Reviewerly/exHarmony huggingface dataset:
Implementation of Genetic Algorithm for School Monitoring System for Matiari District, Sindh, Pakistan

Background: This study focuses on Optimization field of computer science which works well when human beings are unable to solve the problem of finding an efficient path.Methods: The in-depth analysis of the problem has followed by the calculation of the school distances.The detailed route formation has been done so that the distances can be actualized in simulation-based optimization.The optimization is based on Genetic Algorithm and for this purpose a graphical user interface has been developed.Findings: The school monitoring teams were given an optimized path for their route to visit where they can easily save the time and the fuel.Results show that the genetic algorithm has produced an efficient and accurate path for the monitoring team so that the fuel and time can be saved.Improvements/Applications: The current study can be extended by comparing genetic algorithm output to any other optimization algorithms, so that the efficiency and time can be measured.
A Text Categorization Method Using Graph Convolutional Network based on Sparse Representation

Graph convolutional network is a graph-based deep learning approach, which is a significant variant of traditional convolutional neural networks. For text categorization, interdependent information among texts should be taken into consideration. In this paper, we use a graph to modeling the links between different documents, which can be represented by an adjacency matrix. Different from using the existing citation links, we utilize sparse representation to learn the intrinsic relationship from documents information so that more discriminative graph-structure can be learned to improve the accuracy rate of categorization. Then we present a semi-supervised learning approach for text categorization using graph convolutional network. Experiments on citation network show the effectiveness of the method.
Deep Learning Approach for Violence Detection in Urban Areas

Today modern cities tend to grow rapidly. The increased population density brings new challenges in term of public safety. Crime and violence are hard to be detected and managed especially in specific crowd environments like music concerts, sport events or public meetings. To overcome this issue the city administration should implement monitoring systems capable of detecting and analysing such situations. The work presented here combines two approaches that enable implementation of an efficient solution adapted for this purpose. The first one involves sensor networks that prove to be cost effective solution in a smart city environment. They can benefit on the existing surveillance infrastructure and allows rapid deployment. The second approach uses deep learning techniques. They demonstrate outstanding performances in image and actions classification based on a prior learning process. By combining these two approaches we succeed to obtain a real-time and cost-effective solution designed for urban area surveillance networks.
Usability testing of LUI: A new human‐computer interface for large displays

ABSTRACT A usability study was conducted on a new human‐computer web interface for large displays created by researchers from the Media Lab at MIT (Massachusetts Institute of Technology). Data was collected by observing 12 subjects who were asked interact with a large screen monitor using a set of gestures. The subjects were also asked to complete a survey after they participated. The purpose of the study was to discover new methods of how people interact with a large display using gestures. By understanding how people use this interface, novel applications that reflect user preferences can be designed. Our findings discovered usability and technology issues that were given to the creators of the web‐interface that was evaluated in this study. We also created usability testing protocol that will be used in future studies of this gesture‐based application.
A Matrix Factorization Recommendation Method Based on Multi-grained Cascade Forest

The traditional collaborative filtering method based on matrix factorization regards the users preference as the inner product of users and items implicit features and has limited learning ability. Many studies have focused on the use of deep neural networks to mine the interaction relationship between implicit features, but the learning cost of deep neural networks is too large, and the model lacks interpretability. Therefore, a matrix factorization recommendation method based on multi-grained cascade forest is proposed. Replacing inner product by multi-grained cascade forest with deep structures, rather than deep neural networks, and explore the interactive relationship between users and items. The method experiments on real-world data sets and performs well when comparing with the state of the art methods.
Ensemble-Based Feature Ranking for Semi-supervised Classification

In this paper, we propose three feature ranking scores (Symbolic, Genie3, and Random Forest) for the task of semi-supervised classification. In this task, there are only a few labeled examples in a dataset and many unlabeled. This is a highly relevant task, since it is increasingly easy to obtain unlabeled examples, while obtaining labeled examples is often an expensive and tedious task. Each of the proposed feature ranking scores can be computed by using any of three approaches to learning predictive clustering tree ensembles (bagging, random forests, and extra trees). We extensively evaluate the proposed scores on 8 benchmark datasets. The evaluation finds the most suitable ensemble method for each of the scores, shows that taking into account unlabeled examples improves the quality of a feature ranking, and demonstrates that the proposed feature ranking scores outperform a state-of-the-art semi-supervised feature ranking method SEFR. Finally, we identify the best performing pair of a feature ranking score and an ensemble method.
Unsupervised Deep Fusion Cross-modal Hashing

