Reviewerly/exHarmony huggingface dataset:
Facial Biometric System for Recognition Using Extended LGHP Algorithm on Raspberry Pi

In todays world, where the need for security is paramount and biometric access control systems are gaining mass acceptance due to their increased reliability, research in this area is quite relevant. Also with the advent of IOT devices and increased community support for cheap and small computers like Raspberry Pi its convenient than ever to design a complete standalone system for any purpose. This paper proposes a Facial Biometric System built on the client-server paradigm using Raspberry Pi 3 model B running a novel local descriptor based parallel algorithm. This paper also proposes an extended version of Local Gradient Hexa Pattern with improved accuracy. The proposed extended version of LGHP improved performance as shown in performance analysis. Extended LGHP shows improvement over other state-of-the-art descriptors namely LDP, LTrP, MLBP and LVP on the most challenging benchmark facial image databases, i.e. Cropped Extended Yale-B, CMU-PIE, color-FERET, LFW, and Ghallager database. Proposed system is also compared with various patents having similar system design and intent to emphasize the difference and novelty of the system proposed.
Face recognition for Student Attendance using Raspberry Pi

This paper presents development of face recognition for student attendance using Raspberry Pi. Face recognition is a highly efficient and an accurate tool in enhancing security. With nano devices such as Raspberry Pi and Raspberry Pi night vision cameras, lecturers record student attendance to class with face-to-face identification systems. Small night-vision raspberry cameras are installed on the classroom door frame in the classroom room to capture video, approved to Raspberry Pi for face detection and recognition. The proposed method is implemented on Raspberry Pi and Raspberry Pi night vision which is tested on various standard datasets. Experimental results validate the efficiency of the proposed recognition method.
A TV-based image processing framework for blind color deconvolution and classification of histological images

In digital histopathological image analysis, two conflicting objectives are often pursued: closeness to the original tissue and high classification performance. The former objective tries to recover images (stains) that are as close as possible to the ones obtained by staining the tissue with a single dye. The latter objective requires images that allow the extraction of better features for an improved classification, even if their appearance is not close to single stained tissues. In this paper we propose a framework that achieves both objectives depending on the number of stains used to mathematically decompose the scanned image. The proposed framework uses a total variation prior for each stain together with the similarity to a given reference color-vector matrix. Variational inference and an evidence lower bound are utilized to automatically estimate all the latent variables and model parameters. The proposed methodology is tested on real images and compared to classical and state-of-the-art methods for histopathological blind image color deconvolution and prostate cancer classification.
Deep semantic similarity adversarial hashing for cross-modal retrieval

Cross-modal retrieval has attracted considerate attention due to the rapid development of Internet and social media, and cross-modal hashing has been widely and successfully used in this domain. However, most existing hashing methods consider much little the semantic similarity levels between instances, whereas simply classified the semantic relationship as either similar or dissimilar. Besides, the issue of preservation of semantic similarity of original data between the extracted features is less explored from the existing methods. Due to the heterogeneity between different modalities, similarity of different modality features cannot be calculated directly. Therefore, in this paper, we propose a deep semantic similarity adversarial hashing (DSSAH) for cross-modal retrieval. We first calculate semantic similarity by using both label and feature information to provide a more accurate value for similarity between instances. And then an adversarial modality discriminator is introduced to establish a common feature space where similarity of each modality features can be calculated. Finally, two loss functions referring to inter-modal loss and intra-modal loss are designed to generate high quality hash codes. Experiments on three common datasets for cross-modal retrieval show that DSSAH outperforms state-of-the-art cross-modal hashing methods in cross-modal retrieval applications.
Monitoring IaaS Cloud for Healthcare Systems

Healthcare functionality is enriched by cloud services which offers a perspective for broad integration and interoperability. Cloud-based facilities support healthcare systems to remain connected to remote access devices to various tasks and information. The healthcare actors should have an understanding of the risks and benefits associated with the usage of Cloud Computing resources utilization. Also, they must launch an appropriate contract-based relationship between the Cloud Service Providers and the actors of healthcare systems by means of Service Level Agreements (SLAs). The variation in both demand and supply within the healthcare information affects the use of information technology. Hence, monitoring resources can play an important role in accommodating the healthcare data. To deal with the aforementioned problems; reinforcement learning mechanisms along with the metrics has been used and experimented with the various dynamics of workload to deliver services with quality assurance.
Sinhala Sentiment Analysis using Corpus based Sentiment Lexicon

