Reviewerly/exHarmony huggingface dataset:
Efficient Server-Aided Secure Two-Party Computation in Heterogeneous Mobile Cloud Computing

With the ubiquity of mobile devices and rapid development of cloud computing, mobile cloud computing (MCC) has been considered as an essential computation setting to support complicated, scalable and flexible mobile applications by overcoming the physical limitations of mobile devices with the aid of cloud. In the MCC setting, since many mobile applications (e.g., map apps) interacting with cloud server and application server need to perform computation with the private data of users, it is important to realize secure computation for MCC. In this article, we propose an efficient server-aided secure two-party computation (2PC) protocol for MCC. This is the first work that considers collusion between a malicious garbled circuit evaluator and a semi-honest server while ensuring privacy and correctness. Also, it can guarantee fairness when collusion does not exist. The security analysis shows that our protocol can securely compute any function f(x, y) against different types of adversaries in the malicious model. Also, the experimental performance analysis shows that this work outperforms the previous works for at least 10 times with the same security level.
Measurement of Objective Video Quality in Social Cloud Based on Reference Metric

This paper explores the objective of the present video quality analysis (VQA) and measures the full reference metrics keeping in view the quality degradation. During the research work, we conduct experiments on different social clouds (SCs) and low-quality videos. Selected videos are uploaded to SC to assess differences in video service and quality. WeChat shows that the average of all videos (Avg = 100), peak signal-to-noise ratio (PSNR), has no impact on other indicators. Therefore, we believe that WeChat provides the best video quality and multimedia services to their users to meet Quality of Service (QoS)/Quality of Experience (QoE).
Maximal Figure-of-Merit Framework to Detect Multi-Label Phonetic Features for Spoken Language Recognition

Bottleneck features (BNFs) generated with a deep neural network (DNN) have proven to boost spoken language recognition accuracy over basic spectral features significantly. However, BNFs are commonly extracted using language-dependent tied-context phone states as learning targets. Moreover, BNFs are less phonetically expressive than the output layer in a DNN, which is usually not used as a speech feature because of its very high dimensionality hindering further post-processing. In this article, we put forth a novel deep learning framework to overcome all of the above issues and evaluate it on the 2017 NIST Language Recognition Evaluation (LRE) challenge. We use manner and place of articulation as speech attributes, which lead to low-dimensional “universal” phonetic features that can be defined across all spoken languages. To model the asynchronous nature of the speech attributes while capturing their intrinsic relationships in a given speech segment, we introduce a new training scheme for deep architectures based on a Maximal Figure of Merit (MFoM) objective. MFoM introduces non-differentiable metrics into the backpropagation-based approach, which is elegantly solved in the proposed framework. The experimental evidence collected on the recent NIST LRE 2017 challenge demonstrates the effectiveness of our solution. In fact, the performance of speech language recognition (SLR) systems based on spectral features is improved for more than 5% absolute Cavg. Finally, the F1 metric can be brought from 77.6% up to 78.1% by combining the conventional baseline phonetic BNFs with the proposed articulatory attribute features.
Automatic Generation of Graphical User Interface Prototypes from Unrestricted Natural Language Requirements

High-fidelity GUI prototyping provides a meaningful manner for illustrating the developers' understanding of the requirements formulated by the customer and can be used for productive discussions and clarification of requirements and expectations. However, high-fidelity prototypes are time-consuming and expensive to develop. Furthermore, the interpretation of requirements expressed in informal natural language is often error-prone due to ambiguities and misunderstandings. In this dissertation project, we will develop a methodology based on Natural Language Processing (NLP) for supporting GUI prototyping by automatically translating Natural Language Requirements (NLR) into a formal Domain-Specific Language (DSL) describing the GUI and its navigational schema. The generated DSL can be further translated into corresponding target platform prototypes and directly provided to the user for inspection. Most related systems stop after generating artifacts, however, we introduce an intelligent and automatic interaction mechanism that allows users to provide natural language feedback on generated prototypes in an iterative fashion, which accordingly will be translated into respective prototype changes.
Learning Robot Policies Using a High-Level Abstraction Persona-Behaviour Simulator

