Cardiovascular diseases (CVDs) stand as the primary reason of fatalities globally, especially in low- and middle-income countries. In recent years, with the leverage of computer audition technologies, the diagnosis of CVDs through heart sounds become a popular topic. Current models and techniques are trained, validated, and tested on the same dataset, which need to be retrained when encountering new data. To make the best use of sparse data, we propose a Prototypical Network framework with heuristic weight for heart sound recognition. After extracting two different features (Mel Spectrogram and Mel Frequency Cepstral Coefficients) and encoding the features, we calculate the distance between two categories (normal and abnormal), then, a heuristic weight is assigned to the distance that makes the blurred boundaries more distinct. By considering the subject independence, the Unweighted Average Recall (UAR) on the PhysioNet/CinC Challenge 2016 is 68.2 % and 67.7 % on two features, respectively. The capability of our model to work on different datasets is proved by a UAR of 66.4 %, which exceeds the baseline UAR of 58.6 % under a single model.

MOTIVATION: Direct reprogramming (DR) is a process that directly converts somatic cells to target cells. Although DR via small molecules is safer than using transcription factors (TFs) in terms of avoidance of tumorigenic risk, the determination of DR-inducing small molecules is challenging. RESULTS: Here we present a novel in silico method, DIRECTEUR, to predict small molecules that replace TFs for DR. We extracted DR-characteristic genes using transcriptome profiles of cells in which DR was induced by TFs, and performed a variant of simulated annealing to explore small molecule combinations with similar gene expression patterns with DR-inducing TFs. We applied DIRECTEUR to predicting combinations of small molecules that convert fibroblasts into neurons or cardiomyocytes, and were able to reproduce experimentally verified and functionally related molecules inducing the corresponding conversions. The proposed method is expected to be useful for practical applications in regenerative medicine. AVAILABILITY AND IMPLEMENTATION: The code and data are available at the following link: https://github.com/HamanoLaboratory/DIRECTEUR.git.

Translating mental health recognition from clinical research into real-world application requires extensive data, yet existing emotion datasets are impoverished in terms of daily mental health monitoring, especially when aiming for self-reported anxiety and depression recognition. We introduce the Japanese Daily Speech Dataset (JDSD), a large in-the-wild daily speech emotion dataset consisting of 20,827 speech samples from 342 speakers and 54 hours of total duration. The data is annotated on the Depression and Anxiety Mood Scale (DAMS) – 9 self-reported emotions to evaluate mood state including "vigorous", "gloomy", "concerned", "happy", "unpleasant", "anxious", "cheerful", "depressed", and "worried". Our dataset possesses emotional states, activity, and time diversity, making it useful for training models to track daily emotional states for healthcare purposes. We partition our corpus and provide a multi-task benchmark across nine emotions, demonstrating that mental health states can be predicted reliably from self-reports with a Concordance Correlation Coefficient value of .547 on average. We hope that JDSD will become a valuable resource to further the development of daily emotional healthcare tracking.

This paper investigates a novel millimeter wave (mmWave) radar-based inclination estimation to enhance the landing capabilities of unmanned aerial vehicles (UAVs) in poor visibility conditions. Unlike SAR based approaches, this method facilitates one-shot estimation by using known reflector patterns on landing surfaces. As a first step, we evaluate the performance of the estimation with obtained distance information, assuming that the coordinates of the UAV and port are known. The experimental results demonstrate that mmWave radar with centimeter-ordered resolution can estimate the inclination of less than 5° errors.

This paper presents MilliSign, a guidance system based on a batteryless tag to support unmanned aerial vehicles in all-weather conditions. Conventional batteryless guidance systems using visual signs fail to work in inclement weather due to poor visibility. The need for all-weather operation with long-range readability encourages the use of millimeter wave (mmWave) radar, which poses challenges in providing a wide 3-D read range and low-cost operation. To address these challenges, we introduce a corner reflector (CR) array-based chipless RFID tag and a one-shot slant range reading procedure with COTS mmWave radar. We establish a novel design method for the shape and alignment of CR units to decrease the tag's size and expand the 3-D read range. Additionally, we develop a signal-processing pipeline based on Root-MUSIC to achieve accurate power and spatial estimation, which facilitate automatic tag detection. Our evaluation demonstrates that the tag, measuring 292 mm × 600 mm × 19 mm and storing 8 bits, can be read by mmWave radar from a distance of more than 10 m with a viewing angle of more than 30° in elevation and azimuth. Moreover, its performance remains stable in poor visibility conditions and multipath-rich environments.