Sentiment analysis has become a much needed natural language processing technique as the usages of sentiment analysis based tools are getting popular. With sufficient amount of lexical resources available, an accurate sentiment analysis can be performed for English. Sinhala is a morphologically rich but a less resourced language. Availability of lexical resources directly effect on the success of the development of natural language processing tools as well as for further researches. Sentiment lexicon is a primary resource required to perform a lexicon based sentiment analysis in any language. We propose a semi-automated framework for constructing a sentiment lexicon for Sinhala Language using a corpus based method. Proposed framework used conjunction as the indicator of polarity for adjectives in a conjoined adjective pair and morphological relationships between adjectives to determine the polarity of adjectives. To the best of our knowledge, such method has not been attempted for sentiment lexicon construction in Sinhala, until this day. The approach was tested for a 3 million POS tagged word corpus and the resultant sentiment lexicon was used to perform a lexicon based sentiment analysis, which produced 65%-69% of accuracy for three different classifiers. Proposed framework demonstrated potential to be more accurate with sufficiently larger text corpuses.
Dynamic Divide-and-Conquer Adversarial Training for Robust Semantic Segmentation

Adversarial training is promising for improving robustness of deep neural networks towards adversarial perturbations, especially on the classification task. The effect of this type of training on semantic segmentation, contrarily, just commences. We make the initial attempt to explore the defense strategy on semantic segmentation by formulating a general adversarial training procedure that can per-form decently on both adversarial and clean samples. We propose a dynamic divide-and-conquer adversarial training (DDC-AT) strategy to enhance the defense effect, by set-ting additional branches in the target model during training, and dealing with pixels with diverse properties to-wards adversarial perturbation. Our dynamical division mechanism divides pixels into multiple branches automatically. Note all these additional branches can be abandoned during inference and thus leave no extra parameter and computation cost. Extensive experiments with various segmentation models are conducted on PASCAL VOC 2012 and Cityscapes datasets, in which DDC-AT yields satisfying performance under both white- and black-box at-tack. The code is available at https://github.com/dvlab-research/Robust-Semantic-Segmentation.
Developing a pedagogical photoreal virtual environment to teach civil engineering

Purpose It is possible for civil engineering (CE) students to graduate from a degree programme without gaining experience on a construction site. The implementation of virtual field trips using virtual reality (VR) in CE education is a development that can address this phenomenon and help facilitate the consolidation of abstract theories into tangible competences. This project aims to solve a fundamental CE education problem: once a structure has been completed, it is often impossible to see how it was built; hence, how can you demonstrate the construction process to a student? Design/methodology/approach This research used the opportunity of a new campus library development to record its construction sequence. This was achieved by visiting the site eight times to take panoramic stereoscopic photos of the construction process. By its nature, using VR as a didactic tool facilitates experiential learning, but this project also incorporates discovery learning and situated cognition to develop students’ understanding of the construction process. Findings The use of VR in education is becoming increasingly common, but the explicit pedagogy used in these environments is rarely obvious or stated. This project draws upon current VR education discussions and explores the development of a VR environment with a pedagogical context. Originality/value The development of the VR resource draws upon the pedagogical frameworks of discovery learning (Bruner, 1961) and situated cognition (Lave and Wenger, 1991). A further unique aspect of this research is the use of stereoscopic cameras to capture the library’s construction over time.
A Novel Sliding Window Approach for Offline Handwritten Character Recognition

The Handwritten character recognition (HCR) is one of the most promising tasks in the field of optical character recognition (OCR). This paper implements a character recognition system from word images in an offline manner. In an offline system, pen stroke information is not available to the recognition system. The approach presented here performs several steps in accordance to get the final recognized characters. A unique feature representation technique is adapted for handwritten segmented characters. These features are generated by vertically scanning the segmented characters using a sliding window. Experiments are performed on a standard benchmark data-set and finally, the class prediction accuracy in terms of similarity measure is computed and compared to evaluate the performance of the system.
Cross-lingual text similarity exploiting neural machine translation models

