Miracle-dz/UniFAR-FTUs huggingface dataset:
Social Influence on Future Review Sentiments: An Appraisal-Theoretic View

Abstract Given the importance of online reviews, as evidenced by extant research, we studied an understudied area of the impact of past reviews on sentiments of future reviews. Past studies in the emergent area of incidence of reviews have investigated the generation of ratings but there seems to be a lacuna in the second dimension of the reviews (i.e., the textual sentiments). Through a controlled experiment (N = 232), we analyzed the impact of past reviews on reviewers’ subsequent texts by using the unique lens of social appraisal theory. We found the influence of the selection of reviews on most e-commerce websites could strongly bias subsequent written review sentiments, and this effect is more pronounced when the reviewer experienced higher disconfirmation. We also observed review writers tend to post extreme reviews in the absence of any benchmark or prior reviews. The study results extend the understanding of users’ reviews under social influence and enhance the appraisal theoretic understanding of review generation.
A New Decomposition Method for Designing Microservices

Many companies are migrating from monolithic architectures to microservice architectures, and they need to decompose their applications in order to create a microservices application. Therefore, the need comes for an approach that helps software architects in the decomposition process. This paper presents a new approach for decomposing monolithic application to a microservices application through analyzing the application programming interface. Our proposed decomposition methodology uses word embedding models to obtain word representations using operation names, as well as, using a hierarchical clustering algorithm to group similar operation names together in order to get suitable microservices. Also, using grid search method to find the optimal parameter values for Affinity Propagation algorithm, which was used for clustering, as well as using silhouette coefficient score to compare the performance of the clustering parameters. The decomposition approach that was introduced in this research consists of the OpenAPI specifications as an input, then extracts the operation names from the specifications and converts them into average word embedding using fastText model. Lastly the decomposition approach is grouping these operation names using Affinity Propagation algorithm. The proposed methodology presented promising results with a precision of 0.84, recall of 0.78 and F-Measure of 0.81.
Optimising wastewater treatment solutions for the removal of contaminants of emerging concern (CECs): a case study for application in India

The aim of this study was to produce optimal wastewater treatment solutions to calculate the removal of different contaminants of emerging concern (CECs) found in developing countries. A new methodology was developed for a decision support tool (WaStewater Decision support OptiMiser, WiSDOM), which focuses on producing treatment solutions suited to treating water for reuse to Indian Water Quality Standards. WiSDOM-CEC analyses the removal of CECs through different treatment solutions and was also used to evaluate the performance of each treatment train solution in terms of the removal of conventional pollutants using multi-objective optimisation and multi-criteria decision analysis. Information was collected on different CECs across different regions of India, and the removal of 18 different CECs through 42 wastewater treatment unit processes for five different regions of India was analysed. Comparisons between similar categories of CECs, such as non-steroidal anti-inflammatory, showed that emerging contaminants all react differently with individual treatment options. For example, the removal of ibuprofen (IBP) and naproxen (NPX) varied from >80% and 0%, respectively, for a solution in Karnataka involving sedimentation, submerged aerated filter, ultrafiltration and nanofiltration. In Tamil Nadu, results ranged from 36.8% to 72% for diclofenac, 10.7% to 66.5% for IBP, and 0% for NPX.
A novel infant cry recognition system using auditory model‐based robust feature and GMM‐UBM

Recognizing infant cry is a meaning work, which can help new parent to understand infant's needs. Mostly, the motivation of existed recognition features is based on the psychoacoustic model. However, weak features are not enough to represent the details of infant cry. To address this issue, we propose a novel infant cry recognition system. In our system, the feature extraction method derived from auditory model, this model can address the auditory neural active representation. Additionally, we designed the intelligent system by a classifier named Gaussian Mixture Model‐Universal Background Model, which has a robust recognition performance to channel imbalance and corroded signal. The experiment results have shown a superior performance. Compared with the High‐order Spectral feature, a state‐of‐the‐art feature, when using Gaussian Mixture Model and General Background Model as classifier, the recognition accuracy improved by 14.6% and 13.7% under clear and corroded signal, respectively.
Joint Recommendations in Multilayer Mobile Social Networks

