Miracle-dz/UniFAR-FTUs huggingface dataset:
Hybrid Attribute Based Encryption and Customizable Authorization in Cloud Computing

Most centralized systems allow data access to its cloud user if a cloud user has a certain set of satisfying attributes. Presently, one method to compete such policies is to use an authorized cloud server to maintain the user data and have access control over it. At times, when one of the servers keeping data is compromised, the security of the user data is compromised. For getting access control, maintaining data security and obtaining precise computing results, the data owners have to keep attribute-based security to encrypt the stored data. During the delegation of data on cloud, the cloud servers may be tampered by the counterfeit cipher-text. Furthermore, the authorized users may be cheated by retorting them that they are unauthorized. Largely the encryption control access attribute policies are complex. In this paper, we present Cipher-text Policy Attribute-Based Encryption for maintaining complex access control over encrypted data with verifiable customizable authorization. The proposed technique provides data confidentiality to the encrypted data even if the storage server is comprised. Moreover, our method is highly secured against collusion attacks. In advance, performance evaluation of the proposed system is elaborated with implementation of the same.
Towards Individualized Affective Human-Machine Interaction

Robots and other autonomous systems interacting with humans should customize their behaviour to their human partner's preferences. We propose a method for learning and generating robot movement customized to individual preferences. Within a reinforcement learning framework, we generate rewards based on facial expressions observed during the robot's motion. Robot motions are parametrized; the rewards are used to modify these motion parameters using Q learning. The proposed approach is evaluated in a user study, using an interactive kinetic sculpture. The system interacts with participants and evolves its motion based on the rewards estimated from the participants' facial expressions. Our results show that, for a subset of participants, the system was able to successfully generate actions that resulted in higher than random rewards. The ability to successfully generate high-reward actions depends on: being able to recognize positive affect from the face, being able to generate actions that are pleasing to the participant, and being able to learn the mapping from rewards to actions.
A Model-Based Approach to Streamlining Distributed Training for Asynchronous SGD

The success of Deep Neural Networks (DNNs) has created significant interest in the development of software tools, hardware architectures, and cloud systems to meet the huge computational demand of their training jobs. A common approach to speeding up an individual job is to distribute training data and computation among multiple nodes, periodically exchanging intermediate results. In this paper, we address two important problems for the application of this strategy to large-scale clusters and multiple, heterogeneous jobs. First, we propose and validate a queueing model to estimate the throughput of a training job as a function of the number of nodes assigned to the job; this model targets asynchronous Stochastic Gradient Descent (SGD), a popular strategy for distributed training, and requires only data from quick, two-node profiling in addition to job characteristics (number of requested training epochs, mini-batch size, size of DNN parameters, assigned bandwidth). Throughput estimations are then used to explore several classes of scheduling heuristics to reduce response time in a scenario where heterogeneous jobs are continuously submitted to a large-scale cluster. These scheduling algorithms dynamically select which jobs to run and how many nodes to assign to each job, based on different trade-offs between service time reduction and efficiency (e.g., speedup per additional node). Heuristics are evaluated through extensive simulations of realistic DNN workloads, also investigating the effects of early termination, a common scenario for DNN training jobs.
CBI-20: Psychometric Properties for the Coping Behaviors Inventory for Alcohol Abuse in Brazil

