Miracle-dz/UniFAR-FTUs huggingface dataset:
Active Learning by Sparse Instance Tracking and Classifier Confidence in Acoustic Emotion Recognition

Data scarcity is an ever crucial problem in the field of acoustic emotion recognition. How to get the most informative data from a huge amount of data by least human work and at the same time to obtain the highest performance is quite important. In this paper, we propose and investigate two active learning strategies in acoustic emotion recognition: Based on sparse instances or based on classifier confidence scores. The first strategy focuses on the problem of unbalanced binary or multiple classes. The latter strategy pays more attention on clearing up the boundary confusion between different classes. Our experimental results show that by using active learning aiming at sparse instances or based on classifier confidence, the amount of transcribed data needed is significantly reduced and the unweigted accuracy boosts greatly as well.
Spectral and Temporal Feature Learning With Two-Stream Neural Networks for Mental Workload Assessment

People’s mental workload profoundly affects their work efficiency and health. Mental workload assessment can be used to effectively avoid serious accidents caused by excessive mental workload. Both electroencephalogram (EEG) spectral features and its temporal features have proven to be useful in addressing this problem. The fusion of the two types of features can provide rich distinguishing information for improving mental workload assessment. Benefiting from the progress of deep learning, this study proposes the two-stream neural networks (TSNN) for fusing the two types of EEG features. Compared with hand-crafted features, the TSNN can learn and fuse EEG features from the spectral and temporal dimensions automatically without prior knowledge. The TSNN includes a spectral stream and a temporal stream. Each stream consists of a convolutional neural network (CNN) and a temporal convolutional network (TCN) to learn spectral or temporal features from EEG topographic maps. To fuse the learned spectral and temporal information, we concatenate the output of the two streams prior to the fully connected layer. EEG data were collected from 17 subjects who performed n-back tasks with easy, medium, and hard difficulty levels, leading to a three-class mental workload classification. The results show that the TSNN achieves an average accuracy of 91.9%, which is a significant improvement over baseline classifiers based on hand-crafted features. The TSNN also outperforms state-of-the-art deep learning methods developed for EEG classification. The results indicate that the proposed structure is promising for fusing spectral and temporal features for mental workload assessment. In addition, it provides a high-precision approach for potential applications during cognitive activities.
Oropharyngeal Granulomas and Tracheal Cartilage Degeneration in Fischer-344 Rats

The development of tracheal cartilage degeneration and inflammation of the seromucinous glands of the oropharynx may be a factor causing early mortalities in long-term studies with Fischer-344 (F-344) rats. The presence of these lesions was investigated in groups of male and female F-344 rats killed at 6, 19, and 32 wk of age. Half of the rats killed at 19 and 32 wk of age were sham treated with water (pH 10) by daily oral gavage for a duration of 13 or 26 wk prior to autopsy in order to detect any influence on the laryngotracheal cavity due to dosing technique. A clear age-associated increase in severity and incidence of chondroid degeneration of the tracheal and laryngeal cartilage was revealed, with an onset as early as 6 wk of age. After 19 wk, a high frequency of oropharyngeal granuloma formation was found. No relationship of the lesions to the gavage technique was apparent. The F-344 rat strain may possess a predisposition for inflammatory and degenerative changes in the oropharyngeal and laryngotracheal cavity, changes that can lead to increased and unexpected mortality rates in nonclinical safety studies (7).
HHEX_23 AA Genotype Exacerbates Effect of Diabetes on Dementia and Alzheimer Disease: A Population-Based Longitudinal Study

