Reviewerly/exHarmony huggingface dataset:
An efficient deep learning model for classification of thermal face images

Purpose The objective of this paper is to perform infrared (IR) face recognition efficiently with convolutional neural networks (CNNs). The proposed model in this paper has several advantages such as the automatic feature extraction using convolutional and pooling layers and the ability to distinguish between faces without visual details. Design/methodology/approach A model which comprises five convolutional layers in addition to five max-pooling layers is introduced for the recognition of IR faces. Findings The experimental results and analysis reveal high recognition rates of IR faces with the proposed model. Originality/value A designed CNN model is presented for IR face recognition. Both the feature extraction and classification tasks are incorporated into this model. The problems of low contrast and absence of details in IR images are overcome with the proposed model. The recognition accuracy reaches 100% in experiments on the Terravic Facial IR Database (TFIRDB).
Heterogeneous Community Question Answering via Social-Aware Multi-Modal Co-Attention Convolutional Matching

Nowadays, community-based question answering (CQA) systems are popular and have accumulated a large number of questions and answers provided by users. How to accurately match relevant answers for a given question is an essential function in CQA tasks. Recent effective methods utilize word-pair interactions between questions and answers for CQA matching. However, these approaches usually encode questions and answers independently and ignore the fact that they can complement and enhance each other to provide better representations and thus more implicit interactions can be captured. In addition, the visual information, social information and the variable-length problem are usually ignored by most existing approaches. In this paper, a Social-aware Multi-modal Co-attention Convolutional Matching method (SMCACM) is proposed, which models the multi-modal content and social context of questions and answers in a unified framework for CQA matching. A novel co-attention network is proposed to extract complementary information from questions and answers to enhance each other for obtaining better representations, through which our model can capture more implicit interactions between questions and answers. In addition to textual content, our model uses object detection techniques and a meta-path based heterogeneous social representation learning approach to take advantage of the visual content and social context in CQA systems, respectively. Finally, a pooling-based convolutional matching network is designed to infer the matching score based on the complemented questions and answers, which can accept variable-length answers as inputs without padding or cutting. Experimental results on two real-world datasets demonstrate the superior performance of SMCACM compared with other state-of-the-art algorithms.
A Systematic Approach to Map the Research Articles’ Sections to IMRAD

The amount of scientific publications is believed to get doubled every five-years. These publications are stored by citation indexes and digital libraries in the form of complete PDF or/and by extracting terms from these documents. This indexing behavior poses several challenges for the scientific community as well as for digital repositories in terms of handling the advanced requirements of a user. For instance, addressing queries like “Give me those papers that contain the term “Pagerank” in their result section” may not be answered unless the papers are indexed section-wise. This issue has been focused by researchers and international prestigious challenges by top venues in the world like Semantic Publishing Challenge in ESWC. One of the important metadata extraction from research papers is the section information such as IMRAD (Introduction, Methodology, Results, and Discussion). Researchers have presented different approaches to identify and map the section-headings to IMRAD sections. The existing studies have employed parameters like dictionary terms, the template of a paper, and in-text citation frequency to map section-headings onto logical sections. The critical analysis of state-of-the-art revealed that some immensely potential features have been ignored, which might result in accurate mapping. In this study, we propose a novel approach that employs new features along with previously well-known features to map sections-headings to IMRAD. The newly proposed features are: (1) variant of In-text Citation count (2) Figure counts, (3) Table counts, and (4) subheading implicit mapping. The employed data set contains 5000 research papers, collected from CiteSeer. The evaluation of the proposed approach and comparisons with state-of-the-art three approaches revealed an improvement of 18.96%, 21.77%, and 9.50% in average precision with Ding et al, Shahid et al, and Habib et al. respectively. This research has significant implications for citation indexes and digital libraries.
OCR Using Computer Vision and Machine Learning

Being a well-researched area, optical character recognition or OCR has seen many advancements. Many state-of-the-art algorithms have been developed that can be used for the purpose of OCR but extracting text from images containing tables while preserving the structure of the table still remains a challenging task. Here, we present an efficient and highly scalable parallel architecture to segment input images containing tabular data with and without borders into cells and reconstruct the tabular data while preserving the tabular format. The performance improvement thus made can be used to ease the tedious task of digitizing tabular data in bulk. The same architecture can be used for regular OCR applications to improve performance if the data is in huge quantities.
Towards a Framework for Measuring the Performance of a Security Operations Center Analyst

