Miracle-dz/UniFAR-FTUs huggingface dataset:
Extracting Emotional Features from ECG by Using Wavelet Transform

One key element of emotion recognition is to extract emotional features effectively from physiological signals. In this paper, a wavelet transform based feature extraction is proposed to recognize emotions through ECG (Electrocardiogram) signals. Four emotional data sets collected on the same day from one subject are decomposed by DWT (Discrete Wavelet Transform) and 84 statistic values of wavelet coefficients are selected as the emotional features according to their amplitude relations. Furthermore, in order to eliminate the negative impacts of material, time and environment, these selected features are normalized with respect to the emotional mode 'Pleasure'. The initial results show that, with the normalized features, the best correct-classification ratio of joy and sadness reaches 92%.
Mass Balance, Metabolite Profile, and In Vitro-In Vivo Comparison of Clearance Pathways of Deleobuvir, a Hepatitis C Virus Polymerase Inhibitor

ABSTRACT The pharmacokinetics, mass balance, and metabolism of deleobuvir, a hepatitis C virus (HCV) polymerase inhibitor, were assessed in healthy subjects following a single oral dose of 800 mg of [14C]deleobuvir (100 μCi). The overall recovery of radioactivity was 95.2%, with 95.1% recovered from feces. Deleobuvir had moderate to high clearance, and the half-life of deleobuvir and radioactivity in plasma were ∼3 h, indicating that there were no metabolites with half-lives significantly longer than that of the parent. The most frequently reported adverse events (in 6 of 12 subjects) were gastrointestinal disorders. Two major metabolites of deleobuvir were identified in plasma: an acyl glucuronide and an alkene reduction metabolite formed in the gastrointestinal (GI) tract by gut bacteria (CD 6168), representing ∼20% and 15% of the total drug-related material, respectively. Deleobuvir and CD 6168 were the main components in the fecal samples, each representing ∼30 to 35% of the dose. The majority of the remaining radioactivity found in the fecal samples (∼21% of the dose) was accounted for by three metabolites in which deleobuvir underwent both alkene reduction and monohydroxylation. In fresh human hepatocytes that form biliary canaliculi in sandwich cultures, the biliary excretion for these excretory metabolites was markedly higher than that for deleobuvir and CD 6168, implying that rapid biliary elimination upon hepatic formation may underlie the absence of these metabolites in circulation. The low in vitro clearance was not predictive of the observed in vivo clearance, likely because major deleobuvir biotransformation occurred by non-CYP450-mediated enzymes that are not well represented in hepatocyte-based in vitro models.
Trajectories of Change in Psychotherapy.

OBJECTIVE The current study used multilevel growth mixture modeling to ascertain groups of patients who had similar trajectories in their psychological functioning over the course of short-term treatment. METHOD A total of 10,854 clients completed a measure of psychological functioning before each session. Psychological functioning was measured by the Behavioral Health Measure, which is an index of well-being, symptoms, and life-functioning. Clients who attended 5 to 25 sessions at 46 different university/college counseling centers and one community mental health center were included in this study. Client diagnoses and the specific treatment approaches were not known. RESULTS A 3-class solution was a good fit to the data. Clients in classes 1 and 3 had moderate severity in their initial psychological functioning scores, and clients in class 2 had more distressed psychological functioning scores. The trajectory for clients in class 1 was typified by early initial change, followed by a plateau, and then another gain in psychological functioning later in treatment. The trajectory for clients in class 2 demonstrated an initial decrease in functioning, followed by a rapid increase, and then a plateau. Last, the clients in class 3 had a steady increase of psychological functioning, in a more linear manner. CONCLUSION The trajectories of change for clients are diverse, and they can ebb and flow more than traditional dose-effect and good-enough level models may suggest.
HiMuV: Hierarchical Framework for Modeling Multi-modality Multi-resolution Data

