Reviewerly/exHarmony huggingface dataset:
Partial face recognition

Face recognition is an ever-growing, challenging and interesting area for the real-time application, such as security, image enhancement or image data recording. A large number of the face recognition algorithm are introduced in the last several decades for all kind of purposes, Including recognizing the occluded faces also known as Partial Face Recognition. In this paper, the author covered all the major techniques and methods (PCA, LDA, SVM, ANN) used for recognition the partial faces lying in different scenarios such as poor illumination pose variations, occlusion on faces, etc. For the effective result, there are various face databases available. These databases are also mentioned in this paper for a better understanding of face image properties and condition. The paper also introduced some of the recent advancement in the partial Face recognition field by covering topics such as CNN and DEEP learning.
The Model-Driven Enterprise Data Fabric: A Proposal Based on Conceptual Modelling and Knowledge Graphs

Enterprise data is typically located in disparate legacy systems and heterogeneous sources. Researchers and business analysts identified the importance of integrating such data sources through a semantic data fabric to support the generation of valuable insights and consolidated views. Still, this objective is hard to attain as information is dispersed in ever-growing enterprise data lakes and silos. Some solutions are very abstract, taking the form of prescriptive enterprise frameworks, and therefore they do not provide operational mappings between data from real systems. In other cases the integration requires technical expertise that may be format-specific and, because of this, it is hard to cover heterogeneous technologies. It would be useful if those working on the enterprise architecture level could express on a high abstraction level the involved data sources and interlinking rules. This paper proposes a solution that enables integration management in a diagrammatic view that does not require expertise with data transformations. In support of this idea, we engineer a modelling method that provides (i) a front-end interface to enable the combination of relevant data with the help of an agile modelling language and (ii) the use of RDF as a common representation that captures the output of the modelled integrations in an Enterprise Knowledge Graph.
Benefits Derived from the Use of Information and Communication Technologies among Rural Farmers in Northeast Nigeria

The study investigated benefited from the use of ICTs in Northeastern Nigeria.A multistage sampling procedure was used to select 230 respondents.Data for the study were collected using interview schedule and analysed with percentages, mean and weighted score and Chi-square and PPMC at α0.05.Results show that the available ICT tools to the respondents were radio (97.4%), mobile phone (92.6%) and television (88.3%).The most perceived benefits of ICTs were keeping in touch with friends with (158.8) and provision of information on weather, disease outbreak and new agricultural techniques (141.7).However, the majority (54.3%) of the respondents had low level of benefit of ICT use.Constraint encountered using ICTs were high cost of maintenance (126.5),high call tariff (126.4) and language barrier (124.8).Significant relationship existed between respondents' age (r= -0.61), sex (χ 2 =56.50) and benefits derived from the use of ICTs.Respondents' low level of benefit derived could be due to language barrier, high cost of tariff and maintenance.Developmental practitioners should continue the use of ICTs to propagate information to target audience while high priority should be given to local language when packaging such information.
Multiclass Learning from Contradictions

We introduce the notion of learning from contradictions, a.k.a Universum learning, for multiclass problems and propose a novel formulation for multiclass universum SVM (MU-SVM). We show that learning from contradictions (using MU-SVM) incurs lower sample complexity compared to multiclass SVM (M-SVM) by deriving the Natarajan dimension for sample complexity for PAC-learnability of MU-SVM. We also propose an analytic span bound for MU-SVM and demonstrate its utility for model selection resulting in $\sim 2-4 \times$ faster computation times than standard resampling techniques. We empirically demonstrate the efficacy of MU- SVM on several real world datasets achieving $>$ 20\% improvement in test accuracies compared to M-SVM. Insights into the underlying behavior of MU-SVM using a histograms-of-projections method are also provided.
Connective Cognition Network for Directional Visual Commonsense Reasoning

Visual commonsense reasoning (VCR) has been introduced to boost research of cognition-level visual understanding, i.e., a thorough understanding of correlated details of the scene plus an inference with related commonsense knowledge. Recent studies on neuroscience have suggested that brain function or cognition can be described as a global and dynamic integration of local neuronal connectivity, which is context-sensitive to specific cognition tasks. Inspired by this idea, towards VCR, we propose a connective cognition network (CCN) to dynamically reorganize the visual neuron connectivity that is contextualized by the meaning of questions and answers. Concretely, we first develop visual neuron connectivity to fully model correlations of visual content. Then, a contextualization process is introduced to fuse the sentence representation with that of visual neurons. Finally, based on the output of contextualized connectivity, we propose directional connectivity to infer answers or rationales. Experimental results on the VCR dataset demonstrate the effectiveness of our method. Particularly, in $Q \to AR$ mode, our method is around 4\% higher than the state-of-the-art method.
Combining Proximity Estimation With Visible Symbol Assignment to Simplify Line-of-Sight Connections in Mobile Industrial Human-Machine Interaction

