Reviewerly/exHarmony huggingface dataset:
Big Data Driven Secure IoT Analytics with Trusted Execution Environments

The growing adoption of IoT devices in our daily life engendered a need for secure systems to safely store or analyze sensitive data, as well as a decentralized data processing system to handle vast amount of streaming data. The cloud services used to store data and process sensitive data are often come out to be vulnerable to outside threats. Moreover, to analyze enormous streaming data swiftly, they are in need of a fast and efficient system. In this paper we propose a framework to maintain confidentiality and integrity of IoT data, which is of paramount importance, and manage large-scale data analytics. We design the framework to preserve data privacy utilizing Trusted Execution Environment (TEE) such as Intel SGX, and end-to-end data encryption mechanism. In addition, we utilize Apache Spark for fast real-time streaming data processing from many IoT devices. We evaluate the framework by performing simple decision making in the SGX securely that involves multiple IoT devices, and a real-time anomaly detection in the streaming data from IoT devices using Spark.
X <sup>2</sup> R <sup>2</sup>

Reidentification-risk analysis and anonymity have received a great deal of attention in the last two decades. While the research community has been developing several privacy notions and the algorithms to achieve them, these tools have faced difficulties in being transferred to the wider audience of practitioners, for they require a considerable amount of data privacy technical knowledge. We demonstrate X 2 R 2 (Explainable Explorative Reidentification Risk), a data anonymization tool for the laymen. X 2 R 2 guides the user through a transparent explorative process, during which the existing reidentification risks are explained and quantified, possible data transformation options are recommended, and the consequences of these operations, in terms of privacy risk and data utility, are clearly shown.
A Novel Spherical Search Based Grey Wolf Optimizer for Optimization Problems

Grey wolf optimizer (GWO) has shown to converge rapidly during the initial stage of a global search, but it still frequently stick into local optimal. In contrast, spherical evolution (SE) adopts a brand new spherical search style and has good abilities of local optimum avoidance. The focus of this research is on incorporating SE into GWO for optimization problems. This hybrid method generates a new generation of individuals by alternating the leadership hierarchy and hunting mechanism of GWO and the spherical search style of SE. The experiment results on CEC2017 benchmark functions indicate the effectiveness of this hybridization, suggesting that grey wolf search mechanism and spherical search style are complementary. This study gives not only more insights into both original algorithms, but also a novel construction method of merging different algorithms.
Performance of the Grade of Membership Model Under a Variety of Sample Sizes, Group Size Ratios, and Differential Group Response Probabilities for Dichotomous Indicators

Social scientists are frequently interested in identifying latent subgroups within the population, based on a set of observed variables. One of the more common tools for this purpose is latent class analysis (LCA), which models a scenario involving k finite and mutually exclusive classes within the population. An alternative approach to this problem is presented by the grade of membership (GoM) model, in which individuals are assumed to have partial membership in multiple population subgroups. In this respect, it differs from the hard groupings associated with LCA. The current Monte Carlo simulation study extended on prior work on the GoM by investigating its ability to recover underlying subgroups in the population for a variety of sample sizes, latent group size ratios, and differing group response profiles. In addition, this study compared the performance of GoM with that of LCA. Results demonstrated that when the underlying process conforms to the GoM model form, the GoM approach yielded more accurate classification results than did LCA. In addition, it was found that the GoM modeling paradigm yielded accurate results for samples as small as 200, even when latent subgroups were very unequal in size. Implications for practice were discussed.
Edge-Aware Color Image Manipulation by Combination of Low-Pass Linear Filter and Morphological Processing of Its Residuals

In the paper, a method of edge-preserving processing of color images, called LPMPR (Low-Pass filter with Morphologically Processed Residuals), is proposed. It combines linear low-pass filtering with non-linear techniques, that allow for selecting meaningful regions of the image, where edges should be preserved. The selection of those regions is based on morphological processing of the linear filter residuals and aims to find meaningful regions characterized by edges of high amplitude and appropriate size. To find them, two methods of morphological image processing are used: reconstruction operator and area opening. The meaningful reconstructed regions are finally combined with the low-pass filtering result to recover the edges’ original shape. Besides, the method allows for controlling the contrast of the output image. The processing result depends on four parameters, the choice of which allows for adjusting the processed image to particular requirements. Results of experiments, showing example filtering results, are also presented in the paper.
HmwkCheck

