Miracle-dz/UniFAR-FTUs huggingface dataset:
An EEG Experimental Study Evaluating the Performance of Texas Instruments ADS1299

Texas Instruments ADS1299 is an attractive choice for low cost electroencephalography (EEG) devices owing to its low power consumption and low input referred noise. To date, there have been no rigorous evaluations of its performance. In this EEG experimental study we evaluated the performance of the ADS1299 against a high quality laboratory-based system. Two self-paced lower limb motor tasks were performed by 22 healthy participants. Recorded power across delta, theta, alpha, and beta EEG bands, the power ratio across the motor tasks, pre-movement noise, and signal-to-noise ratio were obtained for evaluation. The amplitude and time of the negative peak in the movement-related cortical potentials (MRCPs) extracted from the EEG data were also obtained. Using linear mixed models, no statistically significant differences (p > 0.05) were found in any of these measures across the two systems. These findings were further supported by evaluation of cosine similarity, waveform differences, and topographic maps. There were statistically significant differences in MRCPs across the motor tasks in both systems. We conclude that the performance of the ADS1299 in combination with wet Ag/AgCl electrodes is analogous to that of a laboratory-based system in a low frequency (<40 Hz) EEG recording.
Parallel Linear Regression on Encrypted Data

In recent years, the advent of machine learning models on private data has been remarkable. However, in-corporating machine learning techniques to healthcare data is pretty challenging due to the privacy issues of sensitive data which restricts data sharing in plaintext. Ensuring the privacy of individuals in healthcare datasets while constructing a machine learning model is a challenging research problem today. This paper proposes an approximate mathematical model utilizing linear regression on homomorphically encrypted data to predict the disease association of an individual. Furthermore, as these encryption schemes are not efficient considering computation time, we incorporate the multi-core parallelism to make the framework realistic. We experimentally evaluate the performance of the proposed methods and report on the experimental results.
Crossing Cross-Domain Paths in the Current Web

The loading of resources from third-parties has evoked new security and privacy concerns about the current world wide web. Building on the concepts of forced and implicit trust, this paper examines cross-domain transmission control protocol (TCP) connections that are initiated to domains other than the domain queried with a web browser. The dataset covers nearly ten thousand domains and over three hundred thousand TCP connections initiated by querying popular Finnish websites and globally popular sites. According to the results, (i) crossdomain connections are extremely common in the current Web. (ii) Most of these transmit encrypted content, although mixed content delivery is relatively common; many of the cross-domain connections deliver unencrypted content at the same time. (iii) Many of the cross-domain connections are initiated to known web advertisement domains, but a much larger share traces to social media platforms and cloud infrastructures. Finally, (iv) the results differ slightly between the Finnish web sites sampled and the globally popular sites. With these results, the paper contributes to the ongoing work for better understanding crossdomain connections and dependencies in the world wide web.
"I had a solid theory before but it's falling apart": Polarizing Effects of Algorithmic Transparency

The rise of machine learning has brought closer scrutiny to intelligent systems, leading to calls for greater transparency and explainable algorithms. We explore the effects of transparency on user perceptions of a working intelligent system for emotion detection. In exploratory Study 1, we observed paradoxical effects of transparency which improves perceptions of system accuracy for some participants while reducing accuracy perceptions for others. In Study 2, we test this observation using mixed methods, showing that the apparent transparency paradox can be explained by a mismatch between participant expectations and system predictions. We qualitatively examine this process, indicating that transparency can undermine user confidence by causing users to fixate on flaws when they already have a model of system operation. In contrast transparency helps if users lack such a model. Finally, we revisit the notion of transparency and suggest design considerations for building safe and successful machine learning systems based on our insights.
Security analysis in role-based access control

