Miracle-dz/UniFAR-FTUs huggingface dataset:
A Simple, High Sensitive Fiber Optic Microphone Based on Cellulose Triacetate Diaphragm

We present a high sensitive fiber-optic microphone (FOM) with a wide measuring range based on a cellulose triacetate diaphragm in this work. The average signal-to-noise ratio (SNR) of the proposed FOM is measured 50.86 dB in at the frequency range of 100 Hz–20 kHz. In the same spectrum range, the highest SNR for the 2-kHz frequency under 103.16 mPa acoustic pressure is measured at 72.20 dB with 2 Hz resolution bandwidth, which corresponds to a noise-limited minimum detectable acoustic pressure level of 17.91 μPa/√Hz. The proposed sensor has advantages of high sensitivity, low cost, and easy fabrication. According to the assessments based on the obtained results, the FOM can be used for medical spectroscopy, imaging applications, and for long-distance listening and voice recognition in the military-public security.
Exploiting Coarse-to-Fine Task Transfer for Aspect-level Sentiment Classification

Aspect-level sentiment classification (ASC) aims at identifying sentiment polarities towards aspects in a sentence, where the aspect can behave as a general Aspect Category (AC) or a specific Aspect Term (AT). However, due to the especially expensive and labor-intensive labeling, existing public corpora in AT-level are all relatively small. Meanwhile, most of the previous methods rely on complicated structures with given scarce data, which largely limits the efficacy of the neural models. In this paper, we exploit a new direction named coarse-to-fine task transfer, which aims to leverage knowledge learned from a rich-resource source domain of the coarse-grained AC task, which is more easily accessible, to improve the learning in a low-resource target domain of the fine-grained AT task. To resolve both the aspect granularity inconsistency and feature mismatch between domains, we propose a Multi-Granularity Alignment Network (MGAN). In MGAN, a novel Coarse2Fine attention guided by an auxiliary task can help the AC task modeling at the same finegrained level with the AT task. To alleviate the feature false alignment, a contrastive feature alignment method is adopted to align aspect-specific feature representations semantically. In addition, a large-scale multi-domain dataset for the AC task is provided. Empirically, extensive experiments demonstrate the effectiveness of the MGAN.
Protein inference using PIA workflows and PSI standard file formats

Proteomics using LC-MS/MS has become one of the main methods to analyze the proteins in biological samples in high-throughput. But the existing mass spectrometry instruments are still limited with respect to resolution and measurable mass ranges, which is one of the main reasons why shotgun proteomics is the major approach. Here, proteins are digested, which leads to the identification and quantification of peptides instead. While often neglected, the important step of protein inference needs to be conducted to infer from the identified peptides to the actual proteins in the original sample. In this work, we highlight some of the previously published and newly added features of the tool PIA – Protein Inference Algorithms, which helps the user with the protein inference of measured samples. We also highlight the importance of the usage of PSI standard file formats, as PIA is the only current software supporting all available standards used for spectrum identification and protein inference. Additionally, we briefly describe the benefits of working with workflow environments for proteomics analyses and show the new features of the PIA nodes for the KNIME Analytics Platform. Finally, we benchmark PIA against a recently published dataset for isoform detection. PIA is open source and available for download on GitHub (https://github.com/mpc-bioinformatics/pia) or directly via the community extensions inside the KNIME analytics platform.
Evidence theory‐based framework for improving automation in home automation system