Today, vast amounts of private and sensitive data are being shared across a variety of on-line services day-today. Recent technologies increasingly simplify the collection, processing and evaluation of these data. This results in numerous threats to the privacy of users. Although there are legal regulations to protect privacy, users are increasingly faced with the challenge of controlling their data to exercise their rights. In order to implement the existing legal framework and to help users protect their privacy, technological solutions are becoming increasingly important. Within the scope of this paper, the research areas of privacy risk detection will be examined in more detail. For this purpose, the state of the art of privacy sensitive information detection is elaborated and then analyzed by means of a specifically developed classification scheme to identify research gaps and trends. As a result, several research gaps and trends have been identified, demonstrating that further research is required to develop user tailored privacy enhancing tools and ensure adequate privacy protection.

Reputation systems are helpful for our decision making when we have to deal with unfamiliar service providers. However, some of these systems do not preserve privacy of the users, e.g., rating from an individual is obviously shown. Therefore, users may provide dishonest feedback due to the fear of retaliation. Although, some privacy-preserving reputation systems are proposed in the literature, most of them cannot withstand some specific attacks. These attacks include "dishonest queries to similar sets of users" and "collusion between the dishonest querier and all-but-one users". As a result, rating from an individual may be leaked.As the contribution of this work, we propose a novel privacy-preserving reputation system that addresses the aforementioned attacks. The idea is that, the querier firstly specifies a set of users, then the average rating is computed from a random subset of that set. Since the members of the random subset are not known to anyone, the attacks become difficult. We construct our scheme based on a secret sharing technique, specifically ABY3 framework (Mohassel and Rindal, CCS’18) that supports conversion between different data representations. Correctness, security, privacy, and complexity are then discussed, and possible improvements are also suggested. The experiments show that the accuracy of our scheme is similar to the case where privacy is not preserved.

Social networking services (SNS) are often the subject of privacy concerns. Hence users should be careful not to post content with unacceptable privacy risks. In order to correctly determine whether a privacy risk is acceptable, the perceived privacy risk must match the actual privacy risk. One of the objectives of this study is to investigate whether the recognition of privacy risk is consistent with the actual privacy risk. This study investigates the posters ’recognition of privacy risk from postings that infer their attributes and regrets about their actions. On the other hand, privacy information can also be leaked from other people’s posts, to which people respond with "Liking" or "Retweeting." Existing methods for analyzing their posts to suppress their posts may not be effective if personal information is leaked from the responses to the contents of others’ posts. In addition, users may be less likely to perceive the risk of privacy information leakage from responses to the contents of others ’ posts than from their posts. Therefore, this study tests the hypothesis that there is a difference between the users’ recognition of privacy risk in posting by themselves and that caused by responding to contents of others’ posts and evaluates the difficulty of recognizing privacy risk caused by responding compared with that by posting.

We propose a decentralized but collaborative attribute-based tracing mechanism (a signer-identifying mechanism) for group signatures. Instead of a central tracing party in our scheme, a set of tracers satisfying the attribute set used for generating the group signature can identify the signer. Thus our proposal limits the parties who can identify the signer. On the other hand, it decentralized the tracing authority.