This article studies cross-lingual text similarity using neural machine translation models. A straightforward approach based on machine translation is to use translated text so as to make the problem monolingual. Another possible approach is to use intermediate states of machine translation models as recently proposed in the related work, which could avoid propagation of translation errors. We aim at improving both approaches independently and then combine the two types of information, that is, translations and intermediate states, in a learning-to-rank framework to compute cross-lingual text similarity. To evaluate the effectiveness and generalisability of our approach, we conduct empirical experiments on English–Japanese and English–Hindi translation corpora for a cross-lingual sentence retrieval task. It is demonstrated that our approach using translations and intermediate states outperforms other neural network–based approaches and is even comparable with a strong baseline based on a state-of-the-art machine translation system.
High-dimensional count data clustering based on an exponential approximation to the multinomial Beta-Liouville distribution

In this paper, we propose a mixture model for high-dimensional count data clustering based on an exponential-family approximation of the Multinomial Beta-Liouville distribution, which we call EMBL. We deal simultaneously with the problems of fitting the model to observed data and selecting the number of components. The learning algorithm automatically selects the optimal number of components and avoids several drawbacks of the standard Expectation-Maximization algorithm, including the sensitivity to initialization and possible convergence to the boundary of the parameter space. We demonstrate the effectiveness and robustness of the proposed clustering approach through a set of extensive empirical experiments that involve challenging real-world applications. The results reveal that the novel proposed model strives to achieve higher accuracy compared to the state-of-the-art generative models for count data clustering. Furthermore, the superior performance of EMBL demonstrates its flexibility and ability to address the burstiness phenomenon successfully, as well as shows its computational efficiency, especially when dealing with sparse high-dimensional vectors.
Optical Fredkin gate assisted by quantum dot within optical cavity under vacuum noise and sideband leakage

Abstract We propose a deterministic Fredkin gate which can accomplish controlled-swap operation between three-qubit states. The proposed Fredkin gate consists of a photonic system (single photon) and quantum dots (QDs) confined in single-sided cavities (two electron spin states). In our scheme, the control qubit is the polarization state of the single photon, and two electron spin states in QDs play the role of target qubits (swapped states by control qubit). The interaction between a photon and an electron of QD within the cavity (QD-cavity system) significantly affects the performance of Fredkin gate. Thus, through the analysis of the QD-cavity system under vacuum noise and sideband leakage, we demonstrate that reliable interaction and performance of the QD-cavity system with photonic state (photon) can be acquired in our scheme. Consequently, the Fredkin gate proposed in this paper can be experimentally implemented with high feasibility and efficiency.
Clustering quality metrics for subspace clustering

We study the problem of clustering validation, i.e., clustering evaluation without knowledge of ground-truth labels, for the increasingly-popular framework known as subspace clustering. Existing clustering quality metrics (CQMs) rely heavily on a notion of distance between points, but common metrics fail to capture the geometry of subspace clustering. We propose a novel point-to-point pseudometric for points lying on a union of subspaces and show how this allows for the application of existing CQMs to the subspace clustering problem. We provide theoretical and empirical justification for the proposed point-to-point distance, and then demonstrate on a number of common benchmark datasets that our proposed methods generally outperform existing graph-based CQMs in terms of choosing the best clustering and the number of clusters.
Consumer Response to Virtual CSR Experiences

This study examines a unique context for corporate social responsibility (CSR) initiatives in the form of a virtual reality (VR) video featuring a quasi-experimental design that was sponsored by an alcohol company. The study examines whether individual variation in the immersive tendency plays a role in the perception of CSR initiatives in VR videos. This study reveals that consumers (i.e., college students) with a higher level of immersive tendency are more likely to have positive attitudes toward CSR ads, via emotional engagement and telepresence, than those with a lower level of immersive tendency. However, our results show that consumer immersive tendency and emotional engagement with the VR video have no influence on purchase intention. Further, this study reveals that unique characteristics of VR video (e.g., telepresence and emotional engagement) can play a role in evaluating cognitive, attitudinal, and purchase intention in the unique context of VR video by adopting dialogic (emotional) engagement and the hierarchy-of-effect (HOE) model. Theoretical and managerial implications are discussed.
An Ant Colony Optimization approach for symbolic regression using Straight Line Programs. Application to energy consumption modelling

The increase of energy consumption and their direct effects on pollution and global warming have motivated governments to develop new strategies to promote a better usage of energy. One of the most important aspects related to energy efficiency is the need for a suitable model of energy consumption that can be used to make predictions or to aid experts in high level decision making processes. Symbolic regression techniques can be used to discover an energy consumption model that fits these purposes. Traditionally, the problem of symbolic regression has been solved by using genetic programming approaches to find the algebraic expression that best fits the regression problem data, where each expression is encoded as a tree structure. In previous works, we found that a different approach using Straight Line Programs as a representation technique could provide promising results for symbolic regression, although the size of the resulting algebraic expression might be increased when compared to the traditional approach. This work proposes an Ant Colony Optimization algorithm for Straight Line Programs to solve the problem, and makes a study to compare the approach with traditional genetic programming in a real energy consumption modelling problem.
Measuring photography aesthetics with deep CNNs