An easy and better way of life is the topic of the day and stays an important theme for the future. New researches focusing on this aspect are increasingly emerging. In recent years, improving automation in home automation systems and making homes even smarter seem to be a major issue. Automation of daily life functioning of home devices makes life easier for inhabitants, and then saves them from manual adjustments. Home automation systems encompass different communication and networking technologies. They are embedded with sensor networks that perceive information about the environment and occupants. The collected data can mainly be processed to infer sequential patterns defining the home behavior. However, the imperfection, particularly uncertainty of sensor readings, is a significant factor to consider. Also, time is an important aspect when reasoning about home behavior, as each pattern (scenario) performs at a given time throughout the day. Evidence theory is recently gaining great attention. It is suitable for dealing with imperfection of sensor data and for including temporal features into the reasoning process to discover the appropriate pattern to launch. This paper presents a new framework based on evidence theory with the inclusion of time features, for improving automation in home automation systems. The effectiveness of the proposed approach is evaluated by conducting experiments on synthetic dataset. The results show the efficiency of the discovery process when multiple working sensors are available. The results demonstrate also that reliability and accuracy of the process improve significantly when temporal information is provided.
Aff-Wild2: Extending the Aff-Wild Database for Affect Recognition

Automatic understanding of human affect using visual signals is a problem that has attracted significant interest over the past 20 years. However, human emotional states are quite complex. To appraise such states displayed in real-world settings, we need expressive emotional descriptors that are capable of capturing and describing this complexity. The circumplex model of affect, which is described in terms of valence (i.e., how positive or negative is an emotion) and arousal (i.e., power of the activation of the emotion), can be used for this purpose. Recent progress in the emotion recognition domain has been achieved through the development of deep neural architectures and the availability of very large training databases. To this end, Aff-Wild has been the first large-scale "in-the-wild" database, containing around 1,200,000 frames. In this paper, we build upon this database, extending it with 260 more subjects and 1,413,000 new video frames. We call the union of Aff-Wild with the additional data, Aff-Wild2. The videos are downloaded from Youtube and have large variations in pose, age, illumination conditions, ethnicity and profession. Both database-specific as well as cross-database experiments are performed in this paper, by utilizing the Aff-Wild2, along with the RECOLA database. The developed deep neural architectures are based on the joint training of state-of-the-art convolutional and recurrent neural networks with attention mechanism; thus exploiting both the invariant properties of convolutional features, while modeling temporal dynamics that arise in human behaviour via the recurrent layers. The obtained results show premise for utilization of the extended Aff-Wild, as well as of the developed deep neural architectures for visual analysis of human behaviour in terms of continuous emotion dimensions.
Transient Stability Enhancement of Multi-machine Power System using Fuzzy Controlled TCSC

Power system is subjected to sudden changes in load levels. Stability is an important concept which determines the stable operation of power system. In general rotor angle stability is taken as index, but the concept of transient stability, which is the function of operating condition and disturbances deals with the ability of the system to remain intact after being subjected to abnormal deviations. For the improvement of transient stability the general methods adopted are fast acting exciters, circuit breakers and reduction in system transfer reactance. The modern trend is to employ FACTS devices in the existing system for effective utilization of existing transmission resources. These FACTS devices contribute to power flow improvement besides they extend their services in transient stability improvement as well. In this paper, the studies had been carried out in order to improve the Transient Stability of WSCC 9 Bus System with Fixed Compensation on Various Lines and Optimal Location has been investigated using trajectory sensitivity analysis for better results. In order to improve the Transient Stability margin further series FACTS device has been implemented. A fuzzy controlled Thyristor Controlled Series Compensation (TCSC) device has been used here and the results highlight the effectiveness of the application of a TCSC in improving the transient stability of a power system.
Loop-mediated isothermal amplification: rapid molecular detection of virulence genes associated with avian pathogenic Escherichia coli in poultry

