Reviewerly/exHarmony huggingface dataset:
Graph Classification Using Back Propagation Learning Algorithms

Due to the propagation of graph data, there has been a sharp focus on developing effective methods for classifying the graph object. As most of the proposed graph classification techniques though effective are constrained by high computational overhead, there is a consistent effort to improve upon the existing classification algorithms in terms of higher accuracy and less computational time. In this paper, an attempt has been made to classify graphs by extracting various features and selecting the important features using feature selection algorithms. Since all the extracted graph-based features need not be equally important, only the most important features are selected by using back propagation learning algorithm. The results of the proposed study of feature-based approach using back propagation learning algorithm lead to higher classification accuracy with faster computational time in comparison to other graph kernels. It also appears to be more effective for large unlabeled graphs.
Measuring motion behavior to detect spatial disorientation in a VR environment

Real-time detection of spatial disorientation from wearable sensor data is a highly relevant task in the context of situation-aware, proactive navigation assistance technology. Such a detection system can be based on supervised learning of a machine learning model that discriminates normal and disorientation states. Ideally, the training (and test) data would be obtained in a field study consisting of a real-world orientation task, but such field studies can be challenging, and are not guaranteed to cover the disorientation instances one is interested in, due to the large variability of the environment.
Interpretable Topic Modeling Using Near-Identity Cross-Document Coreference Resolution

Topic modeling is a technique used in a broad spectrum of use cases, such as data exploration, summarization, and classification. Despite being a crucial constituent of many use cases, established topic models, such as LDA, often produce statistically valid yet non-meaningful topics, i.e., that cannot easily be interpreted by humans. In turn, the usability of topic modeling approaches, e.g., in document summarization, is non-optimal. We propose a topic modeling approach that uses TCA, a method for also near-identity cross-document coreference resolution. TCA showed promising results when resolving mentions of not only persons and other named entities, but also broad, vague, or abstract concepts. In a preliminary evaluation on news articles, we compare the approach with state-of-the-art topic modeling. We find that (1) the four baselines produce statistically valid yet hollow topics or topics that only refer to events in the dataset but not the events' topical composition. (2) TCA is the only approach that extracts topics that distinctively describe meaningful parts of the dataset.
Improving CIMDE with An Mixed Selection Method

Differential evolution(DE) is a popular evolution algorithm. How to select individuals for mutation operators is a very critical problem. Many researchers proposed improved mutation operators by selecting more outstanding individuals to produce mutated vectors. Collective information-powered mutation operator based differential evolution(CIMDE) proposed an improved collective information-powered mutation operator that uses multiple outstanding individuals as the heuristic information to release the selection pressure. But we observed that the mutation operator will be easily trapped into stagnation. So an improved mixed mutation operator named “current or rand-to-ci_ m best/1” is proposed to help individuals to departure from stagnation. Experiments show that this operator can improve the performance of the algorithm on CEC2013 bentmark functions.
C-Algl Net: Pathological Images Generate Diagnostic Results

The lack of a clear correspondence between feature of lesion areas and corresponding pathological characteristics and the scarcity of high-quality histopathological image sets pose a great challenge to the establishment of interpretable computer-aided diagnostic systems. Therefore, we propose a new deep learning-based model, named as C-ALGL model (CNN-AttendLSTM-GenerateLSTM), which is able to generate visual image results with diagnostic descriptions from input histopathological images in one pass. We use an improved recurrent neural network-based structure that incorporates attentional mechanisms in the LSTM interlayer with altered LSTM parameter delivery pathways. The structure generates visualization results at the attentional mechanism and diagnostic text at the end-connected full-connected layer. We conducted a large number of experiments on the PATHOLOGY-11 skin pathology image dataset and the experimental results proved that the C-ALGL model performed better than benchmark models on this task.
Conceptual Models in Digital Libraries, Archives, and Museums (SIG-CM) 2020

The annual SIG-CM workshop addresses the development, use, and evolution of conceptual models in the context of digital libraries, archives, and museums. The 2020 workshop will focus on emerging themes in information science research related to information modeling, including the development of empirical techniques to develop and evaluate conceptual models, as well as innovations in pedagogy for educating practitioners in knowledge organization and representation. Activities at the workshop will include paper presentations, round-table discussions, and a keynote from an expert in the conceptual and logical foundations of information organization systems.
A Potential Value Preferences Elicitation Approach Based on SC-VPM and KNN

