Reviewerly/exHarmony huggingface dataset:
Indian sign language interpreter using image processing and machine learning

Sign language plays an important part in the hearing/speech impaired person's life as it is the prime medium of communication for them. However, not everyone understands the sign language hence, it becomes difficult for the disabled to communicate in day-to-day life. The sign language constitutes of various hand gestures which can represent various words and expressions. In this paper, the aim is to build a reliable communication interpretation program for interpreting Indian sign language and converting it to a readable output. The task is accomplished using Image processing and Machine learning. Our proposed project can find its applicability in the day to day for communication, it can also work for learning various gestures in gesture based automatic systems.
Cognitive Computing for Human-Machine Interaction: An IBM Watson Implementation

Human-machine interaction (HMI) focuses on interfacing humans and machines in the same domain. It is a multi-disciplinary field with a number of researcher studies emphasis the emerging technologies to cope up with modern HMI systems. The most emerging aspect of it is Cognitive Computing. In theory, much has been achieved but practically very few projects have been materialized so far. IBM Watson is of such kind that combines artificial intelligence and sophisticated analytical software with a mixture of different techniques such as reasoning, machine learning, natural language processing, and others for cognitive computing to design such a questioning-answering machine which is able to answer the questions presented in natural language by building logic and data-driven insight. The purpose of this survey review paper is to unveil compelling, captivating and powerful perspectives of artificial intelligence by exploring one of its unique practical implementations i.e. IBM Watson and review different stages of its implementation.
Review of Blockchain Technology to Address Various Security Issues in Cloud Computing

Being distributed in nature, Cloud is an in-use technology over the last decade where the data is stored on the Cloud service provider's site where the data owner does not have direct access to the storage where her data is stored. This leads to severe security issues. Out of various such issues, data correctness or integrity is one of the major aspects of Cloud data storage security. Researchers have been exploring the options of using blockchain technology to address the data storage correctness concern in Cloud computing. Blockchain technology has provided a new dimension to address the traditional security issues in computing technologies, where every transaction is treated as an atomic operation, and once performed, it becomes immutable. The transactions are stored in a block, and such blocks are linked together to form a chain through a cryptographic hash code. Such chains are distributed in nature in the sense that there is no single central administrative authority to manage them, and any node in the network can verify the correctness of data or transaction. In this paper, we intend to make an exhaustive study on various alternatives to solve the data integrity issues in Cloud computing using blockchain technology. Being distributed, immutable, transparent, cost effective and efficient to use, blockchain is definitely a solution to many issues including Cloud data storage security problems.
Group Delegated ID-Based Proxy Re-encryption for PHR

In general, ID-based proxy re-encryption has the form of transferring data in a 1:1 manner between a data owner and data requestor. Therefore, only the data owner has the authority to decrypt or re-encrypt data encrypted with their public key. However, in an environment with data self-sovereignty, such as a personal health record, data are managed directly. In such circumstances, if the owner of the data becomes unconscious or unable to control the data, there is no way to obtain the data.
Autonomous Delivery Robot AQUILO

The design and structure of the AQUILO Autonomous Delivery Robot are presented in this paper. The AQUILO robot's task is to transport small deliveries in such buildings as offices, schools, hospitals. The AQUILO robot is autonomous, that is, after starting a task it works without human supervision or control. Individual components used in AQUILO robot construction, for example drives, sensors as well as control algorithms are described in this paper. The proprietary system for stair detection used in the AQUILO robot to assure its safe operation is also described. The conclusions also contain information about further works on AQUILO robot development.
Person re-identification from virtuality to reality via modality invariant adversarial mechanism

Person re-identification based on multi-style images helps in crime scene investigation, where only a virtual image (sketch or portrait) of the suspect is available for retrieving possible identities. However, due to the modality gap between multi-style images, standard model of person re-identification cannot achieve satisfactory performance when directly applied to match the virtual images with the real photographs. To address this problem, we propose a modality invariant adversarial mechanism (MIAM) to remove the modality gap between multi-style images. Specifically, the MIAN consists of two parts: a space transformation module to transfer the multi-style person images to a modality-invariant space, and an adversarial learning module “played” between the category classifier and modality classifier to steer the representation learning. The modality classifier discriminates between the real and virtual images while the category classifier predicts the identities of the input transformed images. We explore the space transformation for data augmentation to further bridge the modality gap and facilitate the performance. Furthermore, we build two new datasets for the multi-style Re-ID to evaluate the performance. Extensive experimental results demonstrate the effectiveness of the proposed method on improving the performance against the existing feature learning networks. Further comparison results conducted on different modules in MIAM show that our approach is of favorable generalization ability on alleviating the modality gap to improve the multi-style Re-ID.
Which Strategies are Used in the Design of Technical LA Infrastructure?: A Qualitative Interview Study