ABSTRACT Infections with pathogenic Escherichia coli can lead to different animal‐ and human‐associated diseases. E. coli infections are common in intensive poultry farming, and important economic losses can be expected during infections with avian pathogenic E. coli (APEC) strains followed by colibacillosis. Loop‐mediated isothermal amplification (LAMP) assays were developed for rapid detection of 3 APEC‐associated virulence genes: sitA, traT, and ompT. All 3 LAMP assays are shown to be specific, repeatable, and reproducible. High sensitivities of the assays are shown, where as few as 1,000 bacterial cells/mL can be detected in different matrices. On‐site applicability of this LAMP method is demonstrated through testing of different sample types, from animal swabs and tissues, and from environmental samples collected from 6 commercial poultry farms. All 3 virulence genes were detected at high rates (above 85%) in samples from layer and broiler chickens with clinical signs and, interestingly, high prevalence of those genes was detected also in samples collected from clinically healthy broiler flock (above 75%) while lower prevalence was observed in remaining 3 clinically healthy chicken flocks (less than 75%). Importantly, these virulence genes were detected in almost all of the air samples from 11 randomly selected poultry houses, indicating air as an important route of E. coli spread. Three LAMP assays that target APEC‐associated virulence genes are shown to be sensitive and robust and are therefore applicable for rapid on‐site testing of various sample types, from animal swabs to air. This on‐site LAMP testing protocol offers rapid diagnostics, with results obtained in <35 min, and it can be applied to other important microorganisms to allow the required prompt measures to be taken.
Desired Compensation Nonlinear Cascade Control of High-Response Proportional Solenoid Valve Based on Reduced-Order Extended State Observer

With the gradual improvement of microprocessor, various nonlinear controllers are successfully applied in the electronic hydraulic system (EHS). The high-response proportional solenoid valve (HPSV), as the key component of many middle-end and high-end EHS, attracts many researchers’ close attention. This paper focuses on the method to get a strong robustness and high-performance controller for HPSV. In general, the full-state feedback condition of nonlinear cascade control (NC) cannot be easily satisfied in the industrial application. Therefore, the reduced-order extended state observer (RESO) is proposed to estimate the unmeasured state variables and the lumped uncertainties (mainly the unmodeled uncertainty and disturbance). Then, this paper presents an NC-based RESO for HPSV. To obtain an excellent control performance of NC, the desired compensation is adopted to reduce the interference of measurement noise, especially when the tracking error is reduced to the same order as the measurement noise. The system stability and error boundedness of the desired compensation NC based on RESO (DCNCRESO) are proved theoretically in this paper. To validate the effectiveness of DCNCRESO, several different types of experiments are carried out under different working conditions. Even when the supply pressure has great fluctuation, the DCNCRESO still behaves well, which manifests its strong robustness.
Efficient Network Flow Based Min-cut Balanced Partitioning

We consider the problem of bipartitioning a circuit into two balanced components that minimizes the number of crossing nets. Previously, the Kernighan and Lin type (K&L) heuristics, the simulated annealing approach, and the spectral method were given to solve the problem. However, network flow techniques were overlooked as a viable approach to min-cut balanced bipartition to due its high complexity. In this paper we propose a balanced bipartition heuristic based on repeated max-flow min-cut techniques, and give an efficient implementation that has the same asymptotic time complexity as that of one max-flow computation. We implemented our heuristic algorithm in a package called FBB. The experimental results demonstrate that FBB outperforms the K&L heuristics and the spectral method in terms of the number of crossing nets, and the efficient implementation makes it possible to partition large, circuit instances with reasonable runtime. For example, the average elapsed time for bipartitioning a circuit S35932 of almost 20K gates is less than 20 minutes.
Study on robustness of BRDF model parameter estimation

The effect of Bidirectional Reflectance Distribution Function (BRDF) is one of the most important factors to calibrate reflectance obtained from remotely sensed data. Because the measured BRDF data involve noise caused by various factors, BRDF model parameter estimation tends to become unstable. We examined parameter estimation robustness by using BRDF data measured at paddy fields in Japan, and experimental result of the estimation is described in this paper.
Five Years at the Edge: Watching Internet From the ISP Network

