Reviewerly/exHarmony huggingface dataset:
Meaning maps and deep neural networks are insensitive to meaning when predicting human fixations

An important aspect of vision – control of eye-movements in scene viewing – is intensely debated, with many studies suggesting that people look at scene regions rich in meaning. A recent proposal suggests that the distribution of meaning can be quantified by ‘Meaning Maps’ (MMs). To create MMs, images are segmented into partially overlapping patches, which are rated for their meaningfulness by multiple observers. These ratings are combined into a smooth distribution over the image. If MMs capture the distribution of meaning, and if the deployment of eye-movements in humans is guided by meaning, two predictions arise: first, MMs should be better predictors of gaze position than saliency models, which use image features rather than meaning to predict fixations; second, differences in eye movements that result from changes in meaning should be reflected in equivalent differences in MMs. Here, we tested these predictions. Results show that MMs performed better than the simplest saliency model (GBVS), were similar to a more advanced model (AWS), and were outperformed by DeepGaze II – a model using features from a deep neural network. These data suggest that, similar to saliency models, MMs might not measure meaning but index the distribution of features. Using the SCEGRAM database, we tested this notion directly by comparing scenes containing consistent object-context relationships with identical images, in which one object was contextually inconsistent, thus changing its meaning (e.g., a kitchen with a mug swapped for a toilet roll). Replicating previous studies, regions containing inconsistencies attracted more fixations from observers than the same regions in consistent scenes. Crucially, however, MMs of the modified scenes did not attribute more ‘meaning’ to these regions. DeepGaze II exhibited the same insensitivity to meaning. Both methods are thus unable to capture changes in the deployment of eye-movements induced by changes of an image’s meaning.
Single View Facial Hair 3D Reconstruction

In this work, we introduce a novel energy-based framework that addresses the challenging problem of 3D reconstruction of facial hair from a single RGB image. To this end, we identify hair pixels over the image via texture analysis and then determine individual hair fibers that are modeled by means of a parametric hair model based on 3D helixes. We propose to minimize an energy composed of several terms, in order to adapt the hair parameters that better fit the image detections. The final hairs respond to the resulting fibers after a post-processing step where we encourage further realism. The resulting approach generates realistic facial hair fibers from solely an RGB image without assuming any training data nor user interaction. We provide an experimental evaluation on real-world pictures where several facial hair styles and image conditions are observed, showing consistent results and establishing a comparison with respect to competing approaches.
A Novel Approach to Heterogeneous Multi-class SVM Classification

The usability of multi-class support vector machine for processing and classifying heterogeneous data has defined in proposed study. Multi-class approaches are machine learning algorithms that construct a new subset of learning classifiers and then classify new models by adding the results of the base classifiers in various ways (characteristically by weighted or un-weighted voting). To construct the multi-class support vector machine classifier into subsets, first we have to decide different kernel tricks for getting better results. However, processing heterogeneous data by support vector machine based on different kernel functions have rarely used. The training time, cross-validation, classification accuracy of single class and multi-class are evaluated here. The sum rule for combining the subsets of multi-class classifiers has been used in this study. The experimental results analysis shows that our proposed multi-class approach achieves highest accuracy with heterogeneous data in comparison with other methods.
Patch based Semi-supervised Linear Regression for Face Recognition

To deal with single sample face recognition, this paper presents a patch based semi-supervised linear regression (PSLR) algorithm, which draws facial variation information from unlabeled samples.Each facial image is divided into overlapped patches, and a regression model with mapping matrix will be constructed on each patch.Then, we adjust these matrices by mapping unlabeled patches to [1,1, ••• ,1] T .The solutions of all the mapping matrices are integrated into an overall objective function, which uses ℓ 2,1 -norm minimization constraints to improve discrimination ability of mapping matrices and reduce the impact of noise.After mapping matrices are computed, we adopt majority-voting strategy to classify the probe samples.To further learn the discrimination information between probe samples and obtain more robust mapping matrices, we also propose a multistage PSLR (MPSLR) algorithm, which iteratively updates the training dataset by adding those reliably labeled probe samples into it.The effectiveness of our approaches is evaluated using three public facial databases.Experimental results prove that our approaches are robust to illumination, expression and occlusion.
Memory Management in Resource-Bounded Agents

In intelligent agents, memory has a very important and decisive role for the choice of future behaviors, since it is progressively formed through the agent’s interactions with the external environment. Previous work exists in the logic concerning the formalization of the reasoning on the formation of beliefs and the interaction with the background knowledge in non-omniscient agents. We extend this work by inserting the concept of time through a particular function that assigns a “timing” to beliefs and inferences.
On the Statistical Detection of Adversarial Instances over Encrypted Data

