Reviewerly/exHarmony huggingface dataset:
A Complete Study of P.K.I. (PKI’s Known Incidents)

In this work, we report on a comprehensive analysis of PKI resulting from Certificate Authorities’ (CAs) behavior using over 1300 instances. We found several cases where CAs designed business models that favored the issuance of digital certificates over the guidelines of the CA Forum, root management programs, and other PKI requirements. Examining PKI from the perspective of business practices, we identify a taxonomy of failures and identify systemic vulnerabilities in the governance and practices in PKI. Notorious cases include the “backdating” of digital certificates, the issuance of these for MITM attempts, the lack of verification of a requester’s identity, and the unscrupulous issuance of rogue certificates. We performed a detailed study of 379 of these 1300 incidents. Using this sample, we developed a taxonomy of the different types of incidents and their causes. For each incident, we determined if the incident was disclosed by the problematic CA. We also noted the Root CA and the year of the incident. We identify the failures in terms of business practices, geography, and outcomes from CAs. We analyzed the role of Root Program Owners (RPOs) and differentiated their policies. We identified serial and chronic offenders in the PKI trusted root programs. Some of these were distrusted by RPOs, while others remain being trusted despite failures. We also identified cases where the concentration of power of RPOs was arguably a contributing factor in the incident. We identify these cases where there is a risk of concentration of power and the resulting conflict of interests. Our research is the first comprehensive academic study addressing all verified reported incidents. We approach this not from a machine learning or statistical perspective but, rather, we identify each reported public incident with a focus on identifying patterns of individual lapses. Here we also have a specific focus on the role of CAs and RPOs. Building on this study, we identify the issues in incentive structures that are contributors to the problems.
Evaluation of Crow Search Algorithm (CSA) for Optimization in Discrete Applications

The optimization algorithms are heuristic and metaheuristic. Heuristic algorithms discover the solution by using the trial and error method. On the other hand metaheuristic algorithms find the solution at higher level. There are many nature based metaheuristic algorithms are available. Crow search Algorithm is one of metaheuristic optimization algorithm introduced by Askarzadeh. CSA is inspired from the intelligent behavior of the crow. Crows are intelligent in storing the excess food at hiding places and recovering it whenever it is required. CSA's earlier results show that it has potential of solving various complex engineering-related optimization problems. There are 6 constrained engineering design problems, and CSA is used to optimize these problems. In this paper, A comprehensive study of CSA in different application is given with the analysis as well as the performances of the CSA in various application is discussed.
A non-myopic scheduling method of radar sensors for maneuvering target tracking and radiation control

In decades, the battlefield environment is becoming more and more complex with plenty of electronic equipments. Thus, in order to improve the survivability of radar sensors and satisfy the requirement of maneuvering target tracking with a low probability of intercept, a non-myopic scheduling is proposed to minimize the radiation cost with tracking accuracy constraint. At first, the scheduling problem is formulated as a partially observable Markov decision process (POMDP). Then the tracking accuracy and radiation cost over the future finite time horizon are predicted by the posterior carmér-rao lower bound (PCRLB) and the hidden Markov model filter, respectively. Finally, the proposed scheduling is implemented efficiently by utilizing the branch and bound (B&B) pruning algorithm. Simulation results show that the performance of maneuvering target tracking was improved by the improved interacting multiple model (IMM), and the scheduler time and maximum memory consumption were significant reduced by the present B&B pruning algorithm without losing the optimal solution.
Mining, analyzing, and modeling text written on mobile devices

Abstract We present a method for mining the web for text entered on mobile devices. Using searching, crawling, and parsing techniques, we locate text that can be reliably identified as originating from 300 mobile devices. This includes 341,000 sentences written on iPhones alone. Our data enables a richer understanding of how users type “in the wild” on their mobile devices. We compare text and error characteristics of different device types, such as touchscreen phones, phones with physical keyboards, and tablet computers. Using our mined data, we train language models and evaluate these models on mobile test data. A mixture model trained on our mined data, Twitter, blog, and forum data predicts mobile text better than baseline models. Using phone and smartwatch typing data from 135 users, we demonstrate our models improve the recognition accuracy and word predictions of a state-of-the-art touchscreen virtual keyboard decoder. Finally, we make our language models and mined dataset available to other researchers.
On Comparing and Selecting Approaches to Model Interval-Valued Data as Fuzzy Sets

