Miracle-dz/UniFAR-FTUs huggingface dataset:
Self-Aware Smart Contracts with Legal Relevance

Conventional contracts (CC) that are not machine readable are challenging to interpret, disconnected from ICT-systems and when conflicts occur, tracking their execution is restrictively slow and challenging to enforce. On the other hand, so called selfaware contracts (SAC) that are similar to CCs with respect to legal enforceability, are machine readable and supportable by blockchain-technology in combination with multi-agent systems. SACs do not require qualitative trust between contracting parties because blockchains establish instead a quantitative notion of trust as SAC-related events are immutably stored while software agents manage the connections to trusted information sources. However, currently existing machine-readable contract solutions, i.e., smart contracts, lack suitable obligation- and rights constructs for execution and enforcement. Additionally, current systems do not comprehend the dynamics of legal relationships. This whitepaper address the gap by specifying a so-called SAC-framework that enables blockchain-driven self-aware agents-assisted contracts for a decentralized peer-to-peer (P2P) economy.
Sex-biased expression between guppies varying in the presence of ornamental coloration

Sex-biased gene expression provides a means to achieve sexual dimorphism across a genome largely shared by both sexes. Trinidadian guppies are ideal to examine questions of sex-bias as they exhibit sexual dimorphism in ornamental coloration with male only expression. Here we use RNA-sequencing to quantify whole transcriptome gene expression differences, with a focus on differential expression of color genes between the sexes. We determine whether males express genes positively correlated with coloration at higher levels than females. We find that all the differentially expressed color genes were more highly expressed by males. Males also expressed all known black melanin synthesis genes at higher levels than females, regardless of whether the gene was significantly differentially expressed in the analysis. These differences correlated with the visual color differences between sexes at the stage sampled, as all males had ornamental black coloration apparent. We propose that sexual dimorphism in ornamental coloration is caused by male-biased expression of color genes.
Symbol Digit Modalities Test: A valid clinical trial endpoint for measuring cognition in multiple sclerosis

Background: The need for more robust outcomes in multiple sclerosis (MS) clinical trials has been a main priority of the field for decades. Dissatisfaction with existing measures has led to several consensus meetings and initiatives over the past few decades in hopes of defining and gaining acceptance of measures that are valid, reliable, sensitive to change and progression, and most importantly, relevant to those living with MS. The Multiple Sclerosis Outcome Assessments Consortium (MSOAC) was formed for this purpose. Objective: The objective of this paper is to describe the results of the MSOAC plan to obtain qualification for a cognitive performance measure that meets these requirements. Methods: Using data from 14 MS disease-modifying registration trials, we completed a comprehensive examination of the psychometric qualities of the Symbol Digit Modalities Test (SDMT) and the Paced Auditory Serial Addition Test (PASAT) with the goal of compiling evidence to support the utilization of one of these measures in future clinical trials. Results and conclusion: Consistent with the published literature, the SDMT proved superior to the PASAT. The SDMT should be considered the measure of choice for MS trials in assessing cognitive processing speed.
Soccer Video Super-Resolution via Sub-Pixel Convolutional Neural Network

In this paper, we consider the problem of the soccer video super-resolution (SR). We propose an end-to-end framework based on the sub-pixel convolution neural network and pre-train our network on the image datasets to improve the resolution of soccer video and the speed of the SR algorithm. Different from the general SR methods, our method does not need to interpolate the low-resolution (LR) images first. It means that we directly extract the features from the LR images so the computational cost is low. We also compare our proposed algorithm with the current SR methods, and the experimental results show that our network has better performance. In order to evaluate the effectiveness of SR, we apply the proposed method to the object detection on the soccer videos. The experiments show that integrating the SR technique into the detecting field can improve the accuracy of detection.
Transferring Knowledge From Texts to Images by Combining Deep Semantic Feature Descriptors