For any professional, it is of crucial importance to know not only how coping styles and strategies are present in an individual, but to know about its role to the treatment of alcohol abuse. Moreover, new approaches have emerged in this area in terms of relapse prevention and the counseling by phone can be an alternative. The aim of this study is to examine the factor structure of the Coping Behaviors Inventory (CBI) and to test its invariance across groups face-to-face and phone counseling in Brazil. For this purpose, two studies were carried out: study I, the factor structure was revisited in terms of exploratory factor analysis. Study II, face-to-face and phone counseling were examined through confirmatory factor analysis and multigroup analysis. The results confirmed the 4-factor solution with a revised model for the removal of 16 items. Thus presented, a reduced version with better indexes than the previous versions developed over the last 30 years that was ones reformulated from 60 items. The Internal consistency for study I presented α = 0.90 and homogeneity was between 0.17 and 0.5). In addition the KMO = 0.9 = 0.932, X(df=630)2 = 6091.94, p < 0.0 < 0.001. In study II, cronbach's alpha = 0.91 and homogeneity 0.23–0.61 (telemedicine treatment) and α = 0.90 0.17 to 0.63 (face-to-face treatment). In the CFA, the examination of the current version has better fit than the that the traditional model. Moreover, the new version showed convergent validity with the IDHEA questionnaire. In the multigroup analysis no significant changes between groups to a metric level. Finally, the Brazilian version of inventory showed no differences between the phone counseling and face-to-face participants in a metric level after a multigroup analysis.
Does Meditation Alter Brain Responses to Negative Stimuli? A Systematic Review

Background: Despite several attempts to review and explain how meditation alters the brain and facilitates emotion regulation, the extent to which meditation and emotion regulation strategies share the same neural mechanisms remains unclear. Objective: We aim to understand the influence of meditation on the neural processing of negative emotional stimuli in participants who underwent meditation interventions (naive meditators) and long-term meditators. Methodology: A systematic review was conducted using standardized search operators that included the presence of terms related to emotion, meditation and neuro-imaging techniques in PsycInfo, PubMed, Scopus, and Web of Science databases. Results: Searches identified 882 papers, of which 11 were eligible for inclusion. Results showed a predominance of greater prefrontal/frontal activity related to meditation, which might indicate the increased recruitment of cognitive/attentional control resources in naïve and long-term meditators. This increased frontal activity was also observed when participants were asked to simply react to negative stimuli. Findings from emotion-related areas were scarce but suggested increased insular activity in meditators, potentially indicating that meditation might be associated with greater bodily awareness. Conclusions: Meditation practice prompts regulatory mechanisms when participants face aversive stimuli, even without an explicit request. Moreover, some studies reported increased insular activity in meditators, consistent with the hypothesis that meditation helps foster an interoceptive awareness of bodily and emotional states.
OPTICAL COHERENCE TOMOGRAPHY ANGIOGRAPHY CHANGES AFTER SUBTHRESHOLD MICROPULSE YELLOW LASER IN DIABETIC MACULAR EDEMA.

PURPOSE To assess changes on optical coherence tomography (OCT) angiography in diabetic macular edema (DME) treated with subthreshold micropulse yellow laser (SMPL) over a period of 6 months. METHODS Thirty-five eyes (35 consecutive patients) with treatment-naive DME prospectively underwent (at baseline, 3 and 6 months) best-corrected visual acuity, swept-source OCT angiography/OCT, and fundus autofluorescence. Following parameters were evaluated on OCT angiography in the superficial capillary plexus (SCP) and deep capillary plexus (DCP): the area of foveal avascular zone, number of microaneurysms (MA), area of cysts, and presence of capillary network alterations. Microaneurysm change was also evaluated in 15 fellow eyes, not needing treatment over 6 months. Vessel and perfusion densities were evaluated in the SCP, DCP, and choriocapillaris, with image J. Retina thickness, number of hyperreflective retinal spots, and external limiting membrane integrity were evaluated on OCT. All measurements were performed by two masked graders, independently. RESULTS All patients had diabetes mellitus Type 2 (mean age, 69.4 ± 10.9 years; duration of diabetes mellitus, 15.7 ± 8.7 years; and HbA1c 7.7 ± 1.2%). Mean best-corrected visual acuity at baseline was 69.7 ± 12.0 letters ETDRS, 72.7 ± 10.7 at 3 months (gain 3.1 ± 4.3, P = 0.0049) and 74.3 ± 9.5 at 6 months (gain 4.6 ± 7.2, P < 0.0001). Foveal avascular zone area decreased in the DCP at 6 months (P = 0.01). Area of cysts decreased in the SCP at 3 months and 6 months (P = 0.038; P = 0.049), and in the DCP at 6 months (P = 0.0071). Number of MA decreased at 6 months in the SCP (P = 0.0007) and at 3 months and 6 months in the DCP (P = 0.048; P < 0.0001) in treated eyes. No significant change in number of MA was found in nontreated eyes. There was no statistically significant change in any other OCT angiography/OCT parameter. CONCLUSION Subthreshold micropulse yellow laser induces more pronounced changes in the DCP than in the SCP in DME. These changes occurred as early as 3 months after treatment. The evaluation of specific parameters in the DCP may help in determining treatment response.
Immune Dysfunction and Autoimmunity as Pathological Mechanisms in Autism Spectrum Disorders

