Reviewerly/exHarmony huggingface dataset:
Research on Reliability and Validity of Mobile Networks-Based Automated Writing Evaluation

Pigaiwang, an automated writing evaluation (AWE) online platform, has been increasingly applied into English writing scoring in China. This article conducted research to empirically assess the reliability and validity of Pigaiwang under mobile networks. It introduces the background and previous works for AWE. Then, an experiment was implemented to evaluate the reliability and validity of Pigaiwang automated evaluation by comparing with the results of human raters and corpus-based assessment. The results show that Pigaiwang alone is not an adequate aid for English writing evaluation. It suggests that combining Pigaiwang, human raters, and corpus might be a beneficial trial for improving English writing mobile education.
Online identification of a rotary wing Unmanned Aerial Vehicle from data streams

Until now the majority of the neuro and fuzzy modeling and control approaches for rotary wing Unmanned Aerial Vehicles (UAVs), such as the quadrotor, have been based on batch learning techniques, therefore static in structure, and cannot adapt to rapidly changing environments. Implication of Evolving Intelligent System (EIS) based model-free data-driven techniques in fuzzy system are good alternatives, since they are able to evolve both their structure and parameters to cope with sudden changes in behavior, and performs perfectly in a single pass learning mode which is suitable for online real-time deployment. The Metacognitive Scaffolding Learning Machine (McSLM) is seen as a generalized version of EIS since the metacognitive concept enables the what-to-learn, how-to-learn, and when-to-learn scheme, and the scaffolding theory realizes a plug-and-play property which strengthens the online working principle of EISs. This paper proposes a novel online identification scheme, applied to a quadrotor using real-time experimental flight data streams based on McSLM, namely Metacognitive Scaffolding Interval Type 2 Recurrent Fuzzy Neural Network (McSIT2RFNN). Our proposed approach demonstrated significant improvements in both accuracy and complexity against some renowned existing variants of the McSLMs and EISs.
Spatiotemporal recursive hyperspheric classification with an application to dynamic gesture recognition

The Recursive Hyperspheric Classification (RHC) is a novel algorithm well-suited for classifying noisy, multivariate datasets. Creating a taxonomy of hyperspheres, the RHC algorithm partitions a search space into labeled regions that are consumed in the recognition process of unlabeled data. While the algorithm is robust, classical RHC cannot cope with spatiotemporal data, such as dynamic gestures, because there is no mechanism that will facilitate temporal learning. Nonetheless, there exists a strong demand for computers to actively recognize gestures, for the simplest gesture can encode and convey an abundance of information. Gestures are a natural mode of communication by means of body movement, and they can imply intent. Therefore, in this paper, the Spatiotemporal Recursive Hyperspheric Classification (STRHC) algorithm is introduced, which utilizes a temporal queue, allowing the algorithm to classify and recognize temporal data, including human gestures that are sensed with the Xbox Kinect, a popular motion sensor. When validating its strength, STRHC has achieved up to a 95.33% average recognition rate while harnessing a diverse dataset of gestures.
Augmenting Labeled Probabilistic Topic Model for Web Service Classification

Web service classification has become an urgent demand on service-oriented applications. Most existing classification algorithms mainly rely on the original service descriptions. That leads to low classification accuracy, since it cannot fully reflect the semantic feature specific to a service category. To solve the issue, this article proposes a novel approach for web service classification, including service topic feature extraction, service functionality augmentation, and service classification model learning. The characteristic is that the original service descriptions can be semantically augmented, which is fed to deriving a service classifier via labeled probabilistic topic model. A benefit from this approach is that it can be applied to an online service management platform, where it assists service providers to facilitate the registration process. Extensive experiments have been conducted on a large-scale real-world data set crawled from ProgrammableWeb. The results demonstrate that it outperforms state-of-the-art methods in terms of service classification accuracy and convergence speed.
An improved algorithm for partial clustering