Background Research has suggested that variations within the IDE/HHEX gene region may underlie the association of type 2 diabetes with Alzheimer disease (AD). We sought to explore whether IDE genes play a role in the association of diabetes with dementia, AD, and structural brain changes using data from two community-based cohorts of older adults and a subsample with structural MRI. Methods and Findings The first cohort, which included dementia-free adults aged ≥75 y (n = 970) at baseline, was followed for 9 y to detect incident dementia (n = 358) and AD (n = 271) cases. The second cohort (for replication), which included 2,060 dementia-free participants aged ≥60 y at baseline, was followed for 6 y to identify incident dementia (n = 166) and AD (n = 121) cases. A subsample (n = 338) of dementia-free participants from the second cohort underwent MRI. HHEX_23 and IDE_9 were genotyped, and diabetes (here including type 2 diabetes and prediabetes) was assessed. In the first cohort, diabetes led to an adjusted hazard ratio (HR) of 1.73 (95% CI 1.19–2.32) and 1.66 (95% CI 1.06–2.40) for dementia and AD, respectively, among all participants. Compared to people carrying the GG genotype without diabetes, AA genotype carriers with diabetes had an adjusted HR of 5.54 (95% CI 2.40–7.18) and 4.81 (95% CI 1.88–8.50) for dementia and AD, respectively. There was a significant interaction between HHEX_23-AA and diabetes on dementia (HR 4.79, 95% CI 1.63–8.90, p = 0.013) and AD (HR 3.55, 95% CI 1.45–9.91, p = 0.025) compared to the GG genotype without diabetes. In the second cohort, the HRs were 1.68 (95% CI 1.04–2.99) and 1.64 (1.02–2.33) for the diabetes–AD and dementia–AD associations, respectively, and 4.06 (95% CI 1.06–7.58, p = 0.039) and 3.29 (95% CI 1.02–8.33, p = 0.044) for the interactions, respectively. MRI data showed that HHEX_23-AA carriers with diabetes had significant structural brain changes compared to HHEX_23-GG carriers without diabetes. No joint effects of IDE_9 and diabetes on dementia were shown. As a limitation, the sample sizes were small for certain subgroups. Conclusions A variant in the HHEX_23 gene interacts with diabetes to be associated with a substantially increased risk of dementia and AD, and with structural brain changes among dementia-free elderly people.
Implementing the Multilayer Retinal Model on the Complex-Cell CNN-um Chip Prototype

There is hardly a more important sensory modality for humans and other mammals than vision. The first and best-known part of the visual system is the retina, which is not a mere photoreceptor or static camera but a sophisticated feature preprocessor with continuous input and several parallel output channels. These interacting channels represent the visual scene. Never before has it been known in neuroscience how these channels build up a "visual language". Our mammalian retina model can generate the elements of this visual language. In the present paper the design steps of the implementation of the multilayer CNN retinal model is shown. It is rare that an analogic CNN algorithm has such a sophisticated series of different complex dynamics, meanwhile it is feasible on a recently fabricated complex cell CNN-UM chip. The mammalian retina model is decomposed into a full-custom mixed-signal chip that embeds digitally programmable analog parallel processing and distributed image memory on a common silicon substrate. The chip was designed and manufactured in a standard 0.5 μm CMOS technology and contains approximately 500,000 transistors. It consists of 1024 processing units arranged into a 32×32 grid. The functional features of the chip are in accordance with the second-order complex cell CNN-UM architecture: two CNN layers with programmable inter- and intra-layer connections between cells as well as programmable layer time constants. The uniqueness of this approach, among others, lies in the reprogrammability, i.e. the openness to any new discovery, even after a possible retinal implementation.
A Lightweight Three-Factor Authentication and Key Agreement Scheme in Wireless Sensor Networks for Smart Homes

A wireless sensor network (WSN) is used for a smart home system’s backbone that monitors home environment and controls smart home devices to manage lighting, heating, security and surveillance. However, despite its convenience and potential benefits, there are concerns about various security threats that may infringe on privacy and threaten our home life. For protecting WSNs for smart homes from those threats, authentication and key agreement are basic security requirements. There have been a large number of proposed authentication and key agreement scheme for WSNs. In 2017, Jung et al. proposed an efficient and security enhanced anonymous authentication with key agreement scheme by employing biometrics information as the third authentication factor. They claimed that their scheme resists on various security attacks and satisfies basic security requirements. However, we have discovered that Jung et al.’s scheme possesses some security weaknesses. Their scheme cannot guarantee security of the secret key of gateway node and security of session key and protection against user tracking attack, information leakage attack, and user impersonation attack. In this paper, we describe how those security weaknesses occur and propose a lightweight three-factor authentication and key agreement scheme in WSNs for smart homes, as an improved version of Jung et al.’s scheme. We then present a detailed analysis of the security and performance of the proposed scheme and compare the analysis results with other related schemes.
Dietary exposure to polychlorinated biphenyls and dioxins from infancy until adulthood: A comparison between breast-feeding, toddler, and long-term exposure.

