Reviewerly/exHarmony huggingface dataset:
Estimating transmitted wavefronts in a broad bandwidth based on Zernike coefficients

Existing technologies and methods for measuring transmitted wavefronts typically operate at only a few specific wavelengths. In this paper, we propose a new method for estimating the wavefront distortion of an optical transmission system in a broad bandwidth. We establish the relationship between the transmitted wavefront and wavelength, using Zernike fringe coefficients to represent the wavefront. From simulations of several different types of optical systems, we found that two formulas can be used to express Zernike-wavelength curves: the Conrady dispersion formula and a new formula that we have named the apochromatic characteristic formula. To reduce the influence of measurement errors on predictions, fitting method is biased in favor of simulation rather than experimental data. We illustrate the validity of this technique by reconstructing a wavefront transmitted at 671 nm using Zernike polynomials, demonstrating that the predicted wavefront is very similar to the wavefront measured experimentally. Using the Conrady formula, we illustrate that the wavefront of a monochromatic system can be predicted for any wavelength in a broad range, using data from three standard wavelengths. As Seidel coefficients correspond to Zernike coefficients, the relationship between optical aberration and wavelength detailed here can also be applied to areas such as optical design, optical computing and adaptive optics.
Selective Detection and Segmentation of Cervical Cells

Accurate detection and segmentation of cervical cells is often considered as a critical prerequisite of the prediction of dysplasia or cancer either by a pap smear or the lately developed liquid-based cytology (LBC). The computer-aided detection in microscope images can relieve the pathologists from strenuous manual labors with higher accuracy and efficiency. In the segmentation tasks of real-life clinical data, one challenging issue is the mis-identification of other cells, such as inflammatory cells, with similar appearance of nuclei in shape, size and texture. With a large distribution in the whole slide, even overlap up to 50% to 75% percentage of normal or abnormal cells, these cells are usually detected and segmented as nuclei. In this paper, compared with the typical three-catalogue segmentation methods of nuclei, cytoplasm and background proposed in the literature, we provide a discrimination between inflammatory cells and nuclei by adding a new catalogue. We present two novel convolutional neural networks (CNN), a deeply fine-tuned model and a trained from scratch model. The models enable us to sensitively detect and remove background noises such as mucus or red blood cells. We also profile a detailed performance comparison between these two methods, with the advantages of either network presented. The experiments are based on the sufficient clinical dataset we collected, and the results show the effectiveness of proposed approaches in selective cell detection and segmentation.
Group Emotion Recognition in Adverse Face Detection

Most techniques for group emotion recognition rely on detection on faces of people and then aggregating the facial information to interpret the group emotion of a given image. However, several faces in the image may be occluded, non-frontal, or indistinguishable, i.e., too many faces in a single image (crowd). This paper focuses on such cases and investigate alternate frameworks which does not involve face detection. The developed frameworks are applied on two datasets – EmotiC and Group Affect Database 3.0 and the results are shown to be competitive with face detection (MTCNN) based approaches.
Exploring the Design Space of Sankey Diagrams for the Food-Energy-Water Nexus

In this work, we define a set of design requirements relating to Sankey diagrams for supporting food-energy-water nexus understanding and propose the network embodied sectoral trajectory diagram design, a visualization design that incorporates a number of characteristics from Sankey diagrams, treemaps, and graphs, to improve the readability and minimize the negative impact of edge crossings that are common in traditional Sankey diagrams.
Adaptation Vectors for Instructional Agents

This paper examines the application of intelligent agents to guide and adapt instruction in a class of learning technologies known as adaptive instructional systems (AISs). AISs are artificially-intelligent, computer-based systems that guide learning experiences by tailoring instruction and recommendations based on the goals, needs, and preferences of each individual learner or team in the context of domain learning objectives. Intelligent agents are autonomous entities which observe the conditions in their environments through percepts (e.g., sensors) and then act upon the environment using actuators. Their activity is intelligently directed to strategies (plans for action) and tactics (actions executed by the AIS) which enhance the progress of the learner toward the achievement of assigned goals and objectives. To optimize agent and learner performance we examine a notional set of principles that is needed to guide adaptive instructional designers and system developers toward adaptation vectors that support both instructionally meaningful (effective) and doctrinally correct (relevant and tactically plausible) actions by the AIS. In conclusion, we explore the literature to understand the design of adaptation vectors and provide recommendations for future AIS research and standards development.
The Biharmonic Eigenface