MOTIVATION: Direct reprogramming involves the direct conversion of fully differentiated mature cell types into various other cell types while bypassing an intermediate pluripotent state (e.g. induced pluripotent stem cells). Cell differentiation by direct reprogramming is determined by two types of transcription factors (TFs): pioneer factors (PFs) and cooperative TFs. PFs have the distinct ability to open chromatin aggregations, assemble a collective of cooperative TFs and activate gene expression. The experimental determination of two types of TFs is extremely difficult and costly. RESULTS: In this study, we developed a novel computational method, TRANSDIRE (TRANS-omics-based approach for DIrect REprogramming), to predict the TFs that induce direct reprogramming in various human cell types using multiple omics data. In the algorithm, potential PFs were predicted based on low signal chromatin regions, and the cooperative TFs were predicted through a trans-omics analysis of genomic data (e.g. enhancers), transcriptome data (e.g. gene expression profiles in human cells), epigenome data (e.g. chromatin immunoprecipitation sequencing data) and interactome data. We applied the proposed methods to the reconstruction of TFs that induce direct reprogramming from fibroblasts to six other cell types: hepatocytes, cartilaginous cells, neurons, cardiomyocytes, pancreatic cells and Paneth cells. We demonstrated that the methods successfully predicted TFs for most cell conversions with high accuracy. Thus, the proposed methods are expected to be useful for various practical applications in regenerative medicine. AVAILABILITY AND IMPLEMENTATION: The source code and data are available at the following website: http://figshare.com/s/b653781a5b9e6639972b. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: Direct cell conversion, direct reprogramming (DR), is an innovative technology that directly converts source cells to target cells without bypassing induced pluripotent stem cells. The use of small compounds (e.g. drugs) for DR can help avoid carcinogenic risk induced by gene transfection; however, experimentally identifying small compounds remains challenging because of combinatorial explosion. RESULTS: In this article, we present a new computational method, COMPRENDRE (combinatorial optimization of pathway regulations for direct reprograming), to elucidate the mechanism of small compound-based DR and predict new combinations of small compounds for DR. We estimated the potential target proteins of DR-inducing small compounds and identified a set of target pathways involving DR. We identified multiple DR-related pathways that have not previously been reported to induce neurons or cardiomyocytes from fibroblasts. To overcome the problem of combinatorial explosion, we developed a variant of a simulated annealing algorithm to identify the best set of compounds that can regulate DR-related pathways. Consequently, the proposed method enabled to predict new DR-inducing candidate combinations with fewer compounds and to successfully reproduce experimentally verified compounds inducing the direct conversion from fibroblasts to neurons or cardiomyocytes. The proposed method is expected to be useful for practical applications in regenerative medicine. AVAILABILITY AND IMPLEMENTATION: The code supporting the current study is available at the http://labo.bio.kyutech.ac.jp/~yamani/comprendre. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Drowsiness detection is a crucial step for safe driving. A plethora of efforts has been invested on using pervasive sensor data (e.g., video, physiology) empowered by machine learning to build an automatic drowsiness detection system. Nevertheless, most of the existing methods are based on complicated wearables (e.g., electroencephalogram) or computer vision algorithms (e.g., eye state analysis), which makes the relevant systems hardly applicable in the wild. Furthermore, data based on these methods are insufficient in nature due to limited simulation experiments. In this light, we propose a novel and easily implemented method based on full non-invasive multimodal machine learning analysis for the driver drowsiness detection task. The drowsiness level was estimated by self-reported questionnaire in pre-designed protocols. First, we consider involving environmental data (e.g., temperature, humidity, illuminance, and further more), which can be regarded as complementary information for the human activity data recorded via accelerometers or actigraphs. Second, we demonstrate that the models trained by daily life data can still be efficient to make predictions for the subject performing in a simulator, which may benefit the future data collection methods. Finally, we make a comprehensive study on investigating different machine learning methods including classic ‘shallow’ models and recent deep models. Experimental results show that, our proposed methods can reach 64.6% unweighted average recall for drowsiness detection in a subject-independent scenario.

Threats and risks against supply chains are increasing and a framework to add the trustworthiness of supply chain has been considered. In this framework, organisations in the supply chain validate the conformance to the pre-defined requirements. The results of validations are linked each other to achieve the trustworthiness of the entire supply chain. In this paper, we further consider this framework for data supply chains. First, we implement the framework and evaluate the performance. The evaluation shows 500 digital evidences (logs) can be checked in 0.28 second. We also propose five methods to improve the performance as well as five new functionalities to improve usability. With these functionalities, the framework also supports maintaining the certificate chain.

