Reviewerly/exHarmony huggingface dataset:
Collision Anticipation via Deep Reinforcement Learning for Visual Navigation

Visual navigation is the ability of an autonomous agent to find its way in a large and complex environment based on visual information. It is indeed a fundamental problem in computer vision and robotics. In this paper, we propose a deep reinforcement learning approach which is able to learn to navigate a scene to reach a given visual target, but anticipating the possible collisions with the environment. Technically, we propose a map-less-based model, which follows an actor-critic reinforcement learning method where the reward function has been designed to be collision aware. We offer a thorough experimental evaluation of our solution in the AI2-THOR virtual environment, where the results show that our proposed method: (1) improves the state of the art in terms of number of steps and collisions; (2) is able to converge faster than a model which does not care about the collisions, simply searching for the shortest paths; and (3) offers an interesting generalization capability to reach visual targets that have never been seen during training.
Scene semantics outperform center bias during scene memorization, image saliency models do not

We must shift our attention to process the complex information in real-world scenes. How do we determine where to focus our attention in scenes? Image saliency theory proposes that our attention is ‘pulled’ to scene regions that differ in low-level image features (e.g., color, orientation, and/or luminance) from the surrounding regions. However, most image saliency models also produce substantial scene-independent spatial biases to help capture observer center bias. In the present study, we tested whether image saliency models explain scene attention based on scene-dependent image features or simply their scene-independent center bias. Participants (N=65) viewed 40 real-world scenes for 12 seconds while performing a scene memorization task. For each scene, a fixation density map was computed across all participant fixations to summarize scene attention. An image saliency map for each scene was then computed using three of the most cited image saliency models including the Itti & Koch model (Itti, Koch, & Niebur, 1998), the Graph-based Visual Saliency model (GBVS; Harel, Koch, & Perona, 2006), and the Attention Information Maximization model (AIM; Bruce & Tsotsos, 2007). For comparison, semantic feature maps (“meaning maps”) were generated using human ratings of the informativeness of isolated scene patches (Henderson & Hayes, 2017). The average squared correlation (R2) between the scene fixation density maps and each image saliency model and its spatial bias were computed separately. The image saliency models on average explained 52% less variance in scene fixation density than their spatial bias alone (IttiKoch bias=0.45, IttiKoch=0.19; GBVS bias=0.46, GBVS=0.37; AIM bias=0.41, AIM=0.08). In comparison, the meaning maps explained on average 14% more variance than the spatial bias models. These results suggest that during scene memorization salient scene regions are weaker predictors of scene attention than a simple center bias model, whereas scene semantics explain additional variance beyond spatial center bias.
A comprehensive depth perception model with filter/cross-correlation/filter (F-CC-F) structure

We have developed a new depth perception model with a filter/cross-correlation/filter (F-CC-F) structure, and validated it over a broad range of spatiotemporal conditions. The two eyes’ images pass through first-stage spatiotemporal filters to compute the normalized cross-correlation (CC), which goes through a maximum (MAX) operator for solving the correspondence problem, then goes through a disparity window to compute local depth quantities, and finally through a second-stage of spatiotemporal filtering to give the perceived depth. Previous work (Ding & Levi VSS 2016) revealed two normalization mechanisms: (1) an energy model (cross-correlation is normalized by monocular contrast energies) to predict Dmin and (2) a gain-control model (interocular contrast gain-controls before cross-correlation) to predict Dmax. Here, we have developed a full model with multiple pathways to explain both Dmin and Dmax simultaneously. To test the model, we performed rating-scale experiments to measure disparity sensitivities over the whole range of binocular disparities. Stimuli were random-Gabor-patch (RGP) stereograms, consisting of vertical Gabor patches with random positions and phases, but with a fixed spatial frequency (3.0 cpd). We tested five stereogram profiles (circles and annuli of 1.76 – 7.04 arcdeg radius) and eight durations (13 – 2013 ms), revealing the complex spatiotemporal properties of depth perception. We found that disparity sensitivity depends on the stereogram size, which can be explained by a band-pass DOG (Difference of Gaussian) filter as the second-stage filter. The model has four pathways, two for crossed- and two for uncrossed-disparities. Each pathway has its own normalization mechanism (either Energy or Gain-control model), its own disparity window (a disparity sensitivity function, the product of disparity power and exponential decay functions, which increases with stimulus disparity at small disparities but decreases at large disparities), and its own second-stage filter. These second-stage filters were validated experimentally. This comprehensive new F-CC-F model successfully predicts the entire data-set.
Managing a heterogeneous scientific computing cluster with cloud-like tools: ideas and experience

