Reviewerly/exHarmony huggingface dataset:
Effects of home reading training on reading and quality of life in AMD—a randomized and controlled study

Age-related macular degeneration (AMD) causes reading impairment, reduced quality of life (QoL), and secondary depression. We have shown that support with magnifying aids improved reading speed (RS), emotional and cognitive status, and QoL. The present study investigates whether additional reading training (RT) (after adapting to appropriate visual aids) can further improve vision rehabilitation.Patients with dry AMD were randomly assigned to 2 groups. The primary RT group (P-RTG, n = 25) trained with sequentially presented text (RSVP), and the control group (CG, n = 12) performed placebo training (crossword puzzles) and later crossed over to RT, so that altogether 37 participants performed reading training. Patients trained at home on a PC for 6 weeks. RS was assessed during reading printed paragraphs of text aloud. Using a scanning laser ophthalmoscope, we examined fixation stability and preferred retinal locus (PRL) for fixating a cross, as well as PRL and eye movements during reading single words. We assessed emotional status by Montgomery-Åsberg Depression Rating Scale (MADRS), cognitive status by dementia detection test ( DemTect ) and QoL by Impact of Vision Impairment (IVI) profile. Visual acuity and magnification requirement were examined by standard procedures. All variables were measured before and after placebo training, before and after RT, and after 6 weeks without training (follow-up).RS improved significantly in the P-RTG during RT, but not in the CG during placebo training. The effect remained stable at follow-up. Fixation performance and eye movement variables did not change. Emotional status (MADRS) improved in P-RTG during RT and showed a significant difference of the change of scores between the 2 groups. Complete IVI scores improved significantly during RT and remained stable.The results indicate that patients with AMD, who already use magnifying aids, benefit from additional RT and that it can contribute in preventing depression and improve QoL.The study was registered at the German Clinical Trials Register (DRKS00015609).
Human action recognition from RGB-D data using complete local binary pattern

Human action recognition is an active research domain in Computer Vision and Pattern Recognition due to the challenges such as inter and intra class variation, background clutter, partial occlusion and changes in scale, viewpoint, lighting, appearance etc. Human action recognition aims at determining the activities of a person or a group of persons, as well as on knowledge about the context within which the observed activities take place. As RGB cameras responds easily to illumination changes and surrounding clutters, the worthwhile RGB Depth (RGB-D) camera sensors (e.g. Kinect) is used to improve the action recognition. This paper aims at classifying Human Actions by integrating salient motion features from both RGB and Depth Camera. The methodology includes Salient Information Map generation from both RGB and depth action sequences signposting the motion significant region of the corresponding action sequence. From the Salient Information Map, Sign, Magnitude and Center descriptors representing Complete Local Binary Pattern was extracted. Then the fusion of features from depth and RGB is carried out by Canonical Correlation Analysis accompanied by dimensionality reduction. Multiclass SVM classifier is used for classifying the features in to various action categories. The experimental analysis of the proposed algorithm was carried with MSR Daily Activity 3D Dataset and UTD-MHAD Action Dataset and the recognition rate of 98.75% and 84.12% was obtained.
A shared ontology for integrated highway planning

Many highway agencies have several functional groups responsible for planning for safety, maintenance and rehabilitation (M&R), mobility, and other functions. The functional nature of State Highway Agencies (SHAs) can result in a siloed approach to planning. Such efforts are further challenged by functional groups utilizing legacy systems which lack interoperability. In practice, this leads to redundant planning efforts and potential spatial-temporal conflicts in the projects proposed by the different groups over a planning period. There is a need for an integrated approach to planning supported by information systems. However, the existing literature on formalized knowledge representation fails to adequately account for the level of information needed for cross-functional planning of projects scheduled for the same network. Hence, this study presents an ontology for integrating information to support the cross-functional and spatial-temporal planning of highway projects. The Integrated Highway Planning Ontology (IHP-Onto) is a shared representation of knowledge about pavement assets, M&R planning, and inter-project coordination. Sources of the knowledge acquired included expert interviews, a review of nation-wide studies, and previously published ontologies. The implementation phase included a case study demonstration of the ontology by answering relevant competency questions via SPARQL queries. Based on the data-driven evaluation of the ontology, the precision and recall rates obtained were 97% and 92% respectively. Based on the results of the evaluation approaches, IHP-Onto was demonstrated as being sufficient to represent domain knowledge capable of supporting integrated highway planning.
Optiwords: A new password policy for creating memorable and strong passwords