Principal component analysis (PCA) is an elegant mechanism that reduces the dimensionality of a dataset to bring out patterns of interest in it. The preprocessing of facial images for efficient face recognition is considered to be one of the epitomes among PCA applications. In this paper, we introduce a novel modification to the method of PCA whereby we propose to utilise the inherent averaging ability of the discrete Biharmonic operator as a preprocessing step. We refer to this mechanism as the BiPCA. Interestingly, by applying the Biharmonic operator to images, we can generate new images of reduced size while keeping the inherent features in them intact. The resulting images of lower dimensionality can significantly reduce the computational complexities while preserving the features of interest. Here, we have chosen the standard face recognition as an example to demonstrate the capacity of our proposed BiPCA method. Experiments were carried out on three publicly available datasets, namely the ORL, Face95 and Face96. The results we have obtained demonstrate that the BiPCA outperforms the traditional PCA. In fact, our experiments do suggest that, when it comes to face recognition, the BiPCA method has at least 25% improvement in the average percentage error rate.
Hybrid CBIR method using statistical, DWT‐Entropy and POPMV‐based feature sets

Content-based image retrieval (CBIR) is an image retrieval technique that can retrieve images by matching its feature set values. This research focuses on a novel CBIR method namely hybrid CBIR method using statistical, Discrete Wavelet Transform (DWT)-Entropy and Peak-oriented Octal Pattern-derived Majority Voting (POPMV)-based feature sets (CBIR_SWPOPMV) to efficiently extract the relevant colour images from the colour image dataset. The doctrine of the proposed method is influenced by a novel texture descriptor namely POPMV which is an octal pattern based on the histogram peak information, to bring about a majority voting-based feature set and three histogram-based feature sets. Furthermore, to improve the retrieval accuracy, the DWT-based Entropy feature set and the statistical feature set are also included. Finally, the Euclidean distance-based matching process brings more favourable relevant images with respect to the query image. The proposed methodology is experimentally compared with the existing recent CBIR versions by using seven standard databases such as Corel-1k, USPTex, MIT-VisTex, KTH-TIPS, KTH-TIPS2a, KTH-TIPS2a, Colored Brodatz and a user-contributed database named DB_VEG.
SCADA Testbed for Vulnerability Assessments, Penetration Testing and Incident Forensics

Industrial control systems are critical assets as they interact with real-life aspects of our daily life. These systems often run 24/7 to control and monitor critical industrial and infrastructure processes. The demand to integrate them with the Internet has opened them up for cyber-attacks. The need for skilled expertise starting at the academic level in defending and investigating these critical assets is ever growing. In this paper, the authors design and deploy a Supervisory Control and Data Acquisition (SCADA) lab at Sam Houston State University (SHSU) with a limited budget. The lab is designed to stimulate a near-world industrial setting specifically for Industrial cyber-security research (penetration testing, vulnerability analysis and incident forensics) as an accompaniment to the digital forensics education curriculum at the University.
Medical Image Encryption with Integrity Using DNA and Chaotic Map

In current era the medical field is digitalizing. In digitalization, internet is playing the main role which is open source. Hence security is the major issue, particularly in medical field integrity, confidentiality and security are very significant issues. To provide this, a novel approach using SHA-256, Deoxyribonucleic Acid (DNA) cryptography and a chaotic map is proposed in this paper. The integrity for digital medical image is very important because slight modification also becomes very big problem. Diagnosing exact disease from slightly modified digital medical image is highly impossible. Hence in this paper we are mainly concentrating on integrity. For integrity purpose initially, the hash key is generated using SHA-256 and is hidden in the Least Significant Byte (LSB) of digitalized medical image. The image is encoded using DNA coding rules to provide security. The encoded DNA matrix pixels are shuffled and diffused using Chen's hyper chaotic map. The simulation results proved that the proposed method is resist against differential, exhaustive and statistical attacks.
Software Fault Proneness Prediction with Group Lasso Regression: On Factors that Affect Classification Performance

Machine learning algorithms have been used extensively for software fault proneness prediction. This paper presents the first application of Group Lasso Regression (G-Lasso) for software fault proneness classification and compares its performance to six widely used machine learning algorithms. Furthermore, we explore the effects of two factors on the prediction performance: the effect of imbalance treatment using the Synthetic Minority Over-sampling Technique (SMOTE), and the effect of datasets used in building the prediction models. Our experimental results are based on 22 datasets extracted from open source projects. The main findings include: (1) G-Lasso is robust to imbalanced data and significantly outperforms the other machine learning algorithms with respect to the Recall and G-Score, i.e., the harmonic mean of Recall and (1- False Positive Rate). (2) Even though SMOTE improved the performance of all learners, it did not have statistically significant effect on G-Lasso's Recall and G-Score. Random Forest was in the top performing group of learners for all performance metrics, while Naive Bayes performed the worst of all learners. (3) When using the same change metrics as features, the choice of the dataset had no effect on the performance of most learners, including G-Lasso. Naive Bayes was the most affected, especially when balanced datasets were used.
Affect-driven Learning Outcomes Prediction in Intelligent Tutoring Systems