Collecting data in Human-Robot Interaction for training learning agents might be a hard task to accomplish. This is especially true when the target users are older adults with dementia since this usually requires hours of interactions and puts quite a lot of workload on the user. This paper addresses the problem of importing the Personas technique from HRI to create fictional patients' profiles. We propose a Persona-Behaviour Simulator tool that provides, with high-level abstraction, user's actions during an HRI task, and we apply it to cognitive training exercises for older adults with dementia. It consists of a Persona Definition that characterizes a patient along four dimensions and a Task Engine that provides information regarding the task complexity. We build a simulated environment where the high-level user's actions are provided by the simulator and the robot initial policy is learned using a Q-learning algorithm. The results show that the current simulator provides a reasonable initial policy for a defined Persona profile. Moreover, the learned robot assistance has proved to be robust to potential changes in the user's behaviour. In this way, we can speed up the fine-tuning of the rough policy during the real interactions to tailor the assistance to the given user. We believe the presented approach can be easily extended to account for other types of HRI tasks; for example, when input data is required to train a learning algorithm, but data collection is very expensive or unfeasible. We advocate that simulation is a convenient tool in these cases.
Agile Scrum Scaling Practices for Large Scale Software Development

Plan-driven software methods, which are also called tradition approaches, promise predictability, stability, and high assurance while Agile methodologies, which is also called light-weight, increased customer satisfaction, lower defect rates, faster development times, and are a solution to rapidly changing requirements. Research works in the software engineering domain report on the increasing use and successes of small-scale and relatively simple software projects with Agile Scrum methods. Recently, however, empirical academic research works and industry experience case studies have confirmed the wide use of Agile Scrum in large-scale and mission-critical software projects. Despite the widely adoption of the method by the software industry, there are still challenges and pressing demands to identify scaling factors for Agile Scrum method. This research is envisioned to identify the specific problems that software companies face when trying to implement Agile Scrum method, in particular related to the implementation of large software development projects. Then, it tries to identify critical success factors of large software development projects that are appropriate for scaling up Agile Scrum. In this research, mixed research methods have been employed to address the research questions. Two Large-scale as well as Safety/Mission-Critical Agile Scrum software development companies are selected for case studies. The findings of the research will help agile practitioners and consultants to identify and adopt Agile Scrum scaling practices in their respective organizational context.
Black-Box Wallets: Fast Anonymous Two-Way Payments for Constrained Devices

Abstract Black-box accumulation (BBA) is a building block which enables a privacy-preserving implementation of point collection and redemption, a functionality required in a variety of user-centric applications including loyalty programs, incentive systems, and mobile payments. By definition, BBA+ schemes (Hartung et al. CCS ‘17) offer strong privacy and security guarantees, such as unlinkability of transactions and correctness of the balance flows of all (even malicious) users. Unfortunately, the instantiation of BBA+ presented at CCS ‘17 is, on modern smartphones, just fast enough for comfortable use. It is too slow for wearables, let alone smart-cards. Moreover, it lacks a crucial property: For the sake of efficiency, the user’s balance is presented in the clear when points are deducted. This may allow to track owners by just observing revealed balances, even though privacy is otherwise guaranteed. The authors intentionally forgo the use of costly range proofs, which would remedy this problem. We present an instantiation of BBA+ with some extensions following a different technical approach which significantly improves efficiency. To this end, we get rid of pairing groups, rely on different zero-knowledge and fast range proofs, along with a slightly modified version of Baldimtsi-Lysyanskaya blind signatures (CCS ‘13). Our prototype implementation with range proofs (for 16 bit balances) outperforms BBA+ without range proofs by a factor of 2.5. Moreover, we give estimates showing that smart-card implementations are within reach.
On the Diversity of Machine Learning Models for System Reliability

The diversity of system components is one of the important contributing factors of reliable and secure software systems. In a software fault-tolerant system using diverse versions of software components, a component failure caused by defects or malicious attacks can be covered by other versions. Machine learning systems can also benefit from such a multi-version approach to improve the system reliability. Nevertheless, there are few studies addressing this issue. In this paper, we experimentally analyze how outputs of machine learning modules can be diversified by using different versions of machine learning algorithms, neural network architectures and perturbated input data. The experiments are conducted on image classification tasks of MNIST data set and Belgian Traffic Sign data set. Different neural network architectures, support vector machines and random forests are used for constructing diverse machine learning models. The diversity is characterized by the coverage of errors over the test samples. We observe that the different machine learning models have quite different error coverages that can be leveraged for system reliability design. Based on the experimental results, we construct the reliability model for three-version machine learning architecture with a diversity measure defined as the intersection of error spaces in the sample space. From the presented reliability model, we derive a necessary condition under which three-version architecture achieves a higher system reliability than a single machine learning module.
Prediction of Academic Performance of Alcoholic Students Using Data Mining Techniques