User-generated textual passwords suffer from the conflict between security and usability. System administrators usually adopt password composition policies to help users choose strong passwords. However, users often use predictable patterns to meet the strict password composition policies and to make passwords easy to remember, which in turn reduces the password strength, or write the password down, which may cause the password to be compromised. To overcome the user-generated password security and usability dilemma, we propose Optiwords, which is a new textual-password creation policy that is based on picture superiority effect, which provides users with a direct “drawing-to-text” method for creating user-friendly passwords. Optiwords helps users design separate line drawings on the keyboard as a “password figure” and choose the characters on the lines of the drawings in a certain sequence as the final textual password. A two-part user study with 127 participants was conducted to compare the usability and security of Optiwords with other three popular password policies. The results showed that there was no statistically significant difference compared Optiwords with Basic8 or 3class8 in memorability. The password strength of Optiwords outperformed Basic8 and 3class8. Compared with Random8, Optiwords had a great advantage in usability.
Comparative geochemical study between the tap waters and the bottled mineral waters in Calabria (Southern Italy) by compositional data analysis (CoDA) developments

This work focuses on comparing the inorganic content of tap and bottled mineral waters in the Calabria region (Southern Italy) performed by using robust Compositional Data Analysis (CoDA) methods and related new developments associated with a classical graphical-numerical approach. Thirty-one samples of tap waters scattered throughout the Calabrian territory and twenty-one bottled natural mineral waters of various Calabrian brands were analyzed for major and trace inorganic components. In addition, a very sensitive method based on cold vapour atomic fluorescence spectrometry (CV-AFS) has been employed to determine total mercury (HgT) concentrations. Compositional data analysis was used to identify the main interrelationships among physico-chemical parameters, highlighting the differences between the tap waters and the bottled mineral waters. Some elements, such as Al, Mn, Fe, Zn, and Pb resulted particularly enriched in the tap waters. This group of enriched elements was probably controlled by corrosion processes from metal pipes, asking for more monitoring on the state of the public distribution water network. Li, Ca, Cr, Ni, and Cd, on the other hand, appear to be mostly contributed by natural sources affecting the overall variability of the bottled mineral waters. In this context, Cr, Cu, Hg, and Pb are mainly enriched in the sparkling bottled mineral waters. Innovative compositional indices did not highlight great differences from the Maximum Allowed Concentrations (MAC) in the two types of water, so that for the health of the consumer it is indifferent to drink tap or bottled mineral water. The results represent a fundamental base to develop monitoring plans in time to check for the maintenance of quality standards.
Intuit before tuning: Type-1 and type-2 fuzzy logic controllers

Although a considerable amount of effort has been put in to show that fuzzy logic controllers have exceptional capabilities of dealing with uncertainty, there are still noteworthy concerns, e.g., the design of fuzzy logic controllers is an arduous task due to the lack of closed-form input–output relationships which is a limitation to interpretability of these controllers. The role of design parameters in fuzzy logic controllers, such as position, shape, and height of membership functions, is not straightforward. Motivated by the fact that the availability of an interpretable relationship from input to output will simplify the design procedure of fuzzy logic controllers, the main aims in this work are derive fuzzy mappings for both type-1 and interval type-2 fuzzy logic controllers, analyse them, and eventually benefit from such a nonlinear mapping to design fuzzy logic controllers. Thereafter, simulation and real-time experimental results support the presented theoretical findings.
Semantic Face Hallucination: Super-Resolving Very Low-Resolution Face Images with Supplementary Attributes