The mobile industrial human-machine interaction plays an important role in the industrial internet of things, since the engineers can use a mobile device to interact with machines that greatly improves the efficiency and safety. Nevertheless, connecting to a specific machine becomes a non-trivial problem due to the massive machines in the network that make the connection list too long to identify the target machine. Some solutions such as QR code scanning and proximity estimation have been proposed to solve this problem. However, they have limited performance in scalability and accuracy correspondingly, and thus cannot satisfy the requirements in most applications. Observing the fact that the engineers generally interact with the machines in their line-of-sight, we propose the LightCon scheme which adopts proximity estimation to estimate the machines in the line-of-sight, and controls the display module of machines to show different visible symbols (colors or numbers). To connect with a specific machine, the engineers just need to select the corresponding symbol on the mobile device. Therefore, they do not have to remember the trivial address of each machine. Furthermore, the symbol assignment algorithm is designed to reduce the complexity of manual symbol selection, and its performance is analyzed theoretically. The performance of LightCon is evaluated in the testbed, and the experimental results prove that LightCon is a promising solution to simplify line-of-sight connections with low complexity.
Communication Issues at the Workplace

Working environment communication is fundamental to cooperation. In addition to the fact that it builds and looks after connections, work environment communication encourages development. However, the barriers in communication can be a hindrance to information being communicated successfully. This study aims to study the communication issues that are faced by Malaysian employees in the workplace. A questionnaire was developed and distributed to Malaysian workers that comprised of various departments in Malaysia. The questionnaire comprises of questions on the communication problems in the context of the workplace in terms of the language barrier and physical environment. The participants that had contributed to this study were as much as 41 respondents. The respondents involved had assessed the survey based on their personal experiences in their respective organizations. Based on the results of the study, it can be seen on how the language barrier and physical environment of the workplace affect the employees’ conditions. The findings of this study can help employers view these challenges more seriously and consider the possible solutions to the problems that have been discussed. This paper is concluded by providing future researchers recommendations on various communication problems in the workplace to be further investigated and analysed in a more comprehensive manner.
Medical Image Edge Detection Using Modified Morphological Edge Detection Approach

International Journal of Computer Sciences and Engineering (A UGC Approved and indexed with DOI, ICI and Approved, DPI Digital Library) is one of the leading and growing open access, peer-reviewed, monthly, and scientific research journal for scientists, engineers, research scholars, and academicians, which gains a foothold in Asia and opens to the world, aims to publish original, theoretical and practical advances in Computer Science,Information Technology, Engineering (Software, Mechanical, Civil, Electronics & Electrical), and all interdisciplinary streams of Computing Sciences. It intends to disseminate original, scientific, theoretical or applied research in the field of Computer Sciences and allied fields. It provides a platform for publishing results and research with a strong empirical component. It aims to bridge the significant gap between research and practice by promoting the publication of original, novel, industry-relevant research.
Detecting “Slippery Slope” and Other Argumentative Stances of Opposition Using Tree Kernels in Monologic Discourse

The aim of this study is to propose an innovative methodology to classify argumentative stances in a monologic argumentative context. Particularly, the proposed approach shows that Tree Kernels can be used in combination with traditional textual vectorization to discriminate between different stances of opposition without the need of extracting highly engineered features. This can be useful in many Argument Mining sub-tasks. In particular, this work explores the possibility of classifying opposition stances by training multiple classifiers to reach different degrees of granularity. Noticeably, discriminating support and opposition stances can be particularly useful when trying to detect Argument Schemes, one of the most challenging sub-task in the Argument Mining pipeline. In this sense, the approach can be also considered as an attempt to classify stances of opposition that are related to specific Argument Schemes.
A Software Defect Learning and Analysis Utilizing Regression Method for Quality Software Development

The program is a complex object consisting of different units with variable degrees of defects.By predicting the effectiveness and frequency of program defects, program managers can make better use of manpower, cost, and time to obtain better quality assurance.It is always possible to have a set of defects that affect designed and predictable units in order to have close association with the subsidiaries.Most of the current defect prediction rating mechanism is derived from learning the previous project data, but it is not sufficient to predict the defect of the new project because the new design may contain a different type of parameter.This paper proposes a Software Defect Learning and Analysis utilizing Regression Method (SDLA-RM) to detect defects and plan a better maintenance strategy, which can support the prediction of a defective or nondefective software unit prior to deployment in any project programs.The SDL-RM mechanism extends Regression Analysis (RAM) to create an effective rulebased model for accurately classifying program faults.This approach improves the predictability of software defects, allowing software development to spend more time testing components that are expected to contain errors.The experimental evaluation is carried out across the NASA-PROMISE repository data sets, that outcome of the results in comparison with existing classifiers suggest the effectiveness and practical perspective in the software development.
Machine Learning Approach for Signature Recognition by HARRIS and SURF Features Detector