In spite of the recent advancements of deep learning based techniques, automatic photo aesthetic assessment still remains a challenging computer vision task. Existing approaches used to focus on providing a single aesthetic score or category (“good” or “bad”) of photograph, rather than quantifying “goodness” or “badness”. The existing algorithms often ignore the importance of different attributes contributing to the artistic quality of the photograph. To obtain the human-interpretability of aesthetic score of photo, we advocate learning the aesthetic attributes alongwith the prediction of the general aesthetic score. We propose a multi-task deep CNN, that collectively learns aesthetic attributes alongwith a general aesthetic score for the photograph. To understand the mathematical representation of the attributes in the proposed model, a visualization technique is proposed using back propagation of gradients. These visualization of attributes correspond to the location of objects in the images in order to find out which part of an image “triggers” the classification outcome, thus providing the insights about the model's understanding of these attributes. This paper proposes an aesthetic feature vector based on the relative foreground position of the object in the image. The proposed aesthetic features outperform the state-of-art methods especially for Rule of Thirds attribute.
Binary LCD Codes and Self-Orthogonal Codes via Simplicial Complexes

Linear complementary dual (LCD) codes and self-orthogonal codes have attracted wide attention in recent years for some practical applications. Hence, in this letter, we use simplicial complexes for construction of an infinite family of binary LCD codes and two infinite families of binary self-orthogonal codes. Moreover, we explicitly determine the weight distributions of these codes. We obtain binary LCD codes which have minimum weights two or three, and we also find some self-orthogonal codes meeting the Griesmer bound. As examples, we also present some (almost) optimal binary self-orthogonal codes and LCD distance optimal codes.
Robotic Swarm Motion Planning for Load Carrying and Manipulating

Certain species of ants can carry out tasks in dense work spaces while maintaining their ability to accurately manipulate heavy loads, and these advantages are of interest to the robotics community. We consider a robotic swarm of $N\ge 6$ agents that assumes the task of moving a load through a cluttered space. This forces the swarm to carefully manipulate the orientation of the load, while transporting it to its destination point. We model this scenario as a 6-PPSS (Prismatic-Prismatic-Spherical-Spherical) redundant mobile platform, having six degrees of freedom. As with insects, the multitude of agents enables sharing the burden of the load in the case that one or more agents are blocked by an obstacle. We model this by a semi-algebraic set of constraints on the distances between the agents and the load. We apply an Extended Kalman Filter routine, in order to estimate their relative locations. We show how the estimation-error is reduced when position-information is shared among the agents. These estimations are then used to calculate the full configuration and investigate the effect of position estimation error on the platform heading error. We show how motion planning can then be calculated in the model's full configuration space and demonstrate this with a distributed control scheme. To reduce the search time, we introduce a variant of the crawling probabilistic road map motion planning algorithm under a set of kinematic constraints and work-space obstacles. Finally, we exemplify our algorithms on several simulated scenarios.
CoCNN: RGB-D deep fusion for stereoscopic salient object detection

Many convolutional neural network (CNN)-based approaches for stereoscopic salient object detection involve fusing either low-level or high-level features from the color and disparity channels. The former method generally produces incomplete objects, whereas the latter tends to blur object boundaries. In this paper, a coupled CNN (CoCNN) is proposed to fuse color and disparity features from low to high layers in a unified deep model. It consists of three parts: two parallel multilinear span networks, a cascaded span network and a conditional random field module. We first apply the multilinear span network to compute multiscale saliency predictions based on RGB and disparity individually. Each prediction, learned under separate supervision, utilizes the multilevel features extracted by the multilinear span network. Second, a proposed cascaded span network, under deep supervision, is designed as a coupling unit to fuse the two feature streams at each scale and integrate all fused features in a supervised manner to construct a saliency map. Finally, we formulate a constraint in the form of a conditional random field model to refine the saliency map based on the a priori assumption that objects with similar saliency values have similar colors and disparities. Experiments conducted on two commonly used datasets demonstrate that the proposed method outperforms previous state-of-the-art methods.