Deep learning techniques have been successfully applied to image processing tasks. Nevertheless, these techniques can be sensitive to the occurrence of small training sets, which is commonly faced when model training requires labelled instances. Transfer learning has been adopted to overcome this limitation by leveraging richer information in auxiliary domains to enhance the learning process in a target domain. In this paper, we proposed a new solution for transfer learning to label images (i.e., the target domain) by reusing labelled textual data (i.e., the auxiliary domain). A convolutional encoder is used to find latent features for images, while a probabilistic generative model is used to find semantic topics (traits) for texts. An ensemble of classifiers is then used to predict semantic topics to new input images according to their latent features. Experiments were performed to evaluate whether the latent features in both domains can actually be related and also to verify the use of the predicted semantic topics to classify images. Promising results were achieved compared to different baselines.
Interaction of CBC Loops Involved in Working Memory Feedback Training

Neuroimaging studies of cognitive learning have identified the important roles of Cortico-Basal ganglia- Cerebellar (CBC) loops, and the neurofeedback training based on real-time functional magnetic resonance imaging (rt-fMRI) has been deemed as a kind of cognitive learning. However, how the connectivity in CBC loops change during the feedback training and the underlying learning mechanism behind the training both remain unclear. In this paper, we firstly used Granger causality model method to construct CBC loops in a working memory feedback training task by rt-fMRI. Then, we examined the interaction changes in CBC loops induced the training. The results showed that the connectivity of fronto- parietal, cortico-basal ganglia (BG) and cortico-cerebellar in CBC loops were significantly enhanced during the training in the experimental group. Further correlation analysis indicated the connectivity changes of cortico-BG were stronger positively correlated with the behavioral improvements. These findings suggest that the interaction between the cortex and BG in the feedback training is an essential factor to the behavioral improvement which makes the individual to complete cognitive learning better.
Analysis of inner structure of VSF-Network

In this paper, a theoretical analysis on the internal structure and learning method of VSF-Network is introduced. It is a hybrid neural network combining hierarchical neural network and the chaotic neural network. The hierarchical neural network learns patterns and recognizes patterns based on the learning results. The chaotic neural network monitors the internal structure of the hierarchical neural network and identifies learned units and unused units. The result of this identification is used for selection of the updating target at the time of updating the weight. With this selective weight update, the hierarchical neural network of the VSF-Network is divided into subnetworks that recognize previously learned patterns and subnetworks that recognize newly learned patterns. The monitoring by chaotic neural network is explained as calculation of eigenspace of its recall process. Furthermore, it is explained from the viewpoint of the combination of the divided linear spaces that the subnetworks obtained by learning can be recognized in combination according to the situation. Finally, the validity and problems of the theoretical explanation are introduced through the analysis of the intermediate layer of VSF-Network during incremental learning.
Attention-Related Brain Activation Is Altered in Older Adults With White Matter Hyperintensities Using Multi-Echo fMRI

Cognitive decline is often undetectable in the early stages of accelerated vascular aging. Attentional processes are particularly affected in older adults with white matter hyperintensities (WMH), although specific neurovascular mechanisms have not been elucidated. We aimed to identify differences in attention-related neurofunctional activation and behavior between adults with and without WMH. Older adults with moderate to severe WMH (n = 18, mean age = 70 years), age-matched adults (n = 28, mean age = 72), and healthy younger adults (n = 19, mean age = 25) performed a modified flanker task during multi-echo blood oxygenation level dependent functional magnetic resonance imaging. Task-related activation was assessed using a weighted-echo approach. Healthy older adults had more widespread response and higher amplitude of activation compared to WMH adults in fronto-temporal and parietal cortices. Activation associated with processing speed was absent in the WMH group, suggesting attention-related activation deficits that may be a consequence of cerebral small vessel disease. WMH adults had greater executive contrast activation in the precuneous and posterior cingulate gyrus compared to HYA, despite no performance benefits, reinforcing the network dysfunction theory in WMH.
Modulation of macrophage polarization by level-1 Yo-Yo intermittent recovery test in young football players