The administration of large role-based access control (RBAC) systems is a challenging problem. In order to administer such systems, decentralization of administration tasks by the use of delegation is an effective approach. While the use of delegation greatly enhances flexibility and scalability, it may reduce the control that an organization has over its resources, thereby diminishing a major advantage RBAC has over discretionary access control (DAC). We propose to use security analysis techniques to maintain desirable security properties while delegating administrative privileges. We give a precise definition of a family of security analysis problems in RBAC, which is more general than safety analysis that is studied in the literature. We show that two classes of problems in the family can be reduced to similar analysis in the RT[↞∩] role-based trust-management language, thereby establishing an interesting relationship between RBAC and the RT framework. The reduction gives efficient algorithms for answering most kinds of queries in these two classes and establishes the complexity bounds for the intractable cases.
'Staged for Living'

We describe more than 19 months of ethnographic fieldwork with people who have embraced minimalism, a lifestyle movement focused on reducing modern life's clutter. We found that for minimalists, the home is a central but porous site for making and staging their values around objects. The home's porous boundary allows minimalists to reinforce their values around objects for others and themselves, but it also necessitates adopting strategies to assert their values when objects---entangled with others' values---move in and out the boundaries of the home. Drawing from our fieldwork, we introduce the concept of the porous boundary. "Porosity" impels us to consider the coupling of objects with values as we do boundary practices for the home. A porous boundary perspective, we argue, can open new design spaces in the development of novel technologies for the home.
Clinical Trail Using A Silver-Coated Screw-Rod System and One-Year Follow-Up of The First 50 Patients

Aim: The occurrence of implant-related infection in all surgical branches is one of the challenges for which a definitive solution has yet to be found. One way to reduce the incidence of implant-related infection is to use implants which are coated with antibacterial materials such as silver. The aim of this study is to investigate if the nanoparticle silver coated spinal implants reduce the implant related infection rates and safe for human use. Method: In this clinical trial performed with 50 patients, we investigated whether or not silver-coated titanium implants alter renal and/or hepatic functions and increase serum silver levels at one year postoperatively. The required stabilization procedure was performed using the “nanoparticle silver coated transpedicular stabilisation system”. Blood and urine samples were taken from each patient at six different time points for detection of any alteration in silver concentration. Silver levels of all samples were investigated spectrophotometrically. Additional serum samples were taken for monitoring liver and kidney functions. Results: All values measured were regarded as safe since they were lower than 5 μg/L. There was no alteration in renal and/or hepatic function, and the amount of silver in urine and serum was at undetectable levels using atomic absorption spectrophotometer. Neither complication was related to silver nor any implant infection was detected in one year followup period. Conclusions: This study showed that, nanoparticular silver coated spinal implants are capable to reduce implantrelated infection rates and these type of implants are safe for human use.
Enhancing the Physical Layer Security of Uplink Non-Orthogonal Multiple Access in Cellular Internet of Things

This paper investigates the physical-layer security of uplink non-orthogonal multiple access (NOMA) in the cellular Internet of Things (IoT) with invoking stochastic geometry. Poisson cluster process-based model is applied to characterize the NOMA uplink transmission scenario, where IoT terminals are located around the serving base station. Considering the severe interference brought by a large number of IoT terminals, inter-cell interference is also taken into account. To enhance the physical-layer security of uplink NOMA transmission with limited overhead increment at IoT terminals, the base stations not only receive the signals from IoT terminals but also keep emitting jamming signals all the time to degrade the performance of any potential eavesdroppers. In order to characterize the physical-layer security performances, we derive expressions of coverage probability and secrecy outage probability. To obtain further insights, network-wide secrecy throughput (NST) and network-wide secrecy energy efficiency (NSEE) are analyzed. It is demonstrated that the security performance can be improved by the proposed full-duplex base station jamming scheme at the cost of reliable performance. The analytical and simulation results show the effects of BS intensity and jamming power on network performances. We also verify that NST and NSEE can be significantly enhanced by our proposed scheme. Using these results, the security of confidential information transmitted by low-complexity IoT terminals can be protected from overhearing.
Generating Market Comments Referring to External Resources

