Reviewerly/exHarmony huggingface dataset:
Ensemble Based Classification of Sentiments Using Forest Optimization Algorithm

Feature subset selection is a process to choose a set of relevant features from a high dimensionality dataset to improve the performance of classifiers. The meaningful words extracted from data forms a set of features for sentiment analysis. Many evolutionary algorithms, like the Genetic Algorithm (GA) and Particle Swarm Optimization (PSO), have been applied to feature subset selection problem and computational performance can still be improved. This research presents a solution to feature subset selection problem for classification of sentiments using ensemble-based classifiers. It consists of a hybrid technique of minimum redundancy and maximum relevance (mRMR) and Forest Optimization Algorithm (FOA)-based feature selection. Ensemble-based classification is implemented to optimize the results of individual classifiers. The Forest Optimization Algorithm as a feature selection technique has been applied to various classification datasets from the UCI machine learning repository. The classifiers used for ensemble methods for UCI repository datasets are the k-Nearest Neighbor (k-NN) and Naïve Bayes (NB). For the classification of sentiments, 15–20% improvement has been recorded. The dataset used for classification of sentiments is Blitzer’s dataset consisting of reviews of electronic products. The results are further improved by ensemble of k-NN, NB, and Support Vector Machine (SVM) with an accuracy of 95% for the classification of sentiment tasks.
Filianore

The (ring) learning with errors (RLWE/LWE) problem is one of the most promising candidates for constructing quantum-secure key exchange protocols. In this work, we design and implement specialized hardware multiplier units for both LWE and RLWE key exchange schemes to maximize their computational efficiency. By exploiting the algebraic structure with aggressive parameter sets, we show that the design and implementation of LWE key exchange on hardware is considerably easier and more flexible than RLWE. Using the proposed architectures, we show that client-side energy-efficiency of LWE-based key exchange can be on the same order, or even (slightly) better than RLWE-based schemes, making LWE an attractive option for designing post-quantum cryptographic suite.
Forecasting Food Sales in a Multiplex Using Dynamic Artificial Neural Networks

In India, food sales are emerging to be a major revenue generator for multiplex operators currently amounting to over $367 million a year. Efficient food sales forecasting techniques are the need of the hour as they help minimize the wastage of resources for the multiplex operators. In this paper, the authors propose a model to make a day-ahead prediction of food sales in one of the top multiplexes in India. Online learning and feature engineering by data correlative analysis in conjecture with a densely connected Neural Network, address the concept drifts and latent time correlations present in the data respectively. A scale independent metric, $$\eta _{comp}$$ is also introduced to measure the success of the models across all food items from the business perspective. The proposed model performs better than the traditional time-series models, and also performs better than the corporate's currently existing model by a factor of 7.7%. This improved performance also leads to a saving of 170 units of food everyday.
Cuckoo-inspired Job Scheduling Algorithm for Cloud Computing

Cloud computing offers a pool of virtual resources to users on demand. Among the main challenge facing cloud computing is job scheduling which is an NP-hard problem. Usually, scheduling should consider job priority, where high-priority jobs should be immediately allocated required resources, whereas low-priority jobs can wait. In this paper, we propose a scheduling algorithm that imitates the parasitic behaviour of the cuckoo bird. Cuckoos reproduce by exploiting the nests of other birds with eggs similar to their own. We have evaluated the proposed cuckoo scheduling algorithm under varying number of nodes and percentage of high priority jobs. The experimental results demonstrate the superiority of our proposed algorithm over the benchmarks in terms of average CPU utilization and average turnaround time for each type of job.
Multi-Source Data Sensing in Mobile Personalized Healthcare Systems: Semantic Linking and Data Mining

The paper introduces an approach to collecting and mining health-related information on the patient based on sensed data from various medical devices as well as from other digitally-enabled sources. The regularly sensed data are semantically linked thus creating an additional information space-semantic layer. On the latter, a linked knowledge-rich structure-semantic network-is maintained and used to construct mobile services. The use of various medical devices and other data sources makes it possible to remotely monitor patients' vital physiological parameters and other important health-related events. It includes sensing the context of the physical environment, which is then coupled with the health state of the patient. Several patients and interested people can be virtually integrated into a group. Consequently, social methods can be used for enhancing the treatment adherence and for motivating the healthcare goals. As such, the healthcare services would have became more focused on the patients and their needs. Ultimately, the mobile healthcare moves towards the vision of At-Home Laboratory (AHL) that diminishes the necessity to visit the hospital and to directly use its facility.
Estimation of the potential of landfill mining and exploration of metal-enriched zones

