Reviewerly/exHarmony huggingface dataset:
Eye tracking and virtual reality

V irtual Reality has the potential to transform the way we work, rest and play.We are seeing use cases as diverse as education and pain management, with new applications being imagined every day.VR technology comes with new challenges, and many obstacles need to be overcome to ensure good user experience.Recently many new Virtual Reality systems with integrated eye tracking have become available.This course presents timely, relevant information on how Virtual Reality (VR) can leverage eye-tracking data to optimize the user experience and to alleviate usability issues surrounding many challenges in immersive VEs.The integration of eye tracking allows us to determine where the viewer is focusing their attention.If we, as the content creators and world builders, need the user to focus on another area of the VE we can use techniques to attract attention to these regions and also we can confirm we are doing so successful as we continually track the users gaze.Advancing these approaches could make the VR experience more comfortable, safe and effective for the user.
A New System-Wide Diversity Measure for Recommendations with Efficient Algorithms

Recommender systems often operate on item catalogs clustered by genres and user bases that have natural clusterings into user types by demographic or psychographic attributes. Prior work on system-wide diversity has mainly focused on defining intent-aware metrics among such categories and maximizing relevance of the resulting recommendations, but this work has not combined the notions of diversity from the two points of view of items and users. In this work, we do the following: (1) We introduce two new system-wide diversity metrics to simultaneously address the problems of diversifying the categories of items that each user sees, diversifying the types of users that each item is shown, and maintaining high recommendation quality. We model this as a subgraph selection problem on the bipartite graph of candidate recommendations between users and items. (2) In the case of disjoint item categories and user types, we show that the resulting problems can be solved exactly in polynomial time, by a reduction to a minimum cost flow problem. (3) In the case of nondisjoint categories and user types, we prove NP-completeness of the objective and present efficient approximation algorithms using the submodularity of the objective. (4) Finally, we validate the effectiveness of our algorithms on the MovieLens-1m dataset and show that algorithms designed for our objective also perform well on sales diversity metrics and even on some intent-aware diversity metrics. Our experimental results justify the validity of our new composite diversity metrics.
Multi Features Based Approach for White Blood Cells Segmentation and Classification in Peripheral Blood and Bone Marrow Images

In this paper, we propose a complete automated framework for white blood cells differential count in peripheral blood and bone marrow images, in order to reduce the analysis time and increase the accuracy of several blood disorders diagnosis. A new colour transformation is first proposed to highlight the white blood cells regions. Then, a marker controlled watershed algorithm is used to segment the region of interest. The nucleus and cytoplasm are subsequently separated. In the identification step, a set of colour, texture and morphological features are extracted from both nucleus and cytoplasm regions. Next, the performances of a random forest classifier on a set of microscopic images are compared and evaluated. The obtained results reveal high recognition accuracies for both segmentation and classification stage.
Human Head Pose and Eye State Based Driver Distraction Monitoring System

One of the major causes of road accidents is driver distraction. Driver distraction is diversion of attention away from activities critical for safe driving. Driver distraction can be categorized into drowsiness and inattentiveness. Drowsiness is a condition in which the driver feels sleepy, therefore cannot pay attention toward road. Inattentiveness is diversion of driver’s attention away from the road. Our system provides facility for monitoring driver’s activities continuously. The in-car camera is mounted to capture live video of driver. Viola–Jones algorithm is used to identify the driver’s non-front-facing frames from video. Inattentiveness is detected if the system identifies consecutive frames having non-frontal face. Drowsiness is identified by continuous monitoring of the eye status, which is either “open” or “closed” using horizontal mean intensity plot of eye region. Once the system detects the distraction, alert is generated in the form of audio. This will reduce the risk of falling asleep in long distance traveling during day and night time.
Context Style Explanation for Recommender Systems

Recommender systems support users by helping them choose items, and explanations for the recommendations further enhance such support. Previous explanation styles were based on information about users and items, such as the demographics of users and contents of items. Contexts, such as “usage scenarios” and “accompanying persons, ” have not been used for explanations, although they influence user's choice of items. In this paper, we propose a context style explanation method, presenting contexts suitable for consuming the recommended items. The expected impacts of context style explanations are as follows: 1) persuasiveness: recognition of a suitable context for usage motivates users to consume items, and 2) usefulness: envisioning a context helps users to make the right choices because the values of items depend on contexts. We evaluate the persuasiveness and usefulness of the context-style explanation by a crowdsourcing-based user study in a restaurant recommendation setting. The context style explanation is compared to the demographic and content style explanations. We also combine the context style and other explanation styles, confirming that hybrid styles improve the persuasiveness and usefulness of the explanation. Further, we investigate the personal preferences for explanation styles and reveal how gender and age relate to such preferences. The contributions of this paper are: the proposal of the novel context style explanation method, the demonstration of the persuasiveness and usefulness of the proposed method by a user study, and the findings of gender- and age-dependence of explanation style preferences.
Combining Part-of-Speech Tags and Self-Attention Mechanism for Simile Recognition