With the rapid spread of mobile devices, many traditional online social network services, like academic applications, start to develop mobile apps and improve their service quality through mobile sensing. A rich friendship among users can provide support for such applications, e.g., help to delegate sensing tasks in mobile crowd sensing. Therefore, studying friend recommendation that offers users’ suggestions on who to connect to, so as to enrich the user's friendship, is necessary to Mobile Social Networks (MSNs). Most existing strategies utilize user's relationship, or similarity, to make the recommendation, which overlooks the existence of multi-type connections among users, e.g., common paper based and common topic based connections among authors in academic networks. To overcome such limitation, we characterize each type of connections by a corresponding network layer and then propose a novel algorithm for joint recommendations in multilayer MSNs. Particularly, two types of results are presented in the paper. (i) Our proposed algorithm, named as Cross-layer 2-hop Path (C2P) algorithm, implements the joint recommendation by suggesting a user establish connections to his cross-layer two-hop neighbors, i.e., those who link to the user by two-hop paths with the two hops belonging to two different layers, respectively. In doing so, each produced recommendation item is a combination of user relationships in both two layers and therefore can better meet user demands. (ii) By analytical derivations, along with further empirical validation on real datasets, we give the performance evaluation on our proposed algorithm. First, we prove that the algorithm is efficiently implementable with a constant complexity of each recommendation in most cases. Then, we evaluate its recommendation performance by two metrics, i.e., accuracy and diversity, where the former metric measures recommendation accuracy and the latter one measures an algorithm's capability to provide diverse recommendation items. Our results show that C2P algorithm is optimal in terms of accuracy and for diversity, its performance is no less than the algorithm that is applied in single layers. And finally, the effectiveness of the proposed algorithm is validated on both synthetic and large-scale real datasets, where it outperforms the baseline algorithms with an up to 32 percent accuracy gain and obtains an approximately 0.5 ratio of the algorithm's diversity to the theoretical upperbound.
Multi-Adversarial Discriminative Deep Domain Generalization for Face Presentation Attack Detection

Face presentation attacks have become an increasingly critical issue in the face recognition community. Many face anti-spoofing methods have been proposed, but they cannot generalize well on "unseen" attacks. This work focuses on improving the generalization ability of face anti-spoofing methods from the perspective of the domain generalization. We propose to learn a generalized feature space via a novel multi-adversarial discriminative deep domain generalization framework. In this framework, a multi-adversarial deep domain generalization is performed under a dual-force triplet-mining constraint. This ensures that the learned feature space is discriminative and shared by multiple source domains, and thus is more generalized to new face presentation attacks. An auxiliary face depth supervision is incorporated to further enhance the generalization ability. Extensive experiments on four public datasets validate the effectiveness of the proposed method.
Edge Computing Security: State of the Art and Challenges

The rapid developments of the Internet of Things (IoT) and smart mobile devices in recent years have been dramatically incentivizing the advancement of edge computing. On the one hand, edge computing has provided a great assistance for lightweight devices to accomplish complicated tasks in an efficient way; on the other hand, its hasty development leads to the neglection of security threats to a large extent in edge computing platforms and their enabled applications. In this paper, we provide a comprehensive survey on the most influential and basic attacks as well as the corresponding defense mechanisms that have edge computing specific characteristics and can be practically applied to real-world edge computing systems. More specifically, we focus on the following four types of attacks that account for 82% of the edge computing attacks recently reported by Statista: distributed denial of service attacks, side-channel attacks, malware injection attacks, and authentication and authorization attacks. We also analyze the root causes of these attacks, present the status quo and grand challenges in edge computing security, and propose future research directions.
Connecting physical and virtual spaces in a HyFlex pedagogic model with a focus on teacher interaction

This article highlights interaction within physical and virtual spaces in a higher education HyFlex learning environment with live streamed lectures and seminars. What kinds of learning spaces do we shape when we connect physical and virtual spaces? How do we interact in these spaces, and how do they affect our interaction? The perspective of ‘designs for learning’ theoretically frames the study. The result show that several different spaces are shaped; physical space, representational space, interactional space, and a ‘liminal space’. The result also implies that a HyFlex model requires an increased didactic awareness of designing for learning.
Performance evaluation of a multiple networked in-vehicle embedded architecture

The paper deals deals with the modelling and the validation of multiple networking computer systems supporting in-vehicle embedded applications. The modelling principle is based on the development of reusable component models with clearly defined interfaces. The building of a whole application model is obtained by integration of these components. This model can therefore be simulated to verify temporal properties and to optimise the implementation. The method is developed and exploited in the context of the CAROSSE project. It is applied to a multiple networked architecture using both CAN and VAN, based on a PSA Peugeot-Citroen application. It has been extended in order to evaluate how a possible use of OSEK/VDX local scheduling with a given configuration of local tasks can influence the performance of the application.
FlexGroup Volumes: A Distributed WAFL File System

The rapid growth of customer applications and datasets has led to demand for storage that can scale with the needs of modern workloads. We have developed FlexGroup volumes to meet this need. FlexGroups combine local WAFL® file systems in a distributed storage cluster to provide a single namespace that seamlessly scales across the aggregate resources of the cluster (CPU, storage, etc.) while preserving the features and robustness of the WAFL file system. In this paper we present the FlexGroup design, which includes a new remote access layer that supports distributed transactions and the novel heuristics used to balance load and capacity across a storage cluster. We evaluate FlexGroup performance and efficacy through lab tests and field data from over 1,000 customer FlexGroups.
Particle swarm optimization versus gradient based methods in optimizing neural network