Since the Industrial Revolution, people have exploited natural resources to manufacture products. Many of the products have later been disposed of in landfills and the like. The aim of this study was to determine the contents and chemical forms of valuable rare metals in landfills and to identify the metal-enriched zones in landfills using non-destructive prospecting methods. Box experiments were performed using four mixtures of sand and iron sulfide particles, and excavated waste samples. Field prospecting studies were performed at an industrial landfill site. As a result, it is possible to establish a grading system for elements based on the maximum contents in the fill, and the condition in the fill is found to potentially affect the metal distribution. In addition, investigations for rare metals coexisting with iron sulfide were performed at each target site. This allowed zones with high rare metal contents in the fill to be identified in a non-destructive manner. Based on the above approach, a basic evaluation of a landfill site that could be used as an "urban mine" was accomplished.
Enhancement of noisy and compressed videos by optical flow and non-local denoising

A new method for denoising video sequences is presented. The method is able to deal with poor quality sequences affected by both noise and compression, as for example mobile phone videos recorded in low-light conditions. In this real scenario, the usual white Gaussian uniform noise assumption fails, and the state-of-the art denoising algorithms are ineffective. The proposed method first estimates a signal dependent noise model at each level of a multi-scale pyramid; then, a variance stabilization transform is applied at each scale; and, finally, a denoising algorithm is applied on the stabilized image sequence. This algorithm takes advantage of self similarity and redundancy of adjacent frames and uses motion compensation by regularized optical flow methods, which permits robust patch comparison in a spatio-temporal volume. The experiments illustrate that the proposed method is able to correctly remove highly correlated noise from real dark and compressed movie sequences. Finally, in order to assess numerically the performance of the proposed algorithm, we introduce a new database of noisy and compressed image sequences with known ground truth. The numerical comparisons with the introduced database corroborate the superior performance of the method.
PIVTONS: Pose Invariant Virtual Try-On Shoe with Conditional Image Completion

Virtual try-on – synthesizing an almost-realistic image for dressing a target fashion item provided the source human photo – has growing needs due to the prevalence of e-commerce and the development of deep learning technologies. However, existing deep learning virtual try-on methods focus on the clothing replacement due to the lack of dataset and cope with flat body segments with frontal poses providing the front view of the target fashion item. In this paper, we present the pose invariant virtual try-on shoe (PIVTONS) to cope with the task of virtual try-on shoe. We collect the paired feet and shoe virtual-try on dataset, Zalando-shoes, containing 14,062 shoes among the 11 categories of shoes. The shoe image only contains a single view of the shoes but the try-on result should show other views of the shoes depending on the original feet pose. We formulate this task as an automatic and labor-free image completion task and design an end-to-end neural networks composing of feature point detector. Through the numerous experiments and ablation studies, we demonstrate the performance of the proposed framework and investigate the parameterizing factors for optimizing the challenging problem.
Enhancing Unsupervised Pretraining with External Knowledge for Natural Language Inference

Unsupervised pretraining such as BERT (Bidirectional Encoder Representations from Transformers) [2] represents the most recent advance on learning representation for natural language, which has helped achieve leading performance on many natural language processing problems. Although BERT can leverage large corpora, we assume it cannot learn all needed semantics and knowledge for natural language inference (NLI). In this paper, we leverage human-authorized external knowledge to further improve BERT, and our results show that BERT, the current state-of-the-art pretraining framework, can benefit from external knowledge.
Robust driver foot tracking and foot gesture recognition using capacitive proximity sensing

Nowadays, there is an increasing trend towards automated driving. This is supported by both driver assistance systems getting more and more available and powerful, and research for car manufacturing industries. As a consequence, driver hands and feet are less involved in vehicle control. Increasing automation will even let them become idle. Recent gesture recognition mainly focuses on hand interaction. This work focuses on possibilities for feet gesture interaction. Many gesture recognition systems rely on computing intensive, privacy concerns causing video systems. Furthermore, these systems require a line of sight and therefore visible interior design integration. The proposed system shows that invisibly integrated capacitive proximity sensors can do the job, too. They do not cause privacy issues and they can be integrated under non-conductive materials. Therefore, there is no visible interior design impact. The proposed solution distinguishes between four feet gestures. There is no limitation to feet movement. Further, an evaluation including six participants and a vehicle legroom mockup shows the system function. This work contributes to the basis of driver foot gesture recognition pointing to further applications and more comprehensive investigations.
ECC Based Encryption Algorithm for Lightweight Cryptography

