Reviewerly/exHarmony huggingface dataset:
Collaborative filtering-based recommendation system for big data

Collaborative filtering algorithm is widely used in the recommendation system of e-commerce website, which is based on the analysis of a large number of users' historical behaviour data, so as to explore the users' interest and recommend the appropriate products to users. In this paper, we focus on how to design a reliable and highly accurate algorithm for movie recommendation. It is worth noting that the algorithm is not limited to film recommendation, but can be applied in many other areas of e-commerce. In this paper, we use Java language to implement a movie recommendation system in Ubuntu system. Benefiting from the MapReduce framework and the recommendation algorithm based on items, the system can handle large datasets. The experimental results show that the system can achieve high efficiency and reliability in large datasets.
Object Detection Using Point Feature Matching Based on SURF Algorithm

Object detection addresses detecting instances of objects of a particular category in digitized images and videos. Every class containing the object is associated with special features that assist in classifying the class to which it belongs. The algorithm used can detect local features and extract them for the concerned object, known as descriptor points [1]. Then compares those extracted features or descriptors with the presumed features of the original image. Matching process among the original image and the target image yields similar features, based on which a decision is made. The mentioned algorithm is called Speeded up Robust Features (SURF) algorithm. The algorithm is based on Scale-invariant feature transform (SIFT) algorithm. The implementation procedure of SURF algorithm along with experiment and its results are stated in the paper. Also, the accuracy of detection of objects using point feature matching methodology has been calculated by means of sensitivity and specificity parameters.
Quantum Walk on Dimensionality Reduced Complete Bipartite Graphs with k Edges Removed

Due to scalability issue in current quantum technologies, many quantum algorithms can only be implemented for small size problems. Scalability remains a bottleneck for current quantum technologies. For solving real-life size hard problems in the near-term, we explore classes of search graphs that can be efficiently reduced to a implementable scale for many quantum algorithms. The reduced Hamiltonian preserves the dynamics of the original Hamiltonian. One of the quantum algorithms we choose for the reduced Hamiltonian is continuous time quantum walk (CTQW). We further show how to determine the correct value of the coupling factor of the underlying CTQW. With wrong couple factor values, the optimality (quadratic speedup) from CTQW might be lost. In this work, we extend the class of reducible graphs to complete bipartite graphs with random $k$ edges removed. We further demonstrate through mathematical proof and simulation experiment using IBM Qiskit to show that the quadratic speed-up is preserved for the CTQW.
EXAMINING INFORMATION ACCURACY ON SOCIAL MEDIA: EMPIRICAL EVIDENT FROM UNITED ARAB EMIRATES (UAE)

Social Media has become an important source of obtaining information and publishing news and articles. Hence, the impact of social media is an important subject for study because journalists are competing with citizens in the dissemination of news. The validity of information on social media is questionable. The focus of this research is identifying the concept of credibility and the prominent credibility standards that must apply in publishing of news. This study examined the relationship between traditional factors in media, acceptance to use of technology, interactive media, quality of news source with the impact of social media. The questionnaire was distributed to 175 respondents from 685 registered journalists in United Arab Emirates by using simple random sampling. The data were analyzed using Partial Least Squares-Structural Equation Modeling (PLS-SEM) to verify the relationships between the variables. The findings of the study largely support the hypothesized relationships proposed in the study model. The findings have implications for knowledge of theories in understanding the relationship between using social media and its impact on the society. The practical contribution of this study will assist journalists in dealing with news on social media, in order to develop the best journalism practices, and makes recommendation for future research directions.
Applying Distributed Cognition Theory to Agile Requirements Engineering

[Context & Motivation] Agile Requirements Engineering (ARE) is a collaborative, team-based process based on frequent elicitation, elaboration, estimation and prioritization of the user requirements, typically represented as user stories. While it is claimed that this Agile approach and the associated RE activities are effective, there is sparse empirical evidence and limited theoretical foundation to explain this efficacy. [Question/problem] We aim to understand and explain aspects of the ARE process by focusing on a cognitive perspective. We appropriate ideas and techniques from Distributed Cognition (DC) theory to analyze the cognitive roles of people, artefacts and the physical work environment in a successful collaborative ARE activity, namely requirement prioritization. [Principal idea/results] This paper presents a field study of two early requirements related meetings in an Agile product development project. Observation data, field notes and transcripts were collected and qualitatively analyzed. We have used DiCoT, a framework for systematically applying DC as a methodological contribution, to analyze the ARE process and explain its efficacy from a cognitive perspective. The analysis identified three main areas of cognitive effort in the ARE process as well as the significant information flows and artefacts. Analysis of these have identified that the use of physical user story cards, specific facilitator skills, and development of shared understanding of the user stories, were all key to the effectiveness of the ARE activity observed. [Contribution] The deeper understanding of cognition involved in ARE provides an empirically evidenced explanation, based on DC theory, of why this way of collaboratively prioritizing requirements was effective. Our result provides a basis for designing other ARE activities.
Towards Unsupervised Image Harmonisation

