Reviewerly/exHarmony huggingface dataset:
ECG Knowledge Discovery Based on Ontologies and Rules Learning for the Support of Personalized Medical Decision Making

The electrocardiogram (ECG) is the most common non-invasive used method for monitoring the heart condition. Combined with other available patient medical data, the manual analysis and evaluation of ECG data become labour intensive and prone to errors. With the increased amount of available digital medical data, there is a need for proper methods to support medical practitioners in the decision-making process. These practitioners base their diagnostic decisions on standardized procedures, combined with field experience. In this paper, we present the conceptual design for an approach to knowledge discovery of ECG data based on ontologies and rules learning using Learning Classifier Systems (LCS). Ontologies can provide a platform and application-independent representation of knowledge based on the patient's medical data. Furthermore, rule-based reasoning provides a mechanism for discovering new knowledge. LCS provide a tool for automatically discovering new rules that are maximally general and can support sequential decision-making process. The use of LCS and rule-based reasoning provide a mechanism for encoding existing and new knowledge that can improve the efficiency of personalized medical treatment.
A High Capacity Text Steganography Utilizing Unicode Zero-Width Characters

Information hiding based on text has become the hottest and most discussed aspect of steganography in recent years, because it eases communication and consumes less memory compared to other types. The lack of redundant information in a text file makes it difficult to hide secret bits into a carrier message (CM) without being noticeable. Therefore, the total amount of hidden bits embeddable in a CM contributes immensely to the system efficiency. Today, individuals share information online with the fear that it could be destroyed by third parties. To achieve a structured text hiding system with high embedding capacity, imperceptibility, robustness and high security concurrently, securing sensitive data through encryption is not enough because attackers are able to suspect that a communication channel exist between the sender and the receiver. Therefore, this paper combines both steganography and cryptography to protect our shared information and to achieve a strong and highly secured non understandable text hiding system. We proposed a method which uses pairs of Zero Width Characters (ZWC) to represent each 4-bit binary classification which provides a high embedding capacity and reduces computational complexity than previous methods. The embedding is done by placing the invisible strings of the secret message (SM) into a single position of the open spaces in the CM which provides high invisibility compared to the reviewed methods. One Time Pad (OTP) and XOR bitwise encryption is used to encrypt the inverted message before the embedding process which provides end-to-end security to the system. The experiment shows that the proposed method provides a very high embedding capacity and high robustness than other literature reviewed.
Iterative Deep Fusion for 3D Semantic Segmentation

Understanding and interpreting a scene is a key task of environment perception for autonomous driving, which is why autonomous vehicles are equipped with a wide range of different sensors. Semantic segmentation of sensor data provides valuable information for this task and is often seen as key enabler. In this paper, we are presenting a deep learning approach for 3D semantic segmentation of lidar point clouds. The proposed architecture uses a range view representation of 3D point clouds and additionally exploits camera features to increase accuracy and robustness. In contrast to other approaches, which fuse lidar and camera feature maps once, we fuse them iteratively and at different scales inside our network architecture. We demonstrate the benefits of the presented iterative deep fusion approach over single fusion approaches on a large benchmark dataset. Our evaluation shows considerable improvements, resulting from the additional use of camera features. Furthermore, our fusion strategy outperforms the current state-of-the-art strategy by a considerable margin. Despite the use of camera features, the presented approach is also trainable solely with point cloud labels.
FPGA based Hybrid Random Number Generators

In this digital era, Pseudo-random number generators (PRNGs) are widely used in several real-time applications. As many types of PRNG are available, the feedback shift register method remains as the popular one. Despite the hype, these methods are unable to produce reliable data due to the presence of repetitive patterns in the sequence. This research work overcomes the aforementioned challenges by developing a hybrid technique. Entropy concept is used to improve the PRNGs quality. Since, Hybrid PRNGs pass in the testing, they can be implemented in real-time applications.
Heterogeneous Information Diffusion Model for Social Recommendation

Social recommendation provides a promising way to alleviate data sparsity and boost recommendation performance in collaborative filtering. How to model social influence is a central problem in the social recommendation. The social data can be represented by two separate graphs together, i.e., a social (user-user) graph and an interaction (user-item) graph. To some extent, this representation leads to loss of heterogeneity. To address this problem, we propose a novel social recommendation model called Heterogeneous Information Diffusion Model (HIDM for short). Specifically, since users are involved in both two subspaces: social subspace and interaction subspace, we design a neural architecture with two modules to aggregate information of the two subspaces. The first module is social aggregation which models the social influence from social connections in layer-wise via GNNs. The second module is item aggregation which captures the item influence from interacted items in a single layer. Moreover, different message functions are designed to encode information in different subspaces. Finally, the user preference representation is obtained by fusing the influence of the two subspaces. The extensive experiments conducted on three realworld datasets show that HIDM outperforms several different state-of-the-art social recommendation models.
Two Factor Hash Verification (TFHV): A Novel Paradigm for Remote Authentication