Abstract The aim of this study was to examine the effect of the level-1 Yo-Yo intermittent recovery test (YYIRT1) on polarization of macrophages in young football players. Fourteen male football players (19.9 ± 1.4 years old) were enrolled in this study. YYIRT1 was performed with 20-meter shuttle runs at increasing speeds and 10-second active recovery in a 5-meter distance between runs till exhaustion. Fasting blood samples were collected before and immediately after YYIRT1. Analysis for macrophage polarization by flow cytometry, reactive oxygen species (ROS) by flow cytometry, biochemical parameters by chemical reactions, and serum cytokines by ELISA were performed. The rating of perceived exertion (RPE) and cardiovascular parameters were recorded. The time to exhaustion was 714.1 ± 114.4 seconds. The oxygen uptake ( ) was 48.7 ± 5.6 mL/min/kg, RPE scale was 19 ± 1, resting heart rate and maximal heart rate were 64.9 ± 8.8 beat/min and 181.9 ± 9.3 beat/min, respectively, indicating a high level of cardiopulmonary fitness. The expression of macrophage-specific CD14 and M1 marker HLA-ABC, but not M2 marker CD206, was down-regulated after YYIRT1. The intracellular ROS levels in macrophages had no significant change. In biochemical profile, the serum levels of lactic dehydrogenase (LDH), a marker of muscle damage, increased after YYIRT1 whereas no significant alteration was noted in creatine phosphokinase (CPK), blood urine nitrogen, creatinine, aspartate transaminase (AST), alanine transaminase (ALT), and C-reactive protein. The serum levels of interleukin (IL)-6, IL-10 and tumor necrosis factor (TNF)-&agr; had no significant change. The YYIRT1 may induce muscle damage accompanied by modulation of macrophage polarization toward suppression of M1 phenotype in young football players.
Adaptive Adversarial Transfer Learning for Electroencephalography Classification

Insufficient training data is a serious problem in all domains related to bioinformatics. Large-scale annotated electroencephalography (EEG) datasets are almost impossible to acquire because biological data acquisition is challenging and quality annotation is costly. Transfer learning relaxes the hypothesis that the training data must be independent and identically distributed (i.i. d.) with the test data, which motivates us to use transfer learning to solve the problem of insufficient training data in bioinformatics. We propose a new approach to transfer knowledge via a deep transfer learning framework, which includes an adaptive sample selection algorithm and a joint adversarial training algorithm. The adaptive sample selection algorithm dynamically adjusts the sample weights during the training process according to the distance between the source domain and the target domain. The joint adversarial training algorithm forces the network to learn a feature extractor suitable for the target domain based on a dataset from the source domain by using an adversarial network and a specific loss function. The experiments demonstrate that our approach has many advantages, such as robustness and accuracy, when applied to EEG classification tasks.
Liposomal codelivery of an SN38 prodrug and a survivin siRNA for tumor therapy

Purpose A liposome-based siRNA–drug combination was evaluated as a potential therapeutic strategy to improve the curative effect. Methods A topoisomerase inhibitor SN38 prodrug was combined with a survivin siRNA through codelivery using transferrin (Tf)-L-SN38/P/siRNA. In this combination, SN38 was conjugated to the cell penetrating peptide TAT through a polyethylene glycol (PEG) linker to synthesize TAT-PEG-SN38. The amphiphilic TAT-PEG-SN38 was used as an ingredient of liposomes to improve the cellular uptake. Protamine was added to form an electrostatic complex with siRNA in the core of the liposomes. Tf was introduced to enable tumor cell targeting of liposomes (Tf-L-SN38/P/siRNA). Results Tf-L-SN38/P/siRNA exhibited a particle size of 148 nm and a ζ-potential of +7.8 mV. The cellular uptake and antitumor activity were dependent on Tf receptor targeting, TAT-PEG-SN38, and siRNA codelivery. Tf-L-SN38/P/siRNA was shown to be considerably more effective than liposomes carrying individual components. This combination induced potent tumor inhibition (76.8%) in HeLa cell xenograft tumor-bearing nude mice. Conclusion These data indicated that Tf-L-SN38/P/siRNA was an effective system for codelivery of SN38 and a survivin siRNA and that its therapeutic potential deserved further evaluation.
Hemodynamic Response Analysis for Mind-Driven Type-writing using a Type 2 Fuzzy Classifier