Simile recognition is to find simile sentences and extract the tenor and vehicle from these sentences. Previous works illustrate that tenors and vehicles are typically noun phrases. A word may have different part-of-speech (POS) labels (e.g., adjectives, adverbs, nouns, and verbs) in different sentences. It is important for the simile recognition task to identify a certain POS information for each word in a sentence. However, existing models use the same word embedding to represent a word, which cannot accurately represent the POS information of this word in different sentences. In this paper, we propose a neural network framework explicitly integrating the POS information into simile recognition task, with additional self-attention mechanism to better capture long term dependencies between any two tokens in sentences. The experimental results show that our proposed models significantly outperform previous state-of-the-art methods in the simile recognition task. We also present an analysis showing that the POS information and self-attention mechanism are effective for the simile recognition task.
MVC-3DC: Software architecture model for designing collaborative augmented reality and virtual reality systems

In this paper, software architecture model “MVC-3DC” for Collaborative Augmented and Virtual Reality Systems design is proposed. This model is the results of merging several aspects: Human-Computer Interaction (HCI), distribution systems, computer-supported cooperative work (CSCW) and new technologies such as augmented reality and virtual reality. MVC-3DC integrates collaboration principles between remote users. MVC-3DC allows a low dependency between components such as the core functions, 3D graphics API and data distribution modes. The proposed architectural model integrates simulation models, SDKs and algorithms for different nodes involved in a collaborative session. This facilitates interoperability and capability to manage heterogeneity and relationship between different nodes participating in the collaborative session. Finally, our model makes it possible to integrate other toolkits without completely changing the structure of collaboration model. A simple adaptation could be made.
Preparing for the Next Cyber Storm: Are You Ready?

On May 12, 2017, the WannaCry ransomware bug struck. The cyberattack infected more than 300,000 computer systems in 150 countries, including several U.K. National Health Service facilities.1The ransomware affected unpatched Windows systems, encrypting data and demanding payment to release it. A patch was available, and if it had been applied to these systems, they would not have been affected.
An Improved Single Shot Detector for Face Detection Using Local Binary Patterns

Face detection plays an important role in indoor surveillance systems. Although there have been many improvements since the 90s, many problems are still unsolved due to the complexity of background, complex computation, long time execution, illumination changes, etc. This paper presents a method to detect faces in a surveillance system under real-world indoor condition. The proposed method combines Background Subtraction Model, Feature-Analysis approach and Image-based approach to localize faces. Many experiments have been conducted with several indoor datasets to evaluate the performance and accuracy of the proposed methods. The result achieves a correct detection rate of 97.072% and processing speed of more than 30 frames per seconds on average.
Hybrid strategy for selecting compact set of clustering partitions

The selection of the most appropriate clustering algorithm is not a straightforward task, given that there is no clustering algorithm capable of determining the actual groups present in any dataset. A potential solution is to use different clustering algorithms to produce a set of partitions (solutions) and then select the best partition produced according to a specified validation measure; these measures are generally biased toward one or more clustering algorithms. Nevertheless, in several real cases, it is important to have more than one solution as the output. To address these problems, we present a hybrid partition selection algorithm, HSS, which accepts as input a set of base partitions potentially generated from clustering algorithms with different biases and aims, to return a reduced and yet diverse set of partitions (solutions). HSS comprises three steps: (i) the application of a multiobjective algorithm to a set of base partitions to generate a Pareto Front (PF) approximation; (ii) the division of the solutions from the PF approximation into a certain number of regions; and (iii) the selection of a solution per region by applying the Adjusted Rand Index. We compare the results of our algorithm with those of another selection strategy, ASA. Furthermore, we test HSS as a post-processing tool for two clustering algorithms based on multiobjective evolutionary computing: MOCK and MOCLE. The experiments revealed the effectiveness of HSS in selecting a reduced number of partitions while maintaining their quality.
Composite Sketch Recognition Using Multi-scale Hog Features and Semantic Attributes