In order to obtain a holistic perspective on learning, technical infrastructure at an institutional level can be advantageous for Learning Analytics (LA). If personal data is collected and processed in such infrastructure, legal requirements are of crucial importance. Recent studies have examined various aspects of LA infrastructure, such as ethical trade-offs and stakeholder needs. However, strategies for designing technical LA infrastructure at the institutional level and strategies for dealing with data protection regulations have been lacking so far, although they are crucial for the adoption of LA in Europe. The purpose of this paper is to examine three research questions: (RQ1) which strategies are currently used to design LA infrastructure; (RQ2) how data protection and privacy affect the design of LA infrastructure; and (RQ3) how could technical measures support the adoption of LA. These research questions were investigated by conducting eleven interviews with LA infrastructure developers representing eight different higher education institutions and ten different infrastructures. According to RQ1, the paper first examines the domain specificity of LA infrastructure and the four design strategies used. The paper also examines, according to RQ2, the interviewees’ awareness of data protection, the conflict with users’ consent, and the data subject rights. Finally, the paper presents, in line with RQ3, the results of the strategies in dealing with trust, stakeholder expectations, and engagement. Researchers and infrastructure developers can use and adopt these findings to improve their strategies for developing technical LA infrastructure with regard to data protection, privacy, and trust.
Modified Semi-Supervised Adversarial Deep Network and Classifier Combination for Segmentation of Satellite Images

Content extraction from satellite images continues to evolve with the application of learning aided approaches. Recently, with the addition of deep learning (DL) based methods, content extraction from satellite images has become more reliable and efficient, yet challenges continue to exist as these methods require a large number of training and annotated images to enable effective learning by these networks. For high-resolution satellite images, limited training data is a familiar problem. Therefore, amongst the DL-based methods, semi-supervised adversarial approaches represent an emerging area of application in content extraction from satellite images. Semi-supervised adversarial methods adopt a combination of unsupervised training and labeled data to process applied inputs to generate reliable classification. In this paper, a semi-supervised adversarial learning method, which includes architectural expansion and several other additions, is reported that is used for content extraction from satellite images. The objective of the work is to design a modified structure of a semi-adversarial network working in concert with a classifier layer to extract the region of interests (ROIs) of the satellite image with limited training and annotated data. The proposed method has been tested with four different input feeding mechanisms which enhance the quality of processed data by generating a higher correlation. Two learning-based networks constitute the core of the semi-supervised adversarial learning method reported in this work. The first is a segmentation network that combines unlabeled data and supervised learning for processing. Next is a discriminator block which is a variant of the popular Convolutional Neural Network (CNN) trained sufficiently to improve the segmentation accuracy. For all the experiments performed, both labeled and unlabeled cases are considered. Adversarial and semi-supervised losses are the cost functions to train the system with images of the DeepGlobe Land Cover Classification Challenge dataset. The outputs of the segmentation network which are the semantic labels of the satellite images are used as inputs to the classifier to extract the ROIs of the satellite image. A comparison of the performance of the classifiers used is also included for ascertaining the most suitable one for the specific combination for the discriminator-segmentation blocks. Experimental data reveal that the proposed work is reliable compared to earlier reported approaches.
Security behaviour of mobile financial service users

Purpose The inception of mobile financial services (MFSs) has positively provoked economic growth and productivity, nonetheless, it has pessimistically caused an upward surge in cybersecurity threat. Customers are progressively becoming conscious of some of the threat and several of them now shun away from some suspicious activities over the internet as a form of protection. This study aims to explore the factors that influence users’ to adopt security behaviour. Design/methodology/approach A synthesis of theories – Self-efficacy and technology threat avoidance theories – was used to examine the security behaviour of users of MFSs. Data was gathered from 530 students in Ghana using convenience sampling technique. Data analysis was carried out using descriptive statistics, inferential statistics and structural equation model. Findings Outcome of the investigation indicate that both mastery experience and verbal persuasion have substantial effect on the avoidance motivation of MFSs users. It was, however, found that emotional state and vicarious experience of users do not influence their avoidance motivation. Also, it was established that avoidance motivation is a positive prognosticator of avoidance behaviour. Practical implications Understanding the security behaviour of MFS users will help the operators to outline strategies to sustain the successes achieved. Originality/value Studies on user security behaviour are rare, especially in sub Saharan Africa, thus, this study will contribute to extant literature by adding a new dimension of user security behaviour.
Optimal Inspection Plans