The field of image synthesis intrinsically relies on the process of image compositing. This process can be automatic or manual, and depends upon artistic intent. Compositing can introduce errors, due to human-detectable differences in the general pixel level transforms of component elements of an image composite. We report on a pilot study evaluating a proof-of-concept automatic image composite harmonisation system consisting of a state-of-the-art deep harmonisation model and a perceptually-based composite luminance artifact detector. We evaluate the performance of both systems on a large data-set of 68128 automatically generated image composites and find that without any task-specific adaptations, the end-to-end system achieves comparable results to the baseline harmoniser fed with ground truth composite masks. We discuss these findings in the context of extending this to an end-to-end, multi-task system.
Autonomous UAV with Learned Trajectory Generation and Control

Unmanned aerial vehicle (UAV) technology is a rapidly growing field with tremendous opportunities for research and applications. To achieve true autonomy for UAVs in the absence of remote control, external navigation aids like global navigation satellite systems and radar systems, a minimum energy trajectory planning that considers obstacle avoidance and stability control will be the key. Although this can be formulated as a constrained optimization problem, due to the complicated non-linear relationships between UAV trajectory and thrust control, it is almost impossible to be solved analytically. While deep reinforcement learning is known for its ability to provide model free optimization for complex system through learning, its state space, actions and reward functions must be designed carefully. This paper presents our vision of different layers of autonomy in a UAV system, and our effort in generating and tracking the trajectory both using deep reinforcement learning (DRL). The experimental results show that compared to conventional approaches, the learned trajectory will need 20% less control thrust and 18% less time to reach the target. Furthermore, using the control policy learning by DRL, the UAV will achieve 58.14% less position error and 21.77% less system power.
Tap-Pair

Augmented Reality (AR) headsets, which allow for a realistic integration between the physical environment and virtual objects, are rapidly coming to customer and enterprise markets. This is largely because they enable a broad range of multi-user applications in which all participants experience the same augmentation of their natural surrounding. However, despite their increasing expansion, there currently exist no implemented methods for secure ad-hoc device pairing of multiple AR headsets. Given the importance of multi-user experiences for future applications of this technology, in this paper we propose two distinct ways to establish secure ad-hoc connections that rely only on typical user interactions in AR: gazing and tapping either at the location of a shared point on the wall or towards the user with whom one wants to connect. To show the feasibility and deployability of the proposed system to existing technology, we build a prototype of Tap-Pair, a system for ad-hoc pairing of AR headsets that is based on Password Authenticated Key Exchange protocols, requires only user interactions that are common in AR, and can be extended to more than two users. The experimental evaluation of the Tap-Pair prototype in a series of measurements at three different locations confirms the feasibility of our proposal, showing that the system built with currently available augmented reality headsets indeed achieves successful pairing in more than 90% of attempts, while keeping the probability of the attacker's success lower than 1e-3.
Secure Spatial Query with Differentially Private Access Pattern

The spatial transformation mechanism (e.g., space-filling curves) enables the spatial query in the encypted spatial data stored on the third party service provider. Although this mechanism will not reveal the original spatial data nor the query content to the service provider, the service provider can learn the relationship between the query tokens and the accessed files, which is called the access pattern. Several researches have demonstrated that the access pattern can be used to recover the content of a query. In this paper, we propose a secure spatial query scheme with differential privacy access pattern. The proposed scheme employs the d-privacy to measure the utilities of the candidate spatial indexes, and applies subsampled exponential mechanism to select the obfuscated spatial index. In this differentially private way, the outsource spatial data is able to resist the query recovery attacks. The experimental results demonstrate that our scheme could generate high quality candidate spatial indexes for the subsampled exponential mechanism, and achieve good precision in the spatial range query.
Evaluation of micro-scale drainage systems in Kelapa Gading, North Jakarta