Many real-world applications are characterized by temporal data collected from multiple modalities, each sampled with a different resolution. Examples include manufacturing processes and financial market prediction. In these applications, an interesting observation is that within the same modality, we often have data from multiple views, thus naturally forming a 2-level hierarchy: with the multiple modalities on the top, and the multiple views at the bottom. For example, in aluminum smelting processes, the multiple modalities include power, noise, alumina feed, etc; and within the same modality such as power, the different views correspond to various voltage, current and resistance control signals and measured responses. For such applications, we aim to address the following challenge, i.e., how can we integrate such multi-modality multi-resolution data to effectively predict the targets of interest, such as bath temperature in aluminum smelting cell and the volatility in financial market. In this paper, for the first time, we simultaneously model the hierarchical data structure and the multi-resolution property via a novel framework named HiMuV. Different from existing work based on multiple views on a single level or a single resolution, the proposed framework is based on the key assumption that the information from different modalities is complementary, whereas the information within the same modality (across different views) is redundant in terms of predicting the targets of interest. Therefore, we introduce an optimization framework where the objective function contains both the prediction loss and a novel regularizer enforcing the consistency among different views within the same modality. To solve this optimization framework, we propose an iterative algorithm based on randomized block coordinate descent. Experimental results on synthetic data, benchmark data, and various real data sets from aluminum smelting processes, and stock market prediction demonstrate the effectiveness and efficiency of the proposed algorithm.
DOF Separation in Mid-air 3D Object Manipulation

Object manipulation is a key feature in almost every virtual environment. However, it is diﬃcult to accurately place an object in immersive virtual environments using mid-air gestures that mimic interactions in the physical world, although being a direct and natural approach. Previous research studied mouse and touch based interfaces concluding that separation of degrees-of-freedom (DOF) led to improved results. In this work, we assess the impact of explicit 6 DOF separation in mid-air manipulation tasks. We implemented a technique based on familiar virtual widgets that allow single DOF control, and compared it against a direct approach and PRISM, which dynamically adjusts the ratio between hand and object motions. Our results suggest that full DOF separation beneﬁts precision in spatial manipulations, at the cost of additional time for complex tasks. From these results we drew some guidelines for object manipulation in mid-air. Based on the guidelines we developed a technique, WISDOM, that combined the positive aspects of the three techniques used before. Our technique provides direct manipulation and scaled movements while maintaining DOF separation oﬀered by our Widgets implementation. In order to validate our proposal we conducted another user evaluation in order to compare it against 6DOF and Widgets. While WISDOM didn’t reduce task time completion, it improved position and rotation error in translation and rotation only tasks and more complex tasks that required both translation and rotation simultaneously compared to the other techniques.
Parental divorce and other determinants of interpersonal trust: Evidence from HILDA panel data

This paper examines the determinants of interpersonal trust with an emphasis on parental divorce using Australian HILDA panel data. The dependent variable is composed of answers to the statement: 'Generally speaking, most people can be trusted'. The analysis is conducted using random effects ordered probit and correlated random effects ordered probit. Mother's higher education is a strong positive determinant for trust for daughters while father's education matters for sons. A working father when the respondent was age 14 is a strong positive determinant for both men and women. An immigrant mother is a negative determinant of trust for both men and women. Residential stability has a strong positive effect for men's interpersonal trust. The results also indicate that there is some correlation between the level of interpersonal trust and parental divorce for both men and women. However, the effect is not strong enough for the 'divorce revolution' to account for the overall lower societal trust in Australia.
Benchmarking an artificial neural network tuned by a genetic algorithm

This thesis starts with a brief introduction to neural networks and the tuning of neural networks using genetic algorithms. An improved genetic algorithm is benchmarked using the technical paper Proben1 as a starting point. The benefits of using a genetic algorithm as well as results of the benchmark tests in comparison to a resilient backpropagation algorithm are discussed. The improved genetic algorithm is not a universal solution to all classification problems. Even though it outperforms the resilient backpropagation algorithm slightly in these benchmark tests more benchmarking on more vast solution domains must be performed.
Host country language ability and expatriate adjustment: the moderating effect of language difficulty

Research has shown that expatriates' host country language ability is positively associated with their adjustment. But does the advantage of expatriates' language ability depend on the difficulty of the host language? To examine this issue, data were collected from expatriates in two European countries, one with an easy, relatively simple language and the other with a difficult, highly complex language. Consistent with Goal-Setting Theory, results indicated a relative advantage of expatriates' language ability in terms of their adjustment in the host country with the difficult language as opposed to the host country with an easy language.
Cloud Asset Pricing Tree (CAPT) - Elastic Economic Model for Cloud Service Providers

Providers are incorporating novel techniques to cope with prospective aspects of trading like resource allocation over future demands and its pricing elasticity that was not foreseen before. To leverage the pricing elasticity of upcoming demand and supply, we employ financial option theory (future contracts) as a mechanism to alleviate the risk in resource allocation over future demands. This study introduces a novel Cloud Asset Pricing Tree (CAPT) model that finds the optimal premium price of the Cloud federation options efficiently. Providers will benefit by this model to make decisions when to buy options in advance and when to exercise them to achieve more economies of scale. The CAPT model adapts its structure to address the price elasticity concerns and makes the demand and provisioning, price inelastic. Our empirical evidences suggest that using the CAPT model, exploits the Cloud market potential as an opportunity for more resource utilization and future capacity planning.
An Empirical Study on the Maturity of the Eclipse Modeling Ecosystem