Expert and intelligent systems use a variety of machine learning techniques to obtain and understand the information inherent in the data. Clustering is one of these techniques, which has become important and popular since it allows classifying an unlabeled dataset into clusters of similar objects. There are many clustering algorithms that have been proposed in the literature. From these algorithms, the Cross-Clustering algorithm is one of the most recent clustering algorithms for partial clustering (clustering where not necessarily all the objects are grouped into clusters), which has provided good results allowing estimating a suitable set of clusters, as well as eliminating outliers. However, this algorithm tends to eliminate too many objects as outliers, which leads to discard a lot of non-outlier objects. Additionally, the Cross-Clustering algorithms spends a lot of time evaluating several combinations of clusterings, trying to determine a suitable number of clusters. To overcome these problems, in this paper, an improved version of the Cross-Clustering algorithm (ICC) is proposed. ICC changes the clustering algorithm used for detecting outliers, as well as it modifies the way outliers are detected. Moreover, a stop criterion allowing to make a fast decision on the estimation of a suitable number of cluster, is also introduced. The performance of the improved Cross-Clustering algorithm is compared with the original algorithm on artificial and real datasets. Our results show that ICC improves the original algorithm and other state of the art clustering algorithms; in both, runtime and clustering quality.
Social Media Based Topic Modeling for Smart Campus: A Deep Topical Correlation Analysis Method

Smart campus builds on characteristic learning and feedback evaluation of diverse students and aims to enable intelligent, accurate, and customized education. Mining social media data, especially topic modeling, from students, provides a non-intrusive method to know the instantaneous thoughts and willings of them. However, it is challenging to deal with multi-modal data (i.e., text, images, and videos contained in the social media data) as well as the modality dependence and missing modality. In this paper, we present a novel deep topical correlation analysis (DTCA) approach, which achieves robust and accurate topic detection for microblogs and simultaneously handles the two challenges aforementioned. In particular, bidirectional recurrent neural networks and convolutional neural networks are used to learn deep textual and visual features, respectively. Then, a canonical correlation analysis-based fusion scheme is proposed, which has two innovations to deal with both modality independence and modality missing, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e.,</i> a filter gate to capture the modality dependency and a matrix-projection based component to handle the missing modality. DTCA is trained in an end-to-end manner, in which the parameters of visual, textual, and cross-modal prediction parts are trained jointly. We further release a large-scale cross-modal twitter dataset for topic detection, denoted as TM-Twitter. On this dataset, extensive and quantitative evaluations are conducted with comparisons to several state-of-the-art and alternative approaches. Significant performance gains are reported to demonstrate the merits of the proposed DTCA.
Computing with High-Dimensional Vectors

Editor's note: The author reviews the principles of high-dimensional (HD) computing as a brain-inspired paradigm, with variables and operations encoded in vectors with high dimensionality (e.g., 10,000). HD computing has been shown to be a robust novel approach with promising applications in language and biosignal processing. -An Chen, Semiconductor Research Corporation.
Framework for Calculating Return on Security Investment (ROSI) for Security-Oriented Organizations

Today's business environment is extremely dynamic and reliant on innovative Information Technology (IT). Such dependence upon technology leads to an increased rate of successful cyber-attacks whose impact is greater than ever. Due to the exponential increase in security breaches, companies should secure their IT systems by adopting appropriate risk management framework. Organizations have to make justified investments in cyber security. However, it is quite challenging to convince higher management to invest in security measures, since such investments cannot be exactly translated into profits. The Return on Security Investment (ROSI) holds great importance to justify such security investments. A large number of ROSI solutions have already been proposed. However, these solutions do not provide any approach to analyze the impact of single security investment upon whole infrastructure. Furthermore, uncertainty of security incident emerges as another important challenge. The existing ROSI frameworks work on approximations, which can be influenced by employees' exposure and experience, resulting in wrong estimation. The objective of this research is to propose a comprehensive framework to measure ROSI effectively by overcoming gaps in the traditional approaches. The framework has been validated with the help of Common Vulnerability Security System (CVSS) attack dataset. The results show that the annual loss in the absence of security mechanisms is very high i.e. 585,553. However, by following the proposed systematic approach to determine ROSI, it can be reduced to 146,388 which is comparatively low. As a result, organization can save its resources, time, money, trust, and reputation in the market.
FM-based: Algorithm research on rural tourism recommendation combining seasonal and distribution features

Recommended technologies for the tourism system, such as the travelocity.com and visiteurope.com, have gained tremendous popularity in the past few years. Although many research works have been dedicated to improving recommendation services and overcoming recommendation challenges, little attention has been attached to the application of recommended technology in Rural Tourism. In order to solve the difficult 'what to choose' problem caused by information overload, this paper combines the features of Rural Tourism, giving a first attempt in this field. First, an effective method of seasonal feature extraction is proposed. Next, a detailed method of geographical distribution feature extraction is described. In particular, a block-number basing on the geographical distribution feature to the Attractions is proposed. Furthermore, the FM-based (Factorization Machines) algorithm is presented as a recommended solution for Rural Tourism. Finally, the comparative experiments are provided to prove the effectiveness of this solution.
Scaling-Based Weight Normalization for Deep Neural Networks

