Reviewerly/exHarmony huggingface dataset:
A Trend Analysis of Machine Learning Research with Topic Models and Mann-Kendall Test

This paper aims to systematically examine the literature of machine learning for the period of 1968~2017 to identify and analyze the research trends.A list of journals from well-established publishers ScienceDirect, Springer, JMLR, IEEE (approximately 23,365 journal articles) related to machine learning is used to prepare a content collection.To the best of our information, it is the first effort to comprehend the trend analysis in machine learning research with topic models: Latent Semantic Analysis (LSA), Latent Dirichlet Allocation (LDA), and LDA with Coherent Model (LDA_CM).The LDA_CM topic model gives the highest topic coherence amongst all topic models under consideration.This study provides a scientific ground that helps to overcome the subjectivity of collective opinion.The Mann-Kendall test is used to understand the trend of the topics.Our findings provide indicative of paradigmatic shifts in research methodology of significant patterns of topical prominence and the evolving research areas.It is used to highlight the evolution regarding the previous and recent trends in research topics in the area of machine learning.Understanding such an intellectual structure and future trends will assist the researchers to adopt the divergent developments of this research in one place.This paper analyzes the overall trends of the machine learning research since 1968, based on the latent topics identified in the period of 2007~2017 that may be helpful to the researchers exploring the recommended areas and publish their research articles.
Aeromagnetic Compensation Algorithm Robust to Outliers of Magnetic Sensor Based on Huber Loss Method

The outliers of the magnetic sensor that are inevitable in special aeromagnetic surveys reduce the robustness of ordinary least-squares (OLS), which are widely used for aeromagnetic compensation. To address this problem, we propose an aeromagnetic compensation algorithm based on the Huber loss method that is robust to outliers. In the proposed method, different weights are assigned to the inliers and outliers using an iteratively reweighted least-squares technique. Although the OLS performs similarly to the proposed method when only 1% of the data are outliers, it is theoretically verified that the proposed method can increase the goodness-of-fit to 0.9963, from 0.6618 in the case of OLS, in the presence of 10% outliers. An experimental platform was constructed to record real magnetic data, with special measures taken to ensure the presence of outliers in the collected data. The results of a flight test using this experimental platform demonstrate that the proposed method increases the improvement ratio to 4.14 from 2.46 when using the OLS.
Methods to estimate entanglement in teleportation experiments

Quantum state teleportation is a protocol where a shared entangled state is used as a quantum channel to transmit quantum information between distinct locations. Here we consider the task of estimating entanglement in teleportation experiments. We show that the data accessible in a teleportation experiment allows to put a lower bound on some entanglement measures, such as entanglement negativity and robustness. Furthermore, we show cases in which the lower bounds are tight. The introduced lower bounds can also be interpreted as quantifiers of the nonclassicality of a teleportation experiment. Thus, our findings provide a quantitative relation between teleportation and entanglement.
OntoSIDES: Ontology-based student progress monitoring on the national evaluation system of French Medical Schools

We introduce OntoSIDES, the core of an ontology-based learning management system in Medicine, in which the educational content, the traces of students' activities and the correction of exams are linked and related to items of an official reference program in a unified RDF data model. OntoSIDES is an RDF knowledge base comprised of a lightweight domain ontology that serves as a pivot high-level vocabulary of the query interface with users, and of a dataset made of factual statements relating individual entities to classes and properties of the ontology. Thanks to an automatic mapping-based data materialization and rule-based data saturation, OntoSIDES contains around 8 millions triples to date, and provides an integrated access to useful information for student progress monitoring, using a powerful query language (namely SPARQL) allowing users to express their specific needs of data exploration and analysis. Since we do not expect end-users to master the raw syntax of SPARQL and to express directly complex queries in SPARQL, we have designed a set of parametrized queries that users can instantiate through a user-friendly interface.
Distance2Pre: Personalized Spatial Preference for Next Point-of-Interest Prediction

Point-of-interest (POI) prediction is a key task in location-based social networks. It captures the user preference to predict POIs. Recent studies demonstrate that spatial influence is significant for prediction. The distance can be converted to a weight reflecting the relevance of two POIs or can be utilized to find nearby locations. However, previous studies almost ignore the correlation between user and distance. When people choose the next POI, they will consider the distance at the same time. Besides, spatial influence varies greatly for different users. In this work, we propose a Distance-to-Preference (Distance2Pre) network for the next POI prediction. We first acquire the user’s sequential preference by modeling check-in sequences. Then, we propose to acquire the spatial preference by modeling distances between successive POIs. This is a personalized process and can capture the relationship in user-distance interactions. Moreover, we propose two preference encoders which are a linear fusion and a non-linear fusion. Such encoders explore different ways to fuse the above two preferences. Experiments on two real-world datasets show the superiority of our proposed network.
TMO: Time Domain Outsourcing Attribute-Based Encryption Scheme for Data Acquisition in Edge Computing

