Miracle-dz/UniFAR-FTUs huggingface dataset:
Benefits of cochlear implantation in elderly patients

Cochlear implantation in elderly patients is a questionable subject. The purpose of this study was to evaluate the procedure and its outcome, the postoperative course, and the audiologic and social benefits of cochlear implantation in this population. Twenty-seven patients older than 60 years were compared with a control group of 15 adult patients. This retrospective study analyzed data concerning the outcome of the procedure, postoperative course, postoperative orthophonic test results, and answers of a questionnaire assessing the changes in communication, perception, and social outcomes. The procedure was uneventful in both groups. Minor complications were not more frequent in elderly patients. Orthophonic test results were comparable in both groups. At 12 months, 83% of the elderly patients had an open-set speech discrimination score above 60%. The benefits of cochlear implantation in terms of the quality of life are not statistically different with younger patients. Cost-utility analysis might support these findings.
Quality of Service-Based Node Relocation Technique for Mobile Sensor Networks

Wireless sensor networks (WSNs) deployed in harsh and unfavorable environments become inoperable because of the failure of multiple sensor nodes. This results into the division of WSNs into small disjoint networks and causes stoppage of the transmission to the sink node. Furthermore, the internodal collaboration among sensor nodes also gets disturbed. Internodal connectivity is essential for the usefulness of WSNs. The arrangement of this connectivity could be setup at the time of network startup. If multiple sensor nodes fail, the tasks assigned to those nodes cannot be performed; hence, the objective of such WSNs will be compromised. Recently, different techniques for repositioning of sensor nodes to recover the connectivity have been proposed. Although capable to restore connectivity, these techniques do not focus on the coverage loss. The objective of this research is to provide a solution for both coverage and connectivity via an integrated approach. A novel technique to reposition neighbouring nodes for multinode failure is introduced. In this technique, neighbouring nodes of the failed nodes relocate themselves one by one and come back to their original location after some allocated time. Hence, it restores both prefailure connectivity and coverage. The simulations show our proposed technique outperforms other baseline techniques.
The Norwegian Institute of Public Health Twin Program of Research: An Update

Abstract The population-based twin program of research at the Norwegian Institute of Public Health (NIPH) was begun in 1992. It consists of a number of questionnaire and clinical interview projects exploring a broad array of mental and physical health outcomes. This article provides a brief update summarizing our research activities, some research highlights, new developments and potentials for further developing the program of twin research. In the most recent years a large effort has concentrated on completing a mental health interview study of Axis I psychiatric and substance use disorders and Axis II personality disorders. Although still in the early planning phases, one of the most significant developments is that an agreement is now in place to centralize the Norwegian twin data into a national Norwegian Twin Registry. This new registry will include twin cohorts born from 1905 onwards. Other resources for building twin projects are described. Nationally, there is great potential for linking the NIPH twin data with other health registries and with information in a number of Norway's large population-based biobank studies. Internationally, platforms such as those developed within GenomEUtwin, for data standards and data sharing and access are greatly facilitating international collaborations in twin research.
Neural Machine Translation With Sentence-Level Topic Context

Traditional neural machine translation (NMT) methods use the word-level context to predict target language translation while neglecting the sentence-level context, which has been shown to be beneficial for translation prediction in statistical machine translation. This paper represents the sentence-level context as latent topic representations by using a convolution neural network, and designs a topic attention to integrate source sentence-level topic context information into both attention-based and Transformer-based NMT. In particular, our method can improve the performance of NMT by modeling source topics and translations jointly. Experiments on the large-scale LDC Chinese-to-English translation tasks and WMT’14 English-to-German translation tasks show that the proposed approach can achieve significant improvements compared with baseline systems.
Electron correlation and screening in model nanostructures

