Reviewerly/exHarmony huggingface dataset:
Angular Deep Supervised Hashing for Image Retrieval

Deep learning based image hashing methods learn hash codes by using powerful feature extractors and nonlinear transformations to achieve highly efficient image retrieval. For most end-to-end deep hashing methods, the supervised learning process relies on pair-wise or triplet-wise information to provide an internal relationship of similarity data. However, the use of pair-wise and triplet loss function is limited not only by expensive training costs but also by quantization errors. In this paper, we propose a novel semantic learning based hashing method for image retrieval to optimize the deep features structure in the hash space from a perspective of angular view. Specifically, we proposed an angular hashing loss function that explicitly improve intra-class compactness and inter-class separability between features in hash space. Geometrically, angular hashing loss can be regarded as imposing hash constraints on hypersphere manifold. In order to solve the training problem on the multi-label case, we further designed a dynamic Softmax training strategy that can directly train the network using gradient descent method. Extensive experiments on two well-known datasets of CIFAR-10 and NUS-WIDE demonstrate that the proposed Angular Deep Supervised Hashing (ADSH) method can generate high-quality and compact binary codes, which can achieve state-of-the-art performance as compared with conventional image hashing and deep learning-based hashing methods.
Implementation of ANN for Predicting the Percentage of Illiteracy in Indonesia by Age Group

Abstract Illiteracy is one of the serious problems experienced by Indonesia. The lack of care from the government and the private sector towards illiterate people makes the illiteracy rate quite high. Because of this, this problem must be one of the government’s targets going forward, because directly or indirectly illiteracy plays a role in increasing the number of poverty in Indonesia. The purpose of this study is to predict the percentage of the illiterate population in Indonesia according to the age group 15-44 years because this age group category is a productive age. The results of this prediction are expected to be a reference and benchmark for the government in determining and making policies to reduce illiteracy rates. The data that will be predicted is the illiteracy rate data for each province in Indonesia sourced from the Indonesian Central Bureau of Statistics from 2011 to 2017. The method used for prediction is Backpropagation Artificial Neural Network. Data analysis and calculation were carried out with the help of Matlab and Microsoft Excel software. This study uses 5 architectures, 4-5-1, 4-6-1, 4-9-1, 4-14-1 and 4-18-1. Of the five models, the best network architecture is 4-14-1 with an accuracy rate of 91% and the Mean Squared Error 0.00274166. Using this 4-14-1 network model, a prediction on the percentage of illiteracy in Indonesia will be calculated for 2018 until 2020.
An Architecture for the Integration of Robots and Sensors for the Care of the Elderly in an Ambient Assisted Living Environment

In this paper, the development of an assistance system for the elderly that combines robots with a network of sensors and actuators is described. The architecture was developed with the aim of interaction. With this reason, the system incorporates particular solutions that are adaptable to the needs of the user, such as a pyramid interaction system, a telepresence robot, a biometric bracelet, and others. In the software, the system is composed of two servers: local and web. The local server is in charge of different modules that interact with direct users and their needs. The web server provides different facilities to external users to access the system remotely. A use case is analyzed where the proposed system is validated.
Extending association rules with graph patterns

We propose a general class of graph-pattern association rules (GPARs) for social network analysis. Extending association rules for itemsets, GPARs can help us discover associations among entities in social networks and identify potential customers. Despite the benefits, GPARs bring us challenges: the problem of GPARs discovery is already intractable, not to mention mining over large social networks. Nonetheless, we show that it is still feasible to discover GPARs from large social networks. We first formalize the GPARs mining problem and decompose it into two subproblems: Frequent pattern mining and rule generation. To address two subproblems, we develop a parallel algorithm along with an optimization strategy to construct DFS code graphs, whose nodes correspond to frequent patterns. We also provide efficient algorithms to generate (resp. representative) GPARs by using (resp. maximal) frequent patterns. Using real-life and synthetic graphs, we experimentally verify that our algorithms not only scale well but can also identify interesting GPARs with high quality among social entities.
Individualising Graphical Layouts with Predictive Visual Search Models

In domains where users are exposed to large variations in visuo-spatial features among designs, they often spend excess time searching for common elements (features) on an interface. This article contributes individualised predictive models of visual search, and a computational approach to restructure graphical layouts for an individual user such that features on a new, unvisited interface can be found quicker. It explores four technical principles inspired by the human visual system (HVS) to predict expected positions of features and create individualised layout templates: (I) the interface with highest frequency is chosen as the template; (II) the interface with highest predicted recall probability (serial position curve) is chosen as the template; (III) the most probable locations for features across interfaces are chosen (visual statistical learning) to generate the template; (IV) based on a generative cognitive model, the most likely visual search locations for features are chosen (visual sampling modelling) to generate the template. Given a history of previously seen interfaces, we restructure the spatial layout of a new (unseen) interface with the goal of making its features more easily findable. The four HVS principles are implemented in Familiariser, a web browser that automatically restructures webpage layouts based on the visual history of the user. Evaluation of Familiariser (using visual statistical learning) with users provides first evidence that our approach reduces visual search time by over 10%, and number of eye-gaze fixations by over 20%, during web browsing tasks.
Deep Learning-Based Sentimental Analysis for Large-Scale Imbalanced Twitter Data

