Miracle-dz/UniFAR-FTUs huggingface dataset:
Activity Recognition Using Hierarchical Hidden Markov Models on Streaming Sensor Data

Activity recognition from sensor data deals with various challenges, such as overlapping activities, activity labeling, and activity detection. Although each challenge in the field of recognition has great importance, the most important one refers to online activity recognition. The present study tries to use online hierarchical hidden Markov model to detect an activity on the stream of sensor data which can predict the activity in the environment with any sensor event. The activity recognition samples were labeled by the statistical features such as the duration of activity. The results of our proposed method test on two different datasets of smart homes in the real world showed that one dataset has improved 4% and reached (59%) while the results reached 64.6% for the other data by using the best methods.
Smell identification function in early-onset Alzheimer’s disease and mild cognitive impairment

ABSTRACT Little is known about olfactory identification (OI) function in early-onset Alzheimer’s disease (EOAD) and early-onset mild cognitive impairment (eoMCI) with age of onset <65 years. We aimed to study OI in EOAD compared with eoMCI and age-matched healthy controls (HC). Nineteen EOAD subjects with mild to moderate dementia, 17 with eoMCI, and 21 HC recruited as a convenience sample from memory services were assessed for cognition, behavioral symptoms, and activities for daily living. The OI was tested using the University of Pennsylvania smell identification test (UPSIT). EOAD participants performed worse compared with eoMCI and HC on cognitive tests and OI (p < 0.001). Although eoMCI had poorer cognitive scores compared to HC, they were similar in their OI function. OI correlated with attention (r = 0.494, p = 0.031), executive functions (r = 0.508, p = 0.026), and praxis (r = 0.455, p = 0.05) within the EOAD group. OI impairment was significantly associated with the diagnosis of EOAD versus eoMCI, but not with eoMCI when compared with HC. OI could potentially be useful in differentiating EOAD from eoMCI. Studies with late-life MCI patients showing OI impairment relative to HC may be attributed to a different disease process. Independent replication in a larger sample is needed to validate these findings.
Designing Interactive Behaviours Beyond the Desktop

As interactions move beyond the desktop, interactive behaviours (effects of actions as they happen, or once they happen) are becoming increasingly complex. This complexity is due to the variety of forms that objects might take, and the different inputs and sensors capturing information, and the ability to create nuanced responses to those inputs. Current interaction design tools do not support much of this rich behaviour authoring. In my work I create prototyping tools that examine ways in which designers can create interactive behaviours. Thus far, I have created two prototyping tools: Pineal and Astral, which examine how to create physical forms based on a smart object's behaviour, and how to reuse existing desktop infrastructures to author different kinds of interactive behaviour. I also contribute conceptual elements, such as how to create smart objects using mobile devices, their sensors and outputs, instead of using custom electronic circuits, as well as devising evaluation strategies used in HCI toolkit research which directly informs my approach to evaluating my tools.
On Kernel Derivative Approximation with Random Fourier Features

Random Fourier features (RFF) represent one of the most popular and wide-spread techniques in machine learning to scale up kernel algorithms. Despite the numerous successful applications of RFFs, unfortunately, quite little is understood theoretically on their optimality and limitations of their performance. To the best of our knowledge, the only existing areas where precise statistical-computational trade-offs have been established are approximation of kernel values, kernel ridge regression, and kernel principal component analysis. Our goal is to spark the investigation of optimality of RFF-based approximations in tasks involving not only function values but derivatives, which naturally lead to optimization problems with kernel derivatives. Particularly, in this paper, we focus on the approximation quality of RFFs for kernel derivatives and prove that the existing finite-sample guarantees can be improved exponentially in terms of the domain where they hold, using recent tools from unbounded empirical process theory. Our result implies that the same approximation guarantee is achievable for kernel derivatives using RFF as for kernel values.
Visual Statistical Learning With Stimuli Presented Sequentially Across Space and Time in Deaf and Hearing Adults