Adversarial instances are malicious inputs designed to fool machine learning models. In particular, motivated and sophisticated attackers intentionally design adversarial instances to evade classifiers which have been trained to detect security violation, such as malware detection. While the existing approaches provide effective solutions in detecting and defending adversarial samples, they fail to detect them when they are encrypted. In this study, a novel framework is proposed which employs statistical test to detect adversarial instances, when data under analysis are encrypted. An experimental evaluation of our approach shows its practical feasibility in terms of computation cost.
Hybrid DV-CV QKD Outperforming Existing QKD Protocols in Terms of Secret-Key Rate and Achievable Distance

To overcome the limitations of both discrete variable (DV) and continuous variable (CV) QKD protocols, in this paper, a hybrid DV-CV QKD protocol is proposed. In the proposed hybrid QKD protocol, Alice simultaneously performs discrete modulation (DM)-based encoding for CV-QKD subsystem and time-phase encoding for DV-QKD on a transmitter side. On receiver side, Bob employs a 1:2 optical space switch to select either DV-QKD receiver or CV-QKD receiver with the optimized probability of selection. Bob further performs the classical postprocessing applied to both subsystems so that resulting joint secure key is derived from both subsystems. The proposed hybrid QKD protocol significantly outperforms previously introduced both Gaussian modulation (GM)- and DM-based CV-QKD protocols as well as DV-QKD protocols in terms of both secret-key rate (SKR) and achievable transmission distance.
Physical-Layer OFDM Data Encryption using Chaotic QAM Mapping

Dynamic QAM mapping schemes are proposed to encrypt the user data in physical-layer using hyper digital chaos for optical orthogonal frequency-division multiplexing (OFDM) transmission. Unlike the traditional physical-layer security schemes such as signal scrambling or permutation of OFDM symbols, the OFDM data is chaotically re-distributed onto any randomized locations on the constellation plane to enhance the security during OFDM data transmission. The proposed dynamic mappings are performed using constellation-shifting mapping, dimension-constrained mapping and circular QAM mapping. The secure mapping schemes are demonstrated and compared in terms of optical OFDM transmission and data security performances. High-level security is achieved with huge key space, due to the introduction of hyper digital chaos in multi-fold dynamic QAM mappings.
MRI Image Compression Using Asymmetric Wavelet Analysis

An exponential rise in amount of medical image data generation has been recorded since two decades. In telemedicine based applications, this enormous amount of data is initially stored and later, transmitted to medical experts for required diagnosis. This whole storage and transmission process may require extremely high memory space and large transmission bandwidth. An attempt has been made to compress MRI (magnetic resonance imaging) data by exploiting neighbouring pixel redundancy found in images along with the relevant diagnostic information. A detailed analysis of MRI image compression using asymmetric wavelet based frequency transformation techniques has been carried out in MATLAB. It implements discrete wavelet transform using asymmetric mother wavelet daubechies (db) in conjunction with a hierarchal embedded bit coding scheme, set partitioning in hierarchal trees. Distinct daubechies (db6, db12, db16 and db20) mother wavelets have been applied to a set of ten MRI image (512 × 512) dataset. The performance of applied compression scheme has been evaluated by analyzing Compression-Ratio achieved, Peak-Signal-to-Noise-Ratio and Bits-Per-Pixel. A high compression ratio (CR - 3.84) with considerably high peak signal to noise ratio (PSNR - 43.35) is achieved with db20 wavelet. This high value of PSNR may help in preserving the diagnostic details in input MRI image data to a great extent and high CR would lead to less storage space followed by small transmission bandwidth requirement as compared to earlier compression techniques. It could definitely lead to development of more efficient remote healthcare systems.
Cross-Correlation Template Matching for Liver Localisation in Computed Tomography

The 2019 Irish Machine Vision and Image Processing Conference (IMVIP 2019), Technological University Dublin, 28-30 August 2019
Learning to generalize like humans using basic-level object labels

Convolutional neural networks (CNNs) have become a standard for modeling several aspects of human visual processing, especially natural object classification, where they rival humans in performance. Most recent work on improving the correspondence between CNNs and humans has focused on low-level architectural modifications, and has paid less attention to changes in training supervision. We identify one way in which the training objective of the network differs greatly from that of humans: CNNs are almost exclusively trained on fine-grained, subordinate-level labels (e.g., Dalmatian), while humans also make use of more coarse-grained, basic-level labels (e.g., dog) that unify otherwise perceptually divergent subordinate classes. We show through a series of experiments that the level of abstraction in the labels used to train the network determines to a large extent how it will generalize, and consequently its correspondence with human generalization behavior.
Evaluation of Students’ perceptions of library service quality using LibQUAL model: The case study in Hospitality Institute in India