Given a tiny face image, existing face hallucination methods aim at super-resolving its high-resolution (HR) counterpart by learning a mapping from an exemplary dataset. Since a low-resolution (LR) input patch may correspond to many HR candidate patches, this ambiguity may lead to distorted HR facial details and wrong attributes such as gender reversal and rejuvenation. An LR input contains low-frequency facial components of its HR version while its residual face image, defined as the difference between the HR ground-truth and interpolated LR images, contains the missing high-frequency facial details. We demonstrate that supplementing residual images or feature maps with additional facial attribute information can significantly reduce the ambiguity in face super-resolution. To explore this idea, we develop an attribute-embedded upsampling network, which consists of an upsampling network and a discriminative network. The upsampling network is composed of an autoencoder with skip-connections, which incorporates facial attribute vectors into the residual features of LR inputs at the bottleneck of the autoencoder, and deconvolutional layers used for upsampling. The discriminative network is designed to examine whether super-resolved faces contain the desired attributes or not and then its loss is used for updating the upsampling network. In this manner, we can super-resolve tiny (16×16 pixels) unaligned face images with a large upscaling factor of 8× while reducing the uncertainty of one-to-many mappings remarkably. By conducting extensive evaluations on a large-scale dataset, we demonstrate that our method achieves superior face hallucination results and outperforms the state-of-the-art.
Multilayer Dense Attention Model for Image Caption

The image caption is a technology that enables us to understand the contents and generate descriptive text, of images using machines. With the development of deep learning, means of using it to understand image content and generate descriptive text has become a hot research topic. This paper proposes a multilayer dense attention model for image caption. A faster recurrent convolutional neural networks (Faster R-CNN) is employed to extract image features as the coding layer, the long short-term memory (LSTM)-attend is used to decode the multilayer dense attention model, and the description text is generated. The model parameters are optimized using strategy gradient optimization in reinforcement learning. Use of dense attention mechanisms in the coding layer can effectively avoid the interference of non-salient information and selectively output the corresponding description text for the decoding process. The experimental results in the field of general images validate the model’s good ability to understand images and generating text.
Lost traffic encryption

Long Term Evolution (LTE) provides the communication infrastructure for both professional and private use cases and has become an integral part of our everyday life. Even though LTE/4G overcomes many security issues of previous standards, recent work demonstrates several attack vectors on the physical and network layers of the LTE stack. We do, however, have only limited insights into the security and privacy aspects of the second layer.
Towards low-complexity wireless technology classification across multiple environments

To cope with the increasing number of co-existing wireless standards, complex machine learning techniques have been proposed for wireless technology classification. However, machine learning techniques in the scientific literature suffer from some shortcomings, namely: (i) they are often trained using data from only a single measurement location, and as such the results do not necessarily generalise and (ii) they typically do not evaluate complexity/accuracy trade-offs of the proposed solutions. To remedy these shortcomings, this paper investigates which resource-friendly approaches are suitable across multiple heterogeneous environments. To this end, the paper designs and evaluates classifiers for LTE, Wi-Fi and DVB-T technologies using multiple datasets to investigate the complexity/accuracy trade-offs between manual feature extraction and automatic feature learning techniques. Our wireless technology classification reaches an accuracy up to 99%. Moreover, we propose the use of data augmentation techniques to extend these results to unseen environments at the cost of only 2% reduction in accuracy. When concerning generalisation capabilities, complex automatic learning techniques surpass simple manual feature extraction approaches. Finally, the complexity of these automatic learning techniques can be significantly reduced by using computationally less intensive received signal strength indicator data while reaching acceptable accuracies in unseen environments (92% vs 97%).
On improving CNNs performance: The case of MNIST

In this note, we follow two directions to improve the performance of CNN classifiers. The first is to apply to CNN units the same improvement techniques that we have successfully used with Stacked Denoising Auto-Encoder classifiers. This leads to obtain a new performance record when classifying MNIST digits. The second consists of applying a Stacked Denoising Auto-Encoder classifier to the output of the best of the previous designs, trying to take advantage of the limitations of CNN architectures. An even better classification record is obtained for MNIST. The above results permit to conclude that combining improvement techniques and stacking deep machines of different nature can be useful to better solve other real-world problems.
Effect of corruption and political instability on enterprises’ innovativeness in Ethiopia: pooled data based

The aim of this study is to investigate the effects of corruption and political instability on Ethiopian enterprises' innovativeness based on pooled data from 644 and 848 enterprises (manufacturing, retailing, and non-retailing services) surveyed in 2011 and 2015, respectively by the World Bank. Both descriptive statistics and structural equation modeling (SEM) were employed for the data analysis. The descriptive statistics results revealed the decline in Ethiopian enterprises' innovativeness during 2015 in comparison with 2011. Similarly, Internet access and training provided to permanent employees somehow decrease over the study period, which are believed to be unfavorable for enterprises' innovativeness. The model results revealed significant greasing effects of corruption, power outage, time and capital budget for R&D activities, exposure to foreign markets, provision of formal training to permanent employees, and number of employees with high school education on Ethiopian enterprises' innovativeness. However, political instability has significant negative effects on enterprises' innovativeness in the country. The study implies that quality of labor, access to training and foreign markets, and political stability are covariates that seek special attention of enterprises and other concerned parties to enhance innovativeness.
Fast Collective Activity Recognition Under Weak Supervision