Recent research works have highlight the invariance or the symmetry that exists in the weight space of a typical neural network and the negative effect of the symmetry on the training neural network due to the Euclidean gradient being not scaling-invariant. Although the problem of the symmetry can be solved by either defining a suitable Riemannian gradient, which is a scale-invariant or placing appropriate constraints on the weights, it will introduce very high-computation cost. In this paper, we first discuss various invariances or symmetries in the weight space, and then we propose to solve the problem via the scaling-invariance of the neural network itself, instead of the scaling-invariant updates methods. The motivation behind our method is that the optimized parameter point in the weight space may be moved from the ill-conditioning region to the flat region via a series of node-wise rescaling without changing the function represented by the neural network. Second, we proposed the scaling-based weight normalization. The proposed method can compatible with the commonly used optimization algorithms and collaborates well with batch normalization. Although our algorithm is very simple, it can accelerate the convergence speed. The additional computation cost introduced by our method is lower. Lastly, the experiments show that our proposed method can improve the performance of various networks architectures over large-scale datasets consistently. Our method outperforms the state-of-the-art methods on CIFAR-100: we obtain test errors as 17.18%.
Application of a Non-Immersive VR, IoT Based Approach to Help Moroccan Students Carry Out Practical Activities in a Personal Learning Style

In the last few years, the evolution of new Information and Communication Technologies (ICT) and networks has enabled the appearance and development of several platforms and tools that serve to operate and distribute the learning content. In some particular domains, especially the scientific one, learners need to work on practical activities, using specific products and equipment to complete, consolidate, or verify their conceptual acquisitions. However, facing the increasing number of learners in Moroccan institutions, it becomes hard and expensive for developing countries, like Morocco, to ensure the appropriate conditions for each learner to perform such activities. The majority of the suggested platforms and tools cannot solve this issue, because of their inefficiency regarding offering students good interactive practical activities. Virtual Reality (VR) and the Internet of Things (IoT), as the two most incredible technologies of the last few decades, can be used as an alternative to create a virtual environment where the learner can carry out practical activities like in the real world. In such an environment, learners interact with both virtual and physical objects. In this research paper, we propose a new approach based on VR and IoT to enhance learning by providing learners with an educational space where they can perform some practical activities. The hybrid proposed approach has been used to create a virtual environment where learners (the final year of high school) can measure ultrasonic velocity in the air. The evaluation results show that the manipulation and coupling of real objects with virtual 3D objects increases in a striking way the learning outcomes of learners, as this allows them to feel linked to the real context.
A Systematic Literature Review About Quantitative Metrics to Evaluate the Usability of E-Commerce Web Sites

Usability has become an essential aspect for the success of the software products. In the E-Commerce domain, the relevance of this quality attribute is even more significant. For this reason, there are methods that can be used to determine whether a software product meets the appropriate level of usability. In the category of quantitative techniques, the metrics are the most used by the specialists to establish the usability degree of a software system. However, the traditional metrics fail to cover aspects that the new emerging types of applications present. This fact leads to the postulation of new metrics that can measure the degree to which these aspects affect the usability. In this paper, the authors present a systematic review about the metrics that are reported in the literature as an assessment tool for the usability of E-Commerce websites.
From Reputation Perspective: A Hybrid Matrix Factorization for QoS Prediction in Location-Aware Mobile Service Recommendation System

With the great development of mobile services, the Quality of Services (QoS) becomes an essential factor to meet end users’ personalized requirement on the nonfunctional performance of mobile services. However, most of the QoS values in real cases are unattainable because a service user would only invoke some specific mobile services. Therefore, how to predict the missing QoS values and recommend high-quality services to end users becomes a significant challenge in mobile service recommendation research. Previous QoS prediction researches demonstrate that the nonfunctional performance of mobile services is closely related to users’ location information. However, most location-aware QoS prediction methods ignore the premise that the obtainable QoS values observed by different users in same location region would probably be untrustworthy, which will lead to inaccurate and unreliable prediction results. To make credible location-aware QoS prediction, we propose a hybrid matrix factorization method integrated location and reputation information (LRMF) to predict the unattainable QoS values. Our approach firstly cluster users into different locational region based on their geographical distribution, and then we compute users’ reputation to identify untrustworthy users in every locational region. Finally, the unknown QoS values can be predicted by integrating locational cluster information and users’ reputation into a hybrid matrix factorization model. Comprehensive experiments are conducted on a public QoS dataset which contains sufficient real-world service invocation records. The evaluation results indicate that our LRMF method can effectively reduce the impact of unreliable users on QoS prediction and make credible mobile service recommendation.
Encryption of 3D Point Cloud Using Chaotic Cat Mapping