The past few years have seen several studies reporting on the role of a Security Operations Center (SOC) analyst and metrics for assessing the performance of analysts. However, research suggests that analysts are dissatisfied with existing metrics as they fail to take into consideration several aspects of their tasks. Existing works advocate for research into this area. A major challenge to devising adequate metrics is that the real work of analysts that needs to be taken into consideration to assess their holistic performance has not been fully discussed. Furthermore, at present, there is no agreement on what constitutes core analysts' functions. Analysts' overall performance in a SOC could be obtained if there is a common agreement on the core functions upon which their performance can be evaluated. In this paper, we propose a framework depicting the core functions of analysts and KPIs that can be used to measure the performance of analysts. To do this, we conducted a thorough analysis of the functions of a SOC described in multiple sources of literature and engaged with several analysts and SOC managers from different industries using qualitative semi-structured interviews. Our research results identify the following: quality of analysts' analysis, quality of analysts' report, time-based measures and the absolute numbers derived from an analyst's tasks as the key performance indicators (KPIs) for assessing analysts' performance. We hope that our findings will stimulate more interest among cybersecurity researchers on assessment methods for analysts.
Improving Multi-Histogram-Based Reversible Watermarking Using Optimized Features and Adaptive Clustering Number

For the multi-histogram-based reversible watermarking (MHRW) scheme, the performance greatly depends on the multi-histogram construction, which remains a challenge in this field. To generate more desirable multi-histograms, this paper improves the MHRW using the Fuzzy C-Means (FCM) clustering technique by developing the following approaches: 1)optimize the original feature set, 2)adopt an alternative FCM (AFCM) clustering method, and 3)determine adaptively the optimal clustering number for low embedding rates. These approaches are then integrated to bring about the proposed scheme, i.e., the improved MHRW (IMHRW). Extensive simulations show that the proposed scheme improves the performance of multi-histogram-based reversible watermarking, and it is comparable to or even better than the state of the arts. This thus demonstrates the feasibility and effectiveness of the proposed scheme.
Adaptive user interface design and analysis using emotion recognition through facial expressions and body posture from an RGB-D sensor

This work presents the design and analysis of an Adaptive User Interface (AUI) for a desktop application that uses a novel solution for the recognition of the emotional state of a user through both facial expressions and body posture from an RGB-D sensor. Six basic emotions are recognized through facial expressions in addition to the physiological state, which is recognized through the body posture. The facial expressions and body posture are acquired in real-time from a Kinect sensor. A scoring system is used to improve recognition by minimizing the confusion between the different emotions. The implemented solution achieves an accuracy rate of above 90%. The recognized emotion is then used to derive an Automatic AUI where the user can use speech commands to modify the User Interface (UI) automatically. A comprehensive user study is performed to compare the usability of an Automatic, Manual, and a Hybrid AUI. The AUIs are evaluated in terms of their efficiency, effectiveness, productivity, and error safety. Additionally, a comprehensive analysis is performed to evaluate the results from the viewpoint of different genders and age groups. Results show that the hybrid adaptation improves usability in terms of productivity and efficiency. Finally, a combination of both automatic and hybrid AUIs result in significantly positive user experience compared to the manual adaptation.
DeepECT: The Deep Embedded Cluster Tree

Abstract The idea of combining the high representational power of deep learning techniques with clustering methods has gained much attention in recent years. Optimizing a clustering objective and the dataset representation simultaneously has been shown to be advantageous over separately optimizing them. So far, however, all proposed methods have been using a flat clustering strategy, with the actual number of clusters known a priori. In this paper, we propose the Deep Embedded Cluster Tree (DeepECT), the first divisive hierarchical embedded clustering method. The cluster tree does not need to know the actual number of clusters during optimization. Instead, the level of detail to be analyzed can be chosen afterward and for each sub-tree separately. An optional data-augmentation-based extension allows DeepECT to ignore prior-known invariances of the dataset, such as affine transformations in image data. We evaluate and show the advantages of DeepECT in extensive experiments.
Graph Database and Relational Database Performance Comparison on a Transportation Network

Facing the problem of structuring irregular data in the big data era, graph databases are a powerful solution to handle link relationship without costly operations and enjoy great flexibility as data model changes. Though it's well-known that graph databases have superior performance in a certain area than relational databases, little effort has been put into investigating the detail of these advantages. In this paper, we report a systematic performance study of graph databases and relational databases on a transportation network. We design a database benchmark considering traversal and searching performance to evaluate system performance in different data organizations, initial states, and running modes. Our results show that graph databases outperform relational databases system in three main graph algorithms testing. Furthermore, we discuss the reasonable practice in applications based on graph databases from our experiment results.
Energy Efficient Elliptical Concave Visibility Graph Algorithm for Unmanned Aerial Vehicle in an Obstacle-rich Environment