Obtaining CPU cycles on an HPC cluster is nowadays relatively simple and sometimes even cheap for academic institutions. However, in most of the cases providers of HPC services would not allow changes on the configuration, implementation of special features or a lower-level control on the computing infrastructure, for example for testing experimental configurations. The variety of use cases proposed by several departments of the University of Torino, including ones from solid-state chemistry, computational biology, genomics and many others, called for different and sometimes conflicting configurations; furthermore, several R&amp;D activities in the field of scientific computing, with topics ranging from GPU acceleration to Cloud Computing technologies, needed a platform to be carried out on. The Open Computing Cluster for Advanced data Manipulation (OCCAM) is a multi-purpose flexible HPC cluster designed and operated by a collaboration between the University of Torino and the Torino branch of the Istituto Nazionale di Fisica Nucleare. It is aimed at providing a flexible and reconfigurable infrastructure to cater to a wide range of different scientific computing needs, as well as a platform for R&amp;D activities on computational technologies themselves. We describe some of the use cases that prompted the design and construction of the system, its architecture and a first characterisation of its performance by some synthetic benchmark tools and a few realistic use-case tests.
SISTEM PAKAR DIAGNOSA PENYAKIT AYAM RAS PETELUR

sophisticated era, especially in the field of computer inspire for the making of computer application System Expert Diagnosis of Chicken Layer layer Disease. The purpose of this study is to create a website application that can be used by laymen either rural farmers or experts who want to add insight into the various symptoms and diseases that attack livestock. There are 45 symptoms that produce 15 diagnoses of disease caused by viruses and bacteria. The programming language used in the design of this expert system is PHP. Meanwhile for the author database using MySQL. The methodology used to develop software is Waterfall. Meanwhile, to build this expert aplik ation writer using a website-based application and the results of this expert application can provide solutions in the form of prevention, how to treatment, and drugs that must be used so as to cope with the disease early on.
Students' acceptance of MySpeechTrainer to improve spoken Academic English

SLaTE 2019: 8th ISCA Workshop on Speech and Language Technology in Education, 20 september 2019
A Framework for Facial Image Analytics Using Deep Learning in Social Sciences Research

Recent advances in artificial intelligence have provided exciting new tools for scientific research that have been applied in many domains, including medicine, engineering, finance, the physical sciences, and online consumer analytics. Although text analytics is now quite a mature approach, image analytics is still developing as research approach and its use is not widespread in the social sciences, particularly owing to the specialized knowledge required in visualization and machine learning. In this paper, we demonstrate how facial image analytics research can be expedited in the social sciences by introducing a simple research framework for integrating facial image classification data into studies. We explain the steps of the framework, including data collection, processing, collation, and analysis. This is accompanied by an example piece of research examining the influence of hosts’ facial expressions on review scores in Airbnb.
Challenges of Using Trusted Computing for Collaborative Data Processing

In recent years many business processes have become more interconnected than ever before. Driven by the advance of the Internet of Things, companies rely on complex data processing chains that span over many collaborating corporations and across different countries. As a result of this development, automated data acquisition and collaborative data usage is now a foundation of many innovative and successful business models. However, despite having a clear interest in sharing valuable data with other stakeholders, data owners simultaneously need to protect their assets against illegitimate use. In order to accommodate this requirement, existing data sharing solutions contain usage control systems capable of enforcing policies on data even after they have been shared. The integrity of these policy enforcement components is often monitored by a trusted platform module (TPM) on the data receiver’s side. In this work we evaluate the adequacy of TPM-based remote attestation for protecting shared data on foreign systems. In order to do so we develop an attacker model that includes privileged system users and expose attack vectors on TPM-protected data sharing applications. We show that TPMs do not provide sufficient protection against malicious administrators from competing stakeholders. Finally, we describe the advantages of using Intel’s Software Guard Extensions (SGX) to protect shared data in hostile environments and propose an enhanced system architecture that includes both SGX enclaves as well as a classical TPM.
Distinguishing Small Targets from Sea Clutter Using Dynamic Models

Radar signal processing for maritime surveillance and reconnaissance is a difficult problem due to the occurrence of sea clutter. The sea surface is highly reflective and agile, therefore the received radar return has a high intensity and it is often correlated in time and space due to undulation. Classical approaches try to suppress the clutter signal on the sensor or measurement level, however such methods usually do not take temporal information over multiple dwells into account. This article provides an analysis of an approach that simultaneously tracks the observed objects and the correlated clutter signals and classifies them based on the correspondence of their state history with the target- and clutter-specific dynamic models. Furthermore, it is demonstrated that this tracking-based classifier maintains good accuracy even on downsampled data, which is representative of a scanning radar. The described method is tested on datasets of the 2006 Fynmeet trial conducted by the Council for Scientific and Industrial Research (CSIR).
Multiphoton Entanglement by Delocalized Single Photon Addition

