Reviewerly/exHarmony huggingface dataset:
Incremental Validation of RDF Graphs

With the scale of RDF graph data dramatically increasing, the demands for specifying constraints on RDF graphs and detecting violations of such constraints become urgent. Recently, the validation of RDF graphs has been attracting increasing research efforts, among which ShEx (Shape Expressions) and SHACL (Shapes Constraint Language) are widely used. However, incremental validation of RDF graphs still needs further study. To this end, we propose an incremental validation method of RDF graphs. Our method differentiates the types of constraint, adjusts the order of schema validation, and validates RDF graphs from a graph theory perspective as well as incrementally validates RDF graphs updates. The experimental results show that the incremental validation method proposed in this paper improves the efficiency of the validation of knowledge graphs updates.
Design and Implementation of a Web-Based Application for Code Smells Repository

Pitfalls in software development process can be prevented by learning from other people's mistakes. Software practitioners and researchers document lessons learned and the knowledge about best practices is spread over literature. Presence of code smells does not indicate that software won’t work, but it will reveal deeper problems and rising risk of failure in future. Software metrics are applied to detect code smells whereas refactoring can remove code smells, improve code quality and make it simpler and cleaner. Detection tools facilitate management of code smells. Knowledge about code smells and related concepts can assist the software maintenance process. Exploratory analysis of code smells carried out in this paper, covers collecting data about code smells, identifying related concepts, categorizing and organizing this knowledge into a code smell repository, which can be made available to software developers. A detailed literature survey is carried out to identify code smells and related concepts. An initial list of 22 code smells proposed in 1999 has grown over the years into 65 code smells. The relationship between code smells, software metrics, refactoring methods and detection tools available in literature is also documented. Templates are designed that capture knowledge about code smells and related concepts. A code smell repository is designed and implemented to maintain all the information gathered about code smells and related concepts and is made available to software practitioners. All the knowledge about code smells found in literature is collected, organized and made accessible.
A New Path to Create Solutions for Quantum Annealing Problems

Quantum computing has already become a technology to be used by large companies in finance, distribution, health care, chemistry, etc.Among the different approaches, quantum annealing is one of the most promising in the short term.However, software development platforms do not offer user-friendly interfaces for the definition of annealing problems.In this paper we present a solution to this problem: QPath ® 's Annealer Compositor that facilitates the definition and execution of annealing algorithms in either quantum annealing or digital annealing computers.An example based on a nurse work schedule is used for illustrating this special interface.
Privacy-Preserving Credit Scoring via Functional Encryption

The majority of financial organizations managing confidential data are aware of security threats and leverage widely accepted solutions (e.g., storage encryption, transport-level encryption, intrusion detection systems) to prevent or detect attacks. Yet these hardening measures do little to face even worse threats posed on data-in-use. Solutions such as Homomorphic Encryption (HE) and hardware-assisted Trusted Execution Environment (TEE) are nowadays among the preferred approaches for mitigating this type of threats. However, given the high-performance overhead of HE, financial institutions —whose processing rate requirements are stringent— are more oriented towards TEE-based solutions. The X-Margin Inc. company, for example, offers secure financial computations by combining the Intel SGX TEE technology and HE-based Zero-Knowledge Proofs, which shield customers’ data-in-use even against malicious insiders, i.e., users having privileged access to the system. Despite such a solution offers strong security guarantees, it is constrained by having to trust Intel and by the SGX hardware extension availability. In this paper, we evaluate a new frontier for X-Margin, i.e., performing privacy-preserving credit risk scoring via an emerging cryptographic scheme: Functional Encryption (FE), which allows a user to only learn a function of the encrypted data. We describe how the X-Margin application can benefit from this innovative approach and —most importantly— evaluate its performance impact.
PCNN for Power Distribution Network

Aiming at the nonlinear relationship between the evaluation indexes and the complex grade of power quality, a power quality evaluation method is proposed based on model of pulse coupled neural network (PCNN). The operating schematic is analyzed according to PCNN modeling. The input power quality grade is controlled by threshold value, and the evaluation grade is output by neuron ignition. The evaluation methods between PCNN and fuzzy neural network are compared through the measured data, the results indicate that PCNN method is more scientific and accurate, and provides a new method to solve the problem on power quality evaluation of distribution network containing microgrid.
PNO: Personalized Network Optimization for Human Pose and Shape Reconstruction