The capture of interval-valued data is becoming an increasingly common approach in data collection (from survey based research to the collation of sensor data) as an efficient method of obtaining information about uncertainty associated with the data in question. To best utilise this data, several methods of aggregating intervals into fuzzy sets have been proposed in the fuzzy set literature, particularly within the field of Computing with Words. Two key examples are the Interval Approach and the Interval Agreement Approach and their respective extensions. Each method takes a fundamentally different approach to constructing fuzzy sets, making different assumptions in respect to the nature and the reliability of the data. The result is noticeably different fuzzy sets that do not share the same statistical properties (such as central-tendency and standard deviation). This begs the question of how these techniques differ in respect to the relationship between the original interval-valued data and the fuzzy sets produced - and thus when and why each of the methods is the most appropriate. This paper compares the results of both methods of constructing fuzzy sets from interval-valued data. Statistical moments of the fuzzy sets are compared against the interval-valued data to evaluate how well key properties of the fuzzy sets match those of the data; for example, does the standard deviation of the fuzzy set represent the standard deviation of the raw interval-valued data? We use comparisons on real-world data to demonstrate how the methods differ and which is more appropriate given the assumptions of the data.
Extreme Points Derived Confidence Map as a Cue for Class-Agnostic Interactive Segmentation Using Deep Neural Network

To automate the process of segmenting an anatomy of interest, we can learn a model from previously annotated data. The learning-based approach uses annotations to train a model that tries to emulate the expert labeling on a new data set. While tremendous progress has been made using such approaches, labeling of medical images remains a time-consuming and expensive task. In this paper, we evaluate the utility of extreme points in learning to segment. Specifically, we propose a novel approach to compute a confidence map from extreme points that quantitatively encodes the priors derived from extreme points. We use the confidence map as a cue to train a deep neural network based on ResNet-101 and PSP module to develop a class-agnostic segmentation model that outperforms state-of-the-art method that employs extreme points as a cue. Further, we evaluate a realistic use-case by using our model to generate training data for supervised learning (U-Net) and observed that U-Net performs comparably when trained with either the generated data or the ground truth data. These findings suggest that models trained using cues can be used to generate reliable training data. Our code is publicly available ( https://github.com/ahmedshahin9/AssistedAnnotator ).
Question Answering System for Legal Bar Examination Using Predicate Argument Structure

We developed a question answering system for legal bar exam, which can explain the way system solves based on underlying logical structures. We focus on the set of subject and object with their predicate, i.e. the predicate argument structure, in order to represent structures of legal documents. We implemented a couple of modules using different searching methods. Our system outputs results using these modules by learning each module's confidence value with SVM. We manually analyzed the difficulty level of the problems whether external knowledge is required or not. We created a structured synonym dictionary specialized to the legal domain, where predicates are categorized with their objects. This synonym dictionary could absorb superficial differences of predicates to solve the problems which do not require external knowledge. We confirmed that the system can solve more than 70% of simple problems. Our system achieved the second best score in Task 4 of the COLIEE 2018 shared task.
High-dimensional data clustering by using local affine/convex hulls

In this paper we propose a novel clustering algorithm that uses local affine/convex hulls for high-dimensional data clustering. In high-dimensional spaces, the sparse and irregular distributions make the nearest-neighbor distances unreliable (hole artifact), and this deteriorates the clustering performance. Therefore, there is a need to fill in these gaps between the nearest samples. To this end, we use local affine/convex hulls of the nearest neighbors of a given sample to fill in the holes, and this greatly improves the Euclidean distance metric and the clustering accuracy. The proposed method can also be seen as the local extension of the well-known iterative subspace clustering algorithms in which the entire cluster is approximated with a single linear/affine subspace. Experimental results show that the proposed method is efficient and it outperforms other subspace clustering algorithms on a wide range of datasets.
Efficient Properties-Based Learning for Mismatch Removal

Mismatch removal is a critical step of feature matching, which is a prerequisite of many visionbased tasks.This paper aims to develop a general and robust method for mismatch removal.To this end, we propose an efficient learning-based mismatch removal method that can significantly improve outlier identification in terms of both accuracy and efficiency.The key idea of our approach is to use a set of properties to describe the putative matches and feed the match representations to a supervised learning procedure learning a binary classifier for mismatch removal.The efficient properties mainly include three aspects: consistency of neighborhood elements, weighted consistency of neighborhood topology, and the stability of correspondence.Different from existing consistency of neighborhood topology, we adopt a weighted strategy to emphasize the effect of different properties with respect to the identified correspondence.The match representations combine the spatial positions of the correspondences with their descriptor reliabilities, which can effectively enlarge the distributions between outliers and inliers.To handle large proportions of outliers, we design a simple strategy to obtain a subset with high ratio inliers guiding the match representations construction process.This strategy can also boost the number of true correspondences without sacrificing the accuracy.Extensive experiments demonstrate our superiority over the state-of-the-art methods.
Opinion Mining in Consumers Food Choice and Quality Perception