Recent advances in wearable and/or biomedical sensing technologies have made it possible to record very long-term, continuous biomedical signals, referred to as biomedical intensive longitudinal data (ILD). To link ILD to clinical applications, such as personalized healthcare and disease prevention, the development of robust and reliable data analysis techniques is considered important. In this review, we introduce multiscale analysis methods for and the applications to two types of intensive longitudinal biomedical signals, heart rate variability (HRV) and spontaneous physical activity (SPA) time series. It has been shown that these ILD have robust characteristics unique to various multiscale complex systems, and some parameters characterizing the multiscale complexity are in fact altered in pathological states, showing potential usability as a new type of ambient diagnostic and/or prognostic tools. For example, parameters characterizing increased intermittency of HRV are found to be potentially useful in detecting abnormality in the state of the autonomic nervous system, in particular the sympathetic hyperactivity, and intermittency parameters of SPA might also be useful in evaluating symptoms of psychiatric patients with depressive as well as manic episodes, all in the daily settings. Therefore, multiscale analysis might be a useful tool to extract information on clinical events occurring at multiple time scales during daily life and the underlying physiological control mechanisms from biomedical ILD.

In this paper, we introduce the concept of a privacy enhancing personal agent that manages a user's privacy policy settings and provides access control functions to ITS services. The personal agent acts as a proxy between a vehicle and service providers, and it automatically decides whether personal data can be sent to a service provider based on the privacy policy settings. The functions of the personal agent are also described. The personal agent provides a common web-based interface, and the quality of data can be controlled through anonymization levels. Our research provides a conceptual model of the personal agent and considers the design of the personal agent based on privacy requirements. Drivers can delegate their user consent role to the personal agent by configuring privacy policy settings on the personal agent. The personal agent is a key component for achieving a secure and reliable data transfer platform between vehicles and service providers.

The objective evaluation of depressive mood is considered to be useful for the diagnosis and treatment of depressive disorders. Thus, we investigated psychobehavioral correlates, particularly the statistical associations between momentary depressive mood and behavioral dynamics measured objectively, in patients with major depressive disorder (MDD) and healthy subjects. Patients with MDD ( n = 14) and healthy subjects ( n = 43) wore a watch-type computer device and rated their momentary symptoms using ecological momentary assessment. Spontaneous physical activity in daily life, referred to as locomotor activity, was also continuously measured by an activity monitor built into the device. A multilevel modeling approach was used to model the associations between changes in depressive mood scores and the local statistics of locomotor activity simultaneously measured. We further examined the cross validity of such associations across groups. The statistical model established indicated that worsening of the depressive mood was associated with the increased intermittency of locomotor activity, as characterized by a lower mean and higher skewness. The model was cross validated across groups, suggesting that the same psychobehavioral correlates are shared by both healthy subjects and patients, although the latter had significantly higher mean levels of depressive mood scores. Our findings suggest the presence of robust as well as common associations between momentary depressive mood and behavioral dynamics in healthy individuals and patients with depression, which may lead to the continuous monitoring of the pathogenic processes (from healthy states) and pathological states of MDD.

In mobile communications, people can be contacted by anyone, anywhere, and anytime, but this might interrupt them. We propose a new mobile telecommunication style, called mobile `Tsunagari-Kan', where users exchange situational cues constantly and casually so they can imagine the situation of the other party. To make this possible, we must decrease users' mental workload caused by frequent communications. We considered that the mental workload could be decreased by exchanging situational cues casually to achieve true `always-on' communications. To link the `flip action' of a cell phone with a communicational behavior, we have developed a test system called the Flip Communicator System based on the Tsunagari-kan concept. This prototype system enables users to exchange situational cues on their cell phones. We carried out a communication field test between seven sets of Japanese mothers and their children. The results indicate that our method can strengthen the ties between a mother and a child.