Emotions detection in social media is very effective to measure the mood of people about a specific topic, news, or product. It has a wide range of applications, including identifying psychological conditions such as anxiety or depression in users. However, it is a challenging task to distinguish useful emotions’ features from a large corpus of text because emotions are subjective, with limited fuzzy boundaries that may be expressed in different terminologies and perceptions. To tackle this issue, this paper presents a hybrid approach of deep learning based on TensorFlow with Keras for emotions detection on a large scale of imbalanced tweets’ data. First, preprocessing steps are used to get useful features from raw tweets without noisy data. Second, the entropy weighting method is used to compute the importance of each feature. Third, class balancer is applied to balance each class. Fourth, Principal Component Analysis (PCA) is applied to transform high correlated features into normalized forms. Finally, the TensorFlow based deep learning with Keras algorithm is proposed to predict high-quality features for emotions classification. The proposed methodology is analyzed on a dataset of 1,600,000 tweets collected from the website ‘kaggle’. Comparison is made of the proposed approach with other state of the art techniques on different training ratios. It is proved that the proposed approach outperformed among other techniques.
Non-Malleable Commitments using Goldreich-Levin List Decoding

We give the first construction of three-round non-malleable commitments from the almost minimal assumption of injective one-way functions. Combined with the lower bound of Pass (TCC 2013), our result is almost the best possible w.r.t. standard polynomial-time hardness assumptions (at least w.r.t. black-box reductions). Our results rely on a novel technique which we call 'bidirectional Goldreich-Levin extraction'. Along the way, we also obtain the first rewind secure delayed-input witness indistinguishable (WI) proofs from only injective one-way functions. We also obtain the first construction of an epsilon-extractable commitment scheme from injective one-way functions. We believe both of these to be of independent interest. In particular, as a direct corollary of our rewind secure WI construction, we are able to obtain a construction of 3-round promise zero-knowledge from only injective one-way functions.
Explaining and exploring job recommendations

The dynamics of the labor market and the tasks with which jobs are being composed are continuously evolving. Job mobility is not evident, and providing effective recommendations in this context has also been found to be particularly challenging. In this paper, we present Labor Market Explorer, an interactive dashboard that enables job seekers to explore the labor market in a personalized way based on their skills and competences. Through a user-centered design process involving job seekers and job mediators, we developed this dashboard to enable job seekers to explore job recommendations and their required competencies, as well as how these competencies map to their profile. Evaluation results indicate the dashboard empowers job seekers to explore, understand, and find relevant vacancies, mostly independent of their background and age.
Neural caption generation over figures

Figures are human-friendly but difficult for computers to process automatically. In this work, we investigate the problem of figure captioning. The goal is to automatically generate a natural language description of a given figure. We create a new dataset for figure captioning, FigCAP. To achieve accurate generation of labels in figures, we propose the Label Maps Attention Model. Extensive experiments show that our method outperforms the baselines. A successful solution to this task allows figure content to be accessible to those with visual impairment by providing input to a text-to-speech system; and enables automatic parsing of vast repositories of documents where figures are pervasive.
Deep Transfer Learning Across Cancer Registries for Information Extraction from Pathology Reports

Automated text information extraction from cancer pathology reports is an active area of research to support national cancer surveillance. A well-known challenge is how to develop information extraction tools with robust performance across cancer registries. In this study we investigated whether transfer learning (TL) with a convolutional neural network (CNN) can facilitate cross-registry knowledge sharing. Specifically, we performed a series of experiments to determine whether a CNN trained with single-registry data is capable of transferring knowledge to another registry or whether developing a cross-registry knowledge database produces a more effective and generalizable model. Using data from two cancer registries and primary tumor site and topography as the information extraction task of interest, our study showed that TL results in 6.90% and 17.22% improvement of classification macro F-score over the baseline single-registry models. Detailed analysis illustrated that the observed improvement is evident in the low prevalence classes.
An Intra-Drift-Free Robust Watermarking Algorithm in High Efficiency Video Coding Compressed Domain