Comments on a stock market often include the reason or cause of changes in stock prices, such as “Nikkei turns lower as yen’s rise hits exporters. ” Generating such informative sentences requires capturing the relationship between different resources, including a target stock price. In this paper, we propose a model for automatically generating such informative market comments that refer to external resources. We evaluated our model through an automatic metric in terms of BLEU and human evaluation done by an expert in finance. The results show that our model outperforms the existing model both in BLEU scores and human judgment.
The Speechmatics Parallel Corpus Filtering System for WMT18

Our entry to the parallel corpus filtering task uses a two-step strategy. The first step uses a series of pragmatic hard ‘rules’ to remove the worst example sentences. This first step reduces the effective corpus size down from the initial 1 billion to 160 million tokens. The second step uses four different heuristics weighted to produce a score that is then used for further filtering down to 100 or 10 million tokens. Our final system produces competitive results without requiring excessive fine tuning to the exact task or language pair. The first step in isolation provides a very fast filter that gives most of the gains of the final system.
Identify, Locate and Separate: Audio-Visual Object Extraction in Large Video Collections Using Weak Supervision

We tackle the problem of audio-visual scene analysis for weakly-labeled data. To this end, we build upon our previous audio-visual representation learning framework to perform object classification in noisy acoustic environments and integrate audio source enhancement capability. This is made possible by a novel use of non-negative matrix factorization for the audio modality. Our approach is founded on the multiple instance learning paradigm. Its effectiveness is established through experiments over a challenging dataset of music instrument performance videos. We also show encouraging visual object localization results.
Prevalence and Factors Influencing Alcohol Use in Pregnancy among Women Attending Antenatal Care in Dodoma Region, Tanzania: A Cross-Sectional Study

Background Alcohol use during pregnancy is high despite the well-established evidence on its adverse pregnancy outcomes and poor child development. Early identification and behavioural modification are of great significance. This study aimed to determine the prevalence and associated factors of alcohol use during pregnancy among women in Dodoma region. Methods 365 randomly selected pregnant women attending antenatal care services in Dodoma region were included. Structured questionnaires were used to assess sociodemographic characteristic and alcohol use. Both descriptive and inferential analyses were used to estimate the prevalence and independent relationships of factors associated with alcohol use in pregnancy, respectively. Results Results showed a prevalence of 15.1% out of the 365 women attending antenatal services in Dodoma region. Prepregnancy alcohol use and having relatives who use alcohol were associated with alcohol use (AOR= 5.19; 95% CI: 4.791-34.867 and AOR=1.57; 95% CI: 1.393-6.248), respectively. Moreover, other associated factors included low education status (AOR=10.636; 95% CI: 1.89-19.844), making local brews as a source of income (AOR=11.44; 95% CI: 1.008-19.86), and not having had complications in previous pregnancies (AOR=4.93; 95% CI: 1.031-23.59). Conclusion There is a significantly high prevalence of alcohol use during pregnancy in Dodoma. Social networks and low social, economic status were associated with alcohol use in pregnancy. There is a need for public health interventions to address alcohol use particularly targeting women of reproductive age with low socioeconomic status.
An Internet-Scale Feasibility Study of BGP Poisoning as a Security Primitive

The relative success or failure in practice of a range of censorship-circumvention, DDoS defense, and congestion discovery systems depend on BGP poisoning's feasibility or impracticality. Using a purpose-built Internet-scale measurement infrastructure spanning 5 Border Gateway Protocol (BGP) routers, 8 previously unused IP prefixes, 5,000 distinct vantage points, and 3 countries, we capture and analyze 1,460 instances of BGP poisoning across 3% of Autonomous Systems (ASes) on the Internet. We also measure the Internet's response to the underlying routing behaviors necessary for widespread success of BGP poisoning and highlight how these findings impact both existing and future security systems and techniques. Fundamentally, our measurement study addresses the growing division between current and future research that assumes active re-routing of traffic is possible versus work that assumes the opposite.
A Methodology for Automatic Selection of Activation Functions to Design Hybrid Deep Neural Networks

