Reviewerly/exHarmony huggingface dataset:
Dataset-Transformation: improving clustering by enhancing the structure with DipScaling and DipTransformation

A data set might have a well-defined structure, but this does not necessarily lead to good clustering results. If the structure is hidden in an unfavourable scaling, clustering will usually fail. The aim of this work is to present techniques—DipScaling and DipTransformation—which enhance the data set by rescaling and transforming its features and thus emphasizing and accentuating its structure. If the structure is sufficiently clear, clustering algorithms will perform far better. We refer to such techniques as “Dataset-Transformations” and try to provide a mathematical framework for them. To show that our algorithms work well, we have conducted extensive experiments on several real-world data sets, where we improve clustering not only for k-means, which is our main focus but also for other standard clustering approaches.
Concept of an Electronic Device Dedicated to Imbalance Disorders Monitoring

In this paper we present the main concept of an electronic device, destined to work as a part of a system devoted to monitoring human gait and movement in daily life. Operation of the device and the whole system is focused on diagnosis of the imbalance disorders, especially in elderly persons. This small electronic device is intended to be worn by the monitored person in non-restraining and comfortable manner. The data collected by the device are to be sent to accompanying data processing unit in form of e.g. a smartphone. After initial processing, the obtained data are then sent to a medical center for raising alert, further processing and storage. The paper is focused on design of a microelectromechanical system (MEMS) and an application-specific integrated circuit (ASIC) responsible for gathering, conditioning and conversion of the data into digital form best suited for quantitative analysis and transmission.
Permutation polynomials of the form <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si1.svg"><mml:mi>x</mml:mi><mml:mo linebreak="goodbreak" linebreakstyle="after">+</mml:mo><mml:mi>γ</mml:mi><mml:msubsup><mml:mrow><mml:mi mathvariant="normal">Tr</mml:mi></mml:mrow><mml:mrow><mml:mi>q</mml:mi></mml:mrow><mml:mrow><mml:msup><mml:mrow><mml:mi>q</mml:mi></mml:mrow><mml:mrow><mml:mi>n</mml:mi></mml:mrow></mml:msup></mml:mrow></mml:msubsup><mml:mo stretchy="false">(</mml:mo><mml:mi>…

In this paper, we investigate permutation polynomials of the form x+γTrqqn(h(x)) over Fqn, which is a further study on a recent work of Kyureghyan and Zieve. Inspired by their computational examples, we present several new permutation polynomials over Fqn based on some known permutations.
Deep Learning for Multimodal Data Fusion

Recent advance in deep learning has enabled realistic image-to-image translation of multimodal data. Along with the development, auto-encoders and generative adversarial networks (GAN) have been extended to deal with multimodal input and output. At the same time, multitask learning has been shown to efficiently and effectively address multiple mutually related recognition tasks. Various scene understanding tasks, such as semantic segmentation and depth prediction, can be viewed as cross-modal encoding / decoding, and hence most of the prior work used multimodal (various types of input) datasets for multitask (various types of output) learning. The inter-modal commonalities, such as one across RGB image, depth, and semantic labels, are being exploited while the study is still at an early stage. In this chapter, we introduce several state-of-the-art encoder–decoder methods on multimodal learning as well as a new approach to cross-modal networks. In particular, we detail a multimodal encoder–decoder networks that harnesses the multimodal nature of multitask scene recognition. In addition to the shared latent representation among encoder–decoder pairs, the model also has shared skip connections from different encoders. By combining these two representation sharing mechanisms, it is shown to efficiently learn a shared feature representation among all modalities in the training data.
Bidirectional approximate reasoning-based approach for decision support

Fuzzy rule-based systems are widely applied for real-world decision support, such as policy formation, public health analysis, medical diagnosis, and risk assessment. However, they face significant challenges when the application problem at hand suffers from the "curse of dimensionality" or "sparse knowledge base". Combination of hierarchical fuzzy rule models and fuzzy rule interpolation offers a potentially efficient and effective approach to dealing with both of these issues simultaneously. In particular, backward fuzzy rule interpolation (B-FRI) facilitates approximate reasoning to be performed given a sparse rule base where rules do not fully cover all observations or the observations are not complete, missing antecedent values in certain available rules. This paper presents a hierarchical bidirectional fuzzy reasoning mechanism by integrating hierarchical rule structures and forward/backward rule interpolation. A computational method is proposed, building on the resulting hierarchical bidirectional fuzzy interpolation to maintain consistency in sparse fuzzy rule bases. The proposed techniques are utilised to address a range of decision support problems, successfully demonstrating their efficacy.
An Efficient Identity-Based Cryptographic Model for Chebyhev Chaotic Map and Integer Factoring Based Cryptosystem