Current paradigms for client-server authentication often rely on username/password schemes. Studies show such schemes are increasingly vulnerable to heuristic and brute-force attacks. This is either due to poor practices by users such as insecure weak passwords, or insecure systems by server operators. A recurring problem in any system which retains information is insecure management policies for sensitive information, such as logins and passwords, by both hosts and users. Increased processing power on the horizon also threatens the security of many popular hashing algorithms. Furthermore, increasing reliance on applications that exchange sensitive information has resulted in increased urgency. This is demonstrated by a large number of mobile applications being deemed insecure by Open Web Application Security Project (OWASP) standards. This paper proposes a secure alternative technique of authentication that retains the current ecosystem, while minimizes attack vectors without inflating responsibilities on users or server operators. Our proposed authentication scheme uses layered encryption techniques alongside a two-part verification process. In addition, it provides dynamic protection for preventing against common cyber-attacks such as replay and man-in-the-middle attacks. Results show that our proposed authentication mechanism outperform other schemes in terms of deployability and resilience to cyber-attacks, without inflating transaction's speed.
Application Of Pizza Sales Data Mining Using Apriori Method

Pizza is a processed food originating from Italy and has been spread in various other countries including one of them in Indonesia. Pizza is a processed food that is currently sought after by various groups of people so as to make the pizza business opportunity very profitable, if it is run in a food business. Currently the pizza business has very favorable prospects when compared to other businesses. Moreover, the targeted target can be from all walks of life from children to adults. Pizza sales transactions that produce sales data every day, have not been able to maximize the use of sales data. Sales data is only stored as an archive, so it becomes a pile of data. Therefore the use of data mining is used to solve this problem. A priori algorithm is a data mining method by using minimum support parameters, minimum confidence and will analyze in the period of every month of sales transactions. This study produces data on the results of the process of association rules from the data collection of sales transactions. From the association rules it can be concluded that the pattern of pizza sales, where consumers more often buy Meatzza and Cheese Mania, as evidenced by the results of calculations using Apriori Algorithm and Rapidminer 5.3, with support of 30% and 60% confidence.
A Smooth Video Summarization Method Based on Frame-Filling

Given the existing video summarization algorithms have too much redundancy in video semantics, and destroy the maximum continuity between video frames, a smooth video summarization generation method is designed in this paper. This method is based on the video keyframe selection, which is mainly divided into three parts. In the first part, this method completes the de-redundancy operation based on the similarity calculated by the essential characteristics of the video frame. The second part completes the shot segmentation processing using the capsule network. The third part can be divided into two sub-parts, key frames selection and filling. The former of this part is selects key-frames based on video semantic, and the latter is smooths summarization based on frame-filling. Experimental results show that the video summarization algorithms designed in this paper can outperform several existing algorithms and achieve excellent results in terms of video content integrity, semantic accuracy and so on.
Creating Norwegian Valence Resources from a Deep Grammar

We present a procedure for generating a valence resources for Norwegian (Bokmål) from a deep grammar. The corpus is presented in the form of IGT (interlinear glossed text) augmented by valence information. Our deep parser is the HPSG-based computational grammar Norsource (Hellan and Bruland 2015), our online IGT repository is TypeCraft (Beermann and Mihaylov 2014), while the sentences of the corpus are taken from the Leipzig Corpus Collection (Goldhahn et al. 2012). We create a common structure for the resources. Our aim is to make the grammatical information encoded in a deep parser more readily accessible for humans and for further processing.
Link Speed Estimation for Traffic Flow Modelling Based on Video Feeds from Monocular Cameras

In this paper, we present a reliable and scalable approach for real-time estimation of link speeds (i.e., traffic speeds on specific road segments) based on video feeds coming from monocular cameras. We detect and track vehicles of specific types, identify anchor points (or keypoints) on them, compute their poses, and use this information to estimate their speeds. We use deep learning methods for vehicle detection, tracking, keypoint detection and localization, and traditional 3D pose estimation techniques for which precise mathematical solutions are available. Thus, our approach exploits the best of both worlds. The proposed approach does not require any physical measurements (extrinsics) in the road scene, making it scalable and easy to install. Our results on video feeds from Bangalore, India, show that the method is able to generalize well for cameras mounted on street light poles, congested traffic situations, and various lighting conditions. Thus, the solution is suitable for emerging market scenarios where traffic tends to be chaotic and dense, and mounting speed sensors or strategically located downward-facing cameras is not feasible. The code and dataset for this work are being made available.
Model-Driven Simulation of Elastic OCCI Cloud Resources