The homework for low-grade pupils often contains simple arithmetic problems, i.e., four arithmetic operations. To evaluate the learning quality of pupils, teachers and parents often need to check the homework manually, which is time and labor consuming. In this paper, we propose a homework auto-checking system HmwkCheck, which checks the four arithmetic operations automatically. Specifically, HmwkCheck utilizes the embedded camera of a smartphone to capture the homework as an image, and then processes the image in the smartphone to detect, segment and recognize both printed characters and handwritten characters. We implement HmwkCheck in an Android smartphone. The experiment results show that HmwkCheck can check homework efficiently, i.e., the average precision, recall and F1-score of character recognition achieve 94.03%, 93.41% and 93.72%, respectively.
Rank over Class: The Untapped Potential of Ranking in Natural Language Processing

Text classification has long been a staple within Natural Language Processing (NLP) with applications spanning across diverse areas such as sentiment analysis, recommender systems and spam detection. With such a powerful solution, it is often tempting to use it as the go-to tool for all NLP problems since when you are holding a hammer, everything looks like a nail. However, we argue here that many tasks which are currently addressed using classification are in fact being shoehorned into a classification mould and that if we instead address them as a ranking problem, we not only improve the model, but we achieve better performance. We propose a novel end-to-end ranking approach consisting of a Transformer network responsible for producing representations for a pair of text sequences, which are in turn passed into a context aggregating network outputting ranking scores used to determine an ordering to the sequences based on some notion of relevance. We perform numerous experiments on publicly-available datasets and investigate the applications of ranking in problems often solved using classification. In an experiment on a heavily- skewed sentiment analysis dataset, converting ranking results to classification labels yields an approximately 22% improvement over state-of-the-art text classification, demonstrating the efficacy of text ranking over text classification in certain scenarios.
An Abnormal Data Analysis and Processing Method for Genealogy Graph Databases

Large amounts of data are generated continually in the real world. The objects in the data and the relationships between them have become increasingly complex. Graph is a powerful tool for representing these data and the complex relationships between them. To effectively describe the entities and connections in these data, the concept of a knowledge graph was proposed, and knowledge graph has become one of the bases for storing graph data, and the importance of it is self-evident. Genealogy data is a kind of graph data where nodes are used to represent the person in the genealogy data, and edges are used to represent the relationship between the person. Moreover, in a genealogy graph database, the "critical" relationship that is unique for an entity is defined. In contrast, the abnormal data derive from the existence of multiple "critical" relationships between entities. In a genealogy graph, abnormal data will cause the wrong relationships and redundant entities. To avoid such abnormal data in genealogy graph database, the abnormal data are categorized into four different types, and the corresponding processing methods are proposed for each type of abnormal data, respectively. The experiment results demonstrate that the processing method can effectively solve these abnormal data.
Using a Deep Learning Model, Content Features, and Author Metadata to Recommend Research Papers

According to the Canadian Science Publishing, there are approximately 2.5 million scientific papers published each year. The huge volume of publications can be contributed to a substantial increase in the total number of academic journals, including the increasing number of predatory or fake scientific journals, which yield high volumes of poor-quality research work. The effect of this scenario is that there is an obsolete jungle of journals to flip through in searching for high-quality and relevant references for researchers, ranging from the ones who simply look for citations to cite or latest development and knowledge in a specific scientific area of study. Querying existing web search engines and research paper archived websites is not the solution to the problem, since they are m-equipped to suggest high quality publications to meet the users' information needs. In solving this problem, we propose an elegant research paper recommender, which is unique compared with existing ones, since besides considering the topics and contents of related publications, it also examines the authority and popularity of each publication to ensure its quality. Conducted empirical study shows that our recommender outperforms existing research paper recommenders and contributes to the design of searching relevant publications.
Trajectory Data Protection based on Differential Privacy k-means

Considering that the existing trajectory data protection methods will result in low data availability, this paper considers the characteristics of trajectory data. It proposes a trajectory data protection method that satisfies differential privacy. It not only protects the privacy of the user's trajectory but also has certain data analyzability. First, with the introduction of the weighted-multi-point judgment method, the infection points in the trajectory are found by setting the threshold of the infection angle. Second, the density of each trajectory point is calculated so as to determine the initial clustering center point. In addition, the privacy protection of the trajectory data is processed with the use of an improved differential privacy k - means method, which improves the availability of the trajectory data on the premise of satisfying the differential privacy. Finally, some numerical experiments are carried out to verify the effectiveness of the method.
A Category-Specific Dictionary Learning Method Tailored for Reconstruction-Based Feature Coding