In this chapter, we consider games for the computation of optimal strategies of how, how often, and when to inspect along a production line, or general industrial process. We review basic concepts of statistical tests, conducted whenever the defender chooses its action to “inspect”, and to understand cheating strategies for the adversary trying to escape detection along the statistical test. This non-detection game is then embedded into an outer sequential game over several stages of inspection, accounting for limited resources and possibilities of the defender to check repeatedly. We also consider inspections as a defense pattern against advanced persistent threat (APT), with two models suitable for two distinct type of APTs: the FlipIt game is discussed as a model when the APT’s goal is to gain longest possible control over an infrastructure, without wishing to damage or destroy it permanently. Complementary to this is the Cut-The-Rope game about defending against an APT whose goal is hitting a vital asset and to destroy or at least permanently damage a critical infrastructure.
Using a Game-Based Mobile App to Enhance Vocabulary Acquisition for English Language Learners

The Test-of-English-for-International-Communication (TOEIC) is an important proficiency test for achieving the benchmark of future employment for English language learners worldwide. However, game-based apps for acquiring TOEIC vocabulary have remained scarce. Therefore, an empirical study was conducted to examine the effects of the self-developed the smartphone app Saving Alice for optimizing the acquisition of TOEIC vocabulary and spelling among EFL learners. Multiple sources of data were collected to investigate how Saving Alice affected vocabulary acquisition, including a demographic survey, pre- and post-tests on TOEIC vocabulary, and semi-structured interviews. Both the quantitative and qualitative findings showed that Saving Alice significantly enhanced the student learning outcomes, and that frequency of using game-based mobile apps (GBMAs) correlated with learning outcomes.
Securing Face Recognition System Using Blockchain Technology

Facial recognition is a wide area of a computer vision which is mostly used for security purpose. The main motive of the face recognition system is to authentic a person from a given training database. The face-based biometric system and surveillance cameras are deployed everywhere, for that need a strong face recognition system. The recognition system needs a large number of training samples that need to store in any storage center but when the face data is collected and stored, that time hackers can access and manipulate that data. So, a platform is presented that is secure and tamper-proof from these data breaches as well as hacks and is not compromising with data availability, by using blockchain to store face images. The absolute infeasibility of editing a historical record in a blockchain makes it tamper-proof (immutable) ensuring security. The storage of data on multiple computers provides accessibility. For face recognition, VGGFace deep neural network is used for automatic extraction of features and logistic regression for classification. $$\ldots $$
Cluster ensemble of valid small clusters

During the last decade, ensemble clustering has been the subject of many researches in data mining. In ensemble clustering, several basic partitions are first generated and then a function is used for the clustering aggregation in order to create a final partition that is similar to all of the basi c partitions as much as possible. Ensemble clustering has been proposed to enhance efficiency, strength, reliability, and stability of the clustering. A common slogan concerning the ensemble clustering techniques is that “the model combining several poorer models is better than a stronger model”. Here at this paper, an ensemble clustering method is proposed using the basic k-means clustering method as its base clustering algorithm. Also, this study could raise the diversity of consensus by adopting some measures. Although our clustering ensemble approach has the strengths of kmeans, such as its efficacy and low complexity, it lacks the drawbacks which the kmeans suffers from; such as its problem in detection of clusters that are not uniformly distributed or in the circular shape. In the empirical studies, we test the proposed ensemble clustering algorithm as well as the other up-to-date cluster ensembles on different data-sets. Based on the experimental results, our cluster ensemble method is stronger than the recent competitor cluster ensemble algorithms and is the most up-to-date clustering method available.
Emotion Recognition from Periocular Features

Image processing and Machine Learning approaches are used for face detection and emotion recognition. There are many features that could be extracted from a facial image, but the focus of this work is on identifying emotions by analyzing the features in the periocular region of the face; the region that consists of the features lying in the area of the immediate vicinity of the eyes. The work is broadly divided into two major modules: facial feature extraction and selection, and classifier training and evaluation. 327 labeled images are used to select seven features (periocular action units). Five classifiers are tested, and the Random Forest classifier provided the highest prediction accuracy at 75.61%, with the best performance observed for the happiness emotion label. The k-Nearest Neighbor classifier follows with a performance of 72% when augmented with Neighborhood Components Analysis. Statistical tests confirm that there is a significant difference between the performance of Random Forest classifier and SVM. The results of this work may serve as inputs for ensemble emotion recognition systems, and also as guidelines for enhancing works involving periocular-feature-based Facial Emotion Recognition systems.
Transparency Enhancing Tools and the GDPR: Do They Match?

