Miracle-dz/UniFAR-FTUs huggingface dataset:
Automatic Ownership Change Detection for IoT devices

Aalto University, P.O. Box 11000, FI-00076 Aalto www.aalto. fi Author Artur Valiev Title Automatic Ownership Change Detection for IoT devices School School of Science Master’s programme Computer, Communication and Information Sciences Major Security and Cloud Computing Code SCI3084 Supervisor Professor N. Asokan, Aalto University, Professor Livshitz I.I. , ITMO University Advisor Dr. Samuel Marchal (Postdoc), Aalto University Level Master’s thesis Date July 2, 2018 Pages 64 Language English Abstract Considering the constant increases in Internet Of Things (IoT) smart home devices prevalence, their ownership is likely to change. This introduces novel privacy issues. Smart home devices store owner’s sensitive information, which needs to be handled securely in case of change in device ownership. Currently employed smart home devices cannot detect changes in their ownership, which raises a great number of privacy and security issues. To address this problem, we propose a system called FoundIoT for automatic detection of IoT device ownership change. FoundIoT provides a technique to detect change of ownership based on device context, which is inferred by monitoring wireless communication channels. Finally, we present a prototype implementation of FoundIoT for the proposed automatic ownership change detection technique. We show that FoundIoT achieves a satisfactory performance. The implementation is supported by a wide range of IoT devices and demonstrates a high speed (up to 1 minute 39 seconds) and 100% accuracy of ownership change detection.Considering the constant increases in Internet Of Things (IoT) smart home devices prevalence, their ownership is likely to change. This introduces novel privacy issues. Smart home devices store owner’s sensitive information, which needs to be handled securely in case of change in device ownership. Currently employed smart home devices cannot detect changes in their ownership, which raises a great number of privacy and security issues. To address this problem, we propose a system called FoundIoT for automatic detection of IoT device ownership change. FoundIoT provides a technique to detect change of ownership based on device context, which is inferred by monitoring wireless communication channels. Finally, we present a prototype implementation of FoundIoT for the proposed automatic ownership change detection technique. We show that FoundIoT achieves a satisfactory performance. The implementation is supported by a wide range of IoT devices and demonstrates a high speed (up to 1 minute 39 seconds) and 100% accuracy of ownership change detection.
MISFOLDING DOMINATES PROTEIN EVOLUTION

The diverse array of protein functions depends upon these molecules' reliable ability to fold into the native structures determined by their amino-acid sequences. Because mutations that alter a protein's sequence frequently disrupt its folding, protein evolution explores protein sequence space conservatively, either by point mutations or recombination between related sequences. Attempts to engineer proteins by co-opting the evolutionary algorithm have also largely proceeded by the stepwise accumulation of beneficial mutations. Other strategies for directed evolution have focused on introducing many mutations at once as a way to increase the likelihood of finding improved variants, attempting to balance higher mutational diversity with lower retention of folding. Using simple models, I explore this tradeoff and find that protein misfolding dominates whether increasing mutation levels increase the number of improved variants. I analyze results of a popular mutagenesis protocol, error-prone PCR, for evidence that coupling between mutations might favor higher mutation levels, as claimed by several groups. A comparison of high-mutation-rate mutagenesis to protein recombination between distantly related proteins reveals qualitative differences in protein tolerance for sequence changes introduced by each method. Mutational tolerance may also be reflected in the rate at which proteins accumulate sequence changes over evolutionary time; why proteins evolve at different rates remains a major open question in biology. An analysis of rate determinants suggests that one major variable, linked to how highly expressed the encoding gene is, dominates the rate of yeast protein evolution. To explain this trend, I hypothesize that proteins are selected to fold properly despite mistranslation, a property I call translational robustness, and test it using genomic data. To examine protein evolution at a higher level of detail, a large-scale simulation is constructed in which simulated organisms, with genomes containing genes expressing computationally foldable proteins at different levels, evolve over millions of generations with protein misfolding imposing the only fitness cost. The results suggest that protein misfolding suffices to explain many significant trends in genome evolution observed across taxa, predict a novel genomic trend which is then identified in yeast, and create insight into the causes of evolutionary rate variation in proteins.
Gait-based Person Identification using Multiple Inertial Sensors

: Inertial such as accelerometers and gyroscopes have gained popularity in recent years for their use in human activity recognition. However, little work has been done on using these sensors for gait-based person identiﬁcation. Gait-based person identiﬁcation turns out to be important in applications such as where different people share the same wearable device and it is desirable to identify who is using the device at a given time while walking. In this research, we present the ﬁrst multi-sensory gait-based person identiﬁcation dataset EJUST-GINR-1 and present our work on gait-based person identiﬁcation using multi-sensory data, by mounting 8 wearable inertial sensory devices on different body locations and use this data to identify the person using it. Two of these sensors are smart watches worn on both wrists. We explore the correlation between each body location and the identiﬁcation accuracy, as well as exploring the effect of fusing pairs of sensory units in different locations, on the ﬁnal classiﬁcation performance.
Implementation of Centralized MPC on the Quadruple-tank Process with Guaranteeing Stability