Activation functions influence behavior and performance of DNNs. Nonlinear activation functions, like Rectified Linear Units (ReLU), Exponential Linear Units (ELU) and Scaled Exponential Linear Units (SELU), outperform the linear counterparts. However, selecting an appropriate activation function is a challenging problem, as it affects the accuracy and the complexity of the given DNN. In this paper, we propose a novel methodology to automatically select the best-possible activation function for each layer of a given DNN, such that the overall DNN accuracy, compared to considering only one type of activation function for the whole DNN, is improved. However, an associated scientific challenge in exploring all the different configurations of activation functions would be time and resource-consuming. Towards this, our methodology identifies the Evaluation Points during learning to evaluate the accuracy in an intermediate step of training and to perform early termination by checking the accuracy gradient of the learning curve. This helps in significantly reducing the exploration time during training. Moreover, our methodology selects, for each layer, the dropout rate that optimizes the accuracy. Experiments show that we are able to achieve on average 7% to 15% Relative Error Reduction on MNIST, CIFAR-10 and CIFAR-100 benchmarks, with limited performance and power penalty on GPUs.
High Grazing Pressure of Geese Threatens Conservation and Restoration of Reed Belts

Reed (Phragmites australis (Cav.) Trin. ex Steud. ) beds are important habitat for marsh birds, but are declining throughout Europe. Increasing numbers of the native marsh bird, the Greylag goose (Anser anser L.), are hypothesized to cause reed bed decline and inhibit restoration of reed beds, but data are largely lacking. In this study, we experimentally tested the effect of grazing by Greylag geese on the growth and expansion of reed growing in belts along lake shorelines. After 5 years of protecting reed from grazing with exclosures, reed stems were over 4-fold denser and taller than in the grazed plots. Grazing pressure was intense with 50–100% of the stems being grazed among years in the control plots open to grazing. After 5 years of protection we opened half of the exclosures and the geese immediately grazed almost 100% of the reed stems. Whereas this did not affect the reed stem density, the stem height was strongly reduced and similar to permanently grazed reed. The next year geese were actively chased away by management from mid-March to mid-June, which changed the maximum amount of geese from over 2300 to less than 50. As a result, reed stem density and height increased and the reed belt had recovered over the full 6 m length of the experimental plots. Lastly, we introduced reed plants in an adjacent lake where no reed was growing and geese did visit this area. After two years, the density of the planted reed was six to nine-fold higher and significantly taller in exclosures compared to control plots where geese had access to the reed plants. We conclude that there is a conservation dilemma regarding how to preserve and restore reed belts in the presence of high densities of Greylag geese as conservation of both reed belts and high goose numbers seems infeasible. We suggest that there are three possible solutions for this dilemma: (1) effects of the geese can be mediated by goose population management, (2) the robustness of the reed marshes can be increased, and (3) at the landscape level, spatial planning can be used to configure landscapes with large reed bed reserves surrounded by unmown, unfertilized meadows.
First-Trimester Uterine Artery Doppler for the Prediction of Preeclampsia in Nulliparous Women: The Great Obstetrical Syndrome Study