Food is the major source for polychlorinated biphenyl (PCB) and dioxin accumulation in the human body. Therefore, investigating food habits from early ages until reproductive age (25 years) is important in order to assess exposure risk for the next generation. The objective of this study was to assess the PCB/dioxin exposure and the relative contribution of different foods to total exposure during preschool age. Particularly, the importance of lactational PCB/dioxin exposure vs. dietary exposure until adulthood was investigated. A cohort of 207 children was studied from birth until preschool age. Based on 3 planar PCBs and 17 2,3,7,8-substituted dibenzo-para-dioxins (PCDDs) and dibenzofurans (PCDFs) measured in breast milk, a model was developed to calculate the cumulative toxic equivalent (TEQ) intake during breast-feeding (0-1 year). In 3. 5-year-old children, daily dietary intake of planar PCB-TEQ and dioxin-TEQ was measured with a validated food questionnaire. Cumulative TEQ intake from 1 to 5 years was estimated using the PCB- and dioxin-TEQ intake measured with the food questionnaire. Cumulative TEQ intake from 6 to 25 years was estimated using national food consumption and contamination data of PCB- and dioxin-TEQ intake. In toddlers, dairy products contributed 43% to PCB-TEQ and 50% to dioxin-TEQ intake. Meat and meat products contributed 14% and 19%, respectively, and processed foods 23% and 15%, respectively. Breast-feeding for 6 months contributed to the cumulative PCB/dioxin TEQ intake until 25 years of age, 12% in boys and 14% in girls. The daily TEQ intake per kilogram body weight is 50 times higher in breast-fed infants and three times higher in toddlers than in adults. Long-term dietary exposure to PCBs and dioxins in men and women is partly due to breast-feeding (12 and 14%, respectively). After weaning, dairy products, processed foods, and meat are major contributors of PCB and dioxin accumulation until reproductive age. Instead of discouraging breast-feeding, maternal transfer of PCBs and dioxins to the next generation must be avoided by enforcement of strict regulations for PCB and dioxin discharge and by reducing consumption of animal products and processed foods in all ages. ImagesFigure 1Figure 2
Analysis of Species, Subgroups, and Endosymbionts of Bemisia tabaci (Hemiptera: Aleyrodidae) From Southwestern Cotton Fields in Turkey

Abstract Bemisia tabaci (Gennadius) (Hemiptera: Aleyrodidae) is one of the most important insect pests worldwide including Turkey. Although there are substantial data regarding species composition of Turkish B. tabaci populations, the situation is still not clear and further investigations are needed. Therefore, in this study, species and subgroups of B. tabaci collected from cotton fields in southwestern part of Turkey (Antalya, Aydın, Denizli, and Muğla) were determined using microsatellite analysis, AluI-based mtCOI polymerase chain reaction–random length polymorphism, and sequencing. Secondary endosymbionts were also determined using diagnostic species-specific PCR. Middle East Asia Minor 1 (MEAM1), Mediterranean (MED) Q1, and MED Q2 were the species and subgroups found in this study. The MED species (85.3%) were found to be more dominant than MEAM1. Species status of B. tabaci varied depending on the location. Although all samples collected from Aydın were found to be Q1, three species and subgroups were found in Muğla. Secondary endosymbionts varied according to species and subgroups. Arsenophonus was found only from Q2, while Hamiltonella was detected in MEAM1 and Q1. In addition, high Rickettsia and low Wolbachia infections were detected in MEAM1 and Q1 populations, respectively. In conclusion, for the first time, we report the presence and symbiotic communities of Q1 from Turkey. We also found that the symbiont complement of the Q1 is more congruent with Q1 from Greece than other regions of the world, which may have some interesting implications for movement of this invasive subgroup.
An Empirical Comparison of Dissimilarity Measures for Time Series Classification