The profound characteristics of factoring problems and chaotic maps are utilized in this article to produce a new identity-based cryptographic scheme. These characteristics include random behavior and high performance (i.e., encryption and decryption) in terms of so many parameters such as computational delay, rate of security, and speed. To illustrate the performance of the proposed cryptographic scheme, some cryptographic attacks are proposed. The mathematical analysis have confirmed that the combined factoring problems and chaotic maps scheme has a high rate of security which satisfy the cryptographic requirements. To the best of our knowledge, this is the first time an ID-based cryptography based on chaotic maps and factoring problems has been proposed.
Triple-Attention Mixed-Link Network for Single-Image Super-Resolution

Single-image super-resolution is of great importance as a low-level computer-vision task. Recent approaches with deep convolutional neural networks have achieved impressive performance. However, existing architectures have limitations due to the less sophisticated structure along with less strong representational power. In this work, to significantly enhance the feature representation, we proposed triple-attention mixed-link network (TAN), which consists of (1) three different aspects (i.e., kernel, spatial, and channel) of attention mechanisms and (2) fusion of both powerful residual and dense connections (i.e., mixed link). Specifically, the network with multi-kernel learns multi-hierarchical representations under different receptive fields. The features are recalibrated by the effective kernel and channel attention, which filters the information and enables the network to learn more powerful representations. The features finally pass through the spatial attention in the reconstruction network, which generates a fusion of local and global information, lets the network restore more details, and improves the reconstruction quality. The proposed network structure decreases 50% of the parameter growth rate compared with previous approaches. The three attention mechanisms provide 0.49 dB, 0.58 dB, and 0.32 dB performance gain when evaluating on Set5, Set14, and BSD100. Thanks to the diverse feature recalibrations and the advanced information flow topology, our proposed model is strong enough to perform against the state-of-the-art methods on the benchmark evaluations.
Supervised image segmentation based on superpixel and improved normalised cuts

Effective and efficient image segmentation is an important task in computer vision. As the full-automatic image segmentation is usually difficult to segment the natural image, it is an excellent solution to use interactive schemes. Here, to overcome the defects of SSNCut in its low quality and speed, the authors proposed a new interactive image segmentation method based on superpixels, must-link, cannot-link constraints and improved normalised cuts. The main contribution of their work is as follows: first, the similarity between two superpixel regions is calculated using Bhattacharyya distance. Second, they adaptively modify the weights of must-link and cannot-link constraints. Compared to SSNCut, their method greatly improves the accuracy of segmentation. Comparative experiments on open datasets show that the proposed method can get better results compared with SSNCut, GrabCut in one cut, interactive segmentation using binary partition tree, interactive graph cut, seed region growing, and simple interactive object extraction.
A Novel Slot-Gated Model Combined With a Key Verb Context Feature for Task Request Understanding by Service Robots

Spoken language understanding (SLU) is a fundamental to service robot handling of natural language task requests. There are two main basic problems in SLU, namely, intent determination (ID) and slot filling (SF). The slot-gated recurrent neural network joint model for the two tasks has been proven to be superior to the single model, and has achieved the most advanced performance. However, in the context of task requests for home service robots, there exists a phenomenon that the information about a current word is strongly dependent on key verbs in the sentence, and it is difficult to capture this relation well with current methods. In this paper, we extract the key instructional verb containing greater core task information based on dependency parsing, and construct a feature that combines the key verb with its contextual information to solve this problem. To further improve the performance of the slot-gated model, we consider the strong relations between intent and slot. By introducing intent attention vectors into the slot attention vectors through the global-level gate and element-level gate, a novel dual slot-gated mechanism is proposed to explicitly model the complex relations between the results of the ID tasks and SF prediction tasks and optimize the global prediction results. Our experimental results on the ATIS dataset and an extended home service task (SRTR) dataset based on FrameNet show that the proposed method outperforms the most advanced methods in both tasks. Especially, for SRTR, the results of SF, ID, and sentence-level semantic frame-filling are improved by 1.7%, 1.1%, and 1.7%, respectively.
A novel side-channel analysis for physical-domain security in cyber-physical systems