Autism spectrum disorders (ASD) are a group of heterogeneous neurological disorders that are highly variable and are clinically characterized by deficits in social interactions, communication, and stereotypical behaviors. Prevalence has risen from 1 in 10,000 in 1972 to 1 in 59 children in the United States in 2014. This rise in prevalence could be due in part to better diagnoses and awareness, however, these together cannot solely account for such a significant rise. While causative connections have not been proven in the majority of cases, many current studies focus on the combined effects of genetics and environment. Strikingly, a distinct picture of immune dysfunction has emerged and been supported by many independent studies over the past decade. Many players in the immune-ASD puzzle may be mechanistically contributing to pathogenesis of these disorders, including skewed cytokine responses, differences in total numbers and frequencies of immune cells and their subsets, neuroinflammation, and adaptive and innate immune dysfunction, as well as altered levels of immunoglobulin and the presence of autoantibodies which have been found in a substantial number of individuals with ASD. This review summarizes the latest research linking ASD, autoimmunity and immune dysfunction, and discusses evidence of a potential autoimmune component of ASD.
Frequency Domain Modeling of a Dipole Antenna Buried in Lossy Half-Space Pocklington equation versus Hallén equation revisited: 80th Anniversary of the Hallén Integral Equation

The paper reviews the frequency domain modeling of buried dipole antenna using both the Pocklington integro-differential equation and Hallén integral equation formulation. The details pertaining to the numerical solution procedures are outlined as, well. Some strengths and weaknesses of both approaches are presented. A typical computational example for the current distribution induced along the dipole embedded in a lossy ground illustrating both methods is given, as well.
A Comparative Study of Laparoscopic versus open Appendicectomy in a District Hospital Setup

Introduction: Laparoscopic appendectomy and open appendectomy are the two methods of choice for appendectomy, the acceptance of laparoscopy over laparotomy still remaining a dogma. The specific aim was to compare the outcomes of both the methods in a district hospital setup. Methods: A non-randomized interventional study was performed in a district hospital for a period of 1 year and follow up period of 3 months in the post op period at regular intervals after 1week, 4 weeks and 12 weeks post-op. Statistical Analysis Used: The data was entered into a Microsoft Excel spreadsheet and then analyzed using the SPSS 20.0. 1 and Graph Pad Prism version 5. Results: Lesser hospital stay, intra and post-op analgesia, post-op pain, smaller scars and earlier ambulation, post-op feed and recovery in case of laparoscopic appendectomy as compared to open appendectomy. Conclusion: Superiority of laparoscopic appendectomy over open appendectomy and can undoubtedly be the surgery of choice for both acute and recurrent acute appendicitis in a district level setup in India with better training, manpower and cost effectiveness.
Breast Mass Classification in Mammograms using Ensemble Convolutional Neural Networks

The paper presents quantitative results of a preliminary study undertaken as part of Decision Support and Information Management System for Breast Cancer (DESIREE). DESIREE is a European-funded project to improve the management of primary breast cancer through image-based, guideline-based, experience-based, and case-based information systems. In this study we explore the use of ensemble deep learning for breast mass classification in mammograms. The proposed method is based on AlexNet with some modifications in order to adapt it to our classification problem. Subsequently, model selection is performed to select the best three results based on the highest validation accuracies during the validation phase. Finally, the prediction is based on the average probability of the models. Experimental evaluation shows that accuracy from individual models ranges between 75% and 77%, but combining the best models (ensemble networks) results in over 80% classification accuracy and aura under the curve.
Healthcare professionals’ attitudes towards remote patient monitoring through sensor networks