Bag-of-Visual-Words (BoVW) is still a useful image classification model when there is not enough data to use Deep Learning. In BoVW model, the practice of reducing the reconstruction errors of local features can improve the classification accuracy owing to the decrease of information loss. Many reconstruction-based coding methods are proposed to learn a visual dictionary and encode local features via minimizing the reconstruction errors of local features with constraints. Besides this, the accuracy can also be improved by learning the category-specific dictionaries and then encoding features based on these dictionaries. By considering the two practices together, we propose a simple category-specific dictionary learning method tailored for reconstruction-based feature coding. Our method can be used as a universal one to improve the classification accuracies of many reconstruction-based coding methods, which is the highlight of our method. Concretely, a universal dictionary is learned by employing a reconstruction-based coding method and then refined for each category to obtain the category-specific dictionary of this category. When encoding a feature by a category-specific dictionary, the visual words for encoding it are decided in advance by the indices, which correspond to the non-zero elements of its coding vector obtained with the universal dictionary. The effectiveness of our method is validated by observing whether there is an accuracy improvement after applying our method. Our results on Scene-15, Caltech-101, and UIUC-Sports datasets show that the accuracies of four representative coding methods are improved by about 0.3% to 2.7%, which experimentally demonstrates the universality and effectiveness of our method.
An OpenSim guided tour in machine learning for e-health applications

OpenSim is a modeling and simulation-based open source software for the purpose of advanced rehabilitation research work. It has an extensive range of applications, which enables rehabilitation by discovering treatments for neurological disorders followed by therapies to cure movement abnormalities. In the field of orthopedics, OpenSim provides the opportunity for surgeons to see the results of surgery before actually operating on the patient. Researchers are able to perform human-robot interaction by simulating humanoid systems. Also, utilization of the OpenSim tool is ideal for ergonomics, which covers workplaces, automobiles, and equipment to study the efficiency of humans. OpenSim monitors the motions and muscle contributions of athletes to maximize performance. This chapter provides a quick summary of critical application areas of OpenSim in e-healthcare and how it is related to the neuromusculoskeletal systems. A detailed investigation of the existing literature is presented, which covers 3D virtual learning environments, OpenSim projects, technical requirements, repurposing for different medical specialties, joint kinematics, joint kinetics, muscle forces, muscle activations, etc. Furthermore, limitations, challenges, and opportunities are also highlighted for conducting a biomechanical simulation. Details of kinematics modeling, human musculoskeletal system control, and dynamics are also presented. Finally, the work is concluded with open research issues and future trends.
Maximum Correntropy Criterion Kalman Filter Based Target Tracking with State Constraints

Since the maximum correntropy criterion (MCC) can capture higher moments of the stochastic signal, the maximum correntropy criterion Kalman filter (MCC-KF) behaves well in state estimation under non-gaussian noise compared to the classic KF. If some prior information such as state constraints is known, we can improve the performance of the MCC-KF by the incorporation of state equality constraints. In this paper, we realize this incorporation through estimation projection technique to deal with the linear and nonlinear constraints. The corresponding algorithm is presented as the main contribution of this paper. Applied in the example of target tracking, the algorithm shows its superiority against its classic counterpart.
Defense Against Adversarial Attack Using PCA

In the paper, aimed at the problem of machine learning security and defense adversarial examples attack, a PCA-based against example attack defense method was proposed, which uses the fast gradient sign method (FGSM) non-target attack method, and the adversary is a white box attack. PCA was performed on the MNIST dataset to defend against escape attack in deep neural network models. The results showed that PCA can defend against adversarial examples, and the effect was best when the dimension reduction dimension is 50.
Anchor-Free Network for Multi-class Object Detection in Remote Sensing Images

The anchor-based object detection algorithms need many hyper-parameters artificial such as the threshold of intersection over union and the size of anchors, which may limit the detection performance to some extent. In order to better solve the problem of multi-class object detection in remote sensing images, this paper proposed an anchor-free object detection network that does not require any hyper-parameters artificial. The network improves the detection effect of small objects by using the improved feature pyramid network to fuse multi-scale feature maps more effectively, and Focal loss and loss of intersection over union (IoU loss) are used as loss functions to optimize the network. The experimental results on the DOTA dataset show that the mean average precision (mAP) of this network is 71.02%, which is at least 10.5% higher than other existing networks. The anchor-free network proposed in this paper achieves superior detection performance in remote sensing images.
Global and Personalized Query Probability for Obfuscation-Based Web Search

Over the past few years, the volumes of digital information have increased dramatically. Web search engines become indispensable to our daily lives due to their capability of filtering relevant information. The web search engines collect users' online behavior data which implies their personalized interest, even contains sensitive information. A malicious attacker may benefit from advertising or manipulating political campaigns, which poses a serious threat to public security. In this paper, we propose two new methods to generate dummy queries base on global and personalized queries' probability. The generated anonymous queries set has greater information entropy which helps to protect user's intent in web search. Experiment results verify the validity of our methods.
SecoArc: A Framework for Architecting Healthy Software Ecosystems