This paper proposes a path planning algorithm for unmanned aerial vehicle (UAV) called Elliptical Concave Visibility Graph (ECoVG). The algorithm, which is based on visibility graph (VG), overcomes the limitations of VG computation time and hence, it can be applied in real-time and in obstacle-rich environments. An experimental investigation has been done to compare the performance between ECoVG and another VG based method namely Equilateral-Space Oriented VG (ESOVG) in terms of computational time and path length. The investigation was done in identical scenarios through simulation to show that the ECoVG has a better computation time than that of ESOVG for its efficient selection of a region in calculating the path. It is also found that the proposed algorithm is energy efficient and complete since it can find a path if one exists.
Insider Threat Detection Using Natural Language Processing and Personality Profiles

This work represents an interdisciplinary effort to proactively identify insider threats, using natural language processing and personality profiles. Profiles were developed for the relevant insider threat types using the five-factor model of personality and were used in a proof-of-concept detection system. The system employs a third-party cloud service that uses natural language processing to analyze personality profiles based on personal content. In the end, an assessment was made over the feasibility of the system using a public dataset.
Improvement of Radio Frequency Identification Security Using New Hybrid Advanced Encryption Standard Substitution Box by Chaotic Maps

Radio Frequency Identification (RFID) technology is widely utilized by businesses, organizations and wireless communication systems. RFID technology is secured using different ways of data encryption, e.g., Advanced Encryption Standard (AES). The Substitution Box (S-Box) is the core of AES. In this paper, a new algorithm is proposed to generate a modified S-Box with new keys, specifically a key and plaintext-dependent S-Box using an improved RC4 encryption algorithm with Logistic Chaotic Maps (LCM). The strength of the proposed S-Box is tested throughout the paper, and compared against the state-of-the-art S-Box implementations, namely, the static S-Box, dynamic S-box, KSA and PRGA S-Box, and RC4 S-Boxes with Henon chaotic maps. The comparison between the state-of-the-art S-Boxes and the proposed S-Box demonstrates that the use of the Logistic Chaotic Map increases the security of the S-Box and makes the differential and linear cryptography more sturdy. In particular, using the strict avalanche test, we demonstrate that the proposed S-Box improves the security by achieving a cipher text bit-flip ratio of 0.4765, which is closer to 0.5 (where half the bits are flipped), while maintaining a minimum elapsed time of 19 milliseconds for encryption and decryption.
Modeling of Distributed Ledgers: Challenges and Future Perspectives

Interest in applications based on distributed ledger technology (DLT) is on the rise, with corporations worldwide shifting from simply exploring DLT's potential to creating productive business cases. However, despite the existence of numerous DLT applications, developers are still lacking proper tools and instruments for evaluating system behavior (e.g., performance) of their applications on different distributed ledgers before deployment. Since the behavior of such applications is highly dependent on the characteristics of the distributed ledger they are built upon and changing the distributed ledger after deployment of an application is difficult, selecting the wrong ledger can have severe negative consequences. To address the issue, we conducted an extensive literature review to identify and synthesize published modeling and simulation approaches for distributed ledgers. Based on the results, this paper also presents a research agenda to improve modeling of the system behavior of distributed ledgers and to support the development of distributed ledgers and viable DLT applications. In doing so, we facilitate informed decision-making for suitable modeling or simulation approach, which helps application developers to identify a suitable distributed ledger before implementing an application. In addition, our work contributes to science, as we provide a comprehensive overview and analysis of extant simulation and modeling approaches in DLT that accumulates the current state-of-the-art in the rapidly growing DLT field.
Detection of speech playback attacks using robust harmonic trajectories

In this paper, a new feature set is proposed for use in a playback attack detector (PAD) aimed at safeguarding a passphrase and speaker-verified protected system that can be remotely accessed from an arbitrary location using an arbitrary telecommunication channel. The new feature set, termed VoicedTracks, is a time-frequency map of the most robust harmonic trajectories in an utterance and serves as an audio fingerprint that can uniquely identify an utterance despite a moderate amount of noise and channel distortion. Experimental results are obtained using a specially designed in-house database; the impact of various noise types and SNR levels is further investigated using a publicly available database. An analysis of playback scores across several combinations of telecommunication channel types, playback devices and additive noise demonstrates robustness of the feature set to channel distortion and additive noise, thus making it suitable for use in a copy-detection based PAD (cd-PAD) designed for applications such as telephone banking. Relative to other cd-PADs the proposed approach was better able to defend against playback attacks when telephone channels were involved. An analysis of its performance across the replay configurations used in the ASVspoof 2017 V2 evaluation set suggests that the proposed cd-PAD is highly effective in detecting those playback attacks that are most likely to spoof the speaker verification system.
Offline Hand Written Urdu Word Spotting Using Random Data Generation