Sensor-based networks have been proposed as a method for the continuous and remote monitoring of patients with chronic illness. Hence, healthcare professionals need to read and provide feedback on the gathered sensor data which represent patient’s vital signs. This qualitative study examined healthcare professionals’ attitudes towards the application of remote patient monitoring through sensor networks in developing regions using semi-structured interviews. Thematic analysis of the interview data revealed that healthcare professionals have experience with digital technologies, moreover, they are willing to use other more advanced technologies that are, wireless, accurate and enable remote and real-time monitoring of patients. According to the healthcare professionals, the benefits of sensor-based networks are: recording patient’s vital signs for longer periods of time; facilitating clinical decision-making; and helping them in providing better treatment which will have a positive impact in the patient’s life. This study confirms that trainings provided to medical personnel before the application of the digital platforms in healthcare helped them to easily adapt these technologies. Furthermore, it did not appear that age was a problem to use digital monitoring technologies in healthcare. Nevertheless, healthcare professionals confirmed that possible limitations for providing distance-based monitoring in developing regions and giving feedback is related to patient’s education and maturity to obey to doctor’s suggestions, doctor’s commitments and available time and the financial aspect of communicating with a patient at a distance.
Learning Groupwise Scoring Functions Using Deep Neural Networks

While in a classification or a regression setting a label or a value is assigned to each individual document, in a ranking setting we determine the relevance ordering of the entire input document list. This difference leads to the notion of relative relevance between documents in ranking. The majority of the existing learning-to-rank algorithms model such relativity at the loss level using pairwise or listwise loss functions. However, they are restricted to pointwise scoring functions, i.e., the relevance score of a document is computed based on the document itself, regardless of the other documents in the list. In this paper, we overcome this limitation by proposing generalized groupwise scoring functions (GSFs), in which the relevance score of a document is determined jointly by groups of documents in the list. We learn GSFs with a deep neural network architecture, and demonstrate that several representative learning-to-rank algorithms can be modeled as special cases in our framework. We conduct evaluation using the public MSLR-WEB30K dataset, and our experiments show that GSFs lead to significant performance improvements both in a standalone deep learning architecture, or when combined with a state-of-the-art tree-based learning-to-rank algorithm.
A Hierarchical Clustering-Based Approach for Software Restructuring at the Package Level

Software refactoring at the package level has a major importance in the field of software architecture, since refactoring increases the internal software quality and is beneficial during software maintenance and evolution. As the requirements for grouping application classes into software packages are hard to identify, clustering is useful, since it is able to uncover hidden patterns in data. In this paper we are investigating software refactoring at the package level by using hierarchical clustering. A hierarchical clustering approach is proposed in order to help software developers in designing well-structured software packages. The approach takes an existing software system and remodularizes it at the package level using hierarchical clustering, in order to obtain better-structured packages. The experimental evaluation of the approach is performed on three open source frameworks and the algorithm has proven to perform well.
Polarization Charges in a High-Performance GaN/InGaN Core/Shell Multiple Quantum Well Nanowire for Solar Energy Harvesting