Accurate models of electron correlation are key to understanding and predicting important physical characteristics that underpin the development of many modern quantum technologies. One of the most widely used approaches to modeling cor- relation is the GW approximation within many-body perturbation theory (MBPT). There are a large number of ‘flavors’ of the GW approximation, with varying levels of computational cost and accuracy. Our aim is to elucidate the various deficiencies and develop novel corrections in order to ameliorate such failings. In this thesis we study simple model systems, whose properties can be determined exactly by numerical solution of the Schrodinger equation, that exhibit the key physical properties present in real quantum systems such as atoms and molecules. We then are able to compare the exact results to that of existing methods to identify the fundamental source of shortcomings, using this insight to develop new approximations. We find that a common systematic error across all flavors of the GW approximation is the ‘self-screening’ error. We develop a conceptually and computationally simple correction that removes the unwanted effect of this error on the charge density and ionization potential. We demonstrate that MBPT methods exhibit Kohn’s concept of advantageous ‘nearsightedness’, unlike the computationally cheaper Kohn-Sham density functional theory (KS-DFT). We attribute this to the non-locality of the potentials used in MBPT. We highlight that this allows approximations to more easily encapsulate advanced aspects of exchange and correlation to be made within MBPT. Hybrid functionals contain these non-local potentials and so benefit from this nearsightedness, and when enforced to obey physically justified constraints, yield extremely accurate densities and ionization potentials. We also extend our investigation to the time-dependent properties of correlated systems. A form of time-dependent many-body perturbation theory, that brings together the simplicity of time-dependent DFT (TDDFT) and sophisticated correlation effects of MBPT is investigated. We show that this approach significantly outperforms common approximations to TDDFT without requiring the more onerous computational cost of non-equilibrium Green’s function methods.
Multivariate Tests for Phase Capacity

To combat the potentially detrimental effects of nonresponse, most surveys repeatedly follow-up with nonrespondents, often targeting a response rate or predetermined number of completes. Each additional recruitment attempt generally brings in a new wave of data, but returns gradually diminish over the course of a static data collection protocol. This is because each subsequent wave tends to contain fewer and fewer new responses, thereby rendering smaller and smaller changes in point estimates. Consequently, point estimates calculated from the accumulating data begin to stabilize. This is the notion of phase capacity, suggesting some form of design change is warranted, such as switching modes, increasing the incentive, or simply discontinuing nonrespondent follow-up. Phase capacity testing methods that have appeared in the literature to date are generally only applicable to a single point estimate. It is unclear how to proceed if conflicting results are obtained following independent tests on two or more point estimates. The purpose of this paper is to introduce two multivariate phase capacity tests, each designed with the aim of providing a universal, yes-or-no phase capacity determination for a battery of point estimates. The two competing methods’ performance is compared via simulation and application using data from the 2011 Federal Employee Viewpoint Survey.
CsiGAN: Robust Channel State Information-Based Activity Recognition With GANs

As a cornerstone service for many Internet of Things applications, channel state information (CSI)-based activity recognition has received immense attention over recent years. However, recognition performance of general approaches might significantly decrease when applying the trained model to the left-out user whose CSI data are not used for model training. To overcome this challenge, we propose a semi-supervised generative adversarial network (GAN) for CSI-based activity recognition (CsiGAN). Based on the general semi-supervised GANs, we mainly design three components for CsiGAN to meet the scenarios that unlabeled data from left-out users are very limited and enhance recognition performance: 1) we introduce a new complement generator, which can use limited unlabeled data to produce diverse fake samples for training a robust discriminator; 2) for the discriminator, we change the number of probability outputs from <inline-formula> <tex-math notation="LaTeX">$k+1$ </tex-math></inline-formula> into <inline-formula> <tex-math notation="LaTeX">$2k+1$ </tex-math></inline-formula> (here, <inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> is the number of categories), which can help to obtain the correct decision boundary for each category; and 3) based on the introduced generator, we propose a manifold regularization, which can stabilize the learning process. The experiments suggest that CsiGAN attains significant gains compared to the state-of-the-art methods.
Observed trends in daily extreme precipitation indices in Aguascalientes, Mexico

Precipitation and its distribution greatly influence the evolution of ecosystems and the development of society. The objective was to analyse trends in extreme precipitation indices at 25 weather stations of Aguascalientes State. Eleven extreme precipitation indices were obtained. The time series of these indices were analysed with the non‐parametric Mann–Kendall test. The number of days above 50 mm, consecutive wet days, and extremely wet days did not have significant trends at any of the weather stations. Each of the indices maximum 1 day precipitation amount, maximum 5 day precipitation amount and number of very heavy precipitation days showed a significant positive trend at 12% of the weather stations; both the number of heavy precipitation days and the number of very wet days had significant positive trends at 8% of the weather stations; the simple daily intensity index, consecutive dry days and annual total wet‐day precipitation showed significant positive trends at 20%, 36% and 4% of weather stations, respectively. The intensity index was the only one that showed a significant negative trend, which happened at 4% of the weather stations. In a small part of Aguascalientes, the precipitation intensity, the number of rainy days and the accumulated total increased in short periods. Also, in a reduced region, daily precipitation intensity decreased; and in another very small area the number of dry days increased. For mitigating the effects of these phenomena, it is suggested that water be used more efficiently for sustained agricultural production systems and ecosystem management. The results of the present study will be of great importance in future economic planning in the Aguascalientes State.
Attribution of growing season vegetation activity to climate change and human activities in the Three-River Headwaters Region, China

