measuring the intensity of several possible emotions depicted in a facial expression image is a challenging task. Previous studies have dealt with this challenge by using label-distribution learning (LDL) and focusing on optimizing a conditional probability function that attempts to reduce the relative entropy of the predicted distribution with respect to the target distribution, which leads to a lack of generality of the model. In this work, we propose a deep learning framework for LDL that uses convolutional neural network (CNN) features to increase the generalization of the trained model. Our framework, which we call EDL-LBCNN, enhances the features extracted by CNNs by incorporating a local binary convolutional (LBC) layer to acquire texture information from the face images. We evaluate our EDL-LBCNN framework on the s-JAFFE dataset. Our experimental results show that the EDL-LBCNN framework can effectively deal with LDL for human emotion recognition and attain a stronger performance than that of state-of-the-art methods.
Cutting the network? Facebook's Libra currency as a problem of organisation

Abstract This essay explores the organisational character of Facebook's Libra currency by undertaking a critical reading of documents published by the Libra Association. Drawing on the conceptual work of Marilyn Strathern and Michel Serres, it illustrates how ownership cuts the network and encourages parasitism as a means of driving future profit. Central to this is the claim that Libra is not an exercise in democratising money, but rather, the opposite: Libra is run as a club, for the benefit of club members. The conceptual theme of ‘cutting’ is used to organise the argument. Rather than a cutting-edge technology, Libra's true innovation is organisational and consists in overturning the decentralised character of blockchain, such that distributed ledger technology is re-centralised by big tech firms. Outsiders are thus cut-off from Libra; only those inside the club have the right to participate in Libra and its governance. This position also affords members an exclusive capacity to take a cut of the profits generated through Libra. As a private organisation, members have sole rights to future profits generated from the Libra ecosystem and are in this way incentivised to create new product opportunities over time.
Design of logistics information system in the finished product warehouse with the waterfall method: review literature

Abstract The process of sending and loading finished goods is very important. According to some experts, transportation activities spend 44% of the total cost. Even in Indonesia transportation costs can reach more than 60%. In practice, one way of saving on transportation costs can be done by minimizing the waiting time for loading finished products in the warehouse. The slow delivery of information from the logistics department to the warehouse can cause high load times for finished products. This can lead to delays in product delivery to consumers. The method used in this paper is the Waterfall method. The Waterfall method provides a software flow approach in sequence or sequentially starting from analysis, design, implementation, verification, and maintenance phase.
To BERT or Not to BERT Dealing with Possible BERT Failures in an Entailment Task

In this paper we focus on an Natural Language Inference task. Being given two sentences, we classify their relation as NEUTRAL, ENTAILMENT or CONTRADICTION. Considering the achievements of BERT (Bidirectional Encoder Representations from Transformers) in many Natural Language Processing tasks, we use BERT features to create our base model for this task. However, several questions arise: can other features improve the performance obtained with BERT? If we are able to predict the situations in which BERT will fail, can we improve the performance by providing alternative models for these situations? We test several strategies and models, as alternatives to the standalone BERT model in the possible failure situations, and we take advantage of semantic features extracted from Discourse Representation Structures.