Abstract Deploying a cloud configuration in a real cloud platform is mostly cost- and time- consuming, as large number of cloud resources have to be rented for the time needed to run the configuration. Thereafter, cloud simulation tools are used as a cheap alternative to test cloud configuration. However, most of the existing cloud simulation tools require extensive technical skills and do not support simulation of any kind of cloud resources. In this context, using a model-driven approach can be helpful as it allows developers to efficiently describe their needs at a high level of abstraction. To do, we propose, in this article, a model-driven engineering approach based on the Open Cloud Computing Interface(OCCI) standard metamodel and CloudSim toolkit. We firstly extend OCCI metamodel for the supporting simulation of any kind of cloud resources. Afterward, to illustrate the extensibility of our approach, we enrich the proposed metamodel by new simulation capabilities. As proof of concept, we study the elasticity and pricing strategies of Amazon Web Services (AWS). This article benefits from OCCIware Studio to design an OCCI simulation extension and to provide a simulation designer for designing cloud configurations to be simulated. We detail the approach process from defining an OCCI simulation extension until the generation and the simulation of the OCCI cloud configurations. Finally, we validate the proposed approach by providing a realistic experimentation to study its usability, the resources coverage rate and the cost. The results are compared with the ones computed from AWS.
Investigating Similarity Metrics for Convolutional Neural Networks in the Case of Unstructured Pruning

Deep Neural Networks (DNNs) are essential tools of modern science and technology. The current lack of explainability of their inner workings and of principled ways to tame their architectural complexity triggered a lot of research in recent years. There is hope that, by making sense of representations in their hidden layers, we could collect insights on how to reduce model complexity—without performance degradation—by pruning useless connections. It is natural then to ask the following question: how similar are representations in pruned and unpruned models? Even small insights could help in finding principled ways to design good lightweight models, enabling significant savings of computation, memory, time and energy. In this work, we investigate empirically this problem on a wide spectrum of similarity measures, network architectures and datasets. We find that the results depend critically on the similarity measure used and we discuss briefly the origin of these differences, concluding that further investigations are required in order to make substantial advances.
Indonesian Ethnicity Recognition Based on Face Image Using Uniform Local Binary Pattern (ULBP) and Color Histogram

Ethnicity is one of identity every human has and can be used to categorize individuals in populations or large groups. We presented an Indonesian ethnicity recognition based on facial images using Uniform Local Binary Pattern (ULBP) and Color Histogram as a feature extraction method. We used the five largest ethnic groups in Indonesia, namely Sundanese, Javanese, Banjar, Buginese, and Malay. In the experiment, we used Random Forest as a classification method. The research obtained a performance accuracy of 98.25% using 2290 facial images.
Parallel-Connected Residual Channel Attention Network for Remote Sensing Image Super-Resolution

In recent years, convolutional neural networks (CNNs) have obtained promising results in single-image super-resolution (SR) for remote sensing images. However, most existing methods are inadequate for remote sensing image SR due to the high computational cost required. Therefore, enhancing the representation ability with fewer parameters and a shorter prediction time is a challenging and critical task for remote sensing image SR. In this paper, we propose a novel CNN called a parallel-connected residual channel attention network (PCRCAN). Specifically, inspired by group convolution, we propose a parallel module with feature aggregation modules in PCRCAN. The parallel module significantly reduces the model parameters and fully integrates feature maps by widening the network architecture. In addition, to reduce the difficulty of training a complex deep network and improve model performance, we use a residual channel attention block as the basic feature mapping unit instead of a single convolutional layer. Experiments on a public remote sensing dataset UC Merced land-use dataset revealed that PCRCAN achieved higher accuracy, efficiency, and visual improvement than most state-of-the-art methods.
Error-Correction Method Based on LDPC for Quantum Key Distribution Systems

We proposed an efficient error correction method for QKD systems using optimal LDPC check matrix. We divide sifted keys into 50 multiple parts, and each part creates syndrome using optimal check matrix. With this syndrome we have corrected the error of the sifted keys. By the proposed algorithm, we can correct less than 3 errors in each part of the sifted keys. The simulation results show that the error correction is performed efficiently.
Independent credible: Secure communication architecture of Android devices based on TrustZone