The existing watermarking algorithms in H.265/high efficiency video coding (HEVC) compressed domain cannot achieve intra-frame distortion drift-free and resist the recompression and signal processing attacks simultaneously. Therefore, the application of HEVC watermarking in copyright protection is limited in practice. To solve the problem, this paper proposes an intra-drift-free robust watermarking algorithm, which uses a multi-coefficients modification method to embed the watermark into intra prediction residual pixels of 4 × 4 luminance transform blocks in the spatial domain. Based on the spatial-domain embedding method, watermark information can be extracted from the blocks that have been attacked and merged into 8 × 8 size, which improves the robustness. To further improve the performance of the watermarking algorithm, the proposed algorithm uses a combination of three thresholds according to the application requirements. Experimental results on a publicly available video database (JCT-VC) have justified the effectiveness of the proposed algorithm which has better imperceptibility and robustness than existing algorithms. In addition, the proposed algorithm has high real-time performance and is suitable for application scenarios where the video is distributed on a large scale.
Classification of diffuse liver diseases based on ultrasound images with multimodal features

This paper describes a method of classification of diffuse liver diseases based on ultrasound images with multimodal features. The CNN network were used to extract image structure features, while Multi-scale Gray-level Co-occurrence Matrix (MGLCM) and Wavelet Multi-sub-bands Co-occurrence Matrix(WMCM) were used to extract image texture features. These two kinds of features were combined into multimodal features, then used as the input of a lightGBM classifier. 2942 liver ultrasound images have been classified using the proposed method. Classification accuracies of normal, fatty liver disease and liver fibrosis are 82.1%, 85.0%, and 80.9%, respectively. It can be seen from contrast experiments that the method proposed in this paper can improve the overall classification accuracy by 5.4%.
Reducing the Model Variance of a Rectal Cancer Segmentation Network

In preoperative imaging, the demarcation of rectal cancer with magnetic resonance images provides an important basis for cancer staging and treatment planning. Recently, deep learning has greatly improved the state-of-the-art method in automatic segmentation. However, limitations in data availability in the medical field can cause large variance and consequent overfitting to medical image segmentation networks. In this study, we propose methods to reduce the model variance of a rectal cancer segmentation network by adding a rectum segmentation task and performing data augmentation; the geometric correlation between the rectum and rectal cancer motivated the former approach. Moreover, we propose a method to perform a bias-variance analysis within an arbitrary region-of-interest (ROI) of a segmentation network, which we applied to assess the efficacy of our approaches in reducing model variance. As a result, adding a rectum segmentation task reduced the model variance of the rectal cancer segmentation network within tumor regions by a factor of 0.90; data augmentation further reduced the variance by a factor of 0.89. These approaches also reduced the training duration by a factor of 0.96 and a further factor of 0.78, respectively. Our approaches will improve the quality of rectal cancer staging by increasing the accuracy of its automatic demarcation and by providing rectum boundary information since rectal cancer staging requires the demarcation of both rectum and rectal cancer. Besides such clinical benefits, our method also enables segmentation networks to be assessed with bias-variance analysis within an arbitrary ROI, such as a cancerous region.
Fingerprint Surface-Based Detection of Web Bot Detectors

Web bots are used to automate client interactions with websites, which facilitates large-scale web measurements. However, websites may employ web bot detection. When they do, their response to a bot may differ from responses to regular browsers. The discrimination can result in deviating content, restriction of resources or even the exclusion of a bot from a website. This places strict restrictions upon studies: the more bot detection takes place, the more results must be manually verified to confirm the bot’s findings. To investigate the extent to which bot detection occurs, we reverse-analysed commercial bot detection. We found that in part, bot detection relies on the values of browser properties and the presence of certain objects in the browser’s DOM model. This part strongly resembles browser fingerprinting. We leveraged this for a generic approach to detect web bot detection: we identify what part of the browser fingerprint of a web bot uniquely identifies it as a web bot by contrasting its fingerprint with those of regular browsers. This leads to the fingerprint surface of a web bot. Any website accessing the fingerprint surface is then accessing a part unique to bots, and thus engaging in bot detection. We provide a characterisation of the fingerprint surface of 14 web bots. We show that the vast majority of these frameworks are uniquely identifiable through well-known fingerprinting techniques. We design a scanner to detect web bot detection based on the reverse analysis, augmented with the found fingerprint surfaces. In a scan of the Alexa Top 1 Million, we find that 12.8% of websites show indications of web bot detection.
Big data and prediction: Four case studies

Has the rise of data-intensive science, or 'big data', revolutionized our ability to predict? Does it imply a new priority for prediction over causal understanding, and a diminished role for theory and human experts? I examine four important cases where prediction is desirable: political elections, the weather, GDP, and the results of interventions suggested by economic experiments. These cases suggest caution. Although big data methods are indeed very useful sometimes, in this paper's cases they improve predictions either limitedly or not at all, and their prospects of doing so in the future are limited too.
DGStream: High quality and efficiency stream clustering algorithm

