Reviewerly/exHarmony huggingface dataset:
A 2D Evaluation of Altmetrics Influence in Citation Growth: Case Study of Indian Research Articles in PLoS Journals

The general approach of research impact evaluation is entirely based upon citations. But in the current Web2.0 era, this citation based evaluation process often do not include the generic discussions people make in social media, blogs, online scholarly discussion forums or even the online readership of the article. Altmetrics, often referred as the article level metrics is a tool that have emerged, which crowd source all the non-citation based discussions from various web2.0 platforms and tries to reflect the overhaul attention that an article gets. Since its introduction, altmetrics have been able to gather a good attention from the scientific community, and researchers are trying to evaluate if it does have scholarly value to complement the citation based impact measure process. The current study tries to evaluate altmetrics penetration in Indian research articles and the influence of altmetrics in the citation growth process. The problem was addressed taking the example of Indian research articles published in PLoS journals. Two hypothesis were set for the study and PLoS altmetrics data were taken to run the t-test for paired sample mean to test the hypothesis. Findings from the t-test estimated a low significant p value, hence rejecting the null hypothesis and accepting the alternate hypothesis proving the positive influence of altmetrics in the citation growth process for the Indian research articles in PLoS journals. Also the presence of altmetrics data in the Indian research articles of PLoS journals is evaluated in the study.
Structure Analysis and Generation of X.509 Digital Certificate Based on National Secret

The X.509 format certificate is a widely used digital certificate standard and is a series of data used to identify the identity information of parties to a communication. The design is based on the national secret algorithm SM2, SM3, replacing the ECDSA algorithm and the SHA-256 algorithm in the strongswan source code with the SM2 and SM3 algorithms respectively, Replace the ECDSA-SHA256 certificate with the SM2-SM3 certificate and use the X.509 certificate format for the encryption product.
Residual-Network-Based Supervised Gaze Prediction for First-Person Videos

Gaze prediction is a significant problem in efficiently processing and understanding a large number of incoming visual signals from first-person views (i.e., egocentric vision). Because many visual processes are expensive and human beings do not process the whole visual field, thus knowing the gaze position is an efficient way to understand the salient content of a video and what users pay attention to. However, current methods for gaze prediction are bottom-up methods and cannot incorporate information about user actions. We proposed a supervised gaze prediction framework based on a residual network, which takes the gaze of user action into consideration. Our model uses the features extracted from the VGG-16 deep neural network to predict the gaze position in FPV videos. The deep residual networks are introduced to combine with this model for learning the residual maps. Our proposed method attempts to obtain gaze prediction results with high accuracy. According to the experimental results, the performance of our proposed gaze prediction method is competitive with that of the state-of-the-art approaches.
Self-Similar Epochs: Value in Arrangement

Optimization of machine learning models is commonly performed through stochastic gradient updates on randomly ordered training examples. This practice means that sub-epochs comprise of independent random samples of the training data that may not preserve informative structure present in the full data. We hypothesize that the training can be more effective with {\em self-similar} arrangements that potentially allow each epoch to provide benefits of multiple ones. We study this for matrix factorization -- the common task of learning metric embeddings of entities such as queries, videos, or words from example pairwise associations. We construct arrangements that preserve the weighted Jaccard similarities of rows and columns and experimentally observe training acceleration of 3\%-37\% on synthetic and recommendation datasets. Principled arrangements of training examples emerge as a novel and potentially powerful enhancement to SGD that merits further exploration.
Dynamic Human-Centered Design:Reinventing Design Philosophies for Advanced Technologies

As technology becomes more advanced and saturated in various industries, the role of design becomes equally significant.Traditionally, human-centered design (HCD) has been the main creative approach for the design decisions in numerous applications.However, the role of HCD within the technology raises concerns.This paper examines the design philosophy of the HCD in parallel with rising technologies, specifically artificial intelligence and machine learning systems, and explores the implications of utilizing a more dynamic approach.With HCD, much of the considerations are determined through user research; the dynamic HCD approach is introduced to accommodate the different units of analysis presented by advanced technologies to create more streamlined designs that support and accelerate technological innovation.
Catenary image enhancement method based on Curvelet transform with adaptive enhancement function

