Reviewerly/exHarmony huggingface dataset:
Deep Learning Aided Signal Detection in OFDM Systems with Time-Varying Channels

In this paper, we propose a deep learning aided approach for signal detection in orthogonal frequency-division multiplexing (OFDM) systems with time-varying channels. The method simplifies the architecture of OFDM systems by treating OFDM receivers as a black box. We utilize fully-connected deep neural network (FC-DNN) properly and successfully simulate an end-to-end time-varying OFDM system. Compared with two conventional algorithms well-designed to deal with OFDM systems in time-varying environment, the proposed method does not need to estimate channel parameters to detect signals. Simulation results also demonstrate that the trained DNN model has the ability to remember the characteristics of wireless time-varying channels and provide more accurate and robust signal recovery performance.
Quantifying location privacy in permissioned blockchain-based internet of things (IoT)

Recently, blockchain has received much attention from the mobility-centric Internet of Things (IoT). It is deemed the key to ensuring the built-in integrity of information and security of immutability by design in the peer-to-peer network (P2P) of mobile devices. In a permissioned blockchain, the authority of the system has control over the identities of its users. Such information can allow an ill-intentioned authority to map identities with their spatiotemporal data, which undermines the location privacy of a mobile user. In this paper, we study the location privacy preservation problem in the context of permissioned blockchain-based IoT systems under three conditions. First, the authority of the blockchain holds the public and private key distribution task in the system. Second, there exists a spatiotemporal correlation between consecutive location-based transactions. Third, users communicate with each other through short-range communication technologies such that it constitutes a proof of location (PoL) on their actual locations. We show that, in a permissioned blockchain with an authority and a presence of a PoL, existing approaches cannot be applied using a plug-and-play approach to protect location privacy. In this context, we propose BlockPriv, an obfuscation technique that quantifies, both theoretically and experimentally, the relationship between privacy and utility in order to dynamically protect the privacy of sensitive locations in the permissioned blockchain.
Analyses of Namibian Seasonal Salt Pan Crust Dynamics and Climatic Drivers Using Landsat 8 Time-Series and Ground Data

Salt pans are highly dynamic environments that are difficult to study by in situ methods because of their harsh climatic conditions and large spatial areas. Remote sensing can help to elucidate their environmental dynamics and provide important constraints regarding their sedimentological, mineralogical, and hydrological evolution. This study utilizes spaceborne multitemporal multispectral optical data combined with spectral endmembers to document spatial distribution of surface crust types over time on the Omongwa pan located in the Namibian Kalahari. For this purpose, 49 surface samples were collected for spectral and mineralogical characterization during three field campaigns (2014–2016) reflecting different seasons and surface conditions of the salt pan. An approach was developed to allow the spatiotemporal analysis of the salt pan crust dynamics in a dense time-series consisting of 77 Landsat 8 cloud-free scenes between 2014 and 2017, covering at least three major wet–dry cycles. The established spectral analysis technique Sequential Maximum Angle Convex Cone (SMACC) extraction method was used to derive image endmembers from the Landsat time-series stack. Evaluation of the extracted endmember set revealed that the multispectral data allowed the differentiation of four endmembers associated with mineralogical mixtures of the crust’s composition in dry conditions and three endmembers associated with flooded or muddy pan conditions. The dry crust endmember spectra have been identified in relation to visible, near infrared, and short-wave infrared (VNIR–SWIR) spectroscopy and X-ray diffraction (XRD) analyses of the collected surface samples. According these results, the spectral endmembers are interpreted as efflorescent halite crust, mixed halite–gypsum crust, mixed calcite quartz sepiolite crust, and gypsum crust. For each Landsat scene the spatial distribution of these crust types was mapped with the Spectral Angle Mapper (SAM) method and significant spatiotemporal dynamics of the major surface crust types were observed. Further, the surface crust dynamics were analyzed in comparison with the pan’s moisture regime and other climatic parameters. The results show that the crust dynamics are mainly driven by flooding events in the wet season, but are also influenced by temperature and aeolian activity in the dry season. The approach utilized in this study combines the advantages of multitemporal satellite data for temporal event characterization with advantages from hyperspectral methods for the image and ground data analyses that allow improved mineralogical differentiation and characterization.
A Security Analysis of Captchas with Large Character Sets