The Internet and the way people use it are constantly changing. Knowing traffic is crucial for operating the network, understanding users’ needs, and ultimately improving applications. Here, we provide an in-depth longitudinal view of Internet traffic during 5 years (from 2013 to 2017). We take the point of the view of a national-wide ISP and analyze rich flow-level measurements to pinpoint and quantify changes. We observe the traffic, both from a point of view of users and services. We show that an ordinary broadband subscriber downloaded in 2017 more than twice as much as they used to do 5 years before. Bandwidth hungry video services drove this change at the beginning, while recently social messaging applications contribute to increase of data consumption. We study how protocols and service infrastructures evolve over time, highlighting events that may challenge traffic management policies. In the rush to bring servers closer and closer to users, we witness the birth of the sub-millisecond Internet, with caches located directly at ISP edges. The picture we take shows a lively Internet that always evolves and suddenly changes. To support new analyses, we make anonymized data available at https://smartdata.polito. it/five-years-at-the-edge/.
Reinforcement learning in child molesters

BACKGROUND Child molesters form a heterogeneous group, but one generally shared characteristic is maladaptive, rigid behaviour. Impairments in reinforcement learning may explain these maladaptive tendencies, but this has not been systematically investigated. Further, it is not known if such impairments vary with subtype of child molesters. AIMS To investigate the presence of impairments in reinforcement learning among child molesters and to test for differences in patterns of impairment with subtype. METHODS A group of 59 child molesters was recruited from several prisons in a two-stage screening process, the first using records and the second interview; a comparison group of 33 offenders who had never committed a sex offence and who denied paedophile ideation was similarly recruited; 36 nonoffender comparison men were recruited by social media and word of mouth. Each was asked to perform a probabilistic reversal learning task, in which stimulus-outcome contingencies had to be learned. RESULTS Child molesters, as a group, made significantly more errors on the probabilistic reversal learning task than the nonoffenders; the comparison offenders and the nonoffenders gained similar scores, although findings may have been confounded by older age in the child molester group. Nonpaedophilic child molesters had significantly worse scores than paedophilic child molesters. CONCLUSIONS Child molesters, especially those not diagnosed with paedophilia, have deficits during both the acquisition and reversal of contingencies, suggesting reinforcement learning deficits that may undermine their capacity to benefit maximally from therapy without preliminary work to repair those deficits, possibly in conjunction with extending the offender programmes. Testing before programme entry would enable accurate targeting of scarce resources in this respect.
The Setting Map Methodology for Adjusting the DG Anti-Islanding Protection Considering Multiple Events

This paper proposes a simple and practical methodology to adjust the pick-up and time-delay settings of passive anti-islanding protection commonly used in distributed generation point of common coupling: underfrequency/overfrequency, rate of change of frequency, vector surge, and undervoltage/overvoltage. The proposed methodology, called setting map, provides a graphical approach that helps define the most suitable settings for the passive anti-islanding protection to, simultaneously, maximize the islanding detection capability and minimize incorrect trips due to events other than islanding. Based on that, the methodology also contains an automatic setting selection procedure that calculates the protection settings when there is a large amount of data to analyze. The proposed approach was validated by using a modified version of the IEEE 34-node test feeder with multiple distributed generators. The results showed the good performance of the methodology, since out of 4606 simulated cases, the calculated settings led to 97.86% of correct islanding detection and 0.98% of incorrect trips, considering the six passive protections operating simultaneously.
A mutual authenticated key agreement scheme over insecure networks

Abstract Authenticated password is extensively used to ensure controlled access to the service providers over insecure networks. Many existed password authentication schemes use timestamps to avoid replay attacks. In this article, we argue these existed schemes need extra synchronized time mechanisms. With the expected delay-time limitation, these schemes can be influenced by forgery attacks. Furthermore, the article proposes an enhanced scheme to avoid the possible attacks. Without timestamps, the enhanced scheme providers the feature of mutual authentication and session key agreement between the user and the server.
ViMC -- Interactive Tool for Measuring Software Applications