: This work presents an implementation of a stabilizing model predictive control applied to a nonlinear system. In this work, the quadruple-tank system has been considered. For this process, a precise control benchmark was available and worked on previously. To ensure the asymptotic stability of this nonlinear system, we made a discretized linearized model and applied a centralized MPC controller with terminal cost constraint. The effectiveness of the proposed strategy is illustrated by simulations.
Prediction of Functional Outcome After Acute Ischemic Stroke: Comparison of the CT-DRAGON Score and a Reduced Features Set

Background and Purpose: The CT-DRAGON score was developed to predict long-term functional outcome after acute stroke in the anterior circulation treated by thrombolysis. Its implementation in clinical practice may be hampered by its plethora of variables. The current study was designed to develop and evaluate an alternative score, as a reduced set of features, derived from the original CT-DRAGON score. Methods: This single-center retrospective study included 564 patients treated for stroke, in the anterior and the posterior circulation. At 90 days, favorable [modified Rankin Scale score (mRS) of 0–2] and miserable outcome (mRS of 5–6) were predicted by the CT-DRAGON in 427 patients. Bootstrap forests selected the most relevant parameters of the CT-DRAGON, in order to develop a reduced set of features. Discrimination, calibration and misclassification of both models were tested. Results: The area under the receiver operating characteristic curve (AUROC) for the CT-DRAGON was 0.78 (95% CI 0.74–0.81) for favorable and 0.78 (95% CI 0.72-0.83) for miserable outcome. Misclassification was 29% for favorable and 13.5% for miserable outcome, with a 100% specificity for the latter. National Institutes of Health Stroke Scale (NIHSS), pre-stroke mRS and age were identified as the strongest contributors to favorable and miserable outcome and named the reduced features set. While CT-DRAGON was only available in 323 patients (57%), the reduced features set could be calculated in 515 patients (91%) (p < 0.001). Misclassification was 25.8% for favorable and 14.4% for miserable outcome, with a 97% specificity for miserable outcome. The reduced features set had better discriminative power than CT-DRAGON for both outcomes (both p < 0.005), with an AUROC of 0.82 (95% CI 0.79–0.86) and 0.83 (95% CI 0.77–0.87) for favorable and miserable outcome, respectively. Conclusions: The CT-DRAGON score revealed acceptable discrimination in our cohort of both anterior and posterior circulation strokes, receiving all treatment modalities. The reduced features set could be measured in a larger cohort and with better discrimination. However, the reduced features set needs further validation in a prospective, multicentre study. Clinical Trial Registration: http://www.clinicaltrials. gov. Identifiers: NCT03355690, NCT04092543.
Ruminal Fermentation Pattern, Bacterial Community Composition, and Nutrient Digestibility of Nellore Cattle Submitted to Either Nutritional Restriction or Intake of Concentrate Feedstuffs Prior to Adaptation Period

Beef cattle are key contributors to meat production and represent critical drivers of the global agricultural economy. In Brazil, beef cattle are reared in tropical pastures and finished in feedlot systems. The introduction of cattle into a feedlot includes a period where they adapt to high-concentrate diets. This adaptation period is critical to the success of incoming cattle, as they must adjust to both a new diet and environment. Incoming animals are typically reared on a variety of diets, ranging from poor quality grasses to grazing systems supplemented with concentrate feedstuffs. These disparate pre-adaptation diets present a challenge, and here, we sought to understand this process by evaluating the adaptation of Nellore calves raised on either grazing on poor quality grasses (restriction diet) or grazing systems supplemented with concentrate (concentrate diet). Given that nutrient provisioning from the diet is the sole responsibility of the ruminal microbial community, we measured the impact of this dietary shift on feeding behavior, ruminal fermentation pattern, ruminal bacterial community composition (BCC), and total tract digestibility. Six cannulated Nellore bulls were randomly assigned to two 3 × 3 Latin squares, and received a control, restriction, or concentrate diet. All cohorts were then fed the same adaptation diet to mimic a standard feedlot. Ruminal BCC was determined using Illumina-based 16S rRNA amplicon community sequencing. We found that concentrate-fed cattle had greater dry matter intake (P < 0.01) than restricted animals. Likewise, cattle fed concentrate had greater (P = 0.02) propionate concentration during the adaptation phase than control animals and a lower Shannon’s diversity (P = 0.02), relative to the restricted animals. We also found that these animals had lower (P = 0.04) relative abundances of Fibrobacter succinogenes when compared to control animals during the pre-adaptation phase and lower abundances of bacteria within the Succinivibrio during the finishing phase, when compared to the control animals (P = 0.05). Finally, we found that animals previously exposed to concentrate were able to better adapt to high-concentrate diets when compared to restricted animals. Our study presents the first investigation of the impact of pre-adaptation diet on ruminal BCC and metabolism of bulls during the adaptation period. We suggest that these results may be useful for planning adaptation protocols of bulls entering the feedlot system and thereby improve animal production.
Optimization of a Soft Ensemble Vote Classifier for the Prediction of Chimeric Virus-Like Particle Solubility and Other Biophysical Properties