Neural network modelling has become a great interest for many statisticians to be utilized in various types of data as classification, regression, and time series. It also has been applied in many fields as environment, financial, medical, agriculture and climate change. A lot of parametric methods have been developed to predict time series data such as ARIMA and exponential smoothing. However, requirement of residual assumptions causes limitedness of the models. Time series prediction by using neural network been an interesting study in the forecasting problem. In this model, one of the most interesting discussion is about how to get the optimal weights. Various gradient and non-gradient based methods have been applied in obtaining the network weights. Particle swarm optimization is one non-gradient based algorithm inspired by the behaviour of birds and fish flocks, which move to form certain formations without colliding to get the best position in a multi-dimensional space. In neural network modelling, the number of input and hidden unit give influence to the network architecture. The more complex an architecture, the more network weights must be estimated. In this study, a comparison of particle swarm optimization and some gradient based methods on the optimizing neural network was studied. Comparative studies were performed on both stationary and non-stationary data. Experiments were conducted several times to obtain optimal accuracy and stability of results, through statistics of mean and variance of MSE values.
Supervised nonparametric sparse discriminant analysis for hyperspectral imagery classification

Owing to the high spectral sampling, the spectral information in hyperspectral imagery (HSI) is often highly correlated and contains redundancy. Motivated by the recent success of sparsity preserving based dimensionality reduction (DR) techniques in both computer vision and remote sensing image analysis community, a novel supervised nonparametric sparse discriminant analysis (NSDA) algorithm is presented for HSI classification. The objective function of NSDA aims at preserving the within-class sparse reconstructive relationship for within-class compactness characterization and maximizing the nonparametric between-class scatter simultaneously to enhance discriminative ability of the features in the projected space. Essentially, it seeks for the optimal projection matrix to identify the underlying discriminative manifold structure of a multiclass dataset. Experimental results on one visualization dataset and three recorded HSI dataset demonstrate NSDA outperforms several state-of-the-art feature extraction methods for HSI classification.
HPC with Python: An MPI-parallel implementation of the Lattice Boltzmann Method

The Lattice Boltzmann Method is well suited for high-performance computational fluid dynamics. We show by means of a common two-dimensional test case, the lid-driven cavity problem, that excellent parallel scaling can be achieved in an implementation based on pure Python, using the numpy library and the Message Passing Interface. We highlight opportunities and pitfalls for the implementation of parallel high-performance codes in the high-level language Python.
Trust Model For Distributed Data Sharing in Cloud Computing

Now a day’s Cloud Computing is the rapid growing technology. It is a new method of delivering the distributed resources over internet on an as-needed basis. It reduces capital and operational expenditure. In the same level to technology, number of cloud service providers of data sharing has increased exponentially in the past few years, providing service with more options for the customers to choose with. Customers are not sure whether they can identify a trustworthy cloud provider only based on their SLA’s (Service Level agreements). In this paper a model for Trust Management for sharing data based has been developed, which can help users make an informed choice towards selecting the appropriate cloud service provider as per their requirement. This study presents what the trust is and how trust has been applied in distributed data sharing in cloud computing.
Geographical Correlation-Based Data Collection for Sensor-Augmented RFID Systems

This paper studies the practically important problem of data collection for sensor-augmented RFID systems. However, existing RFID data collection protocols suffer from two common limitations: execution time is naturally in proportion to the number of tags, thus they cannot satisfy time-stringent application scenarios; none of them is complaint with the C1G2 standard, thus they cannot be implemented using Commercial-Off-The-Shelf (COTS) RFID tags. To overcome these two limitations, this paper proposes the <underline>G</underline>eographical correlation-based <underline>R</underline>F-data <underline>C</underline>ollection (GRC) protocol. GRC is fast because it is able to approximately capture the sensing data of all tags by only actually gathering data from a small set of sampled tags. This is based on the observation from the real-world data set that sensing data has a strong geographical correlation, i.e., data gathered from nearby RFID tags has similar values. In GRC, we use a greedy approach to find the minimum sampling tag set to cover the whole monitoring region such that each un-sampled tag has at least one sampled tag nearby. Then, RFID reader runs the Framed Slotted Aloha (FSA) protocol specified in C1G2 standard to collect sensing data from the sampled tags. For each un-sampled tag, we approximate its sensing data by calculating weight-average of the data collected from its nearby sampled tags, where a faraway sampled tag should be given a small weight, and vice versa. Compared with existing RFID data collection schemes, the advantages of GRC are two-fold: (1) Extensive simulation results demonstrate that the time cost of our GRC scheme is only <inline-formula><tex-math notation="LaTeX">$1/28{\sim }1/3$</tex-math><alternatives><mml:math><mml:mrow><mml:mn>1</mml:mn><mml:mo>/</mml:mo><mml:mn>28</mml:mn><mml:mo>∼</mml:mo><mml:mn>1</mml:mn><mml:mo>/</mml:mo><mml:mn>3</mml:mn></mml:mrow></mml:math><inline-graphic xlink:href="liu-ieq1-2923413.gif"/></alternatives></inline-formula> of the state-of-the-art data collection scheme; (2) GRC is totally complaint with C1G2 standard, thus it can be easily deployed on the COTS RFID tags.
A STUDY ON E-LEARNING SYSTEM TO ANALYSE STUDENT PERFORMANCE USING DATA MINING