International Journal of Computer Sciences and Engineering (A UGC Approved and indexed with DOI, ICI and Approved, DPI Digital Library) is one of the leading and growing open access, peer-reviewed, monthly, and scientific research journal for scientists, engineers, research scholars, and academicians, which gains a foothold in Asia and opens to the world, aims to publish original, theoretical and practical advances in Computer Science,Information Technology, Engineering (Software, Mechanical, Civil, Electronics & Electrical), and all interdisciplinary streams of Computing Sciences. It intends to disseminate original, scientific, theoretical or applied research in the field of Computer Sciences and allied fields. It provides a platform for publishing results and research with a strong empirical component. It aims to bridge the significant gap between research and practice by promoting the publication of original, novel, industry-relevant research.
Implementation of Block-Based Hierarchical Prediction for Developing an Error-Propagation-Free Reversible Data Hiding Scheme

This paper proposes a reversible data hiding technique based on the residual histogram shifting technique. To improve the hiding capacity, this study proposes a multiple-round hierarchical prediction mechanism that generates the prediction errors of each image block. The prediction errors of each block are collected to produce the residual histogram and the secret data are then embedded into the residual histogram to obtain the embedded image. Experimental results demonstrate that the proposed technique not only provides good hiding capacity, but also maintains good image quality of the embedded image. In addition, this technique can be easily extended for image integrity protection as it is capable of resisting error propagation.
Survey on Artificial Intelligence

International Journal of Computer Sciences and Engineering (A UGC Approved and indexed with DOI, ICI and Approved, DPI Digital Library) is one of the leading and growing open access, peer-reviewed, monthly, and scientific research journal for scientists, engineers, research scholars, and academicians, which gains a foothold in Asia and opens to the world, aims to publish original, theoretical and practical advances in Computer Science,Information Technology, Engineering (Software, Mechanical, Civil, Electronics & Electrical), and all interdisciplinary streams of Computing Sciences. It intends to disseminate original, scientific, theoretical or applied research in the field of Computer Sciences and allied fields. It provides a platform for publishing results and research with a strong empirical component. It aims to bridge the significant gap between research and practice by promoting the publication of original, novel, industry-relevant research.
User Perceptions of Security and Usability of Mobile-Based Single Password Authentication and Two-Factor Authentication

Two-factor authentication provides a significant improvement over the security of traditional password-based authentication by requiring users to provide an additional authentication factor, e.g., a code generated by a security token. In this decade, single password authentication (SPA) schemes are introduced to overcome the challenges of traditional password authentication, which is vulnerable to the offline dictionary, phishing, honeypot, and man-in-the-middle attacks. Unlike classical password-based authentication systems, in SPA schemes the user is required to remember only a single password (and a username) for all her accounts, while the password is protected against the aforementioned attacks in a provably secure manner. In this paper, for the first time, we implement the state-of-the-art mobile-based SPA system of Acar et al. (2013) as a prototype and assess its usability in a lab environment where we compare it against two-factor authentication (where, in both cases, in addition to the password, the user needs access to her mobile device). Our study shows that mobile-based SPA is as easy as, but less intimidating and more secure than two-factor authentication, making it a better alternative for online banking type deployments. Based on our study, we conclude with deployment recommendations and further usability study suggestions.
Duration Modeling with Semi-Markov Conditional Random Fields for Keyphrase Extraction

Existing methods for keyphrase extraction need preprocessing to generate candidate phrase or post-processing to transform keyword into keyphrase. In this paper, we propose a novel approach called duration modeling with semi-Markov Conditional Random Fields (DM-SMCRFs) for keyphrase extraction. First of all, based on the property of semi-Markov chain, DM-SMCRFs can encode segment-level features and sequentially classify the phrase in the sentence as keyphrase or non-keyphrase. Second, by assuming the independence between state transition and state duration, DM-SMCRFs model the distribution of duration (length) of keyphrases to further explore state duration information, which can help identify the size of keyphrase. Based on the convexity of parametric duration feature derived from duration distribution, a constrained Viterbi algorithm is derived to improve the performance of decoding in DM-SMCRFs. We thoroughly evaluate the performance of DM-SMCRFs on the datasets from various domains. The experimental results demonstrate the effectiveness of proposed model.
Multi-target tracking with an event-based vision sensor and a partial-update GMPHD filter