Collective activity recognition, which tells what activity a group of people is performing, is a cutting-edge research topic in computer vision. Different from action performed by individuals, collective activity needs to consider the complex interactions among different people. However, most previous works require exhaustive annotations such as accurate label information of individual actions, pairwise interactions, and poses, which could not be easily available in practice. Moreover, most of them treat human detection as a decoupled task before collective activity recognition and leverage all detected persons. This not only ignores the mutual relation between the two tasks, which makes it hard for filtering out irrelevant people, but also probably increases the computation burden when reasoning the collective activities. In this paper, we propose a fast weakly supervised deep learning architecture for collective activity recognition. For fast inference, we propose to make the actor detection and weakly supervised collective activity reasoning collaborate in an end-to-end framework by sharing convolutional layers between them. The joint learning makes the two tasks united and reinforced each other, so that it is more effective to filter out the outliers who are not involved in the activity. For the weakly supervised learning, we propose a latent embedding scheme for mining person-group interactive relationship to get rid of the use of any pairwise relation between people and the individual action labels as well. The experimental results show that the proposed framework achieves comparable or even better performance as compared to the state-of-the-art on three datasets. Our joint modelling reasons collective activities at the speed of 22.65 fps, which is the fastest ever known and substantially makes collective activity recognition more towards real-time applications.
Determination of the Blood, Hormone and Obesity Value Ranges that Indicate the Breast Cancer, Using Data Mining Based Expert System

Breast cancer is a dangerous type of cancer that spreads into other organs over time. Therefore, medical studies are being done for the early diagnosis by means of the anthropometric data and blood analysis values besides the mammographic and histological findings. However, medical studies have identified only cancer-related values but the value ranges indicating the cancer have not been determined yet. Concurrently the automated diagnostic systems are being developed to assist medical specialists in biomedical engineering studies. The range of values or boundaries indicating the cancer are automatically determined in biomedical methods, but only the diagnostic result is presented. Because of this, biomedical studies don't provide enough opportunity for medical experts to evaluate the relationship between values and result. In this study, decision trees that is one of data mining method was applied to anthropometric data and blood analysis values to complete the mentioned deficiencies in breast cancer diagnosis aiming studies. The determined value ranges were also presented visually to medical experts understand them easily. The proposed diagnostic system has accuracy rate up to 90.52% and provides value ranges indicating the breast cancer as well as mathematically presents the relations between the values and cancer.
A Preliminary Review of Blockchain-Based Solutions in Higher Education

Blockchain technology is showing its potential to disrupt established business processes. Four types of blockchain initiatives have been identified: Record keeper, efficiency play, digital asset market, and blockchain disruptor. Many different applications have also been emerging within the educational domain, following a student or institution-centric approach. Student-centric solutions simplify the validation process of received credentials, while institution-centric solutions facilitate mainly operational activities of educational institutions. In this paper, we present several use cases addressing different aspects within the educational domain, such as streamlining the process of diploma verification, virtual lifetime learning passport, securing the issued certificates permanently, verifying the accreditation process, automatic recognition of credits, etc. A preliminary review and analysis of identified projects and initiatives show that most of them follow a student-centric approach while facilitating record-keeping.
Comparative Analysis of Dual and Single Axis Solar Tracking System Considering Cloud Cover

The objective of this work is to carry out a comparative performance analysis of three types of solar tracking systems, fixed, single and dual axis, in the context of Bangladesh in order to determine their suitability and cost-effectiveness for different applications. Total incident solar energy on the photovoltaic panel of same size for the three different systems has been determined for the twelve months, first without and then with considering the effect of cloud cover. The findings show that there is no significant difference in energy yield between single axis and dual axis tracker. The percent difference in total incident energy in one whole year between the single and dual axis tracker systems is found to be about 3.96% without considering the effect of cloud which further reduces to 3.44% when the effect of cloud cover is taken into consideration.