The purpose of this study is to evaluate the hospitality students’ perception of library service quality provided by the institute using a well-known measurement model LibQUAL. A structured questionnaire with 22 items on three dimensions of LibQUAL was distributed to undergraduate (UG) and postgraduate (PG) students using Google forms. The mean differences between the UG and PG were analyzed using independent sample t-test. The findings suggest that there is a significant difference in the mean perception of undergraduate and postgraduate students about the library services provided by the institute. This study provides practical insight to library managers about the perception of services provided. The library administrators need to take note of these differences in the perception and bring the necessary changes in their standard operating procedure to improve the quality of services provided.
Generic Inference in Latent Gaussian Process Models

We develop an automated variational method for inference in models with Gaussian process (GP) priors and general likelihoods. The method supports multiple outputs and multiple latent functions and does not require detailed knowledge of the conditional likelihood, only needing its evaluation as a black-box function. Using a mixture of Gaussians as the variational distribution, we show that the evidence lower bound and its gradients can be estimated efficiently using samples from univariate Gaussian distributions. Furthermore, the method is scalable to large datasets which is achieved by using an augmented prior via the inducing-variable approach underpinning most sparse GP approximations, along with parallel computation and stochastic optimization. We evaluate our approach quantitatively and qualitatively with experiments on small datasets, medium-scale datasets and large datasets, showing its competitiveness under different likelihood models and sparsity levels. On the large-scale experiments involving prediction of airline delays and classification of handwritten digits, we show that our method is on par with the state-of-the-art hard-coded approaches for scalable GP regression and classification.
Estudio de caso del modelo clase invertida en un curso de mecánica de cuerpos rígidos

espanolEl objetivo de este articulo es presentar una investigacion en la que se empleo el metodo de estudio de caso utilizando un grupo control, para aplicar el modelo de clase invertida en un curso de mecanica de cuerpos rigidos. Los estudiantes del grupo A recibieron el curso con el modelo clase invertida, mientras que el grupo de control B con el metodo tradicional basado en conferencias magistrales. Este estudio cuali-cuantitativo recolecto informacion de dos fuentes: calificaciones finales del curso y encuestas a los estudiantes. Como resultado, las calificaciones finales del grupo B fueron significativamente mas altas que las del grupo A (p = 0,039). Las calificaciones del grupo B estuvieron relacionadas con el porcentaje de revisiones de las conferencias pregrabadas. En las encuestas, el grupo B califico positivamente la aplicacion del modelo en 8,3±1,4. Concluyendo, que la clase invertida promueve el autoaprendizaje de los estudiantes, por lo que se recomienda en contenidos relacionados con la Fisica y las Matematicas EnglishThe objective of this article is to present an investigation in which the case study method was used using a group control, to apply the inverted class model in a rigid body mechanics course. The students of the group have received the course with the investment model, while the control group with the traditional method is based on lectures. This qualitative-quantitative study collected information from two sources: final course grades and student surveys. As a result, the final scores of group B were also higher than group A (p = 0.039). Group B ratings related to the percentage of pre-recorded conference reviews. In the surveys, group B positively rated the application of the model at 8,3 ± 1,4. In conclusion, that the inverted class promotes student self-learning, which is recommended in the contents related to Physics and Mathematics
A Construction of Traceability Set Systems with Polynomial Tracing Algorithm

A family F of w-subsets of a finite set X is called a set system with the identifiable parent property if for any w-subset contained in the union of some t sets, called traitors, of F at least one of these sets can be uniquely determined, i.e. traced. A set system with traceability property (TSS, for short) allows to trace at least one traitor by minimal distance decoding of the corresponding binary code, and hence the complexity of tracing procedure is of order O(M), where M is the number of users or the code's cardinality. We propose a new construction of TSS which is based on the old Kautz-Singleton concatenated construction with algebraic-geometry codes as the outer code and Guruswami-Sudan decoding algorithm. The resulting codes (set systems) have exponentially many users (codevectors) M and polylog(M) complexity of code construction and decoding, i.e. tracing traitors. This is the first construction of traceability set systems with such properties.
The Effect of Motivation on the Learning Achievement

