Reviewerly/exHarmony huggingface dataset:
A Study and Analysis of Issues and Attacks Related to Recommender System

Information and communication technologies (ICTs) currently play a role in pervasive assistive tools in our daily life. Their usage is fast growing in various domains like healthcare, e-learning, e-government, e-tourism, banking sector, etc. Nowadays, we are living in the information age and entering the age of recommendation. Recommender systems are intelligent information retrieval systems which help in finding relevant information from a large information space based on the interest of the users. In ICT, the recommender system helps in finding accurate services/items from the large space of items. The recommender system is widely accepted and the most important approach that provides the solution for information overhead problem. Even though user spends a lot of time and effort for finding essential and expected information, it may result in lower precision and recall rates. Nowadays, users spend the shortest span of time and want to obtain the best possible items and services. At present, many recommender systems have been developed in several contexts, but these are not sufficient to accomplish the user needs. So, it is essential to develop the highly featured recommender system. For developing and designing such type of systems, many issues and challenges need appropriate attention. The main purpose of this chapter is to investigate and illustrate the current challenges, issue attacks, and applications in recommender systems. In this chapter, we also propose a recommender system model based on collaborative filtering and intention mining where intentions are generated using gender and age information of the users.
Forensic Scanner Identification Using Machine Learning

Due to the increasing availability and functionality of image editing tools, many forensic techniques such as digital image authentication, source identification and tamper detection are important for forensic image analysis. In this paper, we describe a machine learning based system to address the forensic analysis of scanner devices. The proposed system uses deep-learning to automatically learn the intrinsic features from various scanned images. Our experimental results show that high accuracy can be achieved for source scanner identification. The proposed system can also generate a reliability map that indicates the manipulated regions in an scanned image.
Short Paper: Mechanized Proofs of Verifiability and Privacy in a Paper-Based E-Voting Scheme

Electryo is a paper-based voting protocol that implements the Selene mechanism for individual verifiability. This short paper aims to provide the first formal model of Electryo, with security proofs for vote-privacy and individual verifiability. In general, voting protocols are complex constructs, involving advanced cryptographic primitives and strong security guarantees, posing a serious challenge when wanting to analyse and prove security with formal verification tools. Here we choose to use the $${{\,\mathrm{\textsc {Tamarin}}\,}}$$ prover since it is one of the more advanced tools and is able to handle many of the primitives we encounter in the design and analysis of voting protocols.
DeepScope: HCI Platform for Generative Cityscape Visualization

Streetscape visualizations are necessary for the understanding and evaluation of urban design alternatives. Alongside blueprints and textual descriptions, these design aids can affect city-form, building-codes and regulations for decades to come. Yet despite major advancements in computer graphics, crafting high-quality streetscape visualizations is still a complex, lengthy and costly task, especially for real-time, multiparty design sessions. Here we present DeepScope, a generative, lightweight and real-time HCI platform for urban planning and cityscape visualization. DeepScope is composed of a Generative Neural Network (DCGAN) and a Tangible User Interface (TUI) designed for multi-participants urban design sessions and real-time feedback. In this paper we explore the design, development and deployment of the DeepScope platform, as well as discuss the potential implementation of DeepScope in urban design processes.
User-driven Error Detection for Time Series with Events

Anomalies are pervasive in time series data, such as sensor readings. Existing methods for anomaly detection cannot distinguish between anomalies that represent data errors, such as incorrect sensor readings, and notable events, such as the watering action in soil monitoring. In addition, the quality performance of such detection methods highly depends on the configuration parameters, which are dataset specific. In this work, we exploit active learning to detect both errors and events in a single solution that aims at minimizing user interaction. For this joint detection, we introduce an algorithm that accurately detects and labels anomalies with a non-parametric concept of neighborhood and probabilistic classification. Given a desired quality, the confidence of the classification is then used as termination condition for the active learning algorithm. Experiments on real and synthetic datasets demonstrate that our approach achieves F-score above 80% in detecting errors by labeling 2 to 5 points in one data series. We also show the superiority of our solution compared to the state-of-the-art approaches for anomaly detection. Finally, we demonstrate the positive impact of our error detection methods in downstream data repairing algorithms.
TOURGURU: Tour Guide Mobile Application for Tourists