This study investigated visual statistical learning (VSL) in 24 deaf signers and 24 hearing non-signers. Previous research with hearing individuals suggests that SL mechanisms support literacy. Our first goal was to assess whether VSL was associated with reading ability in deaf individuals, and whether this relation was sustained by a link between VSL and sign language skill. Our second goal was to test the Auditory Scaffolding Hypothesis, which makes the prediction that deaf people should be impaired in sequential processing tasks. For the VSL task, we adopted a modified version of the triplet learning paradigm, with stimuli presented sequentially across space and time. Results revealed that measures of sign language skill (sentence comprehension/repetition) did not correlate with VSL scores, possibly due to the sequential nature of our VSL task. Reading comprehension scores (PIAT-R) were a significant predictor of VSL accuracy in hearing but not deaf people. This finding might be due to the sequential nature of the VSL task and to a less salient role of the sequential orthography-to-phonology mapping in deaf readers compared to hearing readers. The two groups did not differ in VSL scores. However, when reading ability was taken into account, VSL scores were higher for the deaf group than the hearing group. Overall, this evidence is inconsistent with the Auditory Scaffolding Hypothesis, suggesting that humans can develop efficient sequencing abilities even in the absence of sound.
Copper-64: a real theranostic agent

Ongoing studies of physiological and pathological processes have led to a corresponding need for new radiopharmaceuticals, especially when studies are limited by the absence of a particular radiolabeled target. Thus, the development of new radioactive tracers is highly relevant and can represent a significant contribution to efforts to elucidate important phenomena in biology. Currently, theranostics represents a new frontier in the fields of medicine and nuclear medicine, with the same compound being used for both diagnosis and treatment. In the human body, copper (Cu) is the third most abundant metal and it plays a crucial role in many biological functions. Correspondingly, in various acquired and inherited pathological conditions, such as cancer and Alzheimer’s disease, alterations in Cu levels have been found. Moreover, a wide spectrum of neurodegenerative disorders are associated with higher or lower levels of Cu, as well as inappropriately bound or distributed levels of Cu in the brain. In human cells, the membrane protein, hCtr1, binds Cu in its Cu(I) oxidation state in an energy-dependent manner. Copper-64 (64Cu) is a cyclotron-produced radionuclide that has exhibited physical properties that are complementary for diagnosis and/or therapeutic purposes. To date, very few reports have described the clinical development of 64Cu as a radiotracer for cancer imaging. In this review, we highlight recent insights in our understanding and use of 64CuCl2 as a theranostic agent for various types of tumors. To the best of our knowledge, no adverse effects or clinically observable pharmacological effects have been described for 64CuCl2 in the literature. Thus, 64Cu represents a revolutionary radiopharmaceutical for positron emission tomography imaging and opens a new era in the theranostic field.
Highly-uniform resonator-based visible spectrometer on a Si3N4 platform with robust and accurate post-fabrication trimming.

A resonator array-based spectrometer for visible/near-infrared (NIR) wavelengths is fabricated on a low-loss silicon nitride (Si3N4) material platform. Ideally, a spectrometer should uniformly sample the input spectrum. However, resonator-based spectrometers, in which each spectral sample corresponds to resonance wavelength of one of the resonators in the array, suffer from wavelength sampling non-uniformity caused by the high sensitivity of the resonant wavelengths of different resonators to the dimensional variations caused by fabrication imperfections. Using an alignment-insensitive post-fabrication trimming technique, we reduce the standard deviation (STD) of resonance wavelength of a 60-channel integrated photonic spectrometer in Si3N4 to a record-low value of 5 pm in the visible wavelength range. This approach can be used to realize wideband and uniform visible spectrometers that are desirable for applications such as optical signal processing and biological sensing.
Indosum: A New Benchmark Dataset for Indonesian Text Summarization

Automatic text summarization is generally considered as a challenging task in the NLP community. One of the challenges is the publicly available and large dataset that is relatively rare and difficult to construct. The problem is even worse for low-resource languages such as Indonesian. In this paper, we present INDOSUM, a new benchmark dataset for Indonesian text summarization. The dataset consists of news articles and manually constructed summaries. Notably, the dataset is almost 200x larger than the previous Indonesian summarization dataset of the same domain. We evaluated various extractive summarization approaches and obtained encouraging results which demonstrate the usefulness of the dataset and provide baselines for future research. The code and the dataset are available online under permissive licenses.
Spacetime: Enabling Fluid Individual and Collaborative Editing in Virtual Reality