Most previous human pose and shape reconstruction methods focus on the generalization ability and learn a prior of the general pose and shape, however the personalized features are often ignored. We argue that the personalized features such as appearance and body shape are always consistent for the specific person and can further improve the accuracy. In this paper, we propose a Personalized Network Optimization (PNO) method to maintain both generalization and personality for human pose and shape reconstruction. The general trained network is adapted to the personalized network by optimizing with only a few unlabeled video frames of the target person. Moreover, we specially propose geometry-aware temporal constraints that help the network better exploit the geometry knowledge of the target person. In order to prove the effectiveness of PNO, we re-design the benchmark of pose and shape reconstruction to test on each person independently. Experiments show that our method achieve the state-of-the-art results in both 3DPW and MPI-INF-3DHP datasets.
Spatio-Temporal Action Detector with Self-Attention

In the field of spatio-temporal action detection, some current studies attempt to solve the problem of action detection by using the one-stage object detectors based on anchor-free. Albeit efficiency, more performance boosts are expected. Towards this goal, a Self-Attention MovingCenter Detector (SAMOC) is proposed, which is blessed with two attractive aspects: 1) to effectively capture motion cues, a spatio-temporal self-attention block is explored to reinforce feature representation by aggregating motion-dependent global contexts, and 2) a link branch serves to model the frame-level object dependency, which promotes the confidence scores of correct actions. Experiments on two benchmark datasets show that SAMOC with the proposed two aspects achieves the state-of-the-art and works in real-time as well.
Construction of a Hierarchical Neural Network Power Source Model for Human Capital Technology Innovation and Benefit Distribution with Big Data Analysis

In this paper, a hierarchical neural network power source model is used to conduct an in-depth analysis and research on human capital technology innovation and revenue distribution. A hierarchical neural network analysis method was chosen to evaluate the human capital value of professional degree master students, and the applicability of the index system was confirmed through errors; moreover, the significance of the output results was analyzed according to the weight assignments of the input, implicit, and output layers. The analysis found that there was a large disagreement in the assessment of their human capital value, which led to the lack of practical utility of human capital. Knowledge-skilled talents have a wealth of theoretical knowledge and can use theories to guide related work. Compared with technically skilled high-skilled talents, their educational level is higher, and they can summarize past intuitive experience into theoretical guidance. Therefore, the hierarchical neural network method we constructed is theoretically effective in assessing the value of the human capital of professional master’s students and the role of the main constituents. Based on the assessment results, we can provide policy-informed suggestions for improving the quality of school education. To quickly verify whether the model can converge during the training process, a simple dataset with only two sequences and the elements in the sequences being real numbers rather than vectors are constructed to speed up the computation; meanwhile, the length of the sequences in this dataset is adjustable to initially verify the model’s ability to alleviate the long-time dependence problem.
Acoustic Side Channel Attack for Device Identification using Deep Learning Models

Side-channel attacks are easy to execute and very hard to detect because of their passive nature. If the side channels can be used to decode the device operation, the same can be used to identify the physical property difference of the devices. We investigated the possibility of differentiating the devices based on acoustic side-channel attacks. The Mel Frequency Cepstral Coefficients (MFCC) acoustic features are extracted from the audio samples recorded from different computing devices including embedded modules, laptops, and PCs. A comparative analysis of classification accuracy for the various machine learning algorithms in terms of Precision and Recall is also presented. Our results show that CNN and LSTM give the desired results with better accuracy among the different classification models considered.
Robust and Accurate 3D Self-Portraits in Seconds

In this paper, we propose an efficient method for robust and accurate 3D self-portraits using a single RGBD camera. Our method can generate detailed and realistic 3D self-portraits in seconds and shows the ability to handle subjects wearing extremely loose clothes. To achieve highly efficient and robust reconstruction, we propose PIFusion, which combines learning-based 3D recovery with volumetric non-rigid fusion to generate accurate sparse partial scans of the subject. Meanwhile, a non-rigid volumetric deformation method is proposed to continuously refine the learned shape prior. Moreover, a lightweight bundle adjustment algorithm is proposed to guarantee that all the partial scans can not only “loop” with each other but also remain consistent with the selected live key observations. Finally, to further generate realistic portraits, we propose non-rigid texture optimization to improve the texture quality. Additionally, we also contribute a benchmark for single-view 3D self-portrait reconstruction, an evaluation dataset that contains 10 single-view RGBD sequences of a self-rotating performer wearing various clothes and the corresponding ground-truth 3D models in the first frame of each sequence. The results and experiments based on this dataset show that the proposed method outperforms state-of-the-art methods on accuracy, efficiency, and generality.
Flower “Dock”-Smart Flowerpot Based on Microcontroller