Recently as applications produce overwhelming data streams, the need for strategies to analyze and cluster streaming data becomes an urgent and a crucial research area for knowledge discovery. The main objective and the key aim of data stream clustering is to gain insights into incoming data. Recognizing all probable patterns in this boundless data which arrives at varying speeds and structure and evolves over time, is very important in this analysis process. The existing data stream clustering strategies so far, all suffer from different limitations, like the inability to find the arbitrary shaped clusters and handling outliers in addition to requiring some parameter information for data processing. For fast, accurate, efficient and effective handling for all these challenges, we proposed DGStream, a new online-offline grid and density-based stream clustering algorithm. We conducted many experiments and evaluated the performance of DGStream over different simulated databases and for different parameter settings where a wide variety of concept drifts, novelty, evolving data, number and size of clusters and outlier detection are considered. Our algorithm is suitable for applications where the interest lies in the most recent information like stock market, or if the analysis of existing information is required as well as cases where both the old and the recent information are all equally important. The experiments, over the synthetic and real datasets, show that our proposed algorithm outperforms the other algorithms in efficiency.
Research on Optimal Landing Trajectory Planning Method between an UAV and a Moving Vessel

The location, velocity, and flight path angle of an autonomous unmanned aerial vehicle (UAV) landing on a moving vessel are key factors for an optimal landing trajectory. To tackle this challenge, this paper proposes a method for calculating the optimal approach landing trajectory between an UAV and a small vessel. A numerical approach (iterative method) is used to calculate the optimal approach landing trajectory, and the initial lead is introduced in the calculation process of the UAV trajectory for the inclination and heading angle for accuracy improvement, so that the UAV can track and calculate the optimal landing trajectory with high precision. Compared with the variational method, the proposed method can calculate an optimal turning direction angle for the UAV during the landing. Simulation experiments verify the effectiveness of the proposed algorithm and give optimal initialization values.
Characterization and environmental impact assessment of gold mine tailings in arid regions: A case study of Barramiya gold mine area, Eastern Desert, Egypt

Geochemical and mineralogical characterization and environmental impact assessment were performed on Barramiya gold mine tailings, Eastern Desert, Egypt. The Barramiya gold mine is situated within ophiolite-decorated Nugrus suture zone, which composed of dismembered sequence of highly deformed and metamorphosed mafic to ultramafic rocks and their hydrothermal alteration products (carbonate rocks), interbedded with meta-volcano clastic rocks of the Nubian Shield. Morphometric parameters were quantified using ArcGIS 10.0 with Shuttle Radar Topography Mission (SRTM) maps. A total of four tailings samples were gathered and analysed by XRF, XRD, ICP-OES/MS and ESEM. Tailings are characterized by homogeneous silty fine sand, high carbonates in the form of dolomite and ankerite, and low sulfides content in the form of arsenopyrite and pyrite relics. High concentration averages of heavy elements are found for As (2936 mg/kg), Cr (237 mg/kg), Sr (219 mg/kg), Ni (150 mg/kg), V (131 mg/kg), Zn (64 mg/kg), Cu (51 mg/kg), Co (19 mg/kg) and Pb (12 mg/kg). They reflect mafic and ultramafic rocks, graphite schists, and quartz veins of the study area. Gypsum is present as secondary phase, identified in the shrinkage crack fillings. Morphometric drainage basin parameters for wadi Barramiya basin show rectangular to subdendritic drainage pattern with a moderate hazard degree. This study recommends designing multiple-layered dry cover, overlain by plant cover at uppermost part for treatment of the gold mine tailings; otherwise, wind and water erosion can easily remove all dissolved and unconsolidated material that absorb heavy elements and would create a potential risk to the surrounding ecosystem.
Cross-dataset deep transfer learning for activity recognition

Convolution Neural Network (CNN) filters learned on one domain can be used as feature extractors on another similar domain. Transferring filters allow reusing datasets across domains and reducing labelling costs. In this paper, four activity recognition datasets were analyzed to study the effects of transferring filters across the datasets. A spectro-temporal ResNet was implemented as a deep, end-to-end learning architecture. We analyzed the number of transferred CNN residual blocks with respect to the size of the target-adaptation data. The analysis showed that transfer learning using small adaptation subsets is more useful when the target domain contains a small number of different activities. Furthermore, the similarity between the domains, participating in the transfer learning scenario, seems to play a role in its success. The most successful transfer achieved an F1-score of 93%, which is an increase of 9 percentage points compared to a domain-specific baseline model.