To handle the large-scale data in terms of storage and searching time, learning to hash becomes popular due to its efficiency and effectiveness in approximate cross-modal nearest neighbors searching. Most existing unsupervised cross-modal hashing methods, to shorten the semantic gap, try to simultaneously minimize the loss of intra-modal similarity and the loss of inter-modal similarity. However, these models can not guarantee in theory these two losses are simultaneously minimized. In this paper, we first theoretically proved that cross-modal hashing could be implemented by protecting both intra-modal and inter-modal similarity with the aid of variational inference technique and point out the problem that maximizing intra and inter-modal similarity is mutually constrained. In this case, we propose an unsupervised cross-modal hashing framework named as Unsupervised Deep Fusion Cross-modal Hashing (UDFCH) which leverages the data fusion to capture the underlying manifold across modalities to avoid above problem. What's more, in order to reduce the quantization loss, we sample hash codes from different Bernoulli distributions through a reparameterization trick. Our UDFCH framework has two stages. The first stage aims at mining the the intra-modal structure of each modality. The second stage aims to determine the modality-aware hash code by sufficiently considering the correlation and manifold structure among modalities. A series of experiments conducted on three benchmark datasets show that the proposed UDFCH framework outperforms the state-of-the-art methods on different cross-modal retrieval tasks.
Dynamic Modeling and Simulation for 2DOF Parallel Mechanism Solar Tracker

This paper aims to model the dynamic behavior of a 2 DOF parallel mechanism which can be used as dual axis solar tracker. Kinematic constraints are determined to account for the dynamic interaction of the parallel mechanism at joints. Then undetermined close form reaction forces at joints are obtained from the kinematic constraints. Finally, a fully determined equation of motion, which is a system of algebraic-differential equations, is obtained by appending kinematic and dynamic equations. For both kinematic and dynamic equations, Cartesian coordinates and Euler parameters are used to describe translation and rotation motions respectively. As a verification, the equation of motion for the mechanism is applied to the case of one body attached to the ground by a spherical joint of which the close form solution is known. The comparison shows good accuracy of numerical method. Having verified the solution with the case mentioned above, three cases of externally applied loads were selected to examine their effects on the orientations of the solar panel. Results showed that the solar panel changed its orientation as expected for each case study.
Diameter Detection of Silicon Single Crystal Based on Brain Storm Optimization Algorithm Hough Transform

In order to improve the speed and accuracy of silicon single crystal diameter detection, this paper proposes an ellipse detection method based on brain storm optimization algorithm Hough transform, this method uses brain storm optimization algorithm to search ellipse parameters that make the accumulator obtains the maximum value after using Hough transform, then the accurate measurement value of silicon single crystal diameter is obtained. The proposed algorithm is simulated on the complete ellipse with Gaussian white noise and silicon single crystal diameter detection engineering experiments, and the results are compared with other intelligent optimization algorithms. Experimental results show that the proposed algorithm can effectively improve the speed and accuracy of ellipse detection with Hough transform, thereby verify the feasibility and effectiveness of the proposed algorithm, and improve the speed and accuracy of silicon single crystal diameter detection.
A Hybrid Ant–Fuzzy Approach for Data Clustering in a Distributed Environment

Mining the relevant documents from distributed databases is a challenging task in the era of ‘big data’. This paper presents a hybrid approach using two different metaheuristics algorithms inspired by nature, namely, the enhanced ant clustering algorithm (EACA) and the fuzzy clustering algorithm, which deal with the uncertainty in data mining. The first algorithm uses the ant colony metaphor, which is one of the most recent nature-inspired metaheuristics, and the second employs the fuzzy clustering approach. The proposed work aims to introduce the fuzzy approach to the ant-based algorithm for both local and global levels, that is, interclustering and interzone clustering, in mining distributed databases, and a new hybrid (ant–fuzzy) algorithm is developed for data clustering under a distributed environment. The proposed cluster-based framework presents the important concepts of web mining and its various real-time applications. The real-time synthetic data and, also, the training datasets from the UCI Machine Learning Repository were employed to evaluate the performance of the algorithms. The performance of the proposed work is compared with the EACA, probabilistic ant-based clustering (PACE), and K-means algorithms in terms of accuracy with the F-measure and the error rate with entropy measure.
Effective 3D modeling method using indirect information of targets for SAR image prediction