In the process of catenary failure diagnosis system based on image processing technique, some catenary images present low contrast, which need to be enhanced.Curvelet transform has the high directional sensitivity and anisotropy, which is suitable for image enhancement because of its optimal sparse representation of image with rich details and edges.First, the catenary image is decomposed by Curvelet transform to get its high and low frequency coefficients, then adjust the high frequency coefficients using the enhancement function.Afterwards, combine the high frequency coefficients and low frequency coefficients by the inverse Curvelet transform, and thus to get the enhanced catenary image.In this paper, Curvelet transform is compared with the traditional enhancement methods.The experimental results show that the proposed method can effectively enhance the low contrast catenary images, the catenary insulator, arm, hanger, pillar and locator part become visible, the details become more obvious.Moreover, as for the online application of catenary failure diagnosis system, efficiency is another important consideration.The experimental results also show that the cost time of catenary image enhancement is within a few tens of seconds, which meets the requirements of catenary failure diagnosis system.
Visual QR Code with Beautication and Self-recovery Based on Data Hiding

For the problemCai, Huili of beautification and self-recoveryYan, Bin of visual QR (Quick Response) Pan, Jeng-Shyang code, this paper proposes two schemes. The first scheme uses data hiding and beautified QR code for self-recovery. By decoding this visual QR code, we can not only access the relevant links embedded in it but also restore the original image. However, the QR code may be maliciously tampered with by the attacker during transmission (e.g., redirected to a phishing website). For tampering protection and recovery of the QR code, the QR code is bound with the background image. This increases the security level when user scans a QR code. Experimental results demonstrate that the proposed algorithm is superior to the reference method in terms of visual quality.
Adaptive Arabic application for enhancing short-term memory of Dyslexic children

Dyslexia is defined according to the U.S. National Institute of Health as “a learning disability that can hinder a person's ability to read, write, spell, and sometimes speak”. In Kuwait, Dyslexia is considered to be one of the most common learning disabilities experienced and diagnosed in school age children. Worldwide research is being conducted to explore the effect of using technology as a positive intervening factor enabling the enhancement of the abilities of dyslexic children.  Several institutions are conducting research on how to enhance dyslexic abilities in the context of the Kuwaiti Arabic culture.  Kuwait Institute for Scientific Research (KISR) has developed an Arabic application called “Sinbad and Jasmin” in collaboration with the Kuwait Dyslexia Association (KDA).  In this paper, we will discuss the design, methodology and evaluation of the developed application, whose aim is to enhance the short-term memory of dyslexic children.  Analysis of results of the study conducted on selected dyslexic students from the KDA is presented.  An assessment of the long term enhancement effect of the developed application on dyslexic conditions will be conducted in the future.
A New Approach for Anonymizing Relational and Transaction Data

For scientific research and business decision purposes, the relational and transaction data often needs to be published. Because individuals’ privacy exists in this data, it needs to be appropriately anonymized before to be released. However, the privacy model (k, km)-anonymity cannot preserve the individual’s privacy with more than m items in transaction attribute. Also, it does not make the diversity constraint and cannot prevent attribute disclosure. Moreover, it considers all items be sensitive in the transaction attribute, which will cause serious information loss. In this paper, the (k, ρ)-anonymity model is proposed, which can solve the problem caused by (k, km)-anonymity. Then an anonymous approach is designed to achieve (k, ρ)-anonymity. Experiment results show that our approach is better than existing anonymous approach for publishing relational and transaction data in utility and security.
Ordinary Least Squares: the Adequacy of Linear Regression Solutions under Multicollinearity and without it

Ordinary Least Squares: the Adequacy of Linear Regression Solutions under Multicollinearity and without itThe article deals with the problem of economic adequacy of solving a linear regression problem by the OLS method.The study uses the following definition of adequacy: a linear regression solution is considered adequate if it not only has correct signs but also correctly reflects the relationship between coefficients of regression in the population.If in this case the coefficient of determination is greater than 0.8, the solution is considered economically adequate.As an indicator of adequacy of a linear regression problem solution it is proposed to use a 10 % level of the coefficient of variability (CV) of the regression coefficients.It is shown that OLS solutions may be not adequate to the solution in the population, although they may be physically correct (with correct signs) and statistically significant.The mentioned result is obtained by using the artificial data population (ADP) algorithm.The ADP allows generating data of any size with known regression coefficients in the whole population, which can be calculated with the aid of the OLS solution for a very large sample.The ADP algorithm makes it possible to change the regular component of the influence of the regressors on the response.Besides, the random changes of regressors in the ADP are divided into two parts.The first part is coherent to the response changes, but the second part is completely random (incoherent).This one allows changing the near-collinearity level of the data by changing the variance of the incoherent noise in regressors.Studies using ADP have shown that with a high probability the OLS solutions are physically incorrect if the sample sizes (n) are less than 23; physically correct but not adequate for 23 < n < 400; adequate for n > 400.Furthermore, it is noted that if the elimination of strongly correlated regressors is not economically justified but is rather a measure of lowering the value of the VIF-factor, the results may be far from the reality.In this regard, it is stated that the use of the MOLS eliminates the need to exclude strongly correlated regressors at all, since the accuracy of the MOLS solution increases with an increase in the VIF.
Facial Components-based Representation for Caricature Face Recognition