With the rapid development of the Internet of Things and the ever-increasing demands of advanced services and applications, edge computing is proposed to move the computing and storage resources near the data source, which improves the response time and saves the bandwidth. However, due to the limited available resources and massive privacy-sensitive user data in edge nodes, there are huge challenges in data security and privacy protection in the edge computing environment. Hence, we propose an efficient time-domain multi-authority outsourcing attribute-based encryption (ABE) scheme (TMO) with a dynamic policy updating method for secure data acquisition and sharing in the edge computing. Specifically, considering that the time is a crucial factor in many real-world application scenarios, we add time-domain information in the encryption algorithm. Besides, to take full advantage of edge computing, TMO extends the multi-authority ABE approach by outsourcing the computation to edge nodes to enhance security and performance. Moreover, to tackle the mobility and frequently changing edge environment, TMO also provides an efficient online policy updating method to manage attribute information and to access policy with low overhead. The security analysis and the experimental results show that TMO can indeed efficiently enhance data security with low overhead in the edge computing environment.
Optimal training and test sets design for machine learning

In this paper, we describe histogram matching, a metric for measuring the distance of two datasets with exactly the same features, and embed it into a mixed integer programming formulation to partition a dataset into fixed size training and test subsets. The partition is done such that the pairwise distances between the dataset and the subsets are minimized with respect to histogram matching. We then conduct a numerical study using a well-known machine learning dataset. We demonstrate that the training set constructed with our approach provides feature distributions almost the same as the whole dataset, whereas training sets constructed via random sampling end up with significant differences. We also show that our method introduces neither positive nor negative bias in prediction accuracy of a decision tree—used as a representative example of a machine learning method.
Knowledge Distillation for Small Foot-print Deep Speaker Embedding

Deep speaker embedding learning is an effective method for speaker identity modelling. Very deep models such as ResNet can achieve remarkable results but are usually too computationally expensive for real applications with limited resources. On the other hand, simply reducing model size is likely to result in significant performance degradation. In this paper, label-level and embedding-level knowledge distillation are proposed to narrow down the performance gap between large and small models. Label-level distillation utilizes the posteriors obtained by a well-trained teacher model to guide the optimization of the student model, while embedding-level distillation directly constrains the similarity between embeddings learned by two models. Experiments were carried out on the VoxCeleb1 dataset. Results show that the proposed knowledge distillation methods can significantly boost the performance of highly compact student models.
Blockchain based crowdfunding systems

&lt;p class="Abstract"&gt;&lt;span&gt;Initially, blockchain is only used as a foundation of cryptocurrency, but today, we can see the rise of this new emerging technology are being implemented in many industries. In the future, most technologies around the world are expected to use blockchain as an efficient way to make online transactions. One of the areas that blockchain technologies can be applied is crowdfunding platforms. The most common problem with current crowdfunding scene in around the world including is that the campaigns are not regulated and some of the crowd-funding campaign turned out to be fraud. Besides, the completion of some projects also was significantly delayed. This project aims to solve these problems by applying Ethereum smart contracts to the crowdfunding site to that the contracts will be fully automatically executed, thus preventing frauds and ensuring that the projects can be delivered within duration given.&lt;/span&gt;&lt;/p&gt;
Predicting Quality of Service via Leveraging Location Information

QoS (Quality of Service) (our approach can be applied to a wide variety of services; in this paper, we focus on Web services) performance is intensively relevant to locations due to the network distance and the Internet connection between users and services. Thus, considering the location information of services and users is necessary. However, the location information has been ignored by most previous work. In this paper, we take both services’ and users’ location information into account. Specifically, we propose a location-aware QoS prediction approach, called LANFM, by exploiting neural network techniques and factorization machine to improve user-perceived experience. First of all, the information (e.g., id and location) of services and users is expressed as embedding vectors by leveraging neural network techniques. Then, the inner product of various embedding vectors, along with the weighted sum of feature vectors, is used to predict the QoS values. It should be noted that the inner product operation could capture the interactions between services and users, which is helpful to predict QoS values of services that have not been invoked by users. A collection of extensive experiments have been carried out on a real-world dataset to validate the effectiveness of the LANFM model.
A cluster-based feature selection method for image texture classification