Cryptography can be considered as toolbox, where potential attackers have access to different computing resources and techniques to try and calculate key values. In a modern cryptography the strength of the cryptographic algorithm is nothing but the key size. So, our objective is to generate strong key value having minimum length of bits which will be useful in lightweight cryptography. Using Elliptic Curve Cryptography (ECC) with algebraic graph we are finding secret key value. ECC is itself a strong algorithm which generates pair of public and private We are generating secret key value with the help of above pair of key. Secret key parameters not shared in network so it will defense against man-in-middle attack. In this paper we are performing encryption and decryption process with mid-point of graph. This algorithm may be applicable for Smart card, Sensors and wireless network security.
Provenance and tectonic setting of the Jurassic Huayacocotla Formation and Alamitos Sandstone, Central Mexico

We present a modified model for the paleogeographic evolution of Mexico during Early and Late Jurassic time that is constrained by the tectonic setting and the weathering conditions of the Early Jurassic Huayacocotla Formation and Late Jurassic Alamitos Sandstone basins in state San Luis Potosí in central Mexico. Framework petrography constrains feldspato-quartzose sandstone (mean of Q68F22L10) and litho-quartzose (mean of Q75F6L19) sandstone compositions for the two units, respectively. The abundant lithic fragments are totally dominated by volcanic fragments. Quartz cathodoluminescence colours and textures from the Alamitos Sandstone supports a large input of volcanic material, but also indicates the presence of metamorphic quartz. Similarly, the geochemical composition is more mafic for the Huayacocotla Formation (Th/Sc: ˜0.6 and Cr/Th: ˜10) than for the Alamitos Sandstone (Th/Sc: ˜1.1 and Cr/Th: ˜48). Also the weathering conditions were less intense during the Early (CIA: ˜60, PIA: ˜61) than the Late Jurassic (CIA ˜85, PIA ˜97). Well preserved lithic fragments and feldspar grains, particularly in the Huayacocotla Formation, indicate that weathering indeed was minor for this unit. We interpret the difference between the two units as a combined result of climate change and tectonic setting. During the Early Jurassic, transport of volcanic detritus probably dominated from the active Nazas arc in the west. Later, additional sources from the metamorphic basement of Mexico were included. During Late Jurassic time strike-slip faulting related to the opening of the Gulf of Mexico may have re-directed the sediment-transport systems. Finally, the degree of weathering was affected by drastic climatic change from arid to humid tropical conditions during the Middle to Upper Jurassic, possibly related to the first incursions of Gulf of Mexico marine environments linked to the rotation of the Yucatan block.
Ontology Driven Feature Engineering for Opinion Mining

In the process of knowledge discovery, the reliability of results depends upon the effectiveness of attributes selected for decision. The curse of dimensionality refers to the phenomenon in which the excessive number of dimensions affect the analysis. In order to eradicate the curse of dimensionality in text analysis, we are proposing an ontology-based semantic measure for intelligent selection/reduction of features. Among the various text mining techniques, ontology-based mining has a significant contribution to the field. The ontology-based semantic measures, which are mathematical models used to find the similarity between various concepts in the ontology, have made a significant contribution to feature engineering. The proposed measure is an amalgamation of semantic similarity, relatedness, and distance. The measure allows performing an in-depth analysis of various semantic relationships between concepts of the English language. The performance of the measure was evaluated against benchmarked dimension reduction techniques such as PCA. The results show improvement by reducing the size of dimensions up to 35%. The results were further evaluated by training a classifier to validate that the features are not creating any underfit/overfit model.
The effects of cue placement on the relative dominance of boundaries and landmark arrays in goal localization

Two types of visual features are identified as reference points used by individuals to encode locations: surface-based boundaries and discrete-object-based landmarks. Previous research show that learning locations relative to a boundary can overshadow learning relative to a landmark, but not vice versa, suggesting that environmental boundaries play a privileged role in representing individual locations. However, other research has revealed that a less accurate cognitive map is derived from boundary-related learning than from landmark-related learning, suggesting that a boundary is less privileged in representing inter-location spatial relations. The current study aims to reconcile these inconsistent findings. Experiment 1, using both a cue-competition paradigm and a cognitive mapping task, replicated the finding that participants preferred a circular boundary to a four-landmark array for encoding four locations (1A), but that the cognitive maps of the locations derived from the landmark array were more accurate (1B). Using the cue-competition paradigm, Experiments 2-4 manipulated the placement and distinctiveness of the two cues. The results showed that manipulating the placement of the landmark array effectively modulated the relative reliance upon the boundary/landmark-array in encoding individual location. Whereas increasing the distinctiveness of the landmark-array alone is not sufficient to eliminate the boundary advantage in localization. We propose that the boundary privilege occurs in selecting reference points for encoding locations due to its relative peripheral placement in the environment, whereas the landmark advantage occurs in inferring inter-location spatial relations due to the common reference point provided by the single landmark.
Sensor Abnormal Detection and Recovery Using Machine Learning for IoT Sensing Systems