A target database plays a key role in automatic target recognition (ATR) using SAR images. To construct a target database, indirect information of the targets such as plastic models, 2-dimensional draft and pictures of them can be used to make target 3-dimensional CAD models when real targets are not accessible. Each one of the indirect information is not enough to extract the accurate shape information of targets and they should be used together to make the 3D CAD models more accurately. We used 3D graphics models that are available on the internet, 2D drafts with size information and many pictures of targets at various aspects to reverse-engineer the 3D shape of the targets in detail. This paper proposes a practical target 3D CAD modeling method using the indirect information of a target and shows the results by comparing the 3D CAD models made with indirect information with the models generated by high precision laserscanning method. In addition to comparing the 3D shape itself, RCS calculation, ISAR imaging, scattering center extraction with electromagnetics (EM) analysis code were adopted to inspect how the difference of two models affects the scattering phenomena and SAR images as a result. The result shows that once we experience the building-comparingcorrecting process several times for the generation of accurate target CAD models, decent level of target CAD models can be made using indirect information only.
Joint Heterogeneous Pair-wise Loss For Top-N Recommendation

We propose a novel pairwise unified recommendation model (short for pairwise URM). The pairwise URM combines two pairwise ranking-oriented collaborative filtering approaches, namely Collaborative Less-is-More Filtering (CLiMF) and Bayesian Personal Ranking (BPR). By sharing common latent features of users and items in BPR and CLiMF, the pairwise URM can benefit from the two methods to improve recommendation qualities. The experimental evaluation is conducted on two real-world datasets with different scales and demonstrates the positive effect of the performance of the pairwise URM.
A method to calculate effective Hamiltonians in quantum information*

Effective Hamiltonian method is widely used in quantum information. We introduce a method to calculate effective Hamiltonians and give two examples in quantum information to demonstrate the method. We also give a relation between the effective Hamiltonian in the Shrödinger picture and the corresponding effective Hamiltonian in the interaction picture. Finally, we present a relation between our effective Hamiltonian method and the James–Jerke method which is currently used by many authors to calculate effective Hamiltonians in quantum information science.
An EAF guard driver to prevent shellcode from removing guard pages

Prevention of shellcode execution can be located at the last defense line against cyberattacks on vulnerabilities. Export address filtering (EAF), a security feature of Windows Defender Exploit Guard in Windows 10, prevents shellcode execution by guarding access to export address tables using guard pages. To prevent and raise awareness of a new bypass technique, we show a proof-of-concept of the new bypass technique that, by calling the NT!NtProtectVirtualMemory function, removes guard pages created by EAF. Windows shellcode with the proof-of-concept code is compatible with various versions of Windows 7 or later. To prevent the new bypass technique, we propose an EAF guard driver that prevents shellcode from removing guard pages. In tests, the driver prevented, without false alarms, the removal of the guard pages.
A Study on the Improvement for a Student’s Sleep-Phase with Wearable Devices

Abstract—In order to achieve the goal of more productive and more efficient education for university students, this study has presumed as the first step that maintaining students’ health consciousness to a certain level is the most important for their study. Since wristband-type wearable devices, one of IoT (Internet of Things) devices are getting popular on campus, this study focuses on one student’s sleep-phase that was far deviated from that of normal fourth grade students and how this problem was overcome by her own efforts. This student realized what was the most significant factor to influence her daily sleep-phase. With this awareness and her determination to spend more active daily life, she successfully shifted approximately four hours toward average starting time of sleep. This study shows how to realize their own daily life pattern by themselves and that making some effort to achieve a small success are quite important, and wristband-type wearable devices make it possible by visualizing sensed data in charts and values. This study also tried to relate the student’s sleep conditions and her concentration during a class with the use of a commercially available glasses-type wearable device, but this did not bring a clear significance in its multiple regression analysis.
Augmented Entanglement of Narrative Chronotopes and Urban Territories

The complex conditions of urban places render it difficult to identify and perceive their multivariate aesthetic characters. The question examined herein is in which ways digital media like Augmented Reality (AR) can facilitate a more comprehensive aesthetic appreciation of a place by individuals, enhance their overall experience and allow them to recognize the aesthetic distinctiveness of places that may be phenomenologically dense with aesthetics, memory, meaning, legibility. The framework proposed is founded on the inherent power of novels as chronotopes of potential dialogical experiences and on four characteristic strategies of AR. Narrative chronotope singularities are fundamental sources for understanding the collective, cultural, historical, social and spatial practices, leading to an understanding of urban environments. So the first step is to extract narrative chronotope analysis content from a novel’s urban substance (buildings, roads, squares), characters, plot and sequence of events. The second step involves a three-dimensional re-creation of urban heritage components. Finally, the AR media is interwoven with the novels based on four strategies: reinforcement of aspects of real-world urban places by digitally overlaying the novel’s setting; recontextualization to achieve the semantic transformation of places as the novel’s significance and meanings are revealed; remembrance by facilitating the emergence of diverse identities and memories; and re-embodiment through achieving a deeper understanding and re-interconnectedness with the aesthetic aspects of urban places. Augmented narrative descriptions restore harmony between body-mind-environment and fiction while ensuring that different times, places and psychological situations coincide. The proposed novel-based digitally-mediated interaction could provide a shift that leads to the embodiment, enhancement and re-conceptualization of the diverse aesthetic dimensions of constructs such as ‘heritage monuments’, ‘local community’, ‘public place’, etc. Article received: April 2, 2019; Article accepted: July 6, 2019; Published online: October 15, 2019; Review article
An Innovative Bifocal Metrology System based on projective techniques for Aerospace Applications