Alcohol consumption by students has become a serious issue nowadays. Addiction to alcohol leads to the poor academic performance of students. This paper describes few algorithms that help to improve the efficiency of academic performance of students addicted to alcohol. In the paper, we are using one of the popular Data Mining technique—“Prediction” and finding out the best algorithm among other algorithms. Our project is to analyze the academic excellence of the college professionals by making use of WEKA toolkit and R Studio. We implement this project by making use of alcohol consumption by student datasets provided by kaggle website. It is composed of 395 tuples and 33 attributes. A classification model is built by making use of Naïve Bayes and ID3. Comparison of accuracy is done between R and WEKA. The prediction is performed in order to find out whether a student can be promoted or demoted in the next academic year when previous year marks are considered.
Usage of Machine Learning in Software Testing

Finding, pinpointing and correcting bugs in the software takes a lot of effort for software developers. Traditional testing requires humans to search and analyze data. Humans being prone to poor assumptions and hence skewed results leads to overlooked bugs. Machine learning helps systems to learn and apply the learned knowledge in the future which helps software testers with more accurate knowledge. Capability of several advanced machine learning techniques such as deep learning in a number of software engineering tasks such as code completion, defect prediction, bug localization, clone detection, code search and learning API sequences. A lot of approaches have been proposed by the researchers over the years at modifying programs automatically. Repairing programs by generate-and-validate techniques gain a lot by producing acceptable patches to the programmers and not overfitting through learning from past history and generating patches from correct code via probabilistic model. These approaches given the right environments play significant role in reducing the effort and time consumption as well as cost of the bug fixing for the software developers. In this chapter, various machine learning algorithms and their impact in software testing and bug fixing are explored. The last chapter concludes with the future of machine learning and predictive analysis and how they might be used for addressing the challenge of reacting faster to dynamic expectations of customers and their needs.
SAVIZ

Due to climate change and the effects of geopolitical and social challenges like the refugee crisis in Europe, the world is facing an unprecedented set of humanitarian problems. According to the United Nations, there is a projected funding shortfall of more than 20 billion dollars in addressing these needs. Technology can play a vital role in mitigating this burden, especially with the advent of real-time social media and advances in areas like Natural Language Processing and machine learning. An important problem addressed by machine learning in current crisis informatics platforms is situation labeling, which can be intuitively defined as semi-automatically assigning one or more actionable labels (such as food, medicine or water) to tweets or documents from a controlled vocabulary. Despite multiple advances, current situation labeling systems are noisy and do not generalize very well to arbitrary crisis data. Consequentially, consumers of these outputs (which include humanitarian responders) are unwilling to trust these outputs without due diligence or provenance. In this paper, we demonstrate an interactive visualization platform called SAVIZ that provides non-technical first responders with such capabilities. SAVIZ is completely built using open-source technologies, can be rendered on a web browser and is backward-compatible with several pre-existing crisis intelligence platforms. We use two real-world scenarios (the 2015 earthquake in Nepal, and the unfolding Ebola crisis in Africa) to illustrate the potential of SAVIZ.
Data analysis of intellectual property policy system based on Internet of Things

ABSTRACT The issuance and implementation of intellectual property policies have promoted the rapid development of intellectual property intermediary services in China, bringing new opportunities and challenges for public sectors of the government. With their continuous development, Internet of Things (IoT) technology and big data have become the analytical tools widely applied in many technical fields. Through the analysis of IoT data, the optimal resource configuration could be obtained, which would guide both governments and enterprise managers to make scientific decisions in terms of future development.
QoS Aware Group-Based Workload Scheduling in Cloud Environment

Cloud computing paradigm provides dynamically scalable resources as a service over the Internet. It promises reduction in capital expenditure as well as operational expenditure. In cloud computing, there are still some challenges need to be solved. Among them, job scheduling is one of the important issues, since the objective of service provider and the cloud user is to utilize the computing resources efficiently. Job scheduling is the process of assigning user jobs to appropriate resources so that it can utilize the cloud resources efficiently. In cloud computing environment, the communication time between the users and resources can have a great impact on job scheduling. The primary objective in cloud environment is to process the tasks provided by user with higher responsiveness. So there is a need of an efficient group-based job scheduling algorithm that assemble the independent fine-grained jobs into groups by taking the communication time into consideration. In this work, we have proposed a group-based scheduling strategy for scheduling fine-grained jobs according to the processing capability and bandwidth of available resources in cloud computing environment.
Gray image segmentation based on fuzzy c-means and artificial bee colony optimization