&lt;p&gt;Computer vision and pattern recognition applications have been counted serious research trends in engineering technology and scientific research content. These applications such as texture image analysis and its texture feature extraction. Several studies have been done to obtain accurate results in image feature extraction and classifications, but most of the extraction and classification studies have some shortcomings. Thus, it is substantial to amend the accuracy of the classification via minify the dimension of feature sets. In this paper, presents a cluster-based feature selection approach to adopt more discriminative subset texture features based on three different texture image datasets. Multi-step are conducted to implement the proposed approach. These steps involve texture feature extraction via Gray Level Co-occurrence Matrix (GLCM), Local Binary Pattern (LBP) and Gabor filter. The second step is feature selection by using K-means clustering algorithm based on five feature evaluation metrics which are infogain, Gain ratio, oneR, ReliefF, and symmetric. Finally, K-Nearest Neighbor (KNN), Naive Bayes (NB) and Support Vector Machine (SVM) classifiers are used to evaluate the proposed classification performance and accuracy. Research achieved better classification accuracy and performance using KNN and NB classifiers that were 99.9554% for Kelberg dataset and 99.0625% for SVM in Brodatz-1 and Brodatz-2 datasets consecutively. Conduct a comparison to other studies to give a unified view of the quality of the results and identify the future research directions.&lt;/p&gt;
Combined Recommendation Algorithm Based on Improved Similarity and Forgetting Curve

The recommendation algorithm in e-commerce systems is faced with the problem of high sparsity of users’ score data and interest’s shift, which greatly affects the performance of recommendation. Hence, a combined recommendation algorithm based on improved similarity and forgetting curve is proposed. Firstly, the Pearson similarity is improved by a wide range of weighted factors to enhance the quality of Pearson similarity for high sparse data. Secondly, the Ebbinghaus forgetting curve is introduced to track a user’s interest shift. User score is weighted according to the residual memory of forgetting function. Users’ interest changing with time is tracked by scoring, which increases both accuracy of recommendation algorithm and users’ satisfaction. The two algorithms are then combined together. Finally, the MovieLens dataset is employed to evaluate different algorithms and results show that the proposed algorithm decreases mean absolute error (MAE) by 12.2%, average coverage 1.41%, and increases average precision by 10.52%, respectively.
Study on Centroid Type-Reduction of Interval Type-2 Fuzzy Logic Systems Based on Noniterative Algorithms

Interval type-2 fuzzy logic systems have favorable abilities to cope with uncertainties in many applications. While the block type-reduction under the guidance of inference plays the central role in the systems, Karnik-Mendel (KM) iterative algorithms are standard algorithms to perform the type-reduction; however, the high computational cost of type-reduction process may hinder them from real applications. The comparison between the KM algorithms and other alternative algorithms is still an open problem. This paper introduces the related theory of interval type-2 fuzzy sets and discusses the blocks of fuzzy reasoning, type-reduction, and defuzzification of interval type-2 fuzzy logic systems by combining the Nagar-Bardini (NB) and Nie-Tan (NT) noniterative algorithms for solving the centroids of output interval type-2 fuzzy sets. Moreover, the continuous version of NT (CNT) algorithms is proved to be accurate algorithms for performing the type-reduction. Four computer simulation examples are provided to illustrate and analyze the performances of two kinds of noniterative algorithms. The NB and NT algorithms are superior to the KM algorithms on both calculation accuracy and time, which afford the potential application value for designer and adopters of type-2 fuzzy logic systems.
Boundary Discriminative Large Margin Cosine Loss for Text-independent Speaker Verification

Deep neural network based speaker embeddings have attracted much attention in text-independent speaker verification task. In addition to the network architecture, an appropriate design of the loss function is crucial for the deep discriminative embedding extractor. Inspired by the success of Large Margin Cosine Loss (LMCL) in face recognition, we propose an enhanced LMCL named boundary discriminative LMCL (BD-LMCL) to emphasize the discriminative information inherited in the speaker boundaries. Unlike LMCL, where all training samples contribute equally for the objective function, only the samples around the speaker boundaries are considered during the network training with BD-LMCL. Specifically, those samples close to the boundaries are dynamically selected using top-k zero-one loss. Experimental results on a short duration corpus Android Cellphone and NIST SRE 2012 demonstrate better performance compared to LMCL and other popular loss functions.
An Ontological Approach to the Description of Visual and Iconographical Representations