In recent years, prominent software companies have succeeded to grow by creating ecosystems of third-party providers and users around their software platforms. The overall well-functioning of such ecosystems is referred to as ecosystem health that is the result of a complex and variable range of architectural design decisions at business and technical levels. Despite a body of work considering the architecture of software ecosystems, there is still a lack of solid methods with a precise foundation to facilitate architectural decision-making by providing automated techniques and tools. To fill this gap, we present SecoArc, which is a pattern-centric ecosystem modeling framework for architecting healthy software ecosystems. SecoArc makes the architectural knowledge of well-established ecosystems available. In this paper, we focus on the SecoArc modeling language, analysis technique, and a ready-to-use tool. SecoArc enables platform providers to design architectural variabilities, assess the architecture concerning the quality attributes of ecosystem health, and deeply compare several competing architectures. We show the application of the SecoArc framework in a case study.
Distributed Ledger and Smart Contract Based Approach for IoT Sensor Applications

Security and traceability of smart sensor data in centrally organized IoT-architectures require a third party of trust. In order to overcome this issue, Distributed Ledger Technologies (DLT) apply consensus mechanisms. Current approaches suggest DLT-based IoT-architectures which are static and only provide limited data precision in specific applications. Thus, they rely on custom tokens and additional technologies such as SQL databases. In addition, the design of the applied smart contracts (sc) allow unauthorized access. In contrast, in this paper an adaptable, scalable and purely DLT-based IoT-architecture for secure and decentral software services is proposed. It employs sc for the secure and decentralized interaction between users, software services and IoT devices, such as smart sensors. Thereby, sc are adjustable and their access is controlled by an address comparison of authorized wallets. Finally, a case-study on a sc based software service for an industrial smart temperature sensor demonstrates applicability and benefits of the proposed approach.
InvaliDB

Traditional databases are optimized for pull-based queries, i.e. they make information available in direct response to client requests. While this access pattern is adequate for mostly static domains, it requires inefficient and slow workarounds (e.g. periodic polling) when clients need to stay up-to-date. Acknowledging reactive and interactive workloads, modern real-time databases such as Firebase, Meteor, and RethinkDB proactively deliver result updates to their clients through push-based real-time queries. However, current implementations are only of limited practical relevance, since they are incompatible with existing technology stacks, fail under heavy load, or do not support complex queries to begin with. To address these issues, we propose the system design InvaliDB which combines linear read and write scalability for real-time queries with superior query expressiveness and legacy compatibility. We compare InvaliDB against competing system designs to emphasize the benefits of our approach. To validate our claims of linear scalability, we further present an experimental evaluation of the InvaliDB prototype that has been serving customers at the Database-as-a-Service company Baqend since July 2017.
Development of a Dataset and a Deep Learning Baseline Named Entity Recognizer for Three Low Resource Languages: Bhojpuri, Maithili, and Magahi

In Natural Language Processing (NLP) pipelines, Named Entity Recognition (NER) is one of the preliminary problems, which marks proper nouns and other named entities such as Location, Person, Organization, Disease and so on. Such entities, without an NER module, adversely affect the performance of a machine translation system. NER helps in overcoming this problem by recognizing and handling such entities separately, although it can be useful in Information Extraction systems also. Bhojpuri, Maithili, and Magahi are low resource languages, usually known as Purvanchal languages. This article focuses on the development of an NER benchmark dataset for Machine Translation systems developed to translate from these languages to Hindi by annotating parts of the available corpora with named entities. Bhojpuri, Maithili, and Magahi corpora of sizes 228,373, 157,468, and 56,190 tokens, respectively, were annotated using 22 entity labels. The annotation considers coarse-grained annotation labels followed by the tagset used in one of the Hindi NER datasets. We also report a Deep Learning baseline that uses an LSTM-CNNs-CRF model. The lower baseline F 1 -scores from the NER tool obtained by using Conditional Random Fields models are 70.56% for Bhojpuri, 73.19% for Maithili, and 84.18% for Magahi. The Deep Learning-based technique (LSTM-CNNs-CRF) achieved 61.41% for Bhojpuri, 71.38% for Maithili, and 86.39% for Magahi. As the results show, LSTM-CNNs-CRF fails to outperform the lower baseline in the case of Bhojpuri and Maithili, which have more data in terms of the number of tokens, but not in terms of the number of named entities. However, the cross-lingual model training of LSTM-CNNs-CRF for Bhojpuri and Maithili performed better than the CRF.