We report about the generation of entangled states obtained by applying delocalized single-photon addition between two uncorrelated light modes populated by coherent states. Such entangled states can contain an arbitrary large number of photons while preserving their non-classical correlations. Besides their importance to study quantum effects in large-scale systems, these states are found to be surprisingly robust against losses and thus well suited to travel over long distances and to be stored in atomic ensembles.
Scene Clutter and Attention Differentially Affect Object Category and Location Representations

Object perception requires the neural representation of both object identity independent of viewing conditions, and the representation of viewing-dependent properties such as location. The seminal view is that such representations emerge respectively in the ventral and dorsal streams. Recent research in monkeys (Hong et al., 2016) has questioned this division observing both types of information in high-level ventral visual cortex when objects are shown in cluttered scenes. However, how such representations emerge over time, how they emerge in the processing cascade of the ventral visual stream, and whether they depend on attention remains unknown. To investigate this, we conducted three experiments in humans using EEG and fMRI, and analyzed data with multivariate pattern classification methods. Experiment 1 (EEG, N=27) investigated the emergence of object category and location representations in time, dependent on clutter level of the background. We found that object location information emerged later when objects were presented in cluttered versus uncluttered scenes. Further analysis of object location and category information showed that the brain carried out the same processing steps with and without scene clutter, but shifted in time. Experiment 2 (fMRI, N=14) corroborated the previous result in space. In particular, when objects were presented in cluttered versus uncluttered scenes, location information emerged in higher regions of the ventral stream hierarchy. Experiment 3 (EEG, N =25) investigated whether disentangling object-specific information from cluttered scenes might require top-down attention. The emergence of location representations depended strongly on attention for cluttered scenes but was independent of attention in uncluttered scenes. In contrast, category representations were affected by attention independent of whether the background was cluttered or not. Together, our results provide a new perspective on the role of ventral visual cortex in object perception and show how attention impacts the underlying neural processing.
Selection of Software Development Methodologies (SDMs) Using Bayesian Analysis

A software development methodology (SDM)—also called systems development methodology—is a formalized approach for the development of software. Although, there are many different SDMs. In this paper, we proposed a method for the selection of an appropriate SDM for a particular project using Bayesian analysis over various factors affecting the selection of SDM. Bayesian is widely used for decision making. There is a statistical model for various categories established by calculating the conditional probability density functions (PDF). We used some factors, i.e., cost, risk, size, and time duration. We classify categories with the maximum posterior probability within the Bayesian framework. Result of performance of the proposed model is shown by experimentation on the dataset using MATLAB and WINBUGS simulation.
A Study on Quality Assessment of the Translation of an Abstract Text English Idioms Errors Made by Jordanian EFL Undergraduate Students by Google Translate.

This study is a descriptive-qualitative study with an embedded-case study research design. The purpose of the study is to describe the quality of the translation on the accuracy in content, acceptability, and readability of translated text of English Idioms Errors Made by Jordanian EFL, Undergraduate Students abstract. The data of the research is an abstract entitled English Idioms Errors Made by Jordanian EFL, Undergraduate Students and its translation in Indonesian. Two methods of data collection were employed: content analysis used to obtain data from both source and target texts, and questionnaire was used to gather data about the accuracy in content, acceptability, and readability. The accuracy in content, acceptability and readability of the translated text were rated by three raters. The results of data analysis show that: (1) The average score for accuracy in content is 1.97.(2) The average score for acceptability is 1.93. (3) The average score for readability is 2.07. The target readers’ response on the translated text is positive. It can be said that English Idioms Errors Made by Jordanian EFL, Undergraduate Students abstract is quite easy for the target readers to understand. As the average score for those three aspects is 1.97, it can be concluded that the translation of the abstract English Idioms Errors Made by Jordanian EFL, Undergraduate Students by Google Translate was rated close to good quality.
Exaggerated Portrait Caricatures Generation Based On Seq2Seq

Exaggerated portrait caricatures can represent peoples explicit facial characteristics. Recently, there have been increasing interests in exaggerated portrait caricatures generation from the real face images. However, existing methods randomly exaggerate facial features and the effects are not diversified. In this paper, we achieve the extraction of facial structure features and explicit geometry features using sequence-to-sequence model. The extracted facial structure features can be well applied to caricatures generation. In order to avoid destroying the recognition of the characters from exaggerating the input face, we also incorporate the matching threshold loss as a penalty term to constrain the exaggerated expression of facial features. Experiment results on the collected dataset indicate the proposed method can finally get satisfied result.
Adaptive control for a class of nonlinear system using general type-2 fuzzy neural networks approximator