Urdu word spotting is among the most challenging tasks in image processing and word spotting of hand written Urdu text is even more so. When it comes to handwritten Urdu documents, variation among the same words of various writers is significant. The orientation and style of the handwriting makes it really challenging for a word spotting system to correctly recognize the instances of the keyword. In this research, we tend to overcome this hurdle. We propose a system that takes a database of hand written Urdu text and generates random, yet, similar images to improve the classifier’s ability to recognize variations caused by difference in handwriting. For image generation, we used geometric transformations and variants of Generative Adversarial Network (GAN). For the word spotting process, Histogram of Oriented Gradients (HOG) features are extracted from ligature images and then used to train a Long Short-Term Memory (LSTM) network for the classification task. This is the first study that focuses on improving word spotting by generating arbitrary samples using GANs and its variants. The system achieved a promising recognition rate of 98.96% due to the sample generation using Cycle-GANs.
An Object Context Integrated Network for Joint Learning of Depth and Optical Flow

Supervised depth prediction and optical flow estimation have achieved promising performance due to the advanced deep network architectures. Since the ground truths are difficult to be collected, many recent works try to learn the depth and flow in an unsupervised manner. However, existing methods only use features from convolutional layers or a simple aggregation of multi-level features to predict the depth and flow maps, which is insufficient to exploit context information. In this paper, we attempt to exploit object contextual information and investigate the effect of the object context for joint learning of depth and optical flow. Specifically, we present a novel combination of object context and the framework of joint learning depth and optical flow. Our proposed network can exploit and integrate the object context for both tasks by aggregating the context according to pair-wise similarities. Furthermore, we adopt the existing spatial pyramid network (SPN) to estimate the depth and flow in a coarse-to-fine strategy effectively. Given temporally adjacent stereo pairs, our network can be trained end-to-end in an unsupervised manner and can predict the depth and flow maps simultaneously. We conduct experiments on two publicly available datasets, KITTI2012 and KITTI2015. Our proposed approach yields comparable performance on both depth and flow tasks, compared to the recent deep learning-based approaches. Experimental results demonstrate that exploiting object contextual information is useful and beneficial for depth and optical flow estimation.
Reasoning over Permissions Regions in Concurrent Separation Logic

We propose an extension of separation logic with fractional permissions, aimed at reasoning about concurrent programs that share arbitrary regions or data structures in memory. In existing formalisms, such reasoning typically either fails or is subject to stringent side conditions on formulas (notably precision) that significantly impair automation. We suggest two formal syntactic additions that collectively remove the need for such side conditions: first, the use of both “weak” and “strong” forms of separating conjunction, and second, the use of nominal labels from hybrid logic. We contend that our suggested alterations bring formal reasoning with fractional permissions in separation logic considerably closer to common pen-and-paper intuition, while imposing only a modest bureaucratic overhead.
Multilabel classification by exploiting data‐driven pair‐wise label dependence

Exploiting label dependence is a widely used approach to boost classification performance for multilabel classification problems. However, most of the traditional label dependence methods have high time complexity, especially when combined with deep neural networks (DNNs). Thus they usually can not be efficiently applied in large-scale data sets. Recent advances in large-scale multilabel classification widely developed pair-wise ranking and structure-driven methods, but label dependence was little exploited. In most of the structure-driven methods, binary relevance (BR) with multiple binary cross-entropy (BCE) loss functions, a simple but effective method, is still the prior solution incorporation with DNNs in large-scale data sets. In this paper, we propose a novel loss function called label dependent cross-entropy (LDCE), which directly introduces label dependence to BCE loss function by data-driven conditional probability. Combined with deep convolutional neural networks (DCNNs), LDCE introduces no extra parameters and induces very little extra computational complexity. Moreover, we develop its tiny variant with sparse label dependence and its learnable version for automatic learning pair-wise label dependence. Within the BR scheme, LDCE outperforms BCE on seven widely used benchmark datasets. We also perform two large-scale multilabel image classification tasks (VOC 2007 and ChestX-ray14) with DCNNs, and LDCE outperforms BCE and achieves comparable results to the state-of-the-art.