Distance and dissimilarity functions are of undoubted importance to Time Series Data Mining. There are literally hundreds of methods proposed in the literature that rely on a dissimilarity measure as the main manner to compare objects. One notable example is the 1-Nearest Neighbor classification algorithm. These methods frequently outperform more complex methods in tasks such as classification, clustering, prediction, and anomaly detection. All these methods leave open the distance or dissimilarity function, being Euclidean distance (ED) and Dynamic Time Warping (DTW) the two most used dissimilarity measures in the literature. This paper empirically compares 48 measures on 42 time series data sets. Our objective is to call the attention of the research community about other dissimilarity measures besides ED and DTW, some of them able to significantly outperform these measures in classification. Our results show that Complex Invariant Distance DTW (CIDDTW) significantly outperforms DTW and that CIDDTW, DTW, CID, Minkowski L-p (p-norm difference with data set-crafted "p" parameter), Lorentzian L-infinity, Manhattan L-1, Average L-1/L-infinity (arithmetic average), Dice distance, and Jaccard distance outperform ED, but only CIDDTW, DTW, and CID outperform ED with statistical significance.
Complex automation of a technological process on the basis of control systems with a three level structure

Modern control methods are suitable for increasing production capabilities of industrial enterprises by employing information technologies, which are often used for modernization of industrial processes without the need of acquiring new equipment. Therefore, automation of the industrial lines is one of the most important directions of the technological progress, as it leads to improvement and modernization of industrial processes. The technological process of tellurium production includes a number of stages, which require automatic regulation of many parameters with a high degree of precision. In this work, we analyze and propose a unified concept of application of modern automation techniques that can be used to build a three-level hieratical control system intended for improving the quality of product.
Speaker Identification and Verification for Intelligent Service Robots

An economical device for dispensing particulate matter uniformly across a surface which includes a rotating cylinder which has wrapped around the surface thereof in abutting fashion, a conventional small sprocket chain thus providing a series of small receptacles of uniform size which when filled with particulate material passes underneath a restrainer so that each container has a uniform quantity of particulate material therein and then as the cylinder rotates the particulate material is dispensed by gravity.
A commentary on methodological issues in stroke caregiver research: lessons learned from three RESCUE intervention studies

ABSTRACT Stroke caregivers represent a distinct population and require unique considerations when conducting intervention research. Unfortunately, there are few published recommendations for conducting intervention trials with stroke caregivers. Thus, the goal of this paper is to provide foundational guidance for stroke caregiving researchers. We describe lessons learned from three trials of the RESCUE intervention to illustrate methodological issues in stroke caregiver research. We identify challenges encountered while conducting a clinical demonstration project, pilot study, and multi-site randomized controlled trial of a problem-solving and skills-building stroke caregiver intervention. We use our research experiences to exemplify or enhance understanding of each issue and describe the strategies we employed to overcome these obstacles. We present issues related to and recommendations for study design, theoretical frameworks, eligibility criteria, recruitment and retention, human subjects protection, intervention design, treatment fidelity, outcome measures, and qualitative methods. Identifying approaches to circumvent methodological challenges can help advance research on and implementation of stroke caregiver programs.
Randomized algorithms

For many applications, a randomized algorithm is either the simplest or the fastest algorithm available, and sometimes both. This book introduces the basic concepts in the design and analysis of randomized algorithms. The first part of the text presents basic tools such as probability theory and probabilistic analysis that are frequently used in algorithmic applications. Algorithmic examples are also given to illustrate the use of each tool in a concrete setting. In the second part of the book, each chapter focuses on an important area to which randomized algorithms can be applied, providing a comprehensive and representative selection of the algorithms that might be used in each of these areas. Although written primarily as a text for advanced undergraduates and graduate students, this book should also prove invaluable as a reference for professionals and researchers.
Q‐Dock: Low‐resolution flexible ligand docking with pocket‐specific threading restraints