We study the vowels detection from brain activation due to vowel sound imageries. At first we experimentally determine the maximum and relatively longer activation that takes place in the frontal or pre frontal lobe during vowel sound Imagination using acquired electroencephalographic signal analysis. Then we capture pre-frontal or frontal vowel sound imagery using a functional near infrared device to extract certain statistical features. Differential evolution based feature selection is used to for dimensionality reduction. The reduced feature set is then used to design an interval type 2 fuzzy classifier to classify the vowels from the pre frontal or frontal f-NIRs response to vowel sound imagination. Experiments undertaken confirm that the proposed classifier outperforms its competitors in classification accuracy for each vowel sound imagery class. They further confirm that the f-NIRs based classification outperforms EEG based modality for better capture of brain activations. Consonants are encoded with two vowel sounds with a space between them. Thus the proposed technique can effectively be used for mind driven type writing of vowels and consonants, serving people suffering from vocal deficiency.
Improving psychological outcomes for orphans living with HIV in Tanzania through a novel intervention to improve resilience: findings from a pilot RCT

ABSTRACT Parental death and HIV are associated with poor mental health for orphans in sub-Saharan Africa. This study aimed to evaluate a theory-based intervention for orphaned children on treatment in Tanzania, in terms of feasibility, acceptability and preliminary outcomes. A pilot RCT with nested focus group (FGD) allocated 48 children to usual care or residential “Memory Work Therapy”. Children completed outcome measures at T0 (baseline), T1 (2 weeks following intervention) and T2 (9 weeks from T0). Forty percent of eligible children assented. T1 intervention effects were found on all total measures: Brief Symptom Inventory d = 0.99, p < . 001; Rosenberg Self Esteem Scale d = 0.65, p = . 001; Strengths and Difficulties Questionnaire d = 0.64, p = . 002; Self-efficacy questionnaire for children both “social” and “emotional” subscales, d = 0.54, p = . 008 and d = 0.63, p = . 002, respectively. At T2, significant effects remained for all measures and SES effect increased from medium to large (T1 = 0.65, T2 = 1.33). FGD data found children valued a calm environment that met their basic needs, enabling them to reflect, highlight support resources, and strengthen ability to recognize personal skills. The intervention has strong feasibility and acceptability, with no attrition or missing data. Memory Work Therapy has high potential effect size and should be tested in a fully powered trial. Clinical trials.gov NCT02180750.
Computational Intelligence and Adaptation in VANETs: Current Research and New Perspectives

The increasing number of moving vehicles along roads and the lack of supporting infrastructure is a wellestablished problem. Major consequences are augmenting of traffic jams, accidents, fuel consumption and pollution. Vehicular Ad hoc NETworks (VANETs) represent opportunities to deal with the aforementioned problems. In VANETS, efficiency and safety to applications are provided using communication support. In efficiency applications, each vehicle is aware of its location. Using this information and communication support, vehicles collaborate to reduce travel time and to improve mobility. In contrast, safety applications aim to reduce or even avoid accidents, and must obey strong timing constraints. In this context, VANETs applications can benefit from Computational Intelligence (CI) and adaptive approaches to implement the required demands. Thus, the contribution of this paper is twofold: $( i)$ we discuss how VANETs can benefit from CI and Artificial Intelligence techniques to make transportation networks more efficient regarding to safety applications, and, $( ii)$ we report our current work and new directions in the development of efficiency applications to VANETs using adaptation and CI techniques.
Extreme Learning Machines for Data Classification Tuning by Improved Bat Algorithm