The paper discusses a tour guide mobile application which uses cloud computing, machine learning and Augmented Reality (AR) to give the user an amazing experience on tourism. This application would guide users through an appropriate route to a traveler's destination while suggesting recommended attractions through the route. Tourists would also be given the opportunity to listen to a narration about certain monuments while they are walking or driving through the suggested route. Additionally, one from the available two of the AR features can be useful when a tourist wants to find which direction a certain attraction is. This feature would be especially useful on a high vantage point allowing the user to enjoy several attractions from the same place all the while receiving interesting facts about them. Also, it would contain various details about that said attraction. Other feature of AR is on 3D object modelling that helps the user to get the experience of Point of Interest (POI).
Model-based clustering with determinant-and-shape constraint

Model-based approaches to cluster analysis and mixture modeling often involve maximizing classification and mixture likelihoods. Without appropriate constrains on the scatter matrices of the components, these maximizations result in ill-posed problems. Moreover, without constrains, non-interesting or "spurious" clusters are often detected by the EM and CEM algorithms traditionally used for the maximization of the likelihood criteria. Considering an upper bound on the maximal ratio between the determinants of the scatter matrices seems to be a sensible way to overcome these problems by affine equivariant constraints. Unfortunately, problems still arise without also controlling the elements of the "shape" matrices. A new methodology is proposed that allows both control of the scatter matrices determinants and also the shape matrices elements. Some theoretical justification is given. A fast algorithm is proposed for this doubly constrained maximization. The methodology is also extended to robust model-based clustering problems.
Anatomy and Lifecycle of a Bitcoin Transaction

Blockchain is a trusted, distributed, peer to peer, open, public, techno-cryptographic ledger to manage and transfer the value of digital assets, to provide immutability, anonymity and security. Bitcoin is a decentralized virtual, digital and programmable currency. Wallet is an interface to user in bitcoin network where we can store send and receive funds. This paper follows a novel approach in elaborating the underlying technical concepts of a transaction which comprises of locking and unlocking scripts, verification. Additionally, it also presents the details of block header that incorporates the concepts of difficulty, Merkle tree and proof of work. The paper highlights the mechanism of resolving ambiguity when there is more than one chain is available.
Do Explicit Review Strategies Improve Code Review Performance?

Context: Code review is a fundamental, yet expensive part of software engineering. Therefore, research on understanding code review and its efficiency and performance is paramount.
Data Migration Need, Strategy, Challenges, Methodology, Categories, Risks, Uses with Cloud Computing, and Improvements in Its Using with Cloud Using Suggested Proposed Model (DMig 1)

Data Migration is a multi-step process that begins with analyzing old data and culminates in data uploading and reconciliation in new applications.With the rapid growth of data, organizations constantly need to migrate data.Data migration can be a complex process as testing must be done to ensure data quality.Migration also can be very costly if best practices are not followed and hidden costs are not identified in the early stage.On the other hand, many organizations today instead of buying IT equipment (hardware and/or software) and managing it themselves, they prefer to buy services from IT service providers.The number of service providers is increasing dramatically and the cloud is becoming the preferred tool for more cloud storage services.However, as more information and personal data are transferred to the cloud, to social media sites, DropBox, Baidu WangPan, etc., data security and privacy issues are questioned.So, academia and industry circles strive to find an effective way to secure data migration in the cloud.Various resolving methods and encryption techniques have been implemented.In this work, we will try to cover many important points in data migration as Strategy, Challenges, Need, methodology, Categories, Risks, and Uses with Cloud computing.Finally, we discuss data migration security and privacy challenge and how to solve this problem by making improvements in it's using with Cloud through suggested proposed model that enhances data security and privacy by gathering Advanced Encryption Standard-256 (ATS256), Data Dispersion Algorithms and Secure Hash Algorithm-512.This model achieves verifiable security ratings and fast execution times.
The Move Maker - Exploring Bodily Preconditions and Surrounding Conditions for Bodily Interactive Play

Interest in interactive bodily play and game design has increased during the last decade, often fueled by the medical industry's focus on exergames and a need for basic movement training. By dividing bodily interactions into bodily preconditions and surrounding conditions for interaction, Move Maker systematically explores basic bodily play dynamics in combination with digital interactive devices. This way, Move Maker offers a movement-based game system challenging basic movement abilities through bodily play explorations. Move Maker is designed for elderly people to play with their grandchildren (through a suite of ready to play minigames) and for designers and physiotherapists wanting to explore and develop novel bodily play constructions.
PalmBoard: Leveraging Implicit Touch Pressure in Statistical Decoding for Indirect Text Entry