The regions obtained by image segmentation need to satisfy both the requirements of uniformity and connectivity. Image segmentation is the process of dividing an image into several specific regions. The result of image segmentation is a set of combinations covering the main feature areas of the who le image. The pixels in an area are similar to some or calculated characteristics, but there are obvious differences between adjacent areas. In this paper, a gray image segmentation algorithm based on fuzzy C-means combined with bee colony algorithm is proposed, which has strong optimization ability for multi-objective problems. By using the fuzzy membership function of the fuzzy C-means algorithm, the optimal clustering centers in the artificial bee colony optimization algorithm can be quickly calculated. It makes image segmentation faster and more accurate. The bee colony search algorithm is optimized and an effective local search algorithm is designed, it makes the bee colony converge to the optimal solution efficiently. Finally, the improved fuzzy C-means and artificial bee colony optimization algorithm are used to improve and optimize the seed region growth method. The multi-criteria are taken as the multi-objective optimization problem, and the segmentation results are finally obtained. Benefiting from our local search program and feature extraction in multi-color space, it makes the stability; efficiency and accuracy of image segmentation are higher.
Obfuscating spatial point tracks with simulated crowding

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Effects of Augmented Reality Mobile Apps on Early Childhood Education Students' Achievement

Research trends on augmented reality (AR) showed that limited studies have been conducted on early education. The main objective of this study was to investigate the effects of an AR mobile apps on early childhood education students' achievement of learning alphabets. In this study, a total of 60 children at the age of five and six years old from a rural kindergarten were involved. A quasi-experimental research was applied in this study. The results showed that the children in the AR mobile apps group had a statistically significant higher mean score than the regular or non-AR mobile apps group. In conclusion, children learn alphabets better by using AR mobile apps than using the regular mobile apps. Therefore, it is recommended that more studies on AR mobile apps should be conducted in early childhood education as the technology helped children to learn at the early age.
LogDet Metric-Based Domain Adaptation

Domain adaptation has proven to be successful in dealing with the case where training and test samples are drawn from two kinds of distributions, respectively. Recently, the second-order statistics alignment has gained significant attention in the field of domain adaptation due to its superior simplicity and effectiveness. However, researchers have encountered major difficulties with optimization, as it is difficult to find an explicit expression for the gradient. Moreover, the used transformation employed here does not perform dimensionality reduction. Accordingly, in this article, we prove that there exits some scaled LogDet metric that is more effective for the second-order statistics alignment than the Frobenius norm, and hence, we consider it for second-order statistics alignment. First, we introduce the two homologous transformations, which can help to reduce dimensionality and excavate transferable knowledge from the relevant domain. Second, we provide an explicit gradient expression, which is an important ingredient for optimization. We further extend the LogDet model from single-source domain setting to multisource domain setting by applying the weighted Karcher mean to the LogDet metric. Experiments on both synthetic and realistic domain adaptation tasks demonstrate that the proposed approaches are effective when compared with state-of-the-art ones.
3D Image Steganography Using Sine Logistic Map and 2D Hyperchaotic Map

This paper proposes a message security scheme that employs 2 layers. The first layer of security involves steganography while the second one involves cryptography. The secret data is encrypted using AES-128, then XORed with a key that is generated from a chaotic 2D Hyperchaotic map. For steganography, a 3D cover image is sliced into a number of 2D images which are shuffled using the Sine Logistic Map. Then, least significant bit (LSB) embedding is applied to the pixels of the shuffled 2D slices of the 3D image. Finally, the 2D slices are rearranged back into their right order, before forming the 3D image back again.
Models of recognition algorithms based on linear threshold functions

Abstract The problem of constructing a model of recognition algorithms in the conditions of high dimensionality of feature space is considered. To solve this problem, a new approach suggests that it takes into account the interconnectedness of the given features. The main concern of this approach is to isolate representative traits and form preferred combinations of traits. A distinctive feature of the proposed model of algorithms is to determine a suitable set of elementary threshold rules within the framework of the preferred combinations of representative features and build a proximity function based on this set of feature combinations. Scientifically, the results of this work in the aggregate represent a new solution to a scientific problem related to improving the reliability of recognition algorithms based on radial functions. The practical significance of the results lies in the fact that the developed algorithms can be applied in medical and technical diagnostics, geological forecasting, biometric identification. To test the performance of the proposed model, experimental studies were performed.