Chimeric virus-like particles (cVLPs) are protein-based nanostructures applied as investigational vaccines against infectious diseases, cancer, and immunological disorders. Low solubility of cVLP vaccine candidates is a challenge that can prevent development of these very substances. Solubility of cVLPs is typically assessed empirically, leading to high time and material requirements. Prediction of cVLP solubility in silico can aid in reducing this effort. Protein aggregation by hydrophobic interaction is an important factor driving protein insolubility. In this article, a recently developed soft ensemble vote classifier (sEVC) for the prediction of cVLP solubility was used based on 91 literature amino acid hydrophobicity scales. Optimization algorithms were developed to boost model performance, and the model was redesigned as a regression tool for ammonium sulfate concentration required for cVLP precipitation. The present dataset consists of 568 cVLPs, created by insertion of 71 different peptide sequences using eight different insertion strategies. Two optimization algorithms were developed that (I) modified the sEVC with regard to systematic misclassification based on the different insertion strategies, and (II) modified the amino acid hydrophobicity scale tables to improve classification. The second algorithm was additionally used to synthesize scales from random vectors. Compared to the unmodified model, Matthew’s Correlation Coefficient (MCC), and accuracy of the test set predictions could be elevated from 0.63 and 0.81 to 0.77 and 0.88, respectively, for the best models. This improved performance compared to literature scales was suggested to be due to a decreased correlation between synthesized scales. In these, tryptophan was identified as the most hydrophobic amino acid, i.e., the amino acid most problematic for cVLP solubility, supported by previous literature findings. As a case study, the sEVC was redesigned as a regression tool and applied to determine ammonium sulfate concentrations for the precipitation of cVLPs. This was evaluated with a small dataset of ten cVLPs resulting in an R2 of 0.69. In summary, we propose optimization algorithms that improve sEVC model performance for the prediction of cVLP solubility, allow for the synthesis of amino acid scale tables, and further evaluate the sEVC as regression tool to predict cVLP-precipitating ammonium sulfate concentrations.
Combined Effects of Parenting in Childhood and Resilience on Work Stress in Nonclinical Adult Workers From the Community

Background Stress responses induced by job stressors are modified by individual factors. Perceived parental bonding and resilience would play important roles as such individual factors. In this study, we analyzed the mediating roles of resilience on parenting, job stressors, and stress responses among adult workers from the community. Methods A total of 528 workers participated in this study after providing written consent, and completed questionnaires on demographic data, as well as Parental Bonding Instrument, Connor-Davidson Resilience Scale, and Brief Job Stress Questionnaire. Associations between perceived parental bonding, resilience, perceived job stressors, and the psychological and physical stress response (PPSR) were analyzed using structural equation modeling. Results Structural equation modeling with covariance structure analysis showed that parental overprotection reduced resilience and increased perceived job stressors and PPSR. Resilience and perceived job stressors and their combination mediated the effect of parental overprotection on PPSR. Resilience mediated the effect of parental overprotection on perceived job stressors. Perceived job stressors mediated the effect of resilience on PPSR. Parental care had opposite effects to parental overprotection, but the difference was small. Conclusion In this study, we showed that perceived parental bonding affects resilience, and that both factors affect the stress response and perceived job stressors. These findings suggest that parental bonding and resilience are major individual factors affecting work stress, and should be noted when considering industrial hygiene measures for individual workers.
The Importance of Cerebellar Connectivity on Simulated Brain Dynamics