Captcha, which can prevent computer programs from attacking websites, has been the most important security technology for many years. The most popularly deployed Captcha is the text-based scheme. The vast majority of the existing text Captchas are designed with English letters and Arabic numerals. Recently, text Captchas with large character sets are being increasingly popular. From the perspective of attackers, larger character set means greater solution space and better theoretical security. However, the security of Captchas with large character sets in real world has never been studied comprehensively. In this article, we introduce a simple, fast, and effective deep learning method to attack these newly emerging Captchas. Taking 11 Chinese Captchas as representatives, we ran our experimental attack on each of them. Our attack achieved high success rates, ranging from 34.7 to 86.9 percent at an average speed of 0.175 seconds on these schemes. All of the results show that the Chinese text Captcha can be easily broken, demonstrating that text Captchas with large character sets are also insecure in existing forms. As a substitute, we proposed a 3D image-based scheme combining semantic comprehension and dragging action. The preliminary experimental results show that it is more robust than current text-based schemes.
Hierarchical Adaptive Multi-task Learning Framework for Patient Diagnoses and Diagnostic Category Classification

The problems a patient suffers from can be summarized in terms of a list of patient diagnoses. The diagnoses are typically organized in a hierarchy (or a lattice structure) in which many different low-level diagnoses are covered by one or more diagnostic categories. An interesting machine learning problem is related to learning of a wide range of diagnostic models (at different levels of abstraction) that can automatically assign a diagnosis or a diagnostic category to a specific patient. While one can always approach this problem by learning models for each diagnostic task independently, an interesting open question is how one can leverage the knowledge of a diagnostic hierarchy to improve the classification and outperform independent diagnostic models. In this work, we study this problem by designing a new hierarchical classification learning framework in which multiple diagnostic classification targets are explicitly related via diagnostic hierarchy relations. By conducting experiments on MIMIC-III data and ICD-9 diagnosis hierarchy, we demonstrate that our framework leads to improved classification performance on individual diagnostic tasks when compared to independently learned diagnostic models. This improvement is stronger for diagnoses with a low prior and smaller number of positive training examples.
Deep Learning Based-Recommendation System

The growth of data in recent years has motivated the emergence of deep learning in many Computer Sciences related fields including Recommender System (RS). Deep learning has emerged as the solution; overcoming the obstacles of traditional recommendation models. Deep learning is able to enhance recommendation quality by learning non-linear and non-trivial user-item relationship, and extracting deep and abstract feature representations for users and items. However, deep learning in RS is still new and flourishing. The contribution of this paper is two-folds. Firstly, we will be providing several insights on the advances of RS focusing on deep-learning models, datasets and evaluation metrics. Secondly, we expand on the current trend and provide several possible research directions in the field of deep learning-based RS.
Amharic OCR: An End-to-End Learning

In this paper, we introduce an end-to-end Amharic text-line image recognition approach based on recurrent neural networks. Amharic is an indigenous Ethiopic script which follows a unique syllabic writing system adopted from an ancient Geez script. This script uses 34 consonant characters with the seven vowel variants of each (called basic characters) and other labialized characters derived by adding diacritical marks and/or removing parts of the basic characters. These associated diacritics on basic characters are relatively smaller in size, visually similar, and challenging to distinguish from the derived characters. Motivated by the recent success of end-to-end learning in pattern recognition, we propose a model which integrates a feature extractor, sequence learner, and transcriber in a unified module and then trained in an end-to-end fashion. The experimental results, on a printed and synthetic benchmark Amharic Optical Character Recognition (OCR) database called ADOCR, demonstrated that the proposed model outperforms state-of-the-art methods by 6.98% and 1.05%, respectively.
A new secret handshake scheme with multi-symptom intersection for mobile healthcare social networks

With the aggravating trend of aging population in modern societies, Mobile Healthcare Social Network (MHSN) is an attracting platform for improving the life quality of elderly patients and offering mutual supports. In a sustainable MHSN system, one essential issue is to protect sensitive healthcare information while providing information-sharing for patients with multiple symptoms. To achieve multi-symptom matching and cross-domain communications for patients in MHSNs, we propose a new Secret Handshake scheme with Multi-Symptom intersection (MSSH), which is derived from a linear-complexity Authorized Private Set Intersection (APSI) based on Schnorr signature. In MSSH, two anonymous patients, who have registered with independent healthcare centers, are able to successfully execute an affiliation-hiding mutual anonymous authentication only if their target authentication policies are satisfied. The authentication policy demands that the designated healthcare center should be matched and the cardinality of their symptoms intersection is not less than a threshold value. Meanwhile, the symptoms outside of the set intersection remain confidential. The formal analysis proves MSSH is secure under the Random Oracle Model (ROM). The experimental results demonstrate that MSSH is efficient and feasible in MHSNs.
Combining synthesis sparse with analysis sparse for single image super-resolution