In this work we present a system for the automatic analysis of text comments related to food products. Systematic analysis means allowing an analyst to have at a glance all the needed aggregated data and results that summarize the meaning of hundreds or thousands of comments, written in natural language. The analysis of the comments, and therefore the choices of the consumers, can therefore constitute a patrimony of very high value for the companies of the sector. At this aim we implemented a system, developed in Python. It uses the state of the art libraries of processing texts written in natural language, because the messages in natural language collected on the domain of food are written in Italian language.
Incorporating Singular Value Decomposition in User-based Collaborative Filtering Technique for a Movie Recommendation System

User-based collaborative filtering (UCF) technique is typically used to build a recommendation system (RS). A wide variety of techniques, such as matrix factorization, cosine similarity and Pearson correlation, have been proposed to improve the performance of the UCF algorithm in order to build more intelligent RSs. In this paper, we first describe the traditional UCF algorithm as the baseline; then we apply various techniques including singular value decomposition (SVD), cosine similarity, and Pearson correlation to examine and compare the performance of a small- scale movie RS. Our preliminary experimental results show that the UCF which used SVD and Pearson correlation performs better than a traditional UCF.
Multimodal Driver Interaction with Gesture, Gaze and Speech

The ever-growing research in computer vision has created new avenues for user interaction. Speech commands and gesture recognition are already being applied in various touch-based inputs. It is, therefore, foreseeable, that the use of multimodal input methods for user interaction is the next phase in development. In this paper, I propose a research plan of novel methods for the use of multimodal inputs for the semantic interpretation of human-computer interaction, specifically applied to a car driver. A fusion methodology has to be designed that adequately makes use of a recognized gesture (specifically finger pointing), eye gaze and head pose for the identification of reference objects, while using the semantics from speech for a natural interactive environment for the driver. The proposed plan includes different techniques based on artificial neural networks for the fusion of the camera-based modalities (gaze, head and gesture). It then combines features extracted from speech with the fusion algorithm to determine the intent of the driver.
Motion Eavesdropper: Smartwatch-based Handwriting Recognition Using Deep Learning

This paper focuses on the real-life scenario that people are handwriting while wearing small mobile devices on their wrists. We explore the possibility of eavesdropping privacy-related information based on motion signals. To achieve this, we elaborately develop a new deep learning-based motion sensing framework with four major components, i.e., recorder, signal preprocessor, feature extractor and handwriting recognizer. First, we integrate a series of simple yet effective signal processing techniques to purify the sensory data to reflect the kinetic property of a handwriting motion. Then we take advantage of properties of Multimodal Convolutional Neural Network (MCNN) to extract abstract features. After that, a bidirectional Long Short-Term Memory (BLSTM) network is exploited to model temporal dynamics. Finally, we incorporate Connectionist Temporal Classification (CTC) algorithm to realize end-to-end handwriting recognition. We prototype our design using a commercial off-the-shelf smartwatch and carry out extensive experiments. The encouraging results reveal that our system can robustly achieve an average accuracy of 64% at character-level and 71.9% at word-level, and 56.6% accuracy rate for words unseen in the training set under certain conditions, which expose the danger of privacy disclosure in daily lives.
Unseen Food Creation by Mixing Existing Food Images with Conditional StyleGAN

In recent years, thanks to the development of generative adversarial networks (GAN), it has become possible to generate food images. However, the quality is still low and it is difficult to generate appetizing and delicious-looking images. In the latest GAN study, StyleGAN enabled high-level feature separation and stochastic variation of generated images by unsupervised learning. However, to manipulate any style, it is necessary to understand the representation of the latent space and to input reference images. In this paper, we propose a conditional version of StyleGAN to control probabilistic fluctuations of disentangled features. The conditional style-based generator can manipulate the style of any domain by providing conditional vectors. By applying the conditional StyleGAN to the food image domain, we successfully have generated higher quality food images than before. In addition, introducing conditional vectors enabled us to control the food categories of generated images and to generate unseen foods by mixing multiple kinds of foods. In the experiment, to show the result of the proposed method explicitly, Food13 dataset is constructed and evaluated by both qualitative evaluation and quantitative evaluation.
TouchGazePath: Multimodal Interaction with Touch and Gaze Path for Secure Yet Efficient PIN Entry