In sensing systems in various environments, such as environmental monitoring and smart power grid systems, sensors are usually unreliable due to improper calibration, low battery levels or hardware failures of the devices. Unreliability may cause users to make erroneous decisions or inaccurate analysis. In this paper, we propose a detect system architecture to avoid the abnormality among the sensors based on machine learning. The detection mechanism has to be in real-time by exploring the correlation among the sensors, and predicting the supplemental values via other correlated sensors. We analyze the fault data pattern in order to classify the fault type of faulty sensors and also to recover the faulty sensors for improving the reliability of sensing systems.
Deep-DFR

Deep neural networks (DNNs), the brain-like machine learning architecture, have gained immense success in data-extensive applications. In this work, a hybrid structured deep delayed feedback reservoir (Deep-DFR) computing model is proposed and fabricated. Our Deep-DFR employs memristive synapses working in a hierarchical information processing fashion with DFR modules as the readout layer, leading our proposed deep learning structure to be both depth-in-space and depth-in-time. Our fabricated prototype along with experimental results demonstrate its high energy efficiency with low hardware implementation cost. With applications on the image classification, MNIST and SVHN, our Deep-DFR yields a 1.26~7.69X reduction on the testing error compared to state-of-the-art DNN designs.
A Text Data Augmentation Approach for Improving the Performance of CNN

Deep learning is an emerging research area in the field of machine learning and its fascinating accuracy has attracted many researchers to apply it in various domains, including computer vision and natural language processing. Traditional machine learning approaches require to invest a good amount of time in feature engineering and related tasks. Deep learning, on the other hand, does not require to define features explicitly; instead, it aims to learn different representations from data automatically. However, it needs large enriched corpus to train deep classification models properly. Overfitting is another challenging issue, which needs enriched corpus. In this paper, we propose a data augmentation approach, which combines n-grams and LDA techniques to identify class-specific phrases to enrich the underlying corpus. We have evaluated the performance of the convolutional neural network on both original and augmented corpus, and it is found that the augmented corpus has lower variance and better validation accuracy in comparison to the original corpus. The proposed data augmentation approach seems very useful for the domains with small data corpus to train deep learning models.
Global parenchymal texture features based on histograms of oriented gradients improve cancer development risk estimation from healthy breasts

The breast dense tissue percentage on digital mammograms is one of the most commonly used markers for breast cancer risk estimation. Geometric features of dense tissue over the breast and the presence of texture structures contained in sliding windows that scan the mammograms may improve the predictive ability when combined with the breast dense tissue percentage.A case/control study nested within a screening program covering 1563 women with craniocaudal and mediolateral-oblique mammograms (755 controls and the contralateral breast mammograms at the closest screening visit before cancer diagnostic for 808 cases) aging 45 to 70 from Comunitat Valenciana (Spain) was used to extract geometric and texture features. The dense tissue segmentation was performed using DMScan and validated by two experienced radiologists. A model based on Random Forests was trained several times varying the set of variables. A training dataset of 1172 patients was evaluated with a 10-stratified-fold cross-validation scheme. The area under the Receiver Operating Characteristic curve (AUC) was the metric for the predictive ability. The results were assessed by only considering the output after applying the model to the test set, which was composed of the remaining 391 patients.The AUC score obtained by the dense tissue percentage (0.55) was compared to a machine learning-based classifier results. The classifier, apart from the percentage of dense tissue of both views, firstly included global geometric features such as the distance of dense tissue to the pectoral muscle, dense tissue eccentricity or the dense tissue perimeter, obtaining an accuracy of 0.56. By the inclusion of a global feature based on local histograms of oriented gradients, the accuracy of the classifier was significantly improved (0.61). The number of well-classified patients was improved up to 236 when it was 208.Relative geometric features of dense tissue over the breast and histograms of standardized local texture features based on sliding windows scanning the whole breast improve risk prediction beyond the dense tissue percentage adjusted by geometrical variables. Other classifiers could improve the results obtained by the conventional Random Forests used in this study.
Data-Driven Edu Chatbots

There are many online courses and contents on the web, so each learner can find the best one and choose it. However, sometimes many online courses are poorly accessible due to the limits of the search engines on the web. The advent of intelligent systems, and online Chatbots, in particular, has brought improvement in various fields. Education Chatbots improve communication, increase productivity, and simplify learning interaction. This study aims to provide an intelligent Edu Chatbot with a high level of customization to learners who have different needs. This way, they can find their personalized learning path dynamically and their customized content without too much time and effort. This is precisely what e-learning needs because of the enormous amount of material on the web.