Abstract Objective This study aimed to estimate the performance of first-trimester uterine artery (UtA) pulsatility index (PI) for the prediction of preeclampsia (PE). Study Design We conducted a prospective cohort study of nulliparous women with singleton gestation at 11 to 13 6/7 weeks. UtA-Doppler's was performed on both UtAs and the mean UtA-PI was reported in multiple of median (MoM) adjusted for gestational age. Using receiver operating characteristic curves and their area under the curves (AUC); we calculated the performance of UtA-PI for the prediction of PE. Proportional hazard models were used to develop prediction models combining UtA-PI and maternal characteristics. Results Out of 4,676 participants with completed follow-up, 232 (4.9%) developed PE, including 202 (4.3%) term and 30 (0.6%) preterm PE. Mean UtA-PI decreased with gestational age between 11 and 13 6/7 weeks (p < 0.001). First-trimester UtA-PI was associated with preterm (AUC: 0.69; 95% CI [confidence interval]: 0.57–0.80) but not with term (AUC: 0.52; 95% CI: 0.48–0.56) PE. UtA-PI combined with maternal characteristics could predict 45% of preterm PE at a false positive rate of 10%. Conclusion First-trimester UtA-PI decreases with gestational age between 11 and 13 6/7 weeks and is associated with the risk of preterm but not term PE.
Typeface Completion with Generative Adversarial Networks

The mood of a text and the intention of the writer can be reflected in the typeface. However, in designing a typeface, it is difficult to keep the style of various characters consistent, especially for languages with lots of morphological variations such as Chinese. In this paper, we propose a Typeface Completion Network (TCN) which takes one character as an input, and automatically completes the entire set of characters in the same style as the input characters. Unlike existing models proposed for image-to-image translation, TCN embeds a character image into two separate vectors representing typeface and content. Combined with a reconstruction loss from the latent space, and with other various losses, TCN overcomes the inherent difficulty in designing a typeface. Also, compared to previous image-to-image translation models, TCN generates high quality character images of the same typeface with a much smaller number of model parameters. We validate our proposed model on the Chinese and English character datasets, which is paired data, and the CelebA dataset, which is unpaired data. In these datasets, TCN outperforms recently proposed state-of-the-art models for image-to-image translation. The source code of our model is available at this https URL.
Evidence Transfer for Improving Clustering Tasks Using External Categorical Evidence

In this paper we introduce evidence transfer for clustering, a deep learning method that can incrementally manipulate the latent representations of an autoencoder, according to external categorical evidence, in order to improve a clustering outcome. By evidence transfer we define the process by which the categorical outcome of an external, auxiliary task is exploited to improve a primary task, in this case representation learning for clustering. Our proposed method makes no assumptions regarding the categorical evidence presented, nor the structure of the latent space. We compare our method, against the baseline solution by performing k-means clustering before and after its deployment. Experiments with three different kinds of evidence show that our method effectively manipulates the latent representations when introduced with real corresponding evidence, while remaining robust when presented with low quality evidence.
Reinforced concrete structures 1

Sveucilisni udžbenik Betonske konstrukcije 1 namijenjen je studentima Građevinskog fakulteta, ali i inženjerima u praksi jer opisuje proracun armiranobetonskih elemenata i konstrukcija prema važecim normama niza EN 1992.
Enhancing Phytoextraction of HMs at Real Scale, by Combining Salicaceae Trees With Microbial Consortia

We applied an integrated phytoremediation system by inoculation of rhizosphere microbes on an area of 2000 m2 that was contaminated by heavy metals. Hybrid poplar (Populus deltoids x Populus nigra) and willow (Salix purpurea subsp. lambertiana) were planted on four parcels of 500 m2. Of these, two parcels were inoculated with an autochthonous consortium specially selected and the other two were not inoculated. The analyses were carried out on plant and soil samples collected at different times: month 0 (T0), 12 (T1), 24 (T2) and 36 (Tf). Total concentrations of HMs in the roots of Populus and Salix showed significant increase in the plants with rhizosphere microflora consortium. The plants treated with the consortium have an increased enzymatic activity of antioxidants and of stress markers. All enzymes showed an important contribution in enzymatic thiol-dependent ROS scavening mechanisms, through its over-expression at time T1 and T2 and a consequent stabilization at Tf which results in adaptive mechanisms. The plant-fungi-bacterium system was a valid candidate for the implementation of phytoremediation systems in the field, since the plants showed a greater capacity for adaptation and accumulation.