The Bifocal Metrology relates to an optical metrology system applicable to any spacecraft (S/C) which needs to track or point another S/C or when it is needed to measure the relative position/attitude, or to control the reciprocal position/attitude between two S/C in six degrees of freedom in a short, medium and far range by using only one sensor that covers fine and coarse accuracies. It is based on an innovative optical projective system embedded on one S/C and a target composed by three light sources (following isosceles triangle shape) mounted on the other one.
A Calibration Method of Anamorphic Lens Camera Based on Virtual 3D Target

For the application of large-field visual measurement, the paper analyses the mathematical model of anamorphic lens imaging and camera distortion model, proposes a camera calibration method based on anamorphic lens for large-field high-precision visual measurement. The method uses a circular spotting on the CMM (Coordinate Measuring Machine) as the target on the basis of the large field of view of the anamorphic lens. The circular spotting is moved precisely to the preset spatial positions by the control handle. Every time the target reaches a certain position in the set space, the camera will collect the image. The precise coordinate position is provided by the coordinate measuring machine which forms a virtual stereo target in space. The centroid coordinates of circular spot can be accurately extracted by using 2D Gauss surface fitting method. Calibration is divided into two steps, the first step is J. Heikkila calibration method based on 3D target. The second step is to take the camera parameters calculated in the first step as the initial value of global optimization, and introduce anamorphic distance, whose initial value is set to 0 mm. The camera parameters can be calibrated by using MATLAB global optimization function fminsearch. The experimental results show that the calibration standard pixel error of anamorphic image using J. Heikkila method is 2.1399 pixels. The calibration standard pixel error of an anamorphic image using the modified camera calibration model is 1.7361 pixels. According to the standard pixel error, the calibration accuracy has improved by 19%. It shows that the method is effective, and the new calibration model has greatly improved the calibration of anamorphic lens compared with the traditional camera model.
Linguistic Feature-Based Praise or Complaint Classification from Customer Reviews

Online reviews are very important in the customer’s decision-making process in selecting the appropriate products in the online shopping portal. These reviews are then analyzed by business organizations to understand customer sentiment w.r.t. product/service. Traditional sentiment analysis techniques identify only positive, negative or neutral sentiment w.r.t. reviews and does not consider informativeness of reviews while analyzing sentiment. The extreme opinions like Praise and complaint sentences are considered as a subset of positive and negative sentences and becomes difficult to find. Praise sentences are more descriptive in nature. Praises contain more nouns, adjectives, intensifiers as compared to plain positive sentences and complaint sentences contain more connectives and adverbs rather than the plain negative sentences. This paper proposes a Linguistic feature-based approach for review sentences filtering and Hybrid feature selection method for classifying review sentence as Praise or Complaint. These Praise and Complaint sentences can be further analyzed by business organizations to identify the reasons for customer satisfaction or dissatisfaction. It can also be used for creating automatic product description from online reviews in terms of pro and con of the product/service. The performance of the four different supervised Machine Learning classifiers, namely Random forest, SVC, KNeighbors, MLP with hybrid feature selection method is evaluated on three domains reviews using the parameters Accuracy, Precision, Recall, and F1-score. The proposed method showed excellent results as compared to the state of art classifiers.
Tailoring Motion Recognition Systems to Children’s Motions

Motion-based applications are becoming increasingly popular among children and require accurate motion recognition to ensure meaningful interactive experiences. However, motion recognizers are usually trained on adults’ motions. Children and adults differ in terms of their body proportions and development of their neuromuscular systems, so children and adults will likely perform motions differently. Hence, motion recognizers tailored to adults will likely perform poorly for children. My PhD thesis will focus on identifying features that characterize children’s and adults’ motions. This set of features will provide a model that can be used to understand children’s natural motion qualities and will serve as the first step in tailoring recognizers to children’s motions. This paper describes my past and ongoing work toward this end and outlines the next steps in my PhD work.