The microcontroller smart planter project has an intelligent watering system which is based on a microcontroller programmed to adjust the upper and lower soil moisture limits, detection functions and so on. It also enables the acquisition, setting and comparison of data signals. In this paper, we discuss the microcontroller-based intelligent flowerpot project, the Flower “Dock”, to understand the purpose, significance and basic conditions of the project, and focus on the design of the microcontroller-based intelligent flowerpot project.
Design and Implementation of an Information Security Platform for the IoT Based on Blockchain

As in the blockchain system, with the continuous access of various devices, there will be information security risks. Based on the security vulnerabilities of the IoT, this article combines blockchain technology to design a distributed Internet of Things information Security management system. The system can access devices through identity authentication strategies, and control the authority of various devices. At the same time, the security arbitration mechanism is integrated into the smart contract. In terms of information management, the data is classified and stored to ensure data security. This article is based on the design and implementation of a blockchain-based IoT information security platform.
Application of the Delphi Technique to Determine the Technological Competencies of a Faculty Member

&lt;p style="text-align:justify"&gt;The formed primary level and dynamic and sustainable development of technological competence provides quality management of teaching activities, increases the efficiency of the educational process, accelerates the achievement of pedagogical goals. Delphi expert assessment technique is increasingly used in the paradigm of pedagogy. Due to the set of advantages and objectivity of assessments, it has become the dominant method of this study. The objective of the study is to determine the current level of manifestation of technological competencies, as well as generalized prospects for development and improvement of the identified level within the selected group of freelance teachers using the technology of independent expert assessments –the Delphi method. In general, the following methods were used in the current study: methods of data collection and coordination, anonymous brainstorming, Delphi expert assessment technique, statistical and mathematical processing of results through Delphi formulas, comparative method, generalisations. The diversified approach to the interpretation of the technological competence of faculty members allowed determining: a) the level of faculty members’ knowledge of modern educational technologies at 89.1%; b) activity-practical aspect of training at 83.0%; c) dissonance between the theoretical and empirical level of teacher training and the algorithm for fulfilling the potential in practice at 21.5%; d) mastery of individual creative technologies for the organisation of an effective educational process at 55.9%; e) forecasted development of technological competencies of faculty members in the 5-year perspective under the condition of application of special control and skill trainings at 50.7%. Conclusion of the study is that according to the arithmetic mean of experts’ assessments of differentiated levels of technological competence, the overall level was 75.1%. The average result of the initial student survey on the estimating of the teachers’ technological competence was 69.7%. The difference of 5.4% between the data allows stating that both methods were relevant in this particular case.&lt;/p&gt;
Using BERT for choosing classifiers in Mandarin

Choosing the most suitable classifier in a linguistic context is a well-known problem in the production of Mandarin and many other languages. The present paper proposes a solution based on BERT, compares this solution to previous neural and rule-based models, and argues that the BERT model performs particularly well on those difficult cases where the classifier adds information to the text.
Multipath TCP Security Issues, Challenges and Solutions

Multipath TCP (MPTCP) is a bidirectional byte stream transport layer protocol introduced by Internet Engineering Task force (IETF) which provides numerous benefits such as higher throughput, reliability, fault tolerance, backward compatibility and load balancing by supporting multi-homing that allows use of multiple paths for data transfer over single network connection still it is vulnerable to many security intrusions such as Denial of Service, session hijacking, SYN Flooding etc. In this paper, the vulnerabilities of MPTCP leading to some potential attacks and their available solutions are focused. Currently many solutions are available but some of them increase the overhead of MPTCP, some are vulnerable to time-shifted attack and rests are not tested properly. The implementation and experiment details of ADD_ADDR and Eavesdropper in initial handshake attacks performed on MPTCP version0 and version1, detailed analysis of all the available solutions, limitations of these solutions and direction to offer the solution over these limitations are covered in this paper.
A New Method for Anomaly Detection and Diagnosis of Ocean Observation System based on Deep Learning