Equipping an Intelligent Tutoring System (ITS) with the ability to interpret affective signals from students could potentially improve the learning experience of students by enabling the tutor to monitor the students' progress and provide timely interventions as well as present appropriate affective reactions via a virtual tutor. Most ITSs equipped with affect modeling capabilities attempt to predict the emotional state of users. However, the focus in this work is instead on trying to directly predict the learning outcomes of students from a stream of video capturing the students faces as they work on a set of math problems. Using facial features extracted from a video stream, we train classifiers to directly predict the success or failure of a student's attempt to answer a question while the student has just begun to work on the problem. In this work, we first introduce a novel dataset of student interactions with MathSpring, a popular ITS. We provide an exploratory analysis of the different problem outcome classes using typical facial action unit activations. We develop baseline models to predict the problem outcome labels of students solving math problems and discuss how early problem outcome labels can be forecasted and utilized to provide possible interventions.
Combinatorial Persistency Criteria for Multicut and Max-Cut

In combinatorial optimization, partial variable assignments are called persistent if they agree with some optimal solution. We propose persistency criteria for the multicut and max-cut problem as well as fast combinatorial routines to verify them. The criteria that we derive are based on mappings that improve feasible multicuts, respectively cuts. Our elementary criteria can be checked enumeratively. The more advanced ones rely on fast algorithms for upper and lower bounds for the respective cut problems and max-flow techniques for auxiliary min-cut problems. Our methods can be used as a preprocessing technique for reducing problem sizes or for computing partial optimality guarantees for solutions output by heuristic solvers. We show the efficacy of our methods on instances of both problems from computer vision, biomedical image analysis and statistical physics.
Zero-Knowledge and Identity-Based Authentication and Key Exchange for Internet of Things

The Internet of Things (IoT) is a dynamic and large-scale infrastructure of uniquely identifiable, potentially resource-constrained, and heterogenous things sensing and influencing their environment to provide services with or without direct human intervention. Moreover, the de facto method applied so far to provide communication confidentiality and integrity is cryptography. This requires an authentic key exchange between two communicating entities, which in turn needs a mutual authentication of the entities. In this paper, we introduce a novel approach supplying zero-knowledge and identity-based authentication with integrated key exchange while meeting the IoT challenges. Our approach is resistant to active man in the middle attacks and does not include any costly cryptographic operations. Also, it does not require any predistribution or pre-sharing with regard to authenticating entities. Additionally, our identity-based scheme allows that a thing can operate autonomously in a secure manner. Furthermore, this scheme provides application-independence without requiring additional components and procedures. After discussing our approach, this paper also presents our prototype implementation and its evaluation.
Design and Implementation of Programmable Multi-Parametric 4-Degrees of Freedom Seismic Waves Ground Motion Simulation IoT Platform

The early warning and disaster management agencies spend billions of dollars to counter and cater earthquakes but it has always been unique accident. In this work, a programmable four degrees of freedom electromechanical seismic wave events simulation platform design is being proposed to study and experiment seismic waves and earthquakes realization in form of ground motions. The platform can be programmed and interfaced through an IoT cloud-based Web application. The rig has been tested in the range of frequencies of extreme seismic waves from 0.1Hz to 178Hz and terrestrial inclinations from -5.000° to 5.000°, which is key contribution of this work. This would be an enabler for a variety of applications such as training self-balancing and calibrating seismic resistant designs and structures in addition to studying and testing seismic detection devices. Nevertheless, it serves as an adequate training colossus for machine learning algorithms and event management expert systems.
A Novel Ensemble Approach for Click-Through Rate Prediction Based on Factorization Machines and Gradient Boosting Decision Trees

Click-Through Rate (CTR) prediction is a significant technique in the field of computational advertising, its accuracy directly affects companies profits and user experience. Achieving great ability of generalization by learning complicated feature interactions behind user behaviors is critical in improving CTR for recommender systems. Factorization Machines (FM) is a hot recommender method for efficiently modeling features' second-order interactions. Nevertheless, FM cannot capture the nonlinear and complex modes implied in the real-world data while it models feature in a linear way and just uses the second-order feature interactions. In this paper, we propose a model named GFM, which is an ensemble learning of FM and Gradient Boosting Decision Trees (GBDT) for recommendations. We use FM to model linear features and second-order feature interactions and use GBDT to model the side information for transforming the raw features to cross-combined features. In addition, we import the attention mechanism to calculate users' latent attention on different features. To illustrate the performance of GFM, we conduct experiments on two real-world datasets, including a movie dataset and a music dataset, the results show that our model is effective in providing accurate recommendations.
Automated Diagnosis and Classification of Cervical Cancer from pap-smear Images