We present TouchGazePath, a multimodal method for entering personal identification numbers (PINs). Using a touch-sensitive display showing a virtual keypad, the user initiates input with a touch at any location, glances with their eye gaze on the keys bearing the PIN numbers, then terminates input by lifting their finger. TouchGazePath is not susceptible to security attacks, such as shoulder surfing, thermal attacks, or smudge attacks. In a user study with 18 participants, TouchGazePath was compared with the traditional Touch-Only method and the multimodal Touch+Gaze method, the latter using eye gaze for targeting and touch for selection. The average time to enter a PIN with TouchGazePath was 3.3 s. This was not as fast as Touch-Only (as expected), but was about twice as fast the Touch+Gaze. TouchGazePath was also more accurate than Touch+Gaze. TouchGazePath had high user ratings as a secure PIN input method and was the preferred PIN input method for 11 of 18 participants.
CAVRN

The virtual reality ecosystem has gained momentum in the gaming, entertainment, and enterprise markets, but is hampered by limitations in concurrent user count, throughput, and accessibility to mass audiences. Based on our analysis of the current state of the virtual reality ecosystem and relevant aspects of traditional media, we propose a set of design hypotheses for practical and effective seated virtual reality experiences of scale. Said hypotheses manifest in the Collective Audience Virtual Reality Nexus (CAVRN), a framework and management system for large-scale (30+ user) virtual reality deployment in a theater-like physical setting. A mixed methodology study of CAVE, an experience implemented using CAVRN, generated rich insights into the proposed hypotheses. We discuss the implications of our findings on content design, audience representation, and audience interaction.
Greedy Learning of Deep Boltzmann Machine (GDBM)’s Variance and Search Algorithm for Efficient Image Retrieval

Despite extensive research on content-based image retrieval, challenges such as low accuracy, incapability to handle complex queries and high time consumption persist.Initially, a preprocessing technique is introduced in this study, a technique that uses a median filter to remove noise to achieve improved accuracy and reliability.Then, Fourier and circularity descriptors are extract in an effective manner correspondent to the texture and affine shape adaptation features.In addition, various descriptors, such as color histogram, color moment, color autocorrelogram and color coherency vector, are extracted as the invariant color features.The multiple ant colony optimization (MACOBTC) approach is implemented with whole features to find relevant features.Finally, the relevant features are utilized for the greedy learning of deep Boltzmann machine classifier (GDBM).The proposed approach obtains effective performance and accurate results on four datasets and is analyzed with various parameters such as accuracy, precision, recall, Jaccard, Dice, and Kappa coefficients.The GDBM provides a 25% increase in accuracy compared with existing techniques, such as the a priori classification algorithm.
Towards Attack and Defense Views to K-Anonymous Using Information Theory Approach

Ensuring multimedia content security is one of the main targets to solve multimedia information security problems and k-anonymous model is a classic and effective model to protect personal information when we publish multimedia data. The k-anonymous security with different k values has always been a hot topic of research, and there is no specific quantitative method for k-anonymous model security without considering any application scenarios or assumptions. In this paper, we give a quantitative method for the capability of attacker and defender under k-anonymous model. More specifically, we present a new general notion of limitation to provide a precise bound of attack and defense capability in a k-anonymous model. Firstly, we establish the attack channel and the defense channel from the point of the attacker's and the defender's view respectively. Moreover, we describe the capability of both attacking and defending sides by introducing the average mutual information, as well as the maximum value of the the average mutual information is the limitation of the capability of both sides, i.e. the channel capacity. Finally, according to the variation curve of the average mutual information amount, we compare the capability of the attacker and defender and analyze the effect of k value on it and get the best interval of k, that is, the value of k should be greater than 8 and less than 40.
Supervised non-parametric discretization based on Kernel density estimation

Nowadays, machine learning algorithms can be found in many applications where the classifiers play a key role. In this context, discretizing continuous attributes is a common step previous to classification tasks, the main goal being to retain as much discriminative information as possible. In this paper, we propose a supervised univariate non-parametric discretization algorithm which allows the use of a given supervised score criterion for selecting the best cut points. The candidate cut points are evaluated by computing the selected score value using kernel density estimation. The computational complexity of the proposed procedure is O(NlogN), where N is the length of the data. Our proposed algorithm generates a low complexity in discretization policies while retaining the discriminative information of the original continuous variables. In order to assess the validity of the proposed method, a set of real and artificial datasets has been used and the results show that the algorithm provides competitive results in terms of performance, a low complexity in the discretization policies and a high performance.