Caricature face recognition is an interesting but also difficult task due to the huge exaggeration between two different face modalities, photos, and caricatures.Therefore, we propose a new representation for recognition that is fused by the representation learned from photos, caricatures, and generated faces.Each generated face contains four main facial components.Photos, caricatures, and generated faces are sent to Photo-ResNet, Caricature-ResNet, and Generated-ResNet to learn specific representations.Then, the learned three representations are sent to a fully connected layer.We adopt Softmax loss and Center Loss for training, which can reduce the distance of intra-class.To test the performance of our proposed representation, we build a new dataset for caricature face recognition, which consists of 259 subjects, with 6490 caricatures and 8143 photos.The dataset we build is the biggest available caricature dataset.Several basic methods are used for caricature face recognition.To test the discrimination of our proposed representation, two more experiments are fulfilled, including searching photos according to the selected caricature (CTP) and searching caricatures according to the selected photo (PTC), and our proposed method performs better than other convolutional neural network (CNN)-based representations.
Towards a Full Integration of Physics and Math Concepts: The Path Full of Traps

He holds a bachelor degree in Physics Engineering and a doctoral degree in Education both from Tecnologico de Monterrey.Dr. de la Garza has been recognized by the government as a member of the Researchers' National System in Mexico (SNI).He also actively participates in the different initiatives of the University mainly those related to interdisciplinary approach of teaching and learning, looking to close the gap between how knowledge is created
An Image Encryption Method Using Henon Map and Josephus Traversal

Image security is a vital issue in multimedia communications. Chaos based image encryption has become very popular now a days due to the properties of chaotic systems such as unpredictability, ergodicity and sensitivity to initial conditions. This paper proposes a chaos based image encryption technique for gray scale images. Image pixels are scrambled using Josephus permutation sequence and Henon map is used in diffusion process. Encrypted image is obtained through permutation and diffusion process. Security analysis shows that the proposed encryption scheme can accomplish good encryption results.
Report-Sensitive Spot-checking in Peer Grading Systems

Peer grading systems make large courses more scalable, provide students with faster and more detailed feedback, and help students to learn by thinking critically about the work of others. A key obstacle to the broader adoption of peer grading systems is motivating students to provide accurate grades. The literature has explored many different approaches to incentivizing accurate grading (which we survey in detail), but the strongest incentive guarantees have been offered by mechanisms that compare peer grades to trusted TA grades with a fixed probability. In this work, we show that less TA work is required when these probabilities are allowed to depend on the grades that students report. We prove this result in a model with two possible grades, arbitrary numbers of agents, no requirement that students grade multiple assignments, arbitrary but homogeneous noisy observation of the ground truth which students can pay a fixed cost to observe, and the possibility of misreporting grades before or after observing this signal. We give necessary and sufficient conditions for our new mechanism's feasibility, prove its optimality under these assumptions, and characterize its improvement over the previous state of the art both analytically and empirically. Finally, we relax our homogeneity assumption, allowing each student and TA to observe the ground truth according to a different noise model.
Diversification in Wikipedia Enriched Advertisement Recommendation for Microblogs

Recommendation diversification, which is the action of suggesting dissimilar products, is an emerging and important issue in recommendation systems. Recent studies show that recommending diverse products increases customer satisfaction. This is valid for advertisement recommendation as well. In this study, we investigate the diversification performance of a new advertisement recommendation algorithm. In the algorithm, sentiment enhanced user profiling is further enhanced by using followee/follower property of microblogs. In advertisement matching, Wikipedia enrichment strategy is used. Evaluation on a set of real world data shows that this new algorithm has better diversification performance compared to generic keyword based matching strategy and a previous study that uses Wikipedia enrichment strategy.
Providing Security for E-Government Document Images Using Digital Watermarking in the Frequency Domain

Many countries around the world have realized the benefits of the e-government platform in peoples' daily life, and accordingly have already made partial implementations of the key e-government processes. However, before full implementation of all potential services can be made, governments demand the deployment of effective information security measures to ensure secrecy and privacy of their citizens. In this paper, a robust watermarking algorithm is proposed to provide copyright protection for e-government document images. The proposed algorithm utilizes two transforms: the Discrete Wavelet Transformation (DWT) and the Singular Value Decomposition (SVD). Experimental results demonstrate that the proposed e-government document images watermarking algorithm performs considerably well compared to existing relevant algorithms.
Adoption of Web 2.0 Applications for Education by Students in Nigeria