This article discusses the effect of students ' intrinsic motivation on their English achievement, to find out the effect of students’ extrinsic motivation on their English achievement, and to find out the effect of the  eighth grade students’ intrinsic and extrinsic motivation together towards their English achievement at SMPN 5 Bantan. The subject of study was the Eighth grade students of SMPN 5 Bantan  totaling 88 students. The data were collected by using a set of questionnaire and documentation. The instruments used were a reliability test, a normality test and a homogenity test, while the data analysis was used product moment by employing  multiple correlation analysis and regression analysis with a significant level of 5%. The analyzed data werte taken from the  scores of Mid semester. The results showed that 1. There was a positive and significant effect of students' intrinsic motivation on their English learning achievement  with a value of r count was greater than r table (0.287> 0.207). 2. There was no significant effect between students’ extrinsic motivation with their English learning achievement. 3. There was a significant relationship between intrinsic and extrinsic motivation of students with  their English learning achievement  where F count was equal to 6.227, while the F table at 4.88 with N = 88 at the significance level of 5%. So, F was calculated from the F table ( 6.227g 4.88), because the findings of this study showed a significant effect. Overall,  hypothesis test matched the research finding. So, if students have higher motivation, their learning   achievement will increase
Holistic User Context-Aware Recommender Algorithm

Existing recommender algorithms lack dynamism, human focus, and serendipitous recommendations. The literature indicates that the context of a user influences user decisions, and when incorporated in recommender systems (RSs), novel and serendipitous recommendations can be realized. This article shows that social, cultural, psychological, and economic contexts of a user influence user traits or decisions. The article demonstrates a novel approach of incorporating holistic user context-aware knowledge in an algorithm to solve the highlighted problems. Web content mining and collaborative filtering approaches were used to develop a holistic user context-aware (HUC) algorithm. The algorithm was evaluated on a social network using online experimental evaluations. The algorithm demonstrated dynamism, novelty, and serendipity with an average of 84% novelty and 85% serendipity.
Sampling Frequency Evaluation on Recurrent Neural Networks Architectures for IoT Real-time Fall Detection Devices

Falls are one of the most frequent causes of injuries in elderly people.Wearable Fall Detection Systems provided a ubiquitous tool for monitoring and alert when these events happen.Recurrent Neural Networks (RNN) are algorithms that demonstrates a great accuracy in some problems analyzing sequential inputs, such as temporal signal values.However, their computational complexity are an obstacle for the implementation in IoT devices.This work shows a performance analysis of a set of RNN architectures when trained with data obtained using different sampling frequencies.These architectures were trained to detect both fall and fall hazards by using accelerometers and were tested with 10-fold cross validation, using the F1-score metric.The results obtained show that sampling with a frequency of 25Hz does not affect the effectiveness, based on the F1-score, which implies a substantial increase in the performance in terms of computational cost.The architectures with two RNN layers and without a first dense layer had slightly better results than the smallest architectures.In future works, the best architectures obtained will be integrated in an IoT solution to determine the effectiveness empirically.
Average volume reference space for large scale registration of whole-body magnetic resonance images

The construction of whole-body magnetic resonance (MR) imaging atlases allows to perform statistical analysis with applications in anomaly detection, longitudinal, and correlation studies. Atlas-based methods require a common coordinate system to which all the subjects are mapped through image registration. Optimisation of the reference space is an important aspect that affects the subsequent analysis of the registered data, and having a reference space that is neutral with respect to local tissue volume is valuable in correlation studies. The purpose of this work is to generate a reference space for whole-body imaging that has zero voxel-wise average volume change when mapped to a cohort.This work proposes an approach to register multiple whole-body images to a common template using volume changes to generate a synthetic reference space, starting with an initial reference and refining it by warping it with a deformation that brings the voxel-wise average volume change associated to the mappings of all the images in the cohort to zero.Experiments on fat/water separated whole-body MR images show how the method effectively generates a reference space neutral with respect to volume changes, without reducing the quality of the registration nor introducing artefacts in the anatomy, while providing better alignment when compared to an implicit reference groupwise approach.The proposed method allows to quickly generate a reference space neutral with respect to local volume changes, that retains the registration quality of a sharp template, and that can be used for statistical analysis of voxel-wise correlations in large datasets of whole-body image data.
The Impact of Preprocessing on Arabic-English Statistical and Neural Machine Translation

Neural networks have become the state-of-the-art approach for machine translation (MT) in many languages. While linguistically-motivated tokenization techniques were shown to have significant effects on the performance of statistical MT, it remains unclear if those techniques are well suited for neural MT. In this paper, we systematically compare neural and statistical MT models for Arabic-English translation on data preprecossed by various prominent tokenization schemes. Furthermore, we consider a range of data and vocabulary sizes and compare their effect on both approaches. Our empirical results show that the best choice of tokenization scheme is largely based on the type of model and the size of data. We also show that we can gain significant improvements using a system selection that combines the output from neural and statistical MT.