Nowadays the more and more customers start to select and use the composite Web service on Internet, at the same time the services with the same functional properties but the different non-functional properties are increasingly emerging on Internet, which cause the information overload. Then the customer is not able to completely understand various composite Web services, and he/she is not able to define reasonable value preferences clearly on them. Therefore, this paper presents a potential value preference elicitation approach based on SC-VPM model and KNN algorithm, so as to support the third-party brokers to recommends top-satisfying services to customers according to the value preferences of the customers. In the approach, the inference rules based on the semantic relationships in SC-VPM model are used to preliminarily supplement the initial customer-value preference matrix firstly, so as to reduce the impact of the matrix sparsity on the following prediction. And then the KNN algorithm is used to identify the value preferences of K nearest neighbors customers, and the value preference vector of the target customer can be predicted and obtained. At last, a case is used to validate the proposed approach.
Point Estimation Theory

Estimation theory is a branch of statistics that deals with estimating interested parameters from random observations (or sampled data). An estimator (also called estimation rules), defining the corresponding rules of inference, can be classified into point and interval estimators. A point estimator gives an approximate value of unknown parameters, whereas an interval estimator calculates an interval that covers the unknown parameters with a high probability. Navigation estimation is often the issue of point estimation, so this chapter only focuses on the relevant contents of point estimation.
Scalable Machine Learning in C++ (CAMEL)

As technology to collect and operate data from everyday tasks has augmented, there is a significant rise in extrapolation concluded from the datasets. This has made Machine Learning seemingly omnipresent in the decision-making processes around the world. From Decision-Driven programmers to Expert Systems, we count on Machine Learning for optimization and increasing the efficiency of subsystems. Yet, we find that Machine Learning today is constrained based on Programming Language used in development. In this paper, we created a library that is purely developed in C++, a widely used compiler language. We also aim to calculate the performance metrics of Compiled versus Interpreted Languages after developing the algorithms. The scientific library “Armadillo” is used to ease many math-related functions and help us traverse the problem of dynamic datasets introduction instead of statically coded matrices. This paper aims to highlight the differences between Compiled and Interpreted languages as well as to find if Compiled languages are a better alternative for ML Algorithms. This research is also aimed to be a continuing effort to be used as a library like TensorFlow, which offers Application Program Interfaces (API) coded in C as a medium. Lastly, we also aim to increase the scalability of these algorithms to remove any language-based constraints. Thus, these are the main reasons for developing C++ Augmented Machine Learning (CAMEL) library.
Research on Safety Evaluation Model of Drilling Operation Based on AHP-BP

Drilling operation is a nonlinear system with characteristics of uncertainty, dynamic and correlation, etc. Back-Propagation (BP)neural network has good fitting ability and data generalization ability for samples with nonlinear changes, and good processing ability for uncertain problems. Analytic Hierarchy Process (AHP) can qualitatively and quantitatively solve problems of hierarchical and multi-objective weight decision, which makes up for the problem that BP neural network algorithm cannot calculate the weight of input nodes. Therefore, this paper combines BP neural network with Analytic Hierarchy Process to construct a drilling operation safety evaluation model, which can effectively predict drilling operation accidents and draw drilling operation risk situation.
DarwiNN

Neuroevolution (NE), defined as the application of evolution-based training methods to Deep Neural Networks (DNNs), has recently demonstrated encouraging results on a variety of learning tasks. NE is highly parallel and relies on DNN inference as its main computational kernel and therefore can potentially leverage large-scale distributed inference-specific hardware infrastructure in the cloud or edge. We introduce chromosome updates (CU), a novel communication-optimized method for distributing NE computation, and DarwiNN, an open-source, GPU-accelerated distributed NE toolbox based on PyTorch, which implements CU and other algorithms for distributing NE.
An RTS-based algorithm for noisy optimization by strategic sample accumulation

This paper proposes a noisy optimization algorithm that can effectively and efficiently deal with the noise in the objective function by appropriately allotting samples to individuals in the population of an overlapping generation model. During the survival selection, our algorithm makes probabilistic decisions to determine when and to whom additional samples should be given, intending to maximally save the samples. Since the samples allotted to individuals are accumulated as long as they remain in the population, long-lasting individuals tend to have a large accumulation and thus a quite accurate evaluation. Test results with benchmark functions confirm the performance of the proposed algorithm.
An improved registration and mosaicking method for remote sensing images under geometric and photometric constraints