Single hidden layer feed forward neural networks are widely used for various practical problems. However, the training process for determining synaptic weights of such neural networks can be computationally very expensive. In this paper we propose a new learning algorithm for learning the synaptic weights of the single hidden layer feedforward neural networks in order to reduce the learning time. We propose combining the upgraded bat algorithm with the extreme learning machine. The proposed approach reduces the number of evaluations needed to train a neural network and efficiently finds optimal input weights and the hidden biases. The proposed algorithm was tested on standard benchmark classification problems and functions and compared with other approaches from literature. The results have shown that our approach produces a satisfactory performance in almost all cases and that it can obtains solutions much faster than the traditional learning algorithms.
Four- and Seven-Point Relative Camera Pose from Oriented Features

Determining relative camera pose is a fundamental problem in computer vision, and pose is often computed from feature correspondences. For point features, a minimum of five correspondences are required to determine the pose between two calibrated cameras, and eight corresponding points can be used to form a linear solution. However, most feature detectors used in practice produce points with an associated orientation. This work demonstrates that with oriented features the relative pose of two cameras can be computed from just four point correspondences, or seven with a linear solution. These new four- and seven-point algorithms do not require any additional sensors or parameters, but exploit information (feature orientation) that is already computed by most existing structure-from-motion systems. On the DTU multi-view stereo data set the four-point algorithm is shown to be 55% faster than the five-point algorithm, and the seven-point linear algorithm gives a 43% speed improvement over the eight-point algorithm.
Forecasting Directional Change Uncertainty Using Probabilistic Fuzzy Systems

Directional change (DC) representations of stock and exchange rate prices have been proposed as a new method for describing and forecasting intra-day price movements. In the DC approach, a time series price curve is transformed into an intrinsic time curve which records price changes that exceed a threshold level and identify upwards and downwards price changes. These price changes are shown to be relevant for creating investment strategies. In this paper we propose the use of a multi-output Probabilistic Fuzzy System (PFS) to forecast upwards and downwards price changes in the intrinsic time curve. The use of PFS allows to model the stochastic uncertainty of the price changes, while maintaining a linguistic description of the system. The proposed model forecasts upwards and downward movements depending on current market conditions. We apply the proposed method to 5-minute intraday data and report the accuracy of the model in estimating and forecasting price changes. In addition, we illustrate how the uncertainty in these forecasts can potentially be used to define DC-based investment strategies. We report the risk-return features of these strategies and compare them with a conventional moving window strategy and an existing DC investment strategy.
Heading Direction Estimation Using Deep Learning with Automatic Large-scale Data Acquisition

Advanced Driver Assistance Systems (ADAS) have experienced major advances in the past few years. The main objective of ADAS includes keeping the vehicle in the correct road direction, and avoiding collision with other vehicles or obstacles around. In this paper, we address the problem of estimating the heading direction that keeps the vehicle aligned with the road direction. This information can be used in precise localization, road and lane keeping, lane departure warning, and others. To enable this approach, a large-scale database 1+ million images) was automatically acquired and annotated using publicly available platforms such as the Google Street View API and OpenStreetMap. After the acquisition of the database, a CNN model was trained to predict how much the heading direction of a car should change in order to align it to the road 4 meters ahead. To assess the performance of the model, experiments were performed using images from two different sources: a hidden test set from Google Street View (GSV) images and two datasets from our autonomous car (IARA). The model achieved a low mean average error of 2.359° and 2.524° for the GSV and IARA datasets, respectively; performing consistently across the different datasets. It is worth noting that the images from the IARA dataset are very different (camera, FOV, brightness, etc.) from the ones of the GSV dataset, which shows the robustness of the model. In conclusion, the model was trained effortlessly (using automatic processes) and showed promising results in real-world databases working in real-time (more than 75 frames per second).