Due to the harsh conditions of the ocean observation environment and the influence of many uncertain factors, data anomalies often appear in the ocean observation system. Therefore, how to detect and diagnosis data anomalies and take corresponding measures has become an urgent problem to be solved. In this paper, a novel method based on deep learning is presented for detecting and diagnosing the data anomalies of ocean observation system, which combines convolution neural network (CNN) and bidirectional long short-term memory recurrent neural network (BiLSTM) with attention mechanism (CBLA for short). By constructing a prediction model based on the CBLA, the short-term trend of ocean observation data is predicted, and the error threshold is set to realize the detection of data anomalies. In addition, through the discussion of different types of data anomalies, the causes of data anomalies are analyzed, and the diagnosis of data anomalies is actively explored. The experimental results show the accuracy of the prediction model and the effectiveness of the overall data anomalies detection and diagnosis algorithm.
Transport Oriented Framework for Context-Aware Services Management

The paper proposes a dynamic adaptive framework that responds to the needs of context-aware services provided by the intelligent transport systems. The executive core of this framework is a software architecture model that ensures the running of an application which is adaptable to context changes. The solution was tested on a case study that highlights provided contextual information through an inter-vehicle communication system.
Models for Narrative Information: A Study

From the literature study, it was observed that there are significantly fewer studies that review ontology- based narrative models. This motivates the current work. A parametric approach was adopted to report the existing ontology-driven models for narrative information. The work considers the narrative and ontology components as parameters. This study hopes to encompass the relevant literature and ontology models together. The work adopts a systematic literature review methodology for an extensive literature selection. The models were selected from the literature using a stratified random sampling technique. The findings illustrate an overview of the narrative models across domains. The study identifies the differences and similarities of knowledge representation in ontology-based narrative information models. This paper will explore the basic concepts and top-level concepts in the models. Besides, this study provides a study of the narrative theories in the context of ongoing research. It also identifies the state-of-the-art literature for ontology-based narrative information. Keywords: ontologies, narrative information, modelling
Automated Segmentation of Leukocytes using Marker-based Watershed Algorithm from Blood Smear Images

The aim of this paper is to perform segmentation of white blood cells (WBCs) using blood smear images with the help of image processing techniques. Traditionally, the process of morphological analysis of cells is performed by a medical expert. This process is quite tedious and time consuming. The equipments used to perform the experiments are very costly and might not be available in all hospitals. Further, the whole process is quite lengthy and prone to error easily because of the lack of standard set of procedure. Hence there is a need for innovative and efficient techniques. An automated image segmentation system can make the blood test process much easier and faster. Segmentation of a nucleus image is one of the most critical tasks in a leukemia diagnosis. In this work, we have investigated and implemented image processing algorithms to segment cells. The proposed model detects WBCs and converts cell images from RGB to HSV color space using Otsu thresholding. The resultant image is then processed with the morphological filter because the segmented image contains noise which affects the system performance. Lastly, the Marker-based watershed algorithm is implemented in which specific marker positions are defined. The proposed model is tested on publically available ALL-IDB2 dataset. The system’s performance was overall examined and resulted in 98.99% overall precision for WBC segmentation.
Correlation Analysis between the Robustness of Sparse Neural Networks and their Random Hidden Structural Priors

Deep learning models have been shown to be vulnerable to adversarial attacks. This perception led to analyzing deep learning models not only from the perspective of their performance measures but also their robustness to certain types of adversarial attacks. We take another step forward in relating the architectural structure of neural networks from a graph theoretic perspective to their robustness. We aim to investigate any existing correlations between graph theoretic properties and robustness of Sparse Neural Networks. Our hypothesis is, that graph theoretic properties as a prior of neural network structures is related to their robustness. To give answer to this hypothesis, we designed an empirical study with neural network models obtained through random graphs used as sparse structural priors for the networks. We additionally investigated the evaluation of a randomly pruned fully connected network as point of reference. We found that robustness measures are independent to initialization methods but show weak correlations with graph properties: higher graph densities correlate with lower robustness, but higher average path lengths and average node eccentricities show negative correlations with robustness measures. We hope to motivate further empirical and analytical research to tightening an answer to our hypothesis.
Prediction of Chaotic Time Series and Resolution of Embedding Dynamics with the ATNN

Chaotic time series prediction is an essential task of signal processing, physiological analysis and economic forecasting. In this paper, we apply the Adaptive Time-Delay Neural Network (ATNN) to study the chaotic Mackey-Glass differential equation prediction problem. We discuss the embedding dynamics, training set size and performance, and prediction over time. We demonstrate this neural network approach to be flexible, to predict the chaotic dynamics, and to achieve good performance.