We investigated how to incorporate implicit touch pressure, finger pressure applied to a touch surface during typing, to improve text entry performance via statistical decoding. We focused on one-handed touch-typing on indirect interface as an example scenario. We first collected typing data on a pressure-sensitive touchpad, and analyzed users' typing behavior such as touch point distribution, key-to-finger mappings, and pressure images. Our investigation revealed distinct pressure patterns for different keys. Based on the findings, we performed a series of simulations to iteratively optimize the statistical decoding algorithm. Our investigation led to a Markov-Bayesian decoder incorporating pressure image data into decoding. It improved the top-1 accuracy from 53% to 74% over a naive Bayesian decoder. We then implemented PalmBoard, a text entry method that implemented the Markov-Bayesian decoder and effectively supported one-handed touch-typing on indirect interfaces. A user study showed participants achieved an average speed of 32.8 WPM with 0.6% error rate. Expert typists could achieve 40.2 WPM with 30 minutes of practice. Overall, our investigation showed that incorporating implicit touch pressure is effective in improving text entry decoding.
When contextual information meets recommender systems: extended SVD++ models

Collaborative filtering approach is widely used in the area of recommender systems. In fact, its predictive accuracy is supported by a large amount of additional information available on the internet, especially in social media. Singular Value Decomposition (SVD) is the most used matrix factorization method in the field of collaborative filtering, which is realized by using a latent factor vector of items and another one of users and introduces the users and item bias information. SVD++ is a derivation of the SVD model. It aims at adding implicit feedback information that is why the results of this model are improved. In this paper, different contributions are applied to SVD++ model through three different approaches which apply modification of latent factor vector while taking into account social and environmental information. Moreover, the experimental results showed that these proposed extensions ameliorate the prediction accuracy in terms of MAE and RMSE compared to SVD++ corresponding to four benchmarks, which are CiaoDVD, Food data, Adom data and Movielens-100k. According to the four used data sets, the experimental results showed that our proposed third contribution is effective in improving the prediction accuracy by 3.7%, 8.2%, 46.7% and 4%, respectively. These results also showed a reduction of the Mean Absolute Error by 0.0.71, 0.125, 0.61 and 0.038. Similar results were also obtained with the first and the second contribution.
Towards Extracting Highlights From Recorded Live Videos: An Implicit Crowdsourcing Approach

Live streaming platforms need to store a lot of recorded live videos on a daily basis. An important problem is how to automatically extract highlights (i.e., attractive short video clips) from these massive, long recorded live videos. However, algorithmic approaches are either domain-specific, which require experts to spend a long time to design, or resource-intensive, which require a lot of training data and/or computing resources. In this paper, we propose LIGHTOR, a novel implicit crowd-sourcing approach to overcome these limitations. The key insight is to collect users' natural interactions with a live streaming platform, and then leverage them to detect highlights. We recruit hundreds of users from Amazon Mechanical Turk, and evaluate the performance of LIGHTOR using two popular games in Twitch. The results show that LIGHTOR can achieve high extraction precision with a small set of training data and low computing resources.
Improving Immersive Experiences for Visitors with Sensory Impairments to the Aquarium of the Pacific

This case study describes the development of a mid-air haptic solution to enhance the immersive experience of visitors who are deaf, blind or wheelchair users to the Aquarium of the Pacific's movie theatre. During the project we found that adding a sense of touch, using an innovative ultrasound technology, to an immersive experience can improve the sense of engagement users have with the content, and can help to improve agreement with the topics presented. We present guidelines on the design of haptic sensations. By describing how this project took place within the tight timelines of a commercial deployment, we hope to encourage more organisations to do similar work.
Evaluating Virtual Reality as a Medium for Vocational Skill Training

There has been an increasing focus to skill, reskill and upskill the Indian population to capitalize the upcoming demographic dividend of the country. To address this, a broad spectrum of government schemes are being introduced, however, most of them have not been able to fulfil their requirements so far. There is a need for finding out ways to develop dynamic, demand-driven, quality conscious training solutions to enhance the ability to adapt to changing technologies and labour market demands. My dissertation work proposes a potential solution by exploring Virtual Reality as a medium to rapidly develop effective, scalable training modules for vocational skills in new-age employment sectors in India. After creating a framework based on formative research and case studies, I am building and evaluating one such system. I further plan to examine the efficacy of such systems and make them easy to be developed by existing vocational trainers with the help of an authoring tool.
Towards more discriminative features for texture recognition