The rapidly growing number of theoretically predicted protein structures requires robust methods that can utilize low‐quality receptor structures as targets for ligand docking. Typically, docking accuracy falls off dramatically when apo or modeled receptors are used in docking experiments. Low‐resolution ligand docking techniques have been developed to deal with structural inaccuracies in predicted receptor models. In this spirit, we describe the development and optimization of a knowledge‐based potential implemented in Q‐Dock, a low‐resolution flexible ligand docking approach. Self‐docking experiments using crystal structures reveals satisfactory accuracy, comparable with all‐atom docking. All‐atom models reconstructed from Q‐Dock's low‐resolution models can be further refined by even a simple all‐atom energy minimization. In decoy‐docking against distorted receptor models with a root‐mean‐square deviation, RMSD, from native of ∼3 Å, Q‐Dock recovers on average 15‐20% more specific contacts and 25–35% more binding residues than all‐atom methods. To further improve docking accuracy against low‐quality protein models, we propose a pocket‐specific protein–ligand interaction potential derived from weakly homologous threading holo‐templates. The success rate of Q‐Dock employing a pocket‐specific potential is 6.3 times higher than that previously reported for the Dolores method, another low‐resolution docking approach. © 2008 Wiley Periodicals, Inc. J Comput Chem 2008
Data resource profile: Household Influenza Vaccine Evaluation (HIVE) Study

Data resource profile: Household Influenza Vaccine Evaluation (HIVE) Study Arnold S Monto,* Ryan E Malosh, Richard Evans, Adam S Lauring, Aubree Gordon, Mark G Thompson, Alicia M Fry, Brendan Flannery, Suzanne E Ohmit, Joshua G Petrie and Emily T Martin, On behalf of the HIVE Study Research Staff Department of Epidemiology, University of Michigan School of Public Health, Ann Arbor, MI, USA, Division of Infectious Diseases, Michigan Medicine, University of Michigan, Ann Arbor, MI, USA and Influenza Division, National Center for Immunization and Respiratory Diseases, Centers for Disease Control and Prevention, Atlanta, GA, USA
Expanding the Diet for DIET: Electron Donors Supporting Direct Interspecies Electron Transfer (DIET) in Defined Co-Cultures

Direct interspecies electron transfer (DIET) has been recognized as an alternative to interspecies H2 transfer as a mechanism for syntrophic growth, but previous studies on DIET with defined co-cultures have only documented DIET with ethanol as the electron donor in the absence of conductive materials. Co-cultures of Geobacter metallireducens and Geobacter sulfurreducens metabolized propanol, butanol, propionate, and butyrate with the reduction of fumarate to succinate. G. metallireducens utilized each of these substrates whereas only electrons available from DIET supported G. sulfurreducens respiration. A co-culture of G. metallireducens and a strain of G. sulfurreducens that could not metabolize acetate oxidized acetate with fumarate as the electron acceptor, demonstrating that acetate can also be syntrophically metabolized via DIET. A co-culture of G. metallireducens and Methanosaeta harundinacea previously shown to syntrophically convert ethanol to methane via DIET metabolized propanol or butanol as the sole electron donor, but not propionate or butyrate. The stoichiometric accumulation of propionate or butyrate in the propanol- or butanol-fed cultures demonstrated that M. harundinaceae could conserve energy to support growth solely from electrons derived from DIET. Co-cultures of G. metallireducens and Methanosarcina barkeri could also incompletely metabolize propanol and butanol and did not metabolize propionate or butyrate as sole electron donors. These results expand the range of substrates that are known to be syntrophically metabolized through DIET, but suggest that claims of propionate and butyrate metabolism via DIET in mixed microbial communities warrant further validation.