Globally, cervical cancer ranks as the fourth most prevalent cancer affecting women. However, cervical cancer can be treated if detected at an early stage. Pap-smear is a good tool for screening of cervical cancer but the manual analysis is error-prone, tedious and time-consuming. The objective of this study was to rule out these limitations by automating the process of cervical cancer classification from pap-smear images by using an enhanced fuzzy c-means algorithm. Simulated annealing coupled with a wrapper filter was used for feature selection. The evaluation results showed that our method outperforms many of previous algorithms in classification accuracy (99.35%), specificity (97.93%) and sensitivity (99.85%), when applied to the Herlev benchmark pap-smear dataset. The overall accuracy, sensitivity and specificity of the classifier on prepared pap-smear slides was 95.00%, 100% and 90.00% respectively. False Negative Rate (FNR), False Positive Rate (FPR) and classification error of 0.00%, 10.00% and 5.00% respectively were obtained. The major contribution of this tool in a cervical cancer screening workflow is that it reduces on the time required by the cytotechnician to screen very many pap-smears by eliminating the obvious normal ones, hence more time can be put on the suspicious slides. The proposed tool has the capability of analyzing 1-2 smears per minute as opposed to the 5-10 minutes per slide in the manual analysis.
Detection and Tracking of Text from Video Using MSER and SIFT

Text that looks in a scene or is explicitly added to video can offer an imperative additional basis of directory evidence as well as evidences for interpreting the video's arrangement and for classification. Computerized text mining from a number of stationary resources quickness up the progression in workplaces, libraries, banks and an assortment of further places. Text extraction can be completed expending a quantity of various methods provisional upon the necessity of system and exactness level. In this paper, we present and implemented two popular algorithms Maximally Stable Extremal Regions (MSER) and Scale Invariant Feature Transform (SIFT) for spotting and tracking text in digital video. We analyzed results with respect to accuracy of text detection and tracking from videos. Experimental results shows that SIFT are 80% more accurate than MSER in the process of detection and tracking for extraction of text from video. Drawbacks of these two algorithms are also identified. This research paper appearance the diverse alterations that can be made to present text mining procedures by means of applying deep learning based recurrent convolution neural networks (CNN) to rectify drawbacks of two popular proposed techniques. CNN have advantages like local spatial consistency in the input (often images), which permit them to have smaller amount weights as some parameters are shared. This process, taking the form of convolutions, makes them especially well-suited to extract relevant information at a low computational cost.
Universal Proxy Re-Encryption

We put forward the notion of universal proxy re-encryption (UPRE). A UPRE scheme enables a proxy to convert a ciphertext under a (delegator) public key of any existing public-key encryption (PKE) scheme into another ciphertext under a (delegatee) public key of any existing PKE scheme (possibly different from the delegator one). The proxy has a re-encryption key generated from the delegator's secret key and the delegatee public key. Thus UPRE generalizes proxy re-encryption by supporting arbitrary PKE schemes and allowing to convert ciphertexts into ones of possibly different PKE schemes. In this work, we provide syntax and definitions for both UPRE and a variant we call relaxed UPRE. The relaxed variant means that decryption algorithms for re-encrypted ciphertexts are slightly modified but still only use the original delegatee secret keys for decryption. construct a UPRE based on probabilistic indistinguishability obfuscation (PIO). It allows us to re-encrypt ciphertexts polynomially many times. construct relaxed UPRE from garbled circuits (GCs). We provide two variants of this construction, one which allows us to re-encrypt ciphertexts polynomially many times, and a second one which satisfies a stronger security requirement but only allows us to re-encrypt ciphertexts a constant number of times.
Understanding the Related-Key Security of Feistel Ciphers From a Provable Perspective

We initiate the provable related-key security treatment for models of practical Feistel ciphers. In detail, we consider Feistel networks with four whitening keys $w_{i}(k)$ , $i=0,1,2,3$ , and round functions of the form $f(\gamma _{j}(k)\oplus X)$ , where $k$ is the master key, $w_{i}$ and $\gamma _{j}$ are efficient transformations, and $f$ is a public ideal function or permutation accessible by the adversary. We investigate the key-schedule conditions that are sufficient for security against XOR-induced related-key attacks up to $2^{n/2}$ adversarial queries. When the key schedules are non-linear , we prove security for four rounds. When only affine key schedules are used, we prove security for six rounds. These also imply secure tweakable Feistel ciphers in the Random Oracle model. By shuffling the key schedules, our model unifies both the DES-like structure (known as Feistel-2 scheme in the cryptanalytic community, also known as key-alternating Feistel due to Lampe and Seurin) and the Lucifer-like model (previously analyzed by Guo and Lin). This allows us to derive concrete implications on these two (more common) models and helps understanding their related-key security difference.