Since the early days of Model-driven Engineering (MDE), our community has been discussing the reasons why MDE had not quickly became mainstream. It is now clear the answer is a mix of technical and social factors, but among the former, the lack of maturity of MDE tools is often mentioned. The goal of this paper is to explore the question of whether this lack of maturity is actually true. We do so by comparing the maturity of over a hundred modeling and non-modeling projects living together in the Eclipse ecosystem. In both cases, we use the word project to refer to a variety of tools, libraries and other artefacts to build and manipulate software components, either at the model or code level. Our maturity model is based on code-centric and community metrics that we evaluate on the repository data for both kinds of projects. Their incubation status is also considered in the assessment. Results show that there are indeed differences between modeling and non-modeling projects, though less than we expected when setting up the study. Moreover, while the incubation status clearly separates non-modeling projects, the same is not true for modeling projects which seem to remain much more stable across their lifespan. We believe our results help to have a better perspective on maturity of modeling support nowadays and provide ideas for further analysis towards their improvement.
Enhanced RGB-D Mapping Method for Detailed 3D Indoor and Outdoor Modeling

RGB-D sensors (sensors with RGB camera and Depth camera) are novel sensing systems that capture RGB images along with pixel-wise depth information. Although they are widely used in various applications, RGB-D sensors have significant drawbacks including limited measurement ranges (e.g., within 3 m) and errors in depth measurement increase with distance from the sensor with respect to 3D dense mapping. In this paper, we present a novel approach to geometrically integrate the depth scene and RGB scene to enlarge the measurement distance of RGB-D sensors and enrich the details of model generated from depth images. First, precise calibration for RGB-D Sensors is introduced. In addition to the calibration of internal and external parameters for both, IR camera and RGB camera, the relative pose between RGB camera and IR camera is also calibrated. Second, to ensure poses accuracy of RGB images, a refined false features matches rejection method is introduced by combining the depth information and initial camera poses between frames of the RGB-D sensor. Then, a global optimization model is used to improve the accuracy of the camera pose, decreasing the inconsistencies between the depth frames in advance. In order to eliminate the geometric inconsistencies between RGB scene and depth scene, the scale ambiguity problem encountered during the pose estimation with RGB image sequences can be resolved by integrating the depth and visual information and a robust rigid-transformation recovery method is developed to register RGB scene to depth scene. The benefit of the proposed joint optimization method is firstly evaluated with the publicly available benchmark datasets collected with Kinect. Then, the proposed method is examined by tests with two sets of datasets collected in both outside and inside environments. The experimental results demonstrate the feasibility and robustness of the proposed method.
Relationship Between Training Intensity and Volume and Hypothyroidism Among Female Runners

Objective:To determine the effect of training intensity on thyroid function among female nonelite runners. Design:Internet-based survey of medical history and training and racing habits of female runners. Participants:A total of 1222 female runners aged ≥35 years. Main Outcome Measures:Self-reported diagnosis of hypothyroidism and details of training and racing. Results:Hypothyroidism was reported by 149 (12.2%). No characteristics of training intensity or duration, including average miles per week, training pace, or years of accumulated running were associated with thyroid dysfunction. Females who began running at or before age 10 were more likely to report a diagnosis of hypothyroidism versus those who began running at an older age (4.7% vs 1.5%, P = 0.018). Conclusions:Our results do not support the concept of overtraining-related hypothyroidism among nonelite female distance runners although our data demonstrated a 3-fold increase in hypothyroidism among those who began a career at or before age 10. Further study is required to confirm and determine a possible mechanism of this association.
Chunkyspread: Heterogeneous Unstructured Tree-Based Peer-to-Peer Multicast

The rising popularity of live IPTV has triggered renewed interest in P2P multicast. In particular, the simple and robust 'swarming' style of P2P multicast is currently favored over more traditional tree-based approaches, which are seen to be complex and fragile. Swarming approaches, however, exhibit a basic control-overhead-versus-latency tradeoff that gears it more towards high-volume, latency-tolerant applications. This paper presents a new unstructured P2P multicast protocol called Chunkyspread that is tree-based yet simple and robust. Chunkyspread uses multiple trees to provide fine-grained control over member load, reacts quickly to membership changes, scales well, and has low overhead. This paper gives a detailed description of Chunkyspread and an apples-to-apples comparison with the DHT-based Splitstream multi-tree P2P multicast algorithm. We show that Chunkyspread exhibits far better control over transmit load than Splitstream, while exhibiting comparable or better latency and responsiveness to churn. This comparison establishes Chunkyspread as the 'best of breed' among tree- based P2P multicast algorithms, thus setting the stage for future comparisons with swarming-based approaches.
Resilience concepts and findings: implications for family therapy