Sparse coding based-methods show great effectiveness in single image super-resolution (SR). Existing methods generally use only synthesis sparse coding. However, the analysis sparse coding model, which is an alternative to the synthesis sparse coding model, is typically neglected In this paper, we propose a novel single image SR method by combining synthesis sparse coding with analysis sparse coding. In contrast to the existing sparse coding-based SR methods, we replace synthesis sparse coding with analysis sparse coding in the low-resolution (LR) feature representation phase. Thus, we use an analysis dictionary to obtain the LR coefficients and use a synthesis dictionary to reconstruct the high-resolution (HR) patches. Due to the introducing of analysis sparse representation, the l0 or l1 optimization is replaced by soft threshold shrinkage, which is a more time-saving method. To improve the convergence of the training model, we introduce a linear mapping function that reveals the relationship between the HR coefficients and LR coefficients. An alternating optimization strategy is adopted to solve the improved training model. Furthermore, global and nonlocal constraints are taken into account to improve the quality of the reconstructed images. Compared with some existing SR methods, the experimental results demonstrate that our proposed method can obtain better performance.
An Under-Sampling Method with Support Vectors in Multi-class Imbalanced Data Classification

Multi-class imbalanced data classification in supervised learning is one of the most challenging research issues in machine learning for data mining applications. Although several data sampling methods have been introduced by computational intelligence researchers in the past decades for handling imbalanced data, still learning from imbalanced data is a challenging task and played as a significant focused research interest as well. Traditional machine learning algorithms usually biased to the majority class instances whereas ignored the minority class instances. As a result, ignoring minority class instances may affect the prediction accuracy of classifiers. Generally, under-sampling and over-sampling methods are commonly used in single model classifiers or ensemble learning for dealing with imbalanced data. In this paper, we have introduced an under-sampling method with support vectors for classifying imbalanced data. The proposed approach selects the most informative majority class instances based on the support vectors that help to engender decision boundary. We have tested the performance of the proposed method with single classifiers (C4.5 Decision Tree classifier and naïve Bayes classifier) and ensemble classifiers (Random Forest and AdaBoost) on 13 benchmark imbalanced datasets. It is explicitly shown by the experimental result that the proposed method produces high accuracy when classifying both the minority and majority class instances compared to other existing methods.
Classification of batik patterns using K-Nearest neighbor and support vector machine

This study is proposed to compare which are the better method to classify Batik image between K-Nearest neighbor and Support Vector Machine using minimum features of GLCM. The proposed steps are started by converting image to grayscale and extracting colour feature using four features of GLCM. The features include Energy, Entropy, Contras, Correlation and 0o, 45o, 90o, and 135o. The classifier features consist of 16 features in total. In the experimental result, there exist comparison of previous works regarding the classification KNN and SVM using multi texton histogram (MTH). The experiments are carried out in the form of calculation of accuracy with data sharing and cross-validation scenario. From the test results, the average accuracy for KNN is 78.3% and 92.3% for SVM in the cross-validation scenario. The scenario for the highest accuracy of data sharing is at 70% for KNN and at 100% for SVM. Thus, it is apparent that the application of the GLCM and SVM method for extracting and classifying batik motifs has been effective and better than previous work.
Assessing Post-hoc Explainability of the BKT Algorithm

As machine intelligence is increasingly incorporated into educational technologies, it becomes imperative for instructors and students to understand the potential flaws of the algorithms on which their systems rely. This paper describes the design and implementation of an interactive post-hoc explanation of the Bayesian Knowledge Tracing algorithm which is implemented in learning analytics systems used across the United States. After a user-centered design process to smooth out interaction design difficulties, we ran a controlled experiment to evaluate whether the interactive or static version of the explainable led to increased learning. Our results reveal that learning about an algorithm through an explainable depends on users' educational background. For other contexts, designers of post-hoc explainables must consider their users' educational background to best determine how to empower more informed decision-making with AI-enhanced systems.
A Semi-supervised method for tumor segmentation in mammogram images