The perception of our heritage is based on sign-functions, which relate visual representations to cognitive types, allowing us to make perceptual judgements over physical objects. The recording of these types of assertions is paramount for the comprehension and analysis of our heritage. The article investigates a theoretical framework for the organization of information related to visual works on the basis of the identity and symbolic value of their single constituent elements. The framework developed is then used as a driver for the grounding of a new ontology called VIR (Visual Representation), constructed as an extension of CIDOC-CRM (CIDOC Conceptual Reference Model). VIR sustains the recording of statements about the different structural units and relationships of a visual representation, differentiating between object and interpretative act. The result, tested with data describing Byzantine and Renaissance artworks, presents solutions for describing symbols and meanings of iconographical objects, providing new clustering methods in relation to their constitutive elements, subjects or interpretations.
An improved security and message capacity using AES and Huffman coding on image steganography

Information security is very important and has been widely implemented. Cryptography and steganography are two common methods that can be implemented to secure and conceal the information. In this research, the proposed AES algorithm for cryptography and DWT for steganography. However, in case of implementing DWT as steganography, there is a weakness which is a lower capacity. Based on DWT’s problem, proposed Huffman Coding to reduce the total of the message’s bit and increase the capacity. In the implementation, a message will be processed by using AES and compressed by using Huffman Coding then conceal in a cover using DWT. After doing several experiments using a 128x128 pixel message image and a 512x512 pixel of the cover image, achieved the average of MSE is 1.5676 and the average of PSNR result is above 40db which is 46.1878.
An agent-administrator-based security mechanism for distributed sensors and drones for smart grid monitoring

Distributed sensors are the eyes and ears of a smart grid which provide information vital for monitoring and controlling the entire power generation, transmission, and distribution systems. Secure exchange of information among the sensing and decision-making entities is essential as failures may bring the entire system on its knees. With the rapid growth in the number of distributed sensors, drones have a myriad of applications. A swarm of drones could also be deployed in war zones and disaster-stricken areas where a secured intercommunication is of paramount importance for survivability and for successful mission completion. In this paper, a secure mechanism is proposed based on mobile agents to secure information exchange with minimum overhead. An Agent Administrator (AA) automatically clones and sends a secure mobile agent (SMA) to the target sensors or drones to scan and check their security status. Then, the dispatched SMAs send feedbacks to the server AA or other members. In case of sensors, the closest terminal unit to which the sensors are directly connected is designated as an AA, which is capable of checking authentication and scanning for vulnerabilities. In the case of drones, any one of them or multiple of them could be designated as the AA and the flagged feedback is broadcast to all other nodes or drones thereby providing them security status updates. A modified Nagle’s Algorithm is also proposed to support real-time video transmission. The experimental results validate the effectiveness and convenience of the proposed system.
Scalable asymmetric image encryption based on phase-truncation in cylindrical diffraction domain

In this paper, a scalable asymmetric image compression and encryption method is proposed based on discrete wavelet transform (DWT) and nonlinear operations in the cylindrical diffraction domain. In this scheme, the grayscale image is processed by a DWT, and four component images are obtained. Then single amplitude ciphertext is obtained by nonlinear operation of phase truncation after cylindrical diffraction. Since the reconstruction from the four component images of DWT is scalable, the proposed method also has scalability. Moreover, the proposed method has high security because it can resist the phase-retrieval attack and prevent information disclosure. Numerical simulations have demonstrated the security and effectiveness of the proposed method.
A Passive Approach for Detecting Image Splicing using Deep Learning and Haar Wavelet Transform

Passive image forgery detection has attracted many researchers in the recent years.Image manipulation becomes easier than before because of the fast development of digital image editing software.Image splicing is one of the most widespread methods for tampering images.Research on detection of image splicing still carries great challenges.In this paper, an algorithm based on deep learning approach and wavelet transform is proposed to detect the spliced image.In the deep learning approach, Convolution Neural Network (CNN) is employed to automatically extract features from the spliced image.CNN is applied and then Haar Wavelet Transform (HWT) is used.Support Vector Machine (SVM) is used later for classification.Additional experiments are performed.That is, Discrete Cosine Transform (DCT) replaces HWT and then Principle Component Analysis (PCA) is applied.The proposed algorithm is evaluated on a publicly available image splicing datasets (CASIA v1.0 and CASIA v2.0).It achieves high accuracy while using a relatively low dimension feature vector.Our results demonstrate that the proposed algorithm is effective and accomplishes better performance for detecting the spliced image.