Abstract Kelapa Gading is one of the sub-districts located in North Jakarta which consists of low-level plains prone to flooding. One of the areas in Kelapa Gading that is most severely affect by the floods is in front of the Mall of Indonesia (MOI). Based on earlier research, it has been reported that one of the causes of flooding in Kelapa Gading is its drainage system’s inability to pass the flood discharge. Therefore, this study discusses the micro-scale drainage system in the MOI catchment area. The purpose of this study is to evaluate the performance of the micro-scale drainage system in the Mall of Indonesia (MOI) area by comparing the results of the calculation of the flood discharge of the planned catchment area with the capacity of the drainage channels using the help of WinTR-20 and HEC-RAS applications. From the simulation results using the two applications, it can be seen that all micro drainage channels in the MOI catchment area are able to pass flood discharge up to period of 25 years, except in reach 6. Based on this, an open drainage system analysis is carried out into a polder system by placing two pumps with a capacity of 0.25 m3/s. With this proposed polder system design, the flooding in the MOI catchment area can contain the discharge up to a 25-year return period floodings.
Examination of Factors Affecting Customer Oriented Product Design in Automotive Industry

Existing competitive environment leads manufacturers to focus on customer expectations more than ever. One of the reasons behind this focus is that the design concepts which are flexible to customer needs add incredible value to the product. In this chapter, customer oriented product design for an automobile is examined according to design concepts and their causal relationships using fuzzy cognitive mapping (FCM) to help automotive industry to be customer focused. Individual assessments are used in the analysis rather than aggregate them to obtain common customer’s preferences. It is found that the relations and the steady states of the concepts are differentiated among the customers. The results of the study can be used as a road map for automotive industry for customer oriented product design.
The importance of healthcare information privacy through blockchain

This chapter outlines the role of blockchain in fastening the healthcare research and empowering patients to control the privacy of their health data in addition to avail a secured regular health monitoring tool. The chapter also deals with fundamental challenges, issues of blockchain, and research outset of blockchain in the field of the healthcare information security.
The Cramer-Rao Lower Bound in the Phase Retrieval Problem

This paper presents an analysis of Cramer-Rao lower bounds (CRLB) in the phase retrieval problem. Previous papers derived Fisher Information Matrices for the phaseless reconstruction setup. Two estimation setups are presented. In the first setup the global phase of the unknown signal is determined by a correlation condition with a fixed reference signal. In the second setup an oracle provides the optimal global phase. The CRLB is derived for each of the two approaches. Surprisingly (or maybe not) they are different.
Web Services as A Solution for Cloud Enterprise Resource Planning Interoperability

Recently, organizations have shown more interest in cloud computing because of the many advantages they provide (cost savings, storage capacity, scalability, and speed of loading). Enterprise resource planning (ERP) systems are one of the most important systems that have been upgraded to cloud computing. In this thesis, we focus on cloud ERP interoperability, which is an important challenge in cloud ERP. Interoperability is the ability of different components to work in independent clouds with no or minimum user effort. More than 20% of the risk rate of cloud adoption is caused by interoperability. Thus, we propose web services as a solution for cloud ERP interoperability. The proposed solution increases interoperability between different cloud service providers and between cloud ERP systems with other applications in a company.
Decision support system for the selection of rice varieties using weighted product method

Abstract The selection of rice varieties is a problem because there are a number of criteria that are considered in decision making. The criteria used in this study for the selection of rice varieties are yield potential, average yield, age of harvest, resistance to brown plant hopper pest and resistance to bacterial leaf blight disease. The concept of decision support system is a model for selecting the type of variety desired based on determined criteria (attributes). In this study using the weighted product method because there are a number of criteria in decision making (Multi Attribute Decision Making). The weighted product method calculates the preference value of each alternative where the alternative chosen is the one that has the greatest relative preference value. Based on the test results using an application program created, the relative preference values of each superior rice variety are obtained. From the calculation of the largest preference value is INPARI 1 with a value of V equal to 0.0404.
Vowels and Prosody Contribution in Neural Network Based Voice Conversion Algorithm with Noisy Training Data

This research presents a neural network based voice conversion (VC) model. While it is a known fact that voiced sounds and prosody are the most important component of the voice conversion framework, what is not known is their objective contributions particularly in a noisy and uncontrolled environment. This model uses a 2-layer feedforward neural network to map the Linear prediction analysis coefficients of a source speaker to the acoustic vector space of the target speaker with a view to objectively determine the contributions of the voiced, unvoiced and supra-segmental components of sounds to the voice conversion model. Results showed that vowels 'a', 'i', 'o' have the most significant contribution in the conversion success. The voiceless sounds were also found to be most affected by the noisy training data. An average noise level of 40 dB above the noise floor were found to degrade the voice conversion success by 55.14 percent relative to the voiced sounds. The result also shows that for cross-gender voice conversion, prosody conversion is more significant in scenarios where a female is the target speaker.
Automatic region selection method to enhance image-based steganography