Educational Data Mining (EDM) is the field by means of data mining techniques in learning environments. Applying data mining technique in inculcate setting is called as Educational Data Mining (EDM) and is a field that exploits statistical, machine learning, and data-mining (DM) algorithms over the variants of educational data. Existing methods have used features which are mostly associated to educational presentation, family income and family resources, while features belonging to family expenditures and students’ individual information are usually ignored. In this article, an effort is made to examine aforementioned feature sets by collecting the scholarship holding students’ data from different universities of India. Knowledge analytics, discriminative and generative classification models are applied to expect whether a student will be able to absolute his degree or not. We introduce the Weka tool and C4.5 algorithm to analyze replicating studies and discuss the importance of replicating and reproducing previous work. We describe the state of the art in collecting and sharing programming data. To better know the challenges concerned in replicating or reproducing presented studies.
Discovering Hidden Topical Hubs and Authorities Across Multiple Online Social Networks

Finding influential users in online social networks (OSNs) is an important problem with many possible useful applications. Many methods have been proposed to identify influential users in OSNs. PageRank and HITs are two well known examples that determine influential users through link analysis. In recent years, new models that consider both content and social network links have been developed. The Hub and Authority Topic (HAT) model is one that extends HITS to identify topic-specific hubs and authorities by jointly learning hubs, authorities, and topical interests from users’ relationship and textual content. However, many of the previous works are confined to identifying influential users within a single OSN. These models, when applied to multiple OSNs, could not learn influential users under a common set of topics nor address platform preferences. In this paper, we therefore propose the MPHAT model, an extension of HAT, to jointly model the topic-specific hub users, authority users, their topical interests and platform preferences. We evaluate MPHAT against several existing state-of-the-art methods in three tasks: (i) modeling of topics, (ii) platform choice prediction, and (iii) link recommendation. Based on our extensive experiments in multiple OSNs settings using synthetic datasets and real-world datasets from Twitter and Instagram, we show that MPHAT is comparable to state-of-the-art topic models in learning topics but outperforms the state-of-the-art models in platform prediction and link recommendation tasks. We also empirically demonstrate the ability of MPHAT to determine influential users within and across multiple OSNs.
Molecular Dynamics of Kapitza Resistance at the Solid-Liquid Interface

This book implements molecular dynamics atomistic simulation to develop Einstein-Maxwell thermal wall model. The developed modeling approach is shown to provide dual energy and momentum transport exchange across solid-liquid interfaces and nanofluidic channels. The composition of this book has increased our physical understanding regarding nano-scale phenomena of fluid flow, thermal transport and kapitza resistance across interfaces, thin films and nanofluidic channels. The layout and content of the book also makes it suitable for everyone that is interested in Nanotechnology and Molecular Dynamics Technique.
Intelligent Pooling in Thompson Sampling for Rapid Personalization in Mobile Health

Mobile health (mHealth) applications can provide users with essential and timely feedback. From physical activity suggestions, to stress-reduction techniques, mHealth can provide a wide spectrum of effective treatments. Personalizing these interventions might vastly improve their effectiveness, as individuals vary widely in their response to treatment. An optimal mHealth policy must address the question of when to intervene, even as this question is likely to differ between individuals. The high amount of noise due to the in situ delivery of mHealth interventions can cripple the learning rate when a policy only has access to a single user’s data. When there is limited time to engage users, a slow learning rate can pose problems, potentially raising the risk that users leave a study. To speed up learning an optimal policy for each user, we propose learning personalized policies via intelligent use of other users’ data. The proposed learning algorithm allows us to pool information from other users in a principled, adaptive manner. The algorithm combines Thompson sampling with a Bayesian random effects model for the reward function. We use the data collected from a real-world mobile health study to build a generative model and evaluate the proposed algo-rithm in comparison with two natural alternatives: learning the treatment policy separately per person and learning a single treatment policy for all people. This work is motivated by our preparations for a real-world followup study in which the proposed algorithm will be used on a subset of the participants.