The influence of polarization charges on the performance of III-nitride multiple quantum well (MQW) based nanowire photovoltaic devices due to stress and strain at heterointerfaces is investigated. The 7 × GaN/In<italic> <sub>x</sub></italic>Ga<sub>1‐</sub><italic><sub>x</sub></italic>N core/shell MQW triangular nanowire solar cell with {0001}, {1<inline-formula><tex-math notation="LaTeX">$\bar{1}$</tex-math></inline-formula> 0<inline-formula> <tex-math notation="LaTeX">$\bar{1}$</tex-math></inline-formula>}, {<inline-formula><tex-math notation="LaTeX"> $\bar{1}$</tex-math></inline-formula> 10<inline-formula><tex-math notation="LaTeX">$\bar{1}$</tex-math></inline-formula> } or {000<inline-formula><tex-math notation="LaTeX">$\bar{1}$</tex-math></inline-formula>}, {1<inline-formula> <tex-math notation="LaTeX">$\bar{1}$</tex-math></inline-formula> 01}, {<inline-formula><tex-math notation="LaTeX"> $\bar{1}$</tex-math></inline-formula> 101} set of facets is intensively studied by numerical calculations. It is observed that the behavior of QWs in different facets of nanowire solar cells possesses an irregular pattern due to a complex distribution of strain and stress parameters depending on crystallographic orientations. Finally, the effect of polarization charges on optical and electrical performance of the nanowire solar cell is investigated in detail. It reveals that QWs along {000<inline-formula><tex-math notation="LaTeX">$\bar{1}$</tex-math></inline-formula>} facet of the nanowire have a favorable influence on III-nitride nanowire photovoltaic devices. It is interesting to observe that a remarkable efficiency of ∼9.82% with 94.34% fill factor and more than 70% quantum efficiency is achieved from 7 × GaN/In<sub>0.1</sub>Ga<sub>0.9</sub>N MQWs sandwiched between n-GaN and p-GaN layer under 1 Sun AM1.5 illuminations.
Susceptibility of European freshwater fish to climate change: species profiling based on life-history and environmental characteristics

Climate change is expected to strongly affect freshwater fish communities. Combined with other anthropogenic impacts, the impacts will alter species distributions and contribute to population declines and local extinctions. To provide timely management and conservation of fishes, it is relevant to identify species that will be most impacted by climate change and those that will be resilient. Species traits are considered a promising source of information on characteristics that influence resilience to various environmental conditions and impacts. We collated life history traits and climatic niches of 443 European freshwater fish species and compared those identified as susceptible to climate change to those that are considered to be resilient. Significant differences were observed between the two groups in their distribution, life-history and climatic niches, with climate-change susceptible species being distributed more southwardly within Europe, and being characterized by higher threat levels, lower commercial relevance, lower vulnerability to fishing, smaller body size and warmer thermal envelopes. We establish a list of species revealed to be of highest priority for further research and monitoring regarding climate change susceptibility within Europe. The presented approach represents a promising tool, to quickly assess large groups of species regarding their susceptibility to climate change and other threats, and to identify research and management priorities.
Power System Unit Commitment Incorporating Wind Energy and Battery Energy Storage

In this paper, a probabilistic unit commitment model by incorporating wind energy and battery energy storage is developed, as well as a methodology based on forward dynamic programming with the aim of solving the problem. To test the model, a test system with four thermal generating units was used, including a wind farm and an energy storage system. The results of the simulation demonstrate the applicability and good performance of the proposed method and consequent decrease in pollution by using less fossil fuels. Besides, it shows the advantages of using energy storage systems to help at reducing the cost of unit commitment and make the system less susceptible to the uncertainties arising from the inclusion of wind power units in the power system.
Zero-shot Neural Transfer for Cross-lingual Entity Linking

Cross-lingual entity linking maps an entity mention in a source language to its corresponding entry in a structured knowledge base that is in a different (target) language. While previous work relies heavily on bilingual lexical resources to bridge the gap between the source and the target languages, these resources are scarce or unavailable for many low-resource languages. To address this problem, we investigate zero-shot cross-lingual entity linking, in which we assume no bilingual lexical resources are available in the source low-resource language. Specifically, we propose pivot-basedentity linking, which leverages information from a highresource “pivot” language to train character-level neural entity linking models that are transferred to the source lowresource language in a zero-shot manner. With experiments on 9 low-resource languages and transfer through a total of54 languages, we show that our proposed pivot-based framework improves entity linking accuracy 17% (absolute) on average over the baseline systems, for the zero-shot scenario.1 Further, we also investigate the use of language-universal phonological representations which improves average accuracy (absolute) by 36% when transferring between languages that use different scripts.