Image-based steganography is an essential procedure with several practical applications related to information security, user authentication, copyright protection, etc. However, most existing image-based steganographic techniques assume that the pixels that hide the data can be chosen freely, such as random pixel selection, without considering the contents of the input image. So, the “region of interest” such as human faces in the input image might have defected after data hiding even at a low inserting rate, and this will degrade the visual quality especially for the images containing several human faces. With this view, we proposed a novel approach that combines human skin-color detection along with the LSB approach which can choose the embedding regions. The idea behind that is based on the fact that the Human Vision System HVS tends to focus its attention on selectively certain structures of the visual scene instead of the whole image. Practically, human skin-color is good evidence of the existence of human targets in images. To the best of our knowledge, this is the first attempt that employs skin detection in application to steganography which consider the contents of input image and consequently can choose the embedding regions. Moreover, an enhanced RSA algorithm and Elliptic Curve Equation are used to provide a double level of security. In addition, the system embeds noise bits into the resulting stego-image to make the attacker’s task more confusing. Two datasets are used for testing and evaluation. The experimental results show that the proposed approach achieves a significant security improvement with high image quality.
Through the Eyes of Viewers: A Comment-Enhanced Media Content Representation for TED Talks Impression Recognition

Developing computational frameworks for personalized content query and recommendation has sparked numerous research into automatic indexing and retrieval of multimedia data. Assessing viewer impression as an appropriate index of media content is especially important as it links directly to the audience preferences toward media content. Most of the existing machine learning frameworks rely on modeling the media contents solely without considering the potential usefulness of user feedback in order to assess the viewer impressions. In this work, we develop a cross-modal network that projects the multimodal media content through the viewer's comment space in order to learn a joint (content and viewer) embedding space to perform viewer impression recognition. Specifically, we gather a large corpus of TED talks including viewer's online comments for each of the presentation video. Our proposed cross-modal projection network achieves 80.8%, 79.5%, and 80.8% of unweighted average recall (UAR) in binary classification tasks for three different viewer impression ratings (i.e., inspiring, persuasive, and funny, respectively). Our experiments demonstrate intuitively that online user comments reflect the viewer impression the most, but an interesting finding shows that it is important to project the content's information into the user comment space, i.e., through the eyes of the comment, in order to obtain an improved recognition accuracy as compared to simply concatenating content and comment features directly.
Image registration approach with scale-invariant feature transform algorithm and tangent-crossing-point feature

Due to considerable diversities of multimodality remote sensing images in spectral component, the performances of scale-invariant feature transform (or SIFT) may be problematic. In view of this, a model of image registration based on tangent-crossing-point feature is proposed. With the help of tangent-cross-point feature, verifying the correctness of the feature matching is very easy, since it adopts the location information indexed by the correct matching pair of feature-points and transformation information determined by the correct matching pair of curves simultaneously. Experimental results show that this method is more efficient and reliable than the classic SIFT algorithm in some cases.
Saturating the quantum Cram\'er-Rao bound and measuring the related quantum Fisher information in a nitrogen-vacancy center in diamond.

The quantum Cramer-Rao bound sets a fundamental limit on the accuracy of unbiased parameter estimation in quantum systems, relating the uncertainty in determining a parameter to the inverse of the quantum Fisher information. We experimentally demonstrate near saturation of the quantum Cramer-Rao bound in the phase estimation of a solid-state spin system, provided by a nitrogen-vacancy center in diamond. This is achieved by comparing the experimental uncertainty in phase estimation with an independent measurement of the related quantum Fisher information. The latter is finely extracted from coherent dynamical responses of the system under weak parametric modulations, without performing any quantum-state tomography. Our method offers a versatile and powerful tool to explore the fundamental role of the quantum Fisher information in quantum technologies.
Optimal Collision Side-Channel Attacks

Collision side-channel attacks are effective attacks against cryptographic implementations, however, optimality and efficiency of collision side-channel attacks is an open question. In this paper, we show that collision side-channel attacks can be derived using maximum likelihood principle when the distribution of the values of the leakage function is known. This allows us to exhibit the optimal collision side-channel attack and its efficient computation. Finally, we can compute an upper bound for the success rate of the optimal post-processing strategy, and we show that our method and the optimal strategy have success rates close to each other. Attackers can benefit from our method as we present an efficient collision side-channel attack. Evaluators can benefit from our method as we present a tight upper bound for the success rate of the optimal strategy.