The introduction of the General Data Protection Regulation (GDPR) came to further strengthen the need for transparency—one of its main principles—and with it, the users’ empowerment to make service providers more responsible and accountable for processing of personal data. The technological infrastructures are not yet prepared to fully support the principle, but changes are bound to be implemented in the very near future. In this work (1) we comprehensively elicit the requirements one needs to implement transparency as stated in GDPR, and (2) we verify which current Transparency Enhancing Tools (TETs) can fulfil them. We found that work still needs to be done to comply with the European Regulation. However, parts of some TETs can already solve some issues. Work efforts need to be put on the development of new solutions, but also on the improvement and testing of existing ones.
Threat Modeling and Attack Simulations of Connected Vehicles: Proof of Concept

A modern vehicle contains over a hundred Electronic Control Units (ECUs) that communicate over in-vehicle networks, and can also be connected to external networks making them vulnerable to cyber attacks. To improve the security of connected vehicles, threat modeling can be applied to proactively find potential security issues and help manufacturers to design more secure vehicles. It can also be combined with probabilistic attack simulations to provide quantitative security measurements, which has not been commonly used while shown efficient in other domains. This paper reviews research in the field, showing that not much work has been done in the combined area of connected vehicles and threat modeling with attack simulations. We have implemented and conducted attack simulations on two vehicle threat models using a tool called securiCAD. Our work serves as a proof of concept of the approach and indicates that the approach is useful. Especially if more research of vehicle-specific vulnerabilities, weaknesses, and countermeasures is done in order to provide more accurate analyses, and to include this in a more tailored vehicle metamodel.
AgentG: An Engaging Bot to Chat for E-Commerce Lovers

Regular customer assistance chatbots are generally based on dialogues delivered by a human. It faces symbolic issues usually related to data scaling and privacy of one’s information. In this paper, we present AgentG, an intelligent chatbot used for customer assistance. It is built using the deep neural network architecture. It clouts huge-scale and free publicly accessible e-commerce data. Different from the existing counterparts, AgentG takes a great data advantage from in-pages that contain product descriptions along with user-generated data content from these online e-commerce websites. It results in more efficient from a practical point of view as well as cost-effective while answering questions that are repetitive, which helps in providing a freedom to people who work as customer service in order to answer questions with highly accurate answers. We have demonstrated how AgentG acts as an additional extension to the actual stream Web browsers and how it is useful to users in having a better experience who are doing online shopping.
A verifiable and privacy‐preserving multidimensional data aggregation scheme in mobile crowdsensing

Abstract Mobile crowdsensing has become a popular data collection paradigm and has been extensively studied. Since the analysis of sensory data usually reveals privacy of mobile users, data aggregation technology is widely used to avoid privacy disclosure. However, traditional privacy‐preserving data aggregation schemes cannot provide aggregate statistics of multidimensional data in fine‐grained areas or resist collusive attacks. Moreover, the cloud platform is not fully trusted, and it is challenging to verify the correctness of data aggregation results. To provide privacy‐preserving multidimensional statistics within fine‐grained areas and verify results of data aggregation, we propose a verifiable privacy‐preserving data aggregation scheme in this article, which is based on data double‐masking, Shamir's secret sharing, and bilinear mapping. Specifically, a key agreement is utilized to establish mask seeds for users, while the users are required to extend mask values and generate the verification key with bilinear mapping. The cloud platform aggregates masked data and provides proof of computational results. Theoretical analysis demonstrates our scheme can protect data and location privacy, verify the aggregation results, and improve the rubustness to failure. The simulation results show that the computational overhead of mobile devices by using the proposed anticollusion scheme is better than that of homomorphic encryption. In addition, the computational complexity of task requesters is less than that of the PVPA scheme for verification of multidimensional aggregation results.