The brain shows a complex multiscale organization that prevents a direct understanding of how structure, function and dynamics are correlated. To date, advances in neural modeling offer a unique opportunity for simulating global brain dynamics by embedding empirical data on different scales in a mathematical framework. The Virtual Brain (TVB) is an advanced data-driven model allowing to simulate brain dynamics starting from individual subjects’ structural and functional connectivity obtained, for example, from magnetic resonance imaging (MRI). The use of TVB has been limited so far to cerebral connectivity but here, for the first time, we have introduced cerebellar nodes and interconnecting tracts to demonstrate the impact of cerebro-cerebellar loops on brain dynamics. Indeed, the matching between the empirical and simulated functional connectome was significantly improved when including the cerebro-cerebellar loops. This positive result should be considered as a first step, since issues remain open about the best strategy to reconstruct effective structural connectivity and the nature of the neural mass or mean-field models generating local activity in the nodes. For example, signal processing is known to differ remarkably between cortical and cerebellar microcircuits. Tackling these challenges is expected to further improve the predictive power of functional brain activity simulations, using TVB or other similar tools, in explaining not just global brain dynamics but also the role of cerebellum in determining brain states in physiological conditions and in the numerous pathologies affecting the cerebro-cerebellar loops.
Effects of Cardiovascular Interval Training in Healthy Elderly Subjects: A Systematic Review

The aim of this review is to demonstrate the effects of cardiovascular interval training (IT) on healthy elderly subjects. We used the recommendations of the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines. The following variables were observed: resting heart rate (HR), systolic blood pressure (SBP), diastolic blood pressure (DBP), mean arterial pressure (MBP), heart rate variability (HRV), baroreflex activity (BA), and maximal oxygen uptake (VO2max). Studies were searched for in the MedLine, PubMed, and Sport Discus databases considering publications between 1990 and 2019. To find the studies, the keywords used were “Interval and Elderly Training” or “Interval Training and Baroreflex Sensing” or “Interval Training and Aging and Pressure Arterial and Blood Pressure Training” or “Interval Training and Variation in Aging and Heart Rate” or “Interval Training and Sensitivity to the Elderly and Baroreflex” or “Interval Training and Variability in the Elderly and Heart Rate. ” The systematic search identified 1,140 hits. The analysis of the study was performed through a critical review of the content. One thousand one hundred forty articles were identified. Of these, 1,108 articles were excluded by checking the articles and abstracts. Finally, 32 studies were selected for full reading while 26 studies were eliminated because they did not contain a methodology according to the purpose of this review. Thus, six studies were included for the final analysis. The PEDro score was used for analyzing the study quality and found 4,8 ± 1,3 points (range: 3–6). Positive results were found with the different IT protocols in the observed variables. Results show that IT protocols can be an efficient method for functional improvement of cardiovascular and cardiorespiratory variables in the healthy elderly, especially HR, SBP, DBP, MAP, HRV, BA, and VO2max. However, this method can be included in the prescription of aerobic training for the elderly to obtain conditional improvements in the cardiovascular system, thus being an important clinical intervention for the public.
Large-Scale Phenotyping of Livestock Welfare in Commercial Production Systems: A New Frontier in Animal Breeding

Genomic breeding programs have been paramount in improving the rates of genetic progress of productive efficiency traits in livestock. Such improvement has been accompanied by the intensification of production systems, use of a wider range of precision technologies in routine management practices, and high-throughput phenotyping. Simultaneously, a greater public awareness of animal welfare has influenced livestock producers to place more emphasis on welfare relative to production traits. Therefore, management practices and breeding technologies in livestock have been developed in recent years to enhance animal welfare. In particular, genomic selection can be used to improve livestock social behavior, resilience to disease and other stress factors, and ease habituation to production system changes. The main requirements for including novel behavioral and welfare traits in genomic breeding schemes are: (1) to identify traits that represent the biological mechanisms of the industry breeding goals; (2) the availability of individual phenotypic records measured on a large number of animals (ideally with genomic information); (3) the derived traits are heritable, biologically meaningful, repeatable, and (ideally) not highly correlated with other traits already included in the selection indexes; and (4) genomic information is available for a large number of individuals (or genetically close individuals) with phenotypic records. In this review, we (1) describe a potential route for development of novel welfare indicator traits (using ideal phenotypes) for both genetic and genomic selection schemes; (2) summarize key indicator variables of livestock behavior and welfare, including a detailed assessment of thermal stress in livestock; (3) describe the primary statistical and bioinformatic methods available for large-scale data analyses of animal welfare; and (4) identify major advancements, challenges, and opportunities to generate high-throughput and large-scale datasets to enable genetic and genomic selection for improved welfare in livestock. A wide variety of novel welfare indicator traits can be derived from information captured by modern technology such as sensors, automatic feeding systems, milking robots, activity monitors, video cameras, and indirect biomarkers at the cellular and physiological levels. The development of novel traits coupled with genomic selection schemes for improved welfare in livestock can be feasible and optimized based on recently developed (or developing) technologies. Efficient implementation of genetic and genomic selection for improved animal welfare also requires the integration of a multitude of scientific fields such as cell and molecular biology, neuroscience, immunology, stress physiology, computer science, engineering, quantitative genomics, and bioinformatics.