Security of cyber-physical systems against cyber attacks is an important yet challenging problem. Cyber-physical systems are prone to information leakage from the physical domain. The analog emissions, such as magnetic and power, can turn into side channel revealing valuable data, even the crypto key of the system. Template attack is a popular type of side-channel analysis using machine learning technology. Malicious attackers can use template attack to profile the analog emission, then recover the secret key of the system. But conventional template attack requires that the adversary has access to an identical experiment device that he can program to his choice. This study proposes a novel side-channel analysis for physical-domain security in cyber-physical systems. Our contributions are the following three points: (1) Major peak region method for finding points of interests correctly is proposed. (2) A method for establishing templates on the basis of those points of interest still without requiring knowledge of the key is proposed. Several techniques are proposed to improve the quality of the templates as well. (3) A method for choosing attacking traces is proposed to significantly improve the attacking efficiency. Our experiments on three devices show that the proposed method is significantly more effective than conventional template attack. By doing so, we will highlight the importance of performing similar analysis during design time to secure the cyber-physical system.
Fully Convolutional Neural Networks for 3D Vehicle Detection Based on Point Clouds

In this paper, a novel methodology proposed for 3D detection with the purpose of boosting the detection accuracy and keep autonomous vehicles safety. The model takes the point clouds as the input directly. Based on our modified feature pyramid networks and VGG-16 named as FFPNets, which utilizes the one-stage fully convolutional network to detect 3D cars. The experimental result shows the robustness of the model and its superiority. The average precision (AP) of the car for easy, moderate, and hard cases achieves state-of-the-art detection accuracy on KITTI datasets.
Improving Insider Threat Detection Through Multi-Modelling/Data Fusion

Insider threats within organizations can take a variety of forms including fraud, theft of classified or proprietary data, and workplace violence. Insiders can be overt individuals who commit deliberate acts against their organization, or inadvertent insiders who provide access to organizational IT systems. The damage from insider threats was estimated at an annualized average of $4.3M per company across a variety of industries in a 2016 study, which does not include the damage to an organization's reputation, loss of business, and decrease in company value. The Scientific advances to Continuous Insider Threat Evaluation (SCITE) program sponsored by the Intelligence Advanced Research Projects Activity (IARPA) was created to improve insider threat detection. In the first phase of this effort, three competing teams were provided aggregated individual performance data and asked to answer system performance questions on a range of complex challenge problems. The purpose was to be able to detect a subset of individuals from within the overall population of an organization that displayed a behavior or belonged to a group. Teams were scored on three metrics: mean squared error, calibrated certainty interval, and interval score. The team led by Innovative Decisions, Inc. (IDI) used a multiple model approach with data fusion to model these problems. This approach proved far superior to the methods used by the two other teams across all three metrics. The results of this research are applicable to any area where the objective is to identify a very small subset of a much larger group using incomplete, noisy data. Specific areas benefitting education and research include airline passenger screening, vetting of immigration or visa applications, and security clearance reviews.
Human Skeleton Feature Extraction from 2-Dimensional Video of Indonesian Language Sign System (SIBI [Sistem Isyarat Bahasa Indonesia]) Gestures

Indonesian Language Sign System (SIBI) is the official sign language system used in Indonesia. A model that could translate SIBI gesture taken from a video would be very useful for communicating with people with disabilities. One of the features needed to translate SIBI gesture to words is the subject's skeleton. In this paper, we researched a method to extract this feature from 2-Dimensional video. The method reconstructs skeleton model based on the position of head, shoulders, elbows, and hands of the subject. The head is located with haar cascade and the shoulders are pinpointed based on the location of the head. The hands are located with skin segmentation technique and then tracked throughout the video with Lucas-Kanade method. The elbows are extrapolated based on the shoulder and hand points, and the body silhouette. The experiment with LSTM model resulted in maximum testing accuracy of 98.214%.
Hyperparameter Importance Analysis based on N-RReliefF Algorithm

Hyperparameter selection has always been the key to machine learning. The Bayesian optimization algorithm has recently achieved great success, but it has certain constraints and limitations in selecting hyperparameters. In response to these constraints and limitations, this paper proposed the N-RReliefF algorithm, which can evaluate the importance of hyperparameters and the importance weights between hyperparameters. The N-RReliefF algorithm estimates the contribution of a single hyperparameter to the performance according to the influence degree of each hyperparameter on the performance and calculates the weight of importance between the hyperparameters according to the improved normalization formula. The N-RReliefF algorithm analyses the hyperparameter configuration and performance set generated by Bayesian optimization, and obtains the important hyperparameters in random forest algorithm and SVM algorithm. The experimental results verify the effectiveness of the N-RReliefF algorithm.
The Smart Grid Semantic Platform: Synergy between IEC Common Information Model (CIM) and Big Data