Composite sketch recognition belongs to heterogeneous face recognition research, which is of great important in the field of criminal investigation. Because composite face sketch and photo belong to different modalities, robust representation of face feature cross different modalities is the key to recognition. Considering that composite sketch lacks texture details in some area, using texture features only may result in low recognition accuracy, this paper proposes a composite sketch recognition algorithm based on multi-scale Hog features and semantic attributes. Firstly, the global Hog features of the face and the local Hog features of each face component are extracted to represent the contour and detail features. Then the global and detail features are fused according to their importance at score level. Finally, semantic attributes are employed to reorder the matching results. The proposed algorithm is validated on PRIP-VSGC database and UoM-SGFS database, and achieves rank 10 identification accuracy of 88.6% and 96.7% respectively, which demonstrates that the proposed method outperforms other state-of-the-art methods.
Human Activity Recognition Using Smartphone Sensors: A Dense Neural Network Approach

Smartphones are now more and more advanced and the recent smartphone generation incorporates many varied and strong sensors like linear or tri-axial, linear or tri-axial gyroscopes, GPS, etc. The accessibility of this sensor in the mass communication systems provides fascinating possibilities in information mining and machine learning; in addition, the human activity recognition a.k.a. HAR generates a fresh study arena. Wireless sensor-based HAR has an important effect on outpatient surveillance illnesses, physical activity, elderly care, sports and physical therapy. In order to detect ten human actions, we want to walk, walking upstairs, walking downstairs, sitting, standing, lying, using toilet, jogging, writing and typing, positioning the smartphone on hand. We gathered information from both the male and female subject while performing the listed tasks from labeled tri-axial accelerometer and tri-axial gyroscope. In order to achieve necessary sensor information, we developed an Android application with a frequency of 1 Hz. Then we used a model known as DNN for the Dense Neural Network to predict the operations of information from gathered sensors and gyroscope. In addition to DNN for operating studies we have also practiced Support Vector Machines, Decision Tree, K-Nearest Neighbor, Random Forest, Naive Bayes and Logistical Regression. Our key objective was to examine a comparative analysis with statistical and profound classifiers on male and female subject. We also confirmed that information gathered at reduced frequencies can also perform well. We obtained maximum level of precision of 94.38% and 93.35% for male and female data sets only with DNN model respectively.
Harnessing Structures for Value-Based Planning and Reinforcement Learning

Value-based methods constitute a fundamental methodology in planning and deep reinforcement learning (RL). In this paper, we propose to exploit the underlying structures of the state-action value function, i.e., Q function, for both planning and deep RL. In particular, if the underlying system dynamics lead to some global structures of the Q function, one should be capable of inferring the function better by leveraging such structures. Specifically, we investigate the low-rank structure, which widely exists for big data matrices. We verify empirically the existence of low-rank Q functions in the context of control and deep RL tasks (Atari games). As our key contribution, by leveraging Matrix Estimation (ME) techniques, we propose a general framework to exploit the underlying low-rank structure in Q functions, leading to a more efficient planning procedure for classical control, and additionally, a simple scheme that can be applied to any value-based RL techniques to consistently achieve better performance on ''low-rank'' tasks. Extensive experiments on control tasks and Atari games confirm the efficacy of our approach.
Mixed-curvature Variational Autoencoders

It has been shown that using geometric spaces with non-zero curvature instead of plain Euclidean spaces with zero curvature improves performance on a range of Machine Learning tasks for learning representations. Recent work has leveraged these geometries to learn latent variable models like Variational Autoencoders (VAEs) in spherical and hyperbolic spaces with constant curvature. While these approaches work well on particular kinds of data that they were designed for e.g.~tree-like data for a hyperbolic VAE, there exists no generic approach unifying all three models. We develop a Mixed-curvature Variational Autoencoder, an efficient way to train a VAE whose latent space is a product of constant curvature Riemannian manifolds, where the per-component curvature can be learned. This generalizes the Euclidean VAE to curved latent spaces, as the model essentially reduces to the Euclidean VAE if curvatures of all latent space components go to 0.
Designing a Public Key Cryptosystem Based on Quasi-cyclic Subspace Subcodes of Reed-Solomon Codes