Over the past century, vegetation change has been reported at global, national, and regional scales, accompanied by significant climate change and intensified human activities. Among the regions is the rangeland of the Three-River Headwaters Region (TRHR) in China. However, which factor dominates in causing vegetation change in this region is still under considerable debate, and how would the grasslands adapt to the changing environment is largely unknown. To address these issues, we attribute growing season vegetation activity to climate change and human activities, investigate the interactions among different driving variables, and explore the dynamic relationship between vegetation activity and the driving variables. We perform Mann–Kendall trend analysis, Pearson correlation analysis, and partial correlation analysis. The results indicate that the dominant factor for vegetation growth, during the period 1995–2014, was temperature for the southeastern and southern parts of the TRHR, precipitation for the western part, and solar radiation for the northeastern part. The regulation effects of temperature on precipitation and cloud cover contributed to vegetation growth, while grazing activity and population activity offset the positive contribution of climate change. The dynamic relationship between vegetation activity and the driving variables reflected the acclimatization and adaption processes of vegetation, which needs further investigation.
A Mobile-Based Comprehensive Weight Reduction Program for the Workplace (Health-On): Development and Pilot Study

Background There is a growing interest in mobile technology for obesity management. Despite the known effectiveness of workplace-based weight loss programs, there are few studies on mobile phone–delivered interventions. Objective This study aimed to develop and verify an integrated and personalized mobile technology–based weight control program, named Health-On, optimized for workplaces. Methods A weight reduction algorithm was developed for calorie prescription, continuous monitoring, periodic feedback and reevaluation, goal resetting, and offline intervention with behavior-changing strategies. A total of 30 obese volunteers (body mass index ≥25 kg/m2) participated in the 12-week Health-On pilot program. The primary outcome was weight reduction, and secondary outcomes were improved anthropometric measures, metabolic profiles, and fat computed tomography measures, all assessed pre- and postintervention. Results Health-On incorporated proprietary algorithms and several strategies intended to maximize adherence, using compatible online and offline interventions. The mean weight of 30 participants decreased by 5.8%, and median weight also decreased from 81.3 kg (interquartile range [IQR] 77.1-87.8) before intervention to 76.6 kg (IQR 70.8-79.5) after the 12-week intervention period (P<.001). The metabolic profiles and fat measures (blood pressure, glycosylated hemoglobin, total cholesterol, triglyceride, high-density lipoprotein, low-density lipoprotein, alanine aminotransferase, and visceral and subcutaneous adipose tissue; P<.05) also improved significantly. Conclusions In this single-group evaluation of 30 participants before and after the Health-On program, body weight decreased and metabolic profiles and fat measures improved. Follow-up studies are needed to assess effectiveness and long-term adherence.
Promoting content discovery of open repositories : reviewing the impact of optimization techniques (2016-2019)

Ensuring open repositories fulfill the discovery needs of both human and machine users is of growing importance and essential to validate the continued relevance of open repositories to users, and as nodes within open scholarly communication infrastructure. Following positive preliminary results reported elsewhere, this submission reviews the longer term impact of a series of discovery optimization approaches deployed on an open institutional repository. These approaches were designed to support improved content discovery and user engagement, thereby improving content usage. Using Strathprints, the University of Strathclyde repository as a case study, this submission will review the techniques and technical changes deployed on Strathprints and examine the impact of these changes by studying data on web impact, COUNTER usage and web traffic over a 4-year period. Analysis of this unique dataset provides persuasive evidence that specific enhancements to the technical configuration of a repository can generate substantial improvements in its content discovery potential and ergo its content usage, especially over several years. COUNTER usage grew by 62%. Increases in Google 'impressions' (266%) and 'clicks' (104%) were a notable finding, with high levels of statistical significance found in the correlation between clicks and usage (t = 14.30; df = 11; p < 0.0005). Web traffic to Strathprints from Google and Google Scholar was found to increase significantly with growth on some metrics exceeding 1300%. Although some of these results deserve additional scrutiny, the paper nevertheless demonstrates the link between repository optimization and the need for open repositories to assume a proactive development path, especially one that prioritises web impact and discovery.
Discriminating deception from truth and misinformation: an intent-level approach