Local binary patterns (LBP) are considered to be one of the most computationally efficient descriptor that can also be combined jointly among different variants to increase accuracy. In this study, we propose a method to obtain more discriminative 2D LBP features by optimizing projections of a joint LBP distribution onto the marginal histograms. To find a more efficient representation of the feature vector, we seek the least redundant marginal histograms of a joint LBP distribution via optimizing several constraints. In this way, we aim to have a more compact yet accurate feature vector in contrast to the methods that flatten the joint distribution. Experiments we perform on five popular texture datasets show that the feature vectors optimized with the proposed method provide higher recognition rates with the same size vectors and comparable results even with lower dimensional vectors. We also compare the proposed algorithm to more recent texture recognition methods based on convolutional neural networks and show that it can still provide comparable results even though the resulting feature vectors are smaller by orders of magnitude.
Piveau: A Large-Scale Open Data Management Platform Based on Semantic Web Technologies

The publication and (re)utilization of Open Data is still facing multiple barriers on technical, organizational and legal levels. This includes limitations in interfaces, search capabilities, provision of quality information and the lack of definite standards and implementation guidelines. Many Semantic Web specifications and technologies are specifically designed to address the publication of data on the web. In addition, many official publication bodies encourage and foster the development of Open Data standards based on Semantic Web principles. However, no existing solution for managing Open Data takes full advantage of these possibilities and benefits. In this paper, we present our solution "Piveau", a fully-fledged Open Data management solution, based on Semantic Web technologies. It harnesses a variety of standards, like RDF, DCAT, DQV, and SKOS, to overcome the barriers in Open Data publication. The solution puts a strong focus on assuring data quality and scalability. We give a detailed description of the underlying, highly scalable, service-oriented architecture, how we integrated the aforementioned standards, and used a triplestore as our primary database. We have evaluated our work in a comprehensive feature comparison to established solutions and through a practical application in a production environment, the European Data Portal. Our solution is available as Open Source.
A Simple Training Strategy for Graph Autoencoder

Graph autoencoder can map graph data into a low-dimensional space. It is a powerful graph embedding method applied in graph analytics to reduce the computational cost. The training algorithm of a graph autoencoder searches the weight setting for preserving most graph information of the graph data with reduced dimensionality. This paper presents a simple training strategy, which can improve the training performance without significantly increasing time complexity. This strategy can flexibly fit many existing training algorithms. The experimental results confirm the effectiveness of this strategy.
The Effect of Experience on Persona Perceptions

User perceptions of personas affect the adoption of personas for decision-making in real organizations. To investigate how experience affects the way an individual perceives a persona, we conduct an experimental study with individuals less and more experienced with personas. Quantitative results show that previous experience increases several important perceptions, including willingness to use, empathy, likability, and completeness. Results suggest that methods that increase experience (e.g., training, workshops, scenarios) should be applied alongside persona deployment, as desirable persona perceptions increase with individuals' experience.
Deciding When to Trade Data Freshness for Performance in MongoDB-as-a-Service

MongoDB is a popular document store that is also available as a cloud-hosted service. MongoDB internally deploys primary-copy asynchronous replication, and it allows clients to vary the Read Preference, so reads can deliberately be directed to secondaries rather than the primary site. Doing this can sometimes improve performance, but the returned data might be stale, whereas the primary always returns the freshest data value. While state-of-practice is for programmers to decide where to direct the reads at application development time, they do not have full understanding then of workload or hardware capacity. It should be better to choose the appropriate Read Preference setting at runtime, as we describe in this paper. We show how a system can detect when the primary copy is saturated in MongoDB-as-a-Service, and use this to choose where reads should be done to improve overall performance. Our approach is aimed at a cloud-consumer; it assumes access to only the limited diagnostic data provided to clients of the hosted service.
Creation of autonomous groups of combine harvesters and tractors for agriculture based on the Mivar decision-making systems “ROBO!RAZUM”

Abstract The Mivar decision-making system (MDMS) “ROBO!RAZUM” can be used to control autonomous combine harvesters and tractors, which can additionally perform a variety of actions in the process of moving through the field: working in group interaction mode, performing tasks in different weather conditions and even with a faulty or disabled technical vision using the internal map of the area and information from internal inertial sensors or from the GLONASS system. The theoretical basis for the application of MDMS “ROBO!RAZUM” is the result of the practical implementation of MIPRA program, which dramatically reduced the requirements for equipment and time to solve the problems of STRIPS-planning robot actions.
Asian CHI Symposium: HCI Research from Asia and on Asian Contexts and Cultures

This symposium showcases the latest HCI work from Asia and those focusing on incorporating Asian sociocultural factors in their design and implementation. In addition to circulating ideas and envisioning future research in human-computer interaction, this symposium aims to foster social networks among academics (researchers and students) and practitioners and grow a research community from Asia.