In this paper we introduce a code-based cryptosystem using quasi-cyclic generalized subspace subcodes of Generalized Reed-Solomon codes in order to reduce the public key size. In our scheme the underlying Generalized Reed-Solomon code is not secret, so the classical attacks such as square code or folding attacks have no more purpose against it. In addition one part of the security of this scheme is based on hard problems in coding theory like Equivalence Subcodes (ES) Problem. We propose some parameters to reach at least a security level of 128 and 192 bits. We make a public key size comparison with some well established code-based public key encryption schemes. We also see that for the 128 bits security level the key size of our proposals are often better than the code-based schemes in competition for NIST’s second round.
Random phase shifting shadow moiré using a one-dimensional minimizer

The introduction of a random phase-shifting technique into a shadow moiré system, where an equal and known (or unknown) phase step is used to demodulate the phase of interest, is beneficial for the improvement of measurement accuracy. However, in spite of recent advances in optical metrology phase-shifting techniques, simultaneously estimating unequal and unknown phase shifts from three random phase-shifting fringe patterns remains a significant challenge. This paper presents a one-dimensional minimizer-based technique to address this ill-posed problem of phase demodulation from random phase-shifting patterns. In this method, two new sets of connected fringe patterns, without background illumination, are constructed through normalizing the secondary fringe patterns. Then, a generalized phase-shifting algorithm is developed by utilizing the character of the modulation factor's standard deviation distribution. Both numerical simulations and optical experiments are performed to demonstrate the high accuracy and robustness of the proposed method.
Question Bank Similarity Searching System (QB3S) Using NLP and Information Retrieval Technique

Problem Based e-learning(PBeL) in bangla language is one of the most progressing areas of the use of ICT in education. Question Bank(QB) is the main component of any PBeL system. Searching similarity in the complex structure of QB is a challenging task in the development of PBeL system. We have been developed an efficient Question Bank Similarity Searching System(QB3S) to find similar questions, handle duplicate question and rank search result of a query input based on NLP and Information Retrieval techniques. QB3S has four modules: bangla documents processing, question structure analysis and clustered indexing by B+ tree , word-net construction and Information retrieval module. Lexical analysis, stemming by finite automata rules and stopwords removing have been used for bangla document processing. The most challenging procedures of QB3S were Analyzing the structure of data for clustered indexing in the sorted sequential file of the QB database with a B+ tree data structure and improved TF-IDF algorithm with weighted functionality. A Word-net has been used for handling synonyms. Vector Space Model(VSM) has been designed from the value of TF-IDF weighted matrix. By using cosine similarity product rule, we have been Calculated the similarity value between the query input and all mcq of DB from VSM. QB3S has been evaluated in some experimental dataset to find results by imposing different test cases. The accuracy of searching performance which has found to be satisfactory.
PENERAPAN ANALISIS LASSO DAN GROUP LASSO DALAM MENGIDENTIFIKASI FAKTOR-FAKTOR YANG BERHUBUNGAN DENGAN TUBERKULOSIS DI JAWA BARAT

Tuberculosis is the deadliest infectious disease in Indonesia, and West Java is a province with the largest number of tuberculosis cases in Indonesia. This research was conducted to identify variables and groups of variables that could explain the number of tuberculosis cases in West Java. The data used has many explanatory variables, and these variables form groups. LASSO and group LASSO analysis can be used for variables selection and handle data that has many explanatory variables, and group LASSO analysis can be used on data with grouped variables. The results of the LASSO analysis, variables that can explain the number of tuberculosis cases in West Java are the number of people with disabilities, the number of pharmacy staff, the number of malnourished people, the number of people working and the number of cities. According to the group LASSO analysis, the variables that can explain the number of tuberculosis cases in West Java are variables in the health and environmental groups. The government can focus on these factors if they want to reduce the number of tuberculosis cases in West Java.
Evolving an Optimal Decision Template for Combining Classifiers

In this paper, we aim to develop an effective combining algorithm for ensemble learning systems. The Decision Template method, one of the most popular combining algorithms for ensemble systems, does not perform well when working on certain datasets like those having imbalanced data. Moreover, point estimation by computing the average value on the outputs of base classifiers in the Decision Template method is sometimes not a good representation, especially for skewed datasets. Here we propose to search for an optimal decision template in the combining algorithm for a heterogeneous ensemble. To do this, we first generate the base classifier by training the pre-selected learning algorithms on the given training set. The meta-data of the training set is then generated via cross validation. Using the Artificial Bee Colony algorithm, we search for the optimal template that minimizes the empirical 0–1 loss function on the training set. The class label is assigned to the unlabeled sample based on the maximum of the similarity between the optimal decision template and the sample’s meta-data. Experiments conducted on the UCI datasets demonstrated the superiority of the proposed method over several benchmark algorithms.