This paper presents a new automatic and fast satellite image registration approach. In this study, we exploit the invariant associations between different areas of the referenced and sensed images. Mostly existing registration methods are only interested in the extraction of points of interest without taking into account their quality and matching. Our method responds to this problematic because it involves firstly to find a class of separate and independent keypoints which are unchanging to different geometric transformations such as translation, rotation and scaling in addition to photometric constraints and viewpoint adjustments by using the SIFT (Scale Invariant Features Transform) Detector. Then, the matching strategy of SIFT is replaced by a fast k-nearest neighbour algorithm based on the Haar wavelet transform to reduce the space of interest points pair. Finally, to solve the problem of geometric constraints and to estimate the transformation matrix between stereo image pairs, features pairing are filtered using the RANSAC (RANdom Sample Consensus) algorithm. The final warping of the images conforming to the chosen key points is completed by applying a projective transformation and used in a multi-band blending to create a perfect panorama. Experiments on various remote sensing images show the effectiveness and the robustness of our method.
Aggregated Control of Quantum Computations: When Stacked Architectures Are Too Good to Be Practical Soon

Google's quantum supremacy demonstration pitted the world's largest supercomputer against a single quantum chip. A path to further scaleup may involve a large supercomputer working together withquantum chips, instead of in competition with them.
Towards the Intelligent Application of Security Controls

Today, attacks on sensitive data held by organizations and the resulting data breaches are unfortunately all too common. In response to these attacks the organization applies security controls (e.g., encryption) to secure its vulnerabilities. However, these controls are often applied haphazardly, without any idea of their reliability, or any guidance on how they should be applied to account for the priority of the vulnerabilities or a security control's effect on the overall security posture of the organization. This work derives a mathematical model linking the reliability of the security controls to the overall security level of the organization. The paper then combines this model with a method to prioritize vulnerabilities, allowing the organization to more intelligently apply security controls and reach its desired security level goal within negotiated budgetary constraints. The paper illustrates this approach using an application example.
Leveraging Succinct Data Structures for DNA Sequence Mapping on FPGA

The sequencing capability of Next Generation Sequencing platforms continues to increase, but mapping short DNA fragments to large genomic sequences still constitutes a challenge in many bioinformatic applications. In this paper, we present BWaveR, a heterogeneous system leveraging FPGAs to increase the execution efficiency of the sequence matching process, based on succinct data structures that allow to encode large sequences in a minimal memory footprint, while still granting fast queries on them. Our design, evaluated on a Xilinx Alveo U200, results up to 4.9× faster and 26× more power-efficient than Bowtie2, a state-of-the-art equivalent software, run with 16-threads, without any loss in accuracy.
Cognitively-Inspired Inference for Malware Task Identification

Malware reverse-engineering, specifically, identifying the tasks a given piece of malware was designed to perform (e.g., logging keystrokes, recording video, establishing remote access) is a largely human-driven process that is a difficult and time-consuming operation. In this chapter, we present an automated method to identify malware tasks using two different approaches based on the ACT-R cognitive architecture, a popular implementation of a unified theory of cognition. Using three different malware collections, we explore various evaluations for each of an instance-based and rule-based model—including cases where the training data differs significantly from test; where the malware being evaluated employs packing to thwart analytical techniques; and conditions with sparse training data. We find that our approach based on cognitive inference consistently out-performs the current state-of-the art software for malware task identification as well as standard machine learning approaches—often achieving an unbiased F1 score of over 0.9.
Low-Information-Loss Anonymization of Trajectory Data Considering Map Information

Preserving an individual's privacy when publishing data are essential, and anonymization has been getting attention as the solution. When anonymizing data, it is necessary to contemplate the possibilities of linkage with other data which can lead to privacy violation. Trajectory data are one of the data, which contains personal data. Consequently, various anonymization methods of trajectory data have been considered by researchers. However, most research handle trajectory data as polylines connecting location data or as a sequence of location data. In other words, it lacks on considering the connection with map data. In this paper, we will consider the anonymization of trajectory data of moving users matched according to map data, which we will be calling pathing data. According to k-anonymity principle, data can be published if there are k of the same data. We will use k-anonymity principle to quantitively judge the risk of privacy violation and propose two methods that can fulfill the anonymization requirements with low data loss. The two methods are Map Matching to Node (MMtoN) and Map matching to Edge (MMtoE), which judges k-anonymity by segments of pathing data.
Research on Writing Design of College English under Blended Education Mode

With 20 years of development at home and abroad, blended education research has become a common teaching method today with the high development of the Internet. How to make full use of the combination of offline and online methods to improve the learning effect of college English writing under the guidance of blended education theory should be taken into consideration. The author takes the college English writing course of his own school as an example for research of this subject.
Hardware resources analysis of BNNs splitting for FARD-based multi-FPGAs Distributed Systems