Virtual Reality enables users to explore content whose physics are only limited by our creativity. Such limitless environments provide us with many opportunities to explore innovative ways to support productivity and collaboration. We present Spacetime, a scene editing tool built from the ground up to explore the novel interaction techniques that empower single user interaction while maintaining fluid multi-user collaboration in immersive virtual environment. We achieve this by introducing three novel interaction concepts: the Container, a new interaction primitive that supports a rich set of object manipulation and environmental navigation techniques, Parallel Objects, which enables parallel manipulation of objects to resolve interaction conflicts and support design workflows, and Avatar Objects, which supports interaction among multiple users while maintaining an individual users' agency. Evaluated by professional Virtual Reality designers, Spacetime supports powerful individual and fluid collaborative workflows.
A Tutorial on Evaluating the Time-Varying Discrimination Accuracy of Survival Models Used in Dynamic Decision Making

Many medical decisions involve the use of dynamic information collected on individual patients toward predicting likely transitions in their future health status. If accurate predictions are developed, then a prognostic model can identify patients at greatest risk for future adverse events and may be used clinically to define populations appropriate for targeted intervention. In practice, a prognostic model is often used to guide decisions at multiple time points over the course of disease, and classification performance (i.e., sensitivity and specificity) for distinguishing high-risk v. low-risk individuals may vary over time as an individual’s disease status and prognostic information change. In this tutorial, we detail contemporary statistical methods that can characterize the time-varying accuracy of prognostic survival models when used for dynamic decision making. Although statistical methods for evaluating prognostic models with simple binary outcomes are well established, methods appropriate for survival outcomes are less well known and require time-dependent extensions of sensitivity and specificity to fully characterize longitudinal biomarkers or models. The methods we review are particularly important in that they allow for appropriate handling of censored outcomes commonly encountered with event time data. We highlight the importance of determining whether clinical interest is in predicting cumulative (or prevalent) cases over a fixed future time interval v. predicting incident cases over a range of follow-up times and whether patient information is static or updated over time. We discuss implementation of time-dependent receiver operating characteristic approaches using relevant R statistical software packages. The statistical summaries are illustrated using a liver prognostic model to guide transplantation in primary biliary cirrhosis.
Cytokines promote lipolysis in 3T3-L1 adipocytes through induction of NADPH oxidase 3 expression and superoxide production

NADPH oxidase (NOX) enzymes are one of the major superoxide-generating systems in cells. NOX-generated superoxide has been suggested to promote insulin resistance in the liver. However, the role of NOX enzymes in mediating metabolic dysfunction in other insulin target tissues remains unclear. Here, we show that NOX3 expression is induced in differentiated 3T3-L1 adipocytes upon treatment with proinflammatory cytokines. Superoxide production increased concurrently with NOX3 protein expression in cytokine-treated adipocytes, which was inhibited by the NOX inhibitor diphenyleneiodonium (DPI). Treatment of adipocytes with cytokines increased lipolysis and decreased PPARγ activity. Interestingly, treatment with DPI blunted lipolysis activation by cytokines but failed to restore PPARγ activity. siRNA-mediated NOX3 downregulation also prevented cytokine-induced superoxide generation and lipolysis. In line with increasing lipolysis, cytokines increased the phosphorylation of hormone-sensitive lipase (HSL), which was reversed by treatment with DPI and silencing of NOX3 expression. We conclude that NOX3 is a cytokine-inducible superoxide-generating enzyme in adipocytes, which promotes lipolysis through increasing phosphorylation of HSL. This suggests a key role for NOX3-mediated superoxide production in the increased adipocyte lipolysis in inflammatory settings.
Cross-Sectional Association Between Types of Leisure Activities and Self-rated Health According to Gender and Work Status Among Older Japanese Adults

Background Participation in leisure activities (LA) is essential for successful aging. Our aim was to investigate the cross-sectional association of types of LA with self-rated health (SRH) by gender and work status. Methods The target population was all residents aged ≥65 years in a municipality (n = 16,010; response rate, 62.5%). We analyzed 4,044 men and 4,617 women without disabilities. LA were categorized into 14 types. SRH was assessed through the SF-8. Excellent or very good SRH was defined as positive SRH. Covariates included age, marital status, education, subjective economic status, body mass index, chronic diseases, alcohol, smoking, walking time, depression, and cognitive functioning. Multiple logistic regressions were used to calculate the odds ratio (OR) and 95