Tracking a large number of small, similar, high-speed/- agility targets is a challenging problem for current tracking systems that make use of traditional visual sensors. Such targets necessitate very high tracker update rates to keep up with meandering and mutually-occluding target paths. Event-based vision sensors may offer a solution to this problem as they report only &#x201C;event-based&#x201D; pixelwise changes in intensity as they occur with a time resolution approaching 1 &#x3BC;s [1], providing data that is much sparser and higher in time-resolution than traditional vision systems. However, this class of sensor presents unique challenges; for example, a single object in the sensor&#x2019;s field of view may produce multiple synchronous or nearly-synchronous events. In addition, performing direct measurement-to-track association for event data on ms to ms timescales introduces problematic computational burdens for scenarios involving large numbers of targets. The work described in this paper is twofold. We first define and apply an event-clustering procedure to raw events to reduce the amount of data passed into the tracker. This transformation from events to event-clusters provides a) discrimination between event-clusters that correspond to true targets and those that do not and b) reduction in tracking computation time. Second, we define and apply a partial-update Gaussian mixture probability hypothesis density (GMPHD) filter [2] for tracking using event-cluster data. We demonstrate increased computational performance over the standard GMPHD filter while achieving comparable tracking performance per the optimal sub-pattern assignment (OSPA) metric [3].
NARVAL: A no-reference video quality tool for real-time communications

In this paper we introduce two new no-reference metrics and compare their performance to state-of-the-art metrics on six publicly available datasets having a large variety of distortions and characteristics. Our two metrics, based on neural networks, combine the following features: histogram of oriented gradients, edges detection, fast fourier transform, CPBD, blur and contrast measurement, temporal information, freeze detection, BRISQUE and Video BLIINDS. They perform better than Video BLIINDS and BRISQUE on the six datasets used in this study, including one made up of natural videos that have not been artificially distorted. Our metrics show a good generalization as they achieved high performance on the six datasets.
ACD<sup>3</sup>GPSO: automatic clustering-based algorithm for multi-robot task allocation using dynamic distributed double-guided particle swarm optimization

Purpose The multi-robot task allocation (MRTA) problem is a challenging issue in the robotics area with plentiful practical applications. Expanding the number of tasks and robots increases the size of the state space significantly and influences the performance of the MRTA. As this process requires high computational time, this paper aims to describe a technique that minimizes the size of the explored state space, by partitioning the tasks into clusters. In this paper, the authors address the problem of MRTA by putting forward a new automatic clustering algorithm of the robots' tasks based on a dynamic-distributed double-guided particle swarm optimization, namely, ACD 3 GPSO. Design/methodology/approach This approach is made out of two phases: phase I groups the tasks into clusters using the ACD 3 GPSO algorithm and phase II allocates the robots to the clusters. Four factors are introduced in ACD 3 GPSO for better results. First, ACD 3 GPSO uses the k-means algorithm as a means to improve the initial generation of particles. The second factor is the distribution using the multi-agent approach to reduce the run time. The third one is the diversification introduced by two local optimum detectors LODpBest and LODgBest. The last one is based on the concept of templates and guidance probability Pguid. Findings Computational experiments were carried out to prove the effectiveness of this approach. It is compared against two state-of-the-art solutions of the MRTA and against two evolutionary methods under five different numerical simulations. The simulation results confirm that the proposed method is highly competitive in terms of the clustering time, clustering cost and MRTA time. Practical implications The proposed algorithm is quite useful for real-world applications, especially the scenarios involving a high number of robots and tasks. Originality/value In this methodology, owing to the ACD 3 GPSO algorithm, task allocation's run time has diminished. Therefore, the proposed method can be considered as a vital alternative in the field of MRTA with growing numbers of both robots and tasks. In PSO, stagnation and local optima issues are avoided by adding assorted variety to the population, without losing its fast convergence.
Missing Data Reconstruction for Remote Sensing Images With Weighted Low-Rank Tensor Model

Missing data reconstruction for remote sensing images, such as dead-pixel recovery and cloud removal, is important for remote sensing data applications. Missing information reconstruction is well known as being an ill-posed inverse problem. In this paper, a weighted low-rank tensor regularization model is proposed to handle the problem. The proposed model fully utilizes the correlations in the spatial, spectral, and temporal components of the remote sensing images to adaptively deal with the varied missing data problems, including the Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) band 6 dead line problem, the Landsat scan-line corrector failure (SLC-off) problem, and cloud contamination. A double-weighted treatment is developed to balance the contributions from the different dimensions and preserve the different structures and textures in remote sensing images. The experiments undertaken confirmed the good performance of the proposed method, and the reconstruction results of the proposed method, in both visual effect and quantitative evaluation, were superior to those of the other methods.