Breast cancer is one of the most common cancers in women. Mammogram images have an important role in the treatment of various states of this cancer. In recent years, machine learning methods have been widely used for tumor segmentation in mammogram images. Pixel-based segmentation methods have been presented using both supervised and unsupervised learning approaches. Supervised learning methods are usually fast and accurate, but they usually use a large number of labeled data. Besides, providing these samples is very hard and usually expensive. Unsupervised learning methods do not require the labels of the training data for decision making and they completely ignore the prior knowledge that may lead to a low performance. Semi-supervised learning methods which use a small number of labeled data solve the problem of providing the high number of samples in supervised methods, while they usually result in a higher accuracy in comparison to the unsupervised methods.In this study, we used a semisupervised method for tumor segmentation in which the pixel information is used for the classification. The static and gray level run length matrix features for each pixel are considered as the features, and Fisher discriminant analysis (FDA) is used for feature reduction. A cotraining algorithm based on support vector machine and Bayes classifiers is proposed for tumor segmentation on MIAS data set.The results show that the proposed method outperforms both supervised methods.
Future InBodied: A Framework for Inbodied Interaction Design

Inbodied interaction is an emerging area in HCI that aligns how the body performs internally with our designs to support and optimise human performance. Inbodied Interaction therefore relies on knowledge of our physiology/neurology/kinesiology etc, to blend with HCI methodology. Recent, Inbodied Interaction workshops and summer schools, have been designed to share models of these processes to accelerate access to these areas of specialisation for HCI researchers. As such this one-day-hands-on-studio presents an extension of this work - an Inbodied interaction framework - to (1) make inbodied sciences accessible and (2) usable for HCI practitioners when it comes to crafting experiences, whether for health, performance or play. Our framework also offers a design alternative to cyborging futures that seek to augment human performance, Inbodied Interaction seeks to help discover and optimise human potential. As such, in this studio, we will explore where inbodied interaction fits in the narrative of our future bodies.
Implement Time Based One Time Password and Secure Hash Algorithm 1 for Security of Website Login Authentication

The use of information systems is a solutions to support the operations of the institution. In order to access information systems in accordance with their access rights, usually the user will enter a username and password as the authentication process. However, this has a weakness if the other side is cheating by sniffing or tapping user passwords. This makes the password unsafe to use for access information systems. If the username and password if it is stolen, abuse will occur for the crime or theft of the owner's identity accounts like name, email, telephone number, biological mother's name, account number and others. One solution is to apply two factor authentication method which is Time-Based One Time Password (TOTP) and Secure Algorithm Hash Algorithm 1 (SHA1). With this method, the system Authentication of a website or site does not only depend on the username and password to enter the account user but the user will get a token or code which is used to log in to the user's account. After testing hundred times, the authentication process who use Two Factor Authentication can tackle possible attacks on abuse o user access rights. Time Based Application One Time Password and Secure Hash Algorithm 1 Generate code that can't be the same because of the code it can only be used once with a time limit certain so it is difficult to guess. SHA1 with long input different strings will produce output with a fixed length string of 160 bits. Test results are obtained the results that 30 seconds is enough to prevent hackers log in and take over the account without permission and also prove that two-factor authentication can increase the security of the authentication process well. The time above is the result of testing the process user authentication until the hacker sniffing against tokens to try to take over the account.
Measuring the security culture in organizations: a systematic overview of existing tools

There has been an increase in research into the security culture in organizations in recent years. This growing interest has been accompanied by the development of tools to measure the level of security culture in order to identify potential threats and formulate solutions. This article provides a systematic overview of the existing tools. A total of 16 are identified, of which six are studied in detail. This exploration reveals that there is no validated and widely accepted tool that can be used in different sectors and organizations. The majority of the tools reviewed use only a quantitative method; however, security culture includes very different domains and therefore a mixed-method approach should be used. In contrast to security culture, instruments for measuring safety culture are widely available, and with many similarities between these two domains it is possible that well-established tools for measuring safety culture could be adapted to a security environment.
3D Morphable Face Model for Face Animation

This paper employs a new technology for modeling textured 3D faces. 3D faces can either be generated automatically from one or more photographs, or modeled directly through an intuitive user interface. Users are assisted in two key problems of computer-aided face modeling. It presents two algorithms for 3D face modeling from an image sequence. The first method works by creating an initial estimate using multiframe structure from motion (SfM) reconstruction framework, which is refined by comparing against a generic face model. The comparison is carried out using an energy-function optimization strategy. Results of 3D reconstruction algorithm are presented. The second method presented reconstructs a face model by adapting a generic model to contours of a face over all the frames of an image sequence. The algorithm for pose estimation and 3D face reconstruction relies solely on contours and the system does not require knowledge of rendering parameters (e.g. light direction and intensity). Results relying on finding accurate point correspondences across frames is presented.