FPGAs have proven to be valid architectures to accelerate the inference phase of Convolutional Neural Networks (CNNs). State-of-the-art works also demonstrated that it is possible to take advantage of a distributed FPGA-base system to improve performance, power consumption and scalability of such algorithms. However, the hardware resource usage, communication, and the nodes management become main aspects when dealing with an embedded distributed scenario. In this context, FINN optimizes the FPGA-based CNNs with binarization and FARD is a framework that allows the acceleration of fog computing-based application with FPGAs. In this work, we present how to extend FARD to deal with job-based applications rather than the event-based fog computing scenario. In particular, we analyzed two PYNQ-Z1 connected each other and we implemented a distributed BNN algorithm based on FINN's CnvW2A2. Results show how hardware resources vary according to the division of the network when splitting after each convolutional layer.
Teach-Me DNA: an Interactive Course Using Voice Output in an Augmented Reality System

In this paper, we present an interactive system partly based on voice output in an augmented reality environment. Using a chatbot technology, this system allows the user to engage bidirectional communication with a conversational agent. The present system builds upon our previous work in which the user interacts with 3D virtual objects via voice commands. We describe, in the current document, how we integrate speech output using the Text to Speech Service (TTS API) available on the IBM Watson platform, the goal being to obtain an even more interactive system. We also employ Speech Synthesis Markup Language (SSML) to control some features of the natural-sounding speech thus produced. Moreover, we developed Teach-Me DNA, an interactive application that gives the user (pupil, student) the opportunity to learn and/or revise the DNA molecule's basics as a part of a biological course. On one hand, and via voice inputs the user is simply able to perform 3D selections and 3D manipulations of the molecule and its several components in order to learn about their 3D features and properties, this first part dealing with the vocal entries was fully documented in a previous work. On another hand, TeachMe DNA is here enriched by producing a natural speech when reacting to the user's requests. In fact, an agent's voice is used to respond in real time to student's questions. Definitions and/or explanations are thus given in a very natural way which might immerse more significantly the student in the proposed learning environment based on augmented reality technology.
Privacy Enhancing Machine Learning via Removal of Unwanted Dependencies

The rapid rise of IoT and Big Data has facilitated copious data-driven applications to enhance our quality of life. However, the omnipresent and all-encompassing nature of the data collection can generate privacy concerns. Hence, there is a strong need to develop techniques that ensure the data serve only the intended purposes, giving users control over the information they share. To this end, this article studies new variants of supervised and adversarial learning methods, which remove the sensitive information in the data before they are sent out for a particular application. The explored methods optimize privacy-preserving feature mappings and predictive models simultaneously in an end-to-end fashion. Additionally, the models are built with an emphasis on placing little computational burden on the user side so that the data can be desensitized on device in a cheap manner. Experimental results on mobile sensing and face datasets demonstrate that our models can successfully maintain the utility performances of predictive models while causing sensitive predictions to perform poorly.
Efficient Calculation of Empirical P-values for Association Testing of Binary Classifications

Investigating whether two different classifications of a population are associated, is an interesting problem in many scientific fields. For this reason, various statistical tests to reveal this type of associations have been developed, with the most popular of them being Fisher’s exact test. However it has lately been shown that in some cases this test fails to produce accurate results. An alternative approach, known as randomization tests, was introduced to alleviate this issue, however, such tests are computationally intensive. In this paper, we introduce two novel indexing approaches that exploit frequently occurring patterns in classifications to avoid performing redundant computations during the analysis. We conduct a comprehensive set of experiments using real datasets and application scenarios to show that our approaches always outperform the state-of-the-art, with one approach being faster by an order of magnitude.
Learning and Deduction of Rules for Knowledge Graph Completion

The amount of training data or knowledge determines the precision of Knowledge Graph Completion (KGC), since traditional methods require as much training data as possible for model training or embedding to ensure the accurate expression of the knowledge contained in KG. It is necessary to extend the rules into KGC to improve the precision of KGC with respect to the small amount of training data. In this paper, we propose a framework RuleB for KGC including rule learning and rule deduction. First, we adopt six types of basic rules in the framework for searching the triples in KG. Then, we propose an optimization algorithm RWK based on the random walk strategy and K-sized traverse to reduce the execution time of triple search. Second, we give the corresponding deduction strategy for the different basic rules to obtain the new rule knowledge (NRK). Further, we give the corresponding strategy for combining the NRK and KG to obtain new entity knowledge (NEK), as well as that for NEK-based KGC, where NEK contains new triples that have not been presented in the KG previously. Experimental results of rule learning show the efficiency and accuracy of our methods.