Resilience is a term used to describe relative resistance to psychosocial risk experiences. There is abundant evidence that there is enormous variation in children's responses to such experiences but research to determine the processes underlying the variations needs to take account of several crucial methodological issues. The findings emphasize that multiple risk and protective factors are involved; that children vary in their vulnerability to psychosocial stress and adversity as a result of both genetic and environmental influences; that family-wide experiences tend to impinge on individual children in quite different ways; that the reduction of negative, and increase of positive, chain reactions influences the extent to which the effects of adversity persist over time; that new experiences which open up opportunities can provide beneficial ‘turning- point’ effects; that although positive experiences in themselves do not exert much of a protective effect, they can be helpful if they serve to neutralize some risk factors; and that the cognitive and affective processing of experiences is likely to influence whether or not resilience develops. The implications of these findings for family therapy are considered in terms of the need for therapists to look carefully at the ways in which different risk factors interact; to assess and take account of individual differences in susceptibility; to consider the extent to which risk factors impinge on the individual and, in that connection, to note the importance of patterns of social interaction outside as well as inside the family; to appreciate the role of both the peer group and individual characteristics in the development of negative and positive chain reactions; and to pay attention to the ways in which individuals process their experiences.
Experiments with POS-based restructuring and alignment-based reordering for statistical machine translation

This paper presents the methods which are based on the part-of-speech (POS) and auto alignment information to improve the quality of machine translation result and the word alignment. We utilize different types of POS tag to restructure source sentences and use an alignment-based reordering method to improve the alignment. After applying the reordering method, we use two phrase tables in the decoding part to keep the translation performance. Our experiments on Korean-Chinese show that our methods can improve the alignment and translation results. Since the proposed approach reduces the size of the phrase table, multi-tables are considered. The combination of all these methods together would get the best translation result.
A Novel Architecture of Implementing Error Detecting AES Using PRNS

A new method using polynomial residue number systems (PRNS) is introduced in this paper to protect the Advanced Encryption Standard (AES) against faults attacks. By using PRNS, the byte based AES operations over GF(28) are decomposed into several parallel operations that use its residues over smaller fields. In this implementation, three GF(24) irreducible polynomials are selected as the moduli set for the chosen PRNS, including a redundant modulus to achieve error detection. Three GF(24) AES cores are constructed individually according to the chosen moduli. This PRNS architecture brings several advanced features to AES design from the scope of anti-side-channel analysis. Firstly, for each 8-bit GF(28) element, this implementation is capable of detecting up to 4 bit errors that occur in a single GF(24) AES core. Secondly, thanks to the data independency between PRNS operations, the distributed PRNS AES cores have an intrinsic resistance against probing attacks. In addition, due to the introduction of redundant information and the residue representation replacing the original representation, more confusion is added to the system, which may also enhance the design's security. To the authors' knowledge, this is the world's first PRNS AES implementation. Hardware implementation results are also given in this paper.
Isopower maps of the electrogastrogram (EGG) after total gastrectomy or total colectomy

Isopower or topographic electrogastrograms (EGG) correspond to topographic electroencephalograms. Both project the topographic localizations of the spectral frequencies on the abdominal surface or scalp. This paper compares the pre‐operative control isopower EGG maps with those of total gastrectomy or total colectomy.
Fault-tolerant neural network with concurrent error detection and correction capability

Although artificial neural networks (ANNs) are generally considered to be robust, faults in neural network hardware can result in output errors. In order for ANNs to be used in mission-critical areas, they will be required to have the capability of detecting and correcting fault-induced computation errors. In this paper, a fault-tolerant neural network architecture with concurrent error detection and correction capability is proposed. The output of each hidden- and output-layer neuron of the proposed architecture is computed by three different processors or processing elements (PEs), and the computation results are compared. Each PE is also self-testing, and this ensures that if there are similar errors in a majority of the compared PE results, these errors will be detected. The proposed fault-tolerant architecture has been compared with existing fault-tolerant architectures, and simulation results are presented which show that ANNs implemented with the proposed architecture are more reliable and have better fault tolerance.