ABSTRACT Deception detection has been studied for hundreds of years. A particularly challenging problem is to not only identify truth from deception, but also discriminate misinformation, i.e. errors, from deception. Misinformation has generally been ignored in the study of deception detection, but through analysing the foundations of deception, it may be possible to pinpoint a fundamental difference between deception and all other benign communications – namely, the intent of the speaker. We present a detection model that captures a speaker’s intent by measuring his patterns of reasoning. The reasoning patterns of deceivers may serve as indicators of intentional deception. Through empirical studies, these intent-driven reasoning patterns can identify as well as explain deceptive communications.
Aerodynamics of two square back vehicles in platooning configuration

We report on the results of an experimental campaign devoted at quantifying the drag variation of a pack of vehicles when traveling in line. We show the existence of an optimal distance between two vehicles at which they both benefit of a significant drag reduction. This benefit is quickly lost for the leading vehicle, but still persists for the rear one, even at values of the non-dimensional distance between the vehicles (d/L) as large as 3. Flow field analysis carried out at d/L =0.5 and 3 reveal that the structure of the wake behind the rear vehicle is significantly perturbed with respect to the case of isolated model, featuring a reduced extent and lower entrainment.
ECaD: Energy‐efficient routing in flying ad hoc networks

Much progress can be expected in the domain of unmanned aerial vehicle (UAV) communication by the next decade. The cooperation between multiple UAVs in the air exchanging data among themselves can naturally form a flying ad hoc network (FANET). Such networks can be the key support to accomplish several kinds of missions while providing the required assistance to terrestrial networks. However, they are confronted with many challenges and difficulties, which are due to the high mobility of UAVs, the frequent packet losses, and the weak links between UAVs, all affecting the reliability of the data delivery. Furthermore, the unbalanced energy consumption may result in earlier UAV failure and consequently accelerate the decrease of the network lifetime, thus disrupting the overall network. This paper supports the use of the movement information and the residual energy level of each UAV to guarantee a high level of communication stability while predicting a sudden link breakage prior to its occurrence. A robust route discovery process is used to explore routing paths where the balanced energy consumption, the link breakage prediction, and the connectivity degree of the discovered paths are all considered. The performance of the scheme is evaluated through a series of simulations. The outcomes demonstrate the benefits of the proposed scheme in terms of increasing the lifetime of the network, minimizing the number of path failures, and decreasing the packet losses.
Security Monitoring SLA Verification in Clouds: the Case of Data Integrity

The cloud computing business model introduced a new paradigm in terms of ownership of a system. Before the cloud, a user acquires physical infrastructure and uses it by installing and configuring according to her/his needs. In that scenario, the full system is owned by a single entity. In the cloud, when the user outsources a service for a cloud provider the user owns some part of the system while the provider owns the remaining part. Thus, ownership in the cloud is divided between different entities. Clients hosting their information system need to trust and rely on what the providers claim. At the same time providers try to give assurance for some aspects of the provided service (e.g. availability) through service level agreements (SLAs). We aim at extending SLAs to include security monitoring terms. In a previous study [1] we proposed an SLA verification method for security monitoring SLAs describing the performance on an NIDS. In this paper we consider an SLA guaranteeing the integrity of tenants’ data stored in the cloud. The tenant outsources data storage service to a Storage as a Service cloud provider. In such a system the data is owned by the tenant while the provider owns the infrastructure. We consider an SLA offered by the provider to guarantee the integrity of tenants’ data. In this paper, we propose a verification method, i.e. an integrity checking method, which is based on a distributed ledger. Specifically, our proposed method allows both providers and tenants to perform integrity checking without one party relying on the other. The method uses a blockchain as a distributed ledger to store evidences of data integrity. Assuming the ledger as a secure, trusted source of information, the evidence can be used to resolve conflicts between providers and tenants. In addition, we present a prototype implementation and an experimental evaluation to show the feasibility of our verification method and to measure the time overhead introduced.
Comparative Study on Various Congestion Control Protocols: TCP, XCP and RCP

— Today’s modern era of high speed multimedia transmission is demanding faster data transmission of huge amount of data. As a result data traffic is growing faster than capacity of exciting network. So it becomes inescapable to deal with Congestion control. Various congestion protocols are used for avoiding congestion in network. TCP is the most popular congestion protocol in the Internet but it has some limitations, such as fail to utilize network link optimally in high bandwidth-delay product networks, and come out with high load and overhead in the network. To nullify these limitations, several congestion protocols like explicit Control Protocol and Rate Control Protocol have been proposed. These protocols have been proposed previously to work in wired and wireless networks and environments and also have pros and cons. Here we present evaluation study of these protocols that can be taken as a base to building a enhanced version of existing protocol.