The development of mobile internet has brought convenience to people, but the openness and diversity of mobile Internet make it face the security threat of communication privacy data disclosure. In this paper, a trusted android device security communication method based on TrustZone is proposed. Firstly, Elliptic Curve Diffie-Hellman (ECDH) key agreement algorithm is used to make both parties negotiate the session key in the Trusted Execution Environment (TEE), and then, we stored the key safely in the TEE. Finally, TEE completes the encryption and decryption of the transmitted data. This paper constructs a secure communication between mobile devices without a trusted third party and analyzes the feasibility of the method from time efficiency and security. The experimental results show that the method can resist malicious application monitoring in the process of data encryption and ensures the security of the session key. Compared with the traditional scheme, it is found that the performance of the scheme is not significantly reduced.
Perceptron Algorithm and Its Verilog Design

In artificial neural network (ANN), the basic perceptron algorithm plays a significant role in supervised machine learning due to its simple structure. Though it cannot solve some non-linear problems like XOR, however, this feature offers a possibility to build perceptron on a hardware design. Due to high efficiency and defect tolerant, researchers have proposed some ANN accelerators with complicated memory units and specific registers. In this work, we focus on a simplest perceptron and accomplish its hardware design using Verilog HDL. The design module includes one core for learning and four memory units for storing the training data. The study shows that the proximate floating -point simulation of the simple perceptron design can replace the defect-tolerant registers and the simple memory units, thus to make the accelerator a tiny scale, it also demonstrates that the accuracy rate on test set achieved at 98% and the total area cost is only 0.0078 mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> .
Automatic Sheep Counting by Multi-object Tracking

Animal counting is a highly skilled yet tedious task in livestock transportation and trading. To effectively free up the human labour and provide accurate counts for sheep loading/unloading, we develop an auto sheep counting system based on multi-object detection, tracking and extrapolation techniques. Our system has demonstrated more than 99.9% accuracy with sheep moving freely in a race under optimal visual conditions.
Algorithm for Determining Number of Clusters based on Dichotomy

In current clustering algorithms, the number of clusters must be given in advance. And for a given cluster number K has a great impact on the effect of clustering. However, in practice, the specified cluster number K is mostly determined by experience, while partial use of elbow method to find the optimal cluster number requires high time complexity, and the effect is not always optimal. Therefore, there is no unified and effective method to determine the value of cluster number K. To solve this problem, a method to find the number of clusters is proposed. By dichotomizing the current node data each time, the corresponding contour coefficient is obtained, and the size of the contour coefficient before and after calculation is used to judge whether the clustering needs to continue and whether the current node needs to continue adding children nodes until all nodes cannot continue to generate children nodes, and finally a binary tree is formed. And the final number of leaf nodes n of the generated binary tree is taken as the output of the cluster number of the cluster. The experimental results show that the algorithm performs better in the selection of final cluster number and algorithm time.
RTA3: A Real Time Adversarial Attack on Recurrent Neural Networks

Recurrent neural networks are widely used in machine learning systems that process time series data including health monitoring, object tracking in video, and automatic speech recognition (ASR).While much work has been done demonstrating the vulnerability of deep neural networks to socalled adversarial perturbations, the majority of this work has focused on convolutional neural networks that process nonsequential data for tasks like image recognition.We propose that the unique memory and parameter sharing properties of recurrent neural networks make them susceptible to periodic adversarial perturbations that can exploit these unique features.In this paper, we demonstrate a general application of deep reinforcement learning to the generation of periodic adversarial perturbations in a black-box approach to attack recurrent neural networks processing sequential data.We successfully learn an attack policy to generate adversarial perturbations against the DeepSpeech ASR system and further demonstrate that this attack policy generalizes to a set of unseen examples in real time.
Geolocation using GAT with Multiview Learning

Information in social networks plays an important role in many fields such as event detection, disaster warning, etc. However, due to the lack of geographic metadata, the information is often unusable. Therefore, the geolocation using social network data has gradually become a hot research topic. Existing methods mainly use textual contents, and thus poorly exploit the available data, especially the hidden information in the link. To address this issue, we propose two Multiview learning models, M-GAT and M-GCN, based on the Graph Attention and Graph Convolution Network to fuse both the text and link information. By extracting the text features from multiple angles to extend the feature space, our models achieve the best results on the baseline dataset. The visual display of representations collected from a hidden layer illustrates the validity of our models. Experiments on different feature combination show the effectiveness of our proposal.