This study entailed the survey of the use of Web 2.0 for educational purposes by the students inNigeria. The sample of the study constituted of students from seven faculties and one centerwhich were purposively selected. The study used a questionnaire as its instrument of datacollection. Four hundred copies of the questionnaire were distributed among the sample, out ofwhich 380 were returned and found usable. Statistical Package for Social Sciences (SPSS) wasused to analyze the collected data. Statistical methods used for the analyses of the data werefrequency distribution, cross tabulation, T-test, One-way ANOVA and Regression.The study revealed that majority of the students of the University of Ibadan isalready familiar with the term “Web 2.0” and are using it for educational purposes. The studentsindicated that the Internet was their source of information about the term “Web 2.0”. Also,course mates/ friends, lecturers and newspapers/magazines were sources of information. Inexploring the group of students that use Web 2.0 for education purposes, it was discovered thatthe students in higher levels use Web 2.0 tools more than their counterparts in lower levels andmajority of these students are in IT-related departments like computer science, library, archivaland information studies.Keywords: Web 2.0, Education, Blogs, Nigeria
Modified SHA1: A Hashing Solution to Secure Web Applications through Login Authentication

The modified SHA1 algorithm has been developed by expanding its hash value up to 1280 bits from the original size of 160 bit. This was done by allocating 32 buffer registers for variables A, B, C and D at 5 bytes each. The expansion was done by generating 4 buffer registers in every round inside the compression function for 8 times. Findings revealed that the hash value of the modified algorithm was not cracked or hacked during the experiment and testing using powerful online cracking tool, bruteforce and rainbow table such as CrackingStation and Rainbow Crack and bruteforcer which are available online thus improved its security level compared to the original SHA1.
Development of a Trajectory Generation and Tracking Control System for a Quadrotor UAV

In this paper, we describe the development of a trajectory generation and trajectory tracking control system for a quadrotor UAV. The trajectory generation algorithm generates minimum snap trajectories which minimizes effort for actuation of motors, and the geometric control based trajectory tracking controller is utilized for tracking given trajectories precisely. The trajectory tracking controller generates a thrust command and angular rate commands for an inner-loop angular rate controller. To identify flight control performance, we developed a quadrotor platform and carried out flight experiments using circle and figure 8 trajectories. The flight experiment results show that the quadrotor UAV precisely follows the commanded trajectories in real flight environments.
Neutralizing SQL Injection Attack on Web Application Using Server Side Code Modification

SQL Injection attacks pose a very serious security threat to Web applications and web servers. They allow attackers to obtain unrestricted access to the databases underlying the applications and to the potentially sensitive and important information these databases contain. This research, “Neutralizing SQL Injection attack on web application using server side code modification” proposes a method for boosting web security by detecting SQL Injection attacks on web applications by modification on the server code so as to minimize vulnerability and mitigate fraudulent and malicious activities. This method has been implemented on a simple website with a database to register users with an admin that has control privileges. The server used is a local server and the server code was written with PHP as the back end. The front end was designed using MySQL. PHP server side scripting language was used to modify codes. ‘PDO prepare’ a tool to prepare parameters to be executed. The proposed method proved to be efficient in the context of its ability to prevent all types of SQL injection attacks. Acunetix was used to test the vulnerability of the code, and the code was implemented on a simple website with a simple database. Some popular SQL injection attack tools and web application security datasets have been used to validate the model. Unlike most approaches, the proposed method is quite simple to implement yet highly effective. The results obtained are promising with a high accuracy rate for detection of SQL injection attack.
Recognizing Hand Configurations of Brazilian Sign Language Using Convolutional Neural Networks

This paper proposes evaluating three convolutional neural network architectures for recognizing hand configurations of the Brazilian Sign Language (Libras). To improve the generalization of neural networks, two techniques were employed: dropout and L2 regularization. A proprietary database consisting of 12.200 depth images, captured with the Kinect® sensor was used. Two hundred images were captured for each one of 61 Hand Configurations (HC) of Libras. The training and testing subsets were compounded using an interleave technique. An accuracy of 98% was achieved. This value is better than previous results obtained, with the same dataset, using the k-Nearest Neighbor (kNN) and Novelty classifiers, 95.41% and 96.31%, respectively.
Montague Semantics for Lambek Pregroups

Distributional compositional (DisCo) models are functors that compute the meaning of a sentence from the meaning of its words. We show that DisCo models in the category of sets and relations correspond precisely to relational databases. As a consequence, we get complexity-theoretic reductions from semantics and entailment of a fragment of natural language to evaluation and containment of conjunctive queries, respectively. Finally, we define question answering as an NP-complete problem.