Recent innovations in Information Technology, Cloud Computing, Big Data analysis, Internet of Things (IoT) and Artificial Intelligence (AI) are enabling technological solutions that were previously unimaginable, or usually available only for big IT companies, with impacts in both industry and services. The demonstrator named Smart Grid Semantic Platform (SGSP) has been created using these technologies integrating several aspects of the management of an electricity distribution network. The services deployed on the platform concern the visualization of the topographic data and electricity grid assets, as well as the processing and displaying of historical data related to the operation of the networks and/or the result of Big Data analysis. This paper demonstrates how useful the synergy is between the standard semantic model of the electric network (IEC CIM 61968) and the historical data of the time series of medium voltage electricity consumption analyzed using a Big Data platform. Specifically it illustrates the realization of a use case about the calculation of a load index of the electric lines.
An Investigation of 21st-Century Digital Skills on Digital Citizenship Among College Students

The aim of this paper was to explore the relationship between 21st-century digital skills and digital citizenship among college students. A total of 962 college students at a normal university in the central China region were involved in the study. A 21st-century digital skill scale and a digital citizenship scale were employed to measure college students' 21st-century digital skills and digital citizenship respectively, which were proved to be reliable and valid. The results showed that college students' 21st-century digital skill was positively related to their digital citizenship. Furthermore, college students' 21st-century digital skills in communication sharing, collaboration, critical thinking, creativity, and problem solving were significant factors predicting digital citizenship positively. Based on the above findings, the authors propose several strategies to promote college students' digital citizenship.
Retinal Image Registration based on Auto-Adaptive SIFT and Redundant Keypoint Elimination Method

Retinal image matching is a main step in many applications, including retinal image registration and retinal image mosaicking. The A2SIFT algorithm is one of the improved versions of SIFT algorithm, originally proposed for matching aerial imagery. One of the most important weaknesses of A2SIFT algorithm in retinal image matching is the contrast threshold value and the presence of a redundant feature. In this paper, first the contrast threshold value of this method has been modified and optimized for retinal image matching. After that, RKEM algorithm is used to increase its efficiency. The results of the experiments show the high efficiency and accuracy of the proposed method in retinal image matching.
Robust Embedding Framework with Dynamic Hypergraph Fusion for Multi-label Classification

Label embedding is an important family of multi-label classification algorithms which can jointly extract the information of all labels for better performance. However, few works have been done on multi-label embedding methods which can effectively deal with the interference of noisy data during training progress. Inspired by the low-rank hypothesis originating from label correlation, we propose a novel embedding based framework namely Robust Embedding Framework with Dynamic Hypergraph Fusion for multi-label classification (REFDHF), by which a latent space with strong anti-noise and predictive ability can be obtained. Meanwhile, a novel hypergraph fusion technology is designed to explore and utilize the complementary between feature space and label space to make the proposed REFDHF more robust. Furthermore, we conduct a deep extension for REFDHF, which is effectively applied to image annotation. Extensive experimental results on datasets with many labels demonstrate that our proposed approach is significantly better than the existing label embedding algorithms.
Volumetrie Data Augmentation as an Effective Tool in MRI Classification Using 3D Convolutional Neural Network

Deep learning has become a default choice for many researchers in the areas of prediction and automatic diagnostics of different diseases. One of the prerequisites for achieving good results in the domain of an automatic diagnostics is the size and quality of the training set. One of the most serious challenges in the domain of a medical image analysis is the lack of manually annotated data. In this work, we use a relatively simple convolutional neural network to classify MRI images as either cognitively normal or those suffering from Alzheimer's disease. Based on a quantitative analysis of multiple techniques, we propose to use a specific set of data augmentation techniques to improve the generalization of the deep neural network model. The proposed approach was compared with three different approaches using the same neural network - one approach with no data augmentation, one with regularization not specific to visual data (dropout and weight decay) and one with extensive data augmentation specific for images. We conclude that by using the proposed set of augmentation techniques, including novel perturbed normalization, we postponed overfitting, which resulted in a better generalized model.
Bag of ARSRG Words (BoAW)

In recent years researchers have worked to understand image contents in computer vision. In particular, the bag of visual words (BoVW) model, which describes images in terms of a frequency histogram of visual words, is the most adopted paradigm. The main drawback is the lack of information about location and the relationships between features. For this purpose, we propose a new paradigm called bag of ARSRG (attributed relational SIFT (scale-invariant feature transform) regions graph) words (BoAW). A digital image is described as a vector in terms of a frequency histogram of graphs. Adopting a set of steps, the images are mapped into a vector space passing through a graph transformation. BoAW is evaluated in an image classification context on standard datasets and its effectiveness is demonstrated through experimental results compared with well-known competitors.
Leveraging Skill Hierarchy for Multi-Level Modeling with Elo Rating System

In this paper, we are discussing the case of offering retired assessment items as practice problems for the purposes of learning in a system called ACT Academy. In contrast to computer-assisted learning platforms, where students consistently focus on small sets of skills they practice till mastery, in our case, students are free to explore the whole subject domain. As a result, they have significantly lower attempt counts per individual skill.