3D point clouds, a new primitive representation for objects, are spreading among thousands of people through internet software. Thus, the privacy preserving problem of the 3D point cloud should be widely concerned by more and more people. To ensure the safe transmission and use of point cloud, two schemes of encryption have been proposed by using chaotic cat mapping in this paper. The two encryption schemes are tested by using various types of 3D point clouds. In addition, these proposed encryption algorithms are analyzed through key space, sensibility, statistical and encryption time analysis. These analysis results show that the two proposed schemes can resist the common existing cipher attacks and are effective encryption methods for 3D point cloud encryption. At the same time, the two promising encryption algorithms can guarantee the security of the 3D point cloud model transmitted on the Internet.
Bilinear Supervised Hashing Based on 2D Image Features

Hashing has been recognized as an efficient representation learning method to effectively handle big data due to its low computational complexity and memory cost. Most of the existing hashing methods focus on learning the low-dimensional vectorized binary features based on the high-dimensional raw vectorized features. However, the studies on how to obtain preferable binary codes from the original 2D image features for retrieval is very limited. This paper proposes a bilinear supervised discrete hashing (BSDH) method based on 2D image features which utilizes bilinear projections to binarize the image matrix features such that the intrinsic characteristics in the 2D image space are preserved in the learned binary codes. Meanwhile, the bilinear projection approximation and vectorization binary codes regression are seamlessly integrated together to formulate the final robust learning framework. Furthermore, a discrete optimization strategy is developed to alternatively update each variable for obtaining the high-quality binary codes. In addition, two 2D image features, traditional SURF-based FVLAD feature, and CNN-based AlexConv5 feature are designed for further improving the performance of the proposed BSDH method. The results of extensive experiments conducted on four benchmark datasets show that the proposed BSDH method almost outperforms all competing hashing methods with different input features by different evaluation protocols.
Toward Secure and Scalable Computation in Internet of Things Data Applications

The ever-growing of Internet of Things (IoT) data and the new spectrum of data applications have stimulated IoT clients to outsource their data to cloud servers or datacenters. Apart from storage service, the IoT clients also desires the servers to execute functional operations per client’s request. In this paper, we aim to design the secure mechanisms that allow the IoT clients to outsource their encrypted data to geographically distributed servers while supporting homomorphic computation functions. We leverage the distributed index framework to disassemble and spread data evenly across geographically distributed servers while employing the key–value store as the underlying structure for fast data retrieval. To support computing over encrypted data, we customize Shamir’s secret sharing into our mechanisms to design a tunable scheme for the adaption of different IoT application scenarios. In particular, we design three tunable protocols to achieve the effective additive homomorphic computations while approaching efficiency in terms of servers utilization, computation, and storage overhead. Even the designs focus on the additive computation, we show that it can be readily extended to other types of homomorphic computations as well as verifying the correctness of stored data. Based on the proposed protocols, we design system prototypes, deploy them in Amazon Web services, and evaluate our construction experimentally. Through experimental results, we show that our designs can achieve the efficiency in various perspectives.
Jointly Learning Visual Poses and Pose Lexicon for Semantic Action Recognition

A novel method for semantic action recognition through learning a pose lexicon is presented in this paper. A pose lexicon comprises a set of semantic poses, a set of visual poses, and a probabilistic mapping between the visual and semantic poses. This paper assumes that both the visual poses and mapping are hidden and proposes a method to simultaneously learn a visual pose model that estimates the likelihood of an observed video frame being generated from hidden visual poses, and a pose lexicon model establishes the probabilistic mapping between the hidden visual poses and the semantic poses parsed from textual instructions. Specifically, the proposed method consists of two-level hidden Markov models. One level represents the alignment between the visual poses and semantic poses. The other level represents a visual pose sequence, and each visual pose is modeled as a Gaussian mixture. An expectation-maximization algorithm is developed to train a pose lexicon. With the learned lexicon, action classification is formulated as a problem of finding the maximum posterior probability of a given sequence of video frames that follows a given sequence of semantic poses, constrained by the most likely visual pose and the alignment sequences. The proposed method was evaluated on MSRC-12, WorkoutSU-10, WorkoutUOW-18, Combined-15, Combined-17, and Combined-50 action datasets using cross-subject, cross-dataset, zero-shot, and seen/unseen protocols.