Modern software applications are complex, and their maintenance implies elaborate analysis tools that reveal their structure and functionality, for purposes like software understanding or quality assessment. This paper introduces ViMC, a tool we have developed to support software engineers in analyzing the structure of object-oriented software applications, by focusing on measuring the various aspects that define the properties of the entities within a software project. The tool provides a set of predefined software metrics that can be applied to sets of classes and packages within a given software application, while allowing the user to dynamically create new metrics, as needed. The definition of custom metrics is done either through programming, or by interactively combining existing metrics via a user interface.
Use of the Strengths and Difficulties Questionnaire in identifying emotional and behavioural problems in children of parents with a mental illness in Australia

Abstract Thirty six children of parents who attended the adult outpatient service in the Werribee Mercy Mental Health Program were screened for possible emotional and behavioural problems using the Strengths and Difficulties Questionnaire. The proportion of children identified with difficulties was 50 percent, much higher than in the general population. Fifty eight percent of the children had chronic problems and the difficulties were perceived to be moderate to severe in 53 percent of the sample. The burden of these difficulties on the family was also high. Despite this, only a small proportion of children were then referred for further assessment and treatment. More psycho-education for both clinicians in the adult service and for parents is required. Difficulties in recruitment resulted in low numbers for this study. There continues to be difficulty in parents consenting for their children to be assessed.
Membrane-Located Expression of Thioesterase From Acinetobacter baylyi Enhances Free Fatty Acid Production With Decreased Toxicity in Synechocystis sp. PCC6803

It has been previously reported that photosynthetic production of extracellular free fatty acids (FFAs) in cyanobacteria was realized by thioesterases (TesA) mediated hydrolysis of fatty acyl-ACP in cytosol and excretion of the FFA outside of the cell. However, two major issues related to the genetically modified strains need to be addressed before the scale-up commercial application becomes possible: namely, the toxicity of FFAs, and the diversity of carbon lengths of fatty acids that could mimic the fossil fuel. To address those issues, we hypothesized that generating FFAs near membrane could facilitate rapid excretion of the FFA outside of the cell and thus decrease toxicity caused by intracellular FFAs in the cytosolic expression of thioesterase. To realize this, we localized a leaderless thioesterase (AcTesA) from Acinetobacter baylyi on the cytosolic side of the inner membrane of Synechocystis sp. PCC6803 using a membrane scaffolding system. The engineered strain with AcTesA on its membrane (mAcT) produced extracellular FFAs up to 171.9 ± 13.22 mg⋅L-1 compared with 40.24 ± 10.94 and 1.904 ± 0.158 mg⋅L-1 in the cytosol-expressed AcTesA (AcT) and wild-type (WT) strains, respectively. Moreover, the mAcT strain generated around 1.5 and 1.9 times less reactive oxygen species than AcT and WT, respectively. Approximately 78% of total FFAs were secreted with an average rate of 1 mg⋅L-1⋅h-1, which was higher than 0.44 mg⋅L-1⋅h-1 reported previously. In the case of mAcT strain, 60% of total secreted FFAs was monounsaturated (C18:1) which is the preferable biodiesel component. Therefore, the engineered mAcT strain shows enhanced FFAs production with less toxicity which is highly desirable for biodiesel production.
Locally Private Gaussian Estimation

We study a basic private estimation problem: each of $n$ users draws a single i.i. d. sample from an unknown Gaussian distribution, and the goal is to estimate the mean of this Gaussian distribution while satisfying local differential privacy for each user. Informally, local differential privacy requires that each data point is individually and independently privatized before it is passed to a learning algorithm. Locally private Gaussian estimation is therefore difficult because the data domain is unbounded: users may draw arbitrarily different inputs, but local differential privacy nonetheless mandates that different users have (worst-case) similar privatized output distributions. We provide both adaptive two-round solutions and nonadaptive one-round solutions for locally private Gaussian estimation. We then partially match these upper bounds with an information-theoretic lower bound. This lower bound shows that our accuracy guarantees are tight up to logarithmic factors for all sequentially interactive $(\varepsilon,\delta)$-locally private protocols.