In this paper, an indirect adaptive controller with general type-2 fuzzy neural networks (GT2FNN) approximator is proposed for a general class of SISO nonlinear systems. General type-2 fuzzy system (GT2FS) can be considered as a collection of numerous interval type-2 fuzzy systems (IT2FS) by α-plane. By using the recently introduced adaptive modulation factor into GT2FNN, the computational complexity and time-consuming are greatly reduced in the type reduction of IT2FS. The KM algorithm can be avoided in the type reduction of GT2FNN. Compared to the conventional GT2FNN, the proposed GT2FNN has obvious advantages in computational complexity and time consumption. The Lyaponov approach proves the stability of the closed-loop system. The simulation results show that the tracking performance of GT2FNN approximator is better than IT2FNN and T1FNN approximator.
Deep Aesthetic Assessment of Breast Cancer Surgery Outcomes

Breast cancer is a highly mutable and rapidly evolving disease, with a large worldwide incidence. Even though, it is estimated that approximately 90% of the cases are treatable and curable if detected on early staging and given the best treatment. Nowadays, with the existence of breast cancer routine screening habits, better clinical treatment plans and proper management of the disease, it is possible to treat most cancers with conservative approaches, also known as breast cancer conservative treatments (BCCT). With such a treatment methodology, it is possible to focus on the aesthetic results of the surgery and the patient’s Quality of Life, which may influence BCCT outcomes. In the past, this assessment would be done through subjective methods, where a panel of experts would be needed to perform the assessment; however, with the development of computer vision techniques, objective methods, such as BAT© and BCCT.core, which perform the assessment based on asymmetry measurements, have been used. On the other hand, they still require information given by the user and none of them has been considered the gold standard for this task. Recently, with the advent of deep learning techniques, algorithms capable of improving the performance of traditional methods on the detection of breast fiducial points (required for asymmetry measurements) have been proposed and showed promising results. There is still, however, a large margin for investigation on how to integrate such algorithms in a complete application, capable of performing an end-to-end classification of the BCCT outcomes. Taking this into account, this thesis shows a comparative study between deep convolutional networks for image segmentation and two different quality-driven keypoint detection architectures for the detection of the breast contour. One that uses a deep learning model that has learned to predict the quality (given by the mean squared error) of an array of keypoints, and, based on this quality, applies the backpropagation algorithm, with gradient descent, to improve them; another which uses a deep learning model which was trained with the quality as a regularization method and that used iterative refinement, in each training step, to improve the quality of the keypoints that were fed into the network. Although none of the methods surpasses the current state of the art, they present promising results for the creation of alternative methodologies to address other regression problems in which the learning of the quality metric may be easier. Following the current trend in the field of web development and with the objective of transferring BCCT.core to an online format, a prototype of a web application for the automatic keypoint detection was developed and is presented in this document. Currently, the user may upload an image and automatically detect and/or manipulate its keypoints. This prototype is completely scalable and can be upgraded with new functionalities according to the user’s needs .
Curbing Hatred: The Ethnic Diehards' Agitations and 2015 Presidential Election Campaign in Nigeria

Nigeria witnessed bouts of hate speeches during the buildup to the March 28, 2015 Presidential election. Politicians and their allies made use of acerbic words, half-truths and outright lies during their electioneering campaigns. The election was regarded as a ‘do-or-die’ affair by the politicians. Their activities were alarming and apprehensions were rife about the success of the election and peaceful transition of power. Hence, the two major contestants, Goodluck Jonathan and Muhammadu Buhari along with nine party leaders signed the ‘Abuja Accord’ on January 14, 2015.Furthermore, the country experienced a season of acerbic comments, issuance of quit notices and calls for secessions by ethnic jingoists. These activities heated up the polity and caused panic among the citizens of the country. On June 6, 2017, the Arewa Youth Consultative Forum comprising of 19 Northern states of Nigeria issued a 90-day ultimatum to South-Eastern residents in the North to vacate the region before October 1, 2017.This article explores the use of hate speech in electioneering campaign drawing from secondary data i.e. newspaper excerpts; and through the lens of ethics using relevant Communication Practice Codes, Nigerian Press Council Code, Nigerian Broadcasting Code, 2012 and relevant sections of Electoral Act 2010. It also examines the issue of the ethnic diehards’ agitations and quit notice and its implications for national peace, security and justice. The argument and submission of this paper highlights ways of curbing hatred, advancing of peace, acceptance, tolerance and justice.
Double Nearest-Neighbor Error Correcting Codes on Hexagonal Signal Constellation

A new class of double nearest-neighbor errorcorrecting codes on hexagonal constellation in the two dimensional space is presented. The proposed code is a linear [n,n−3] code over GF(p) where p = 6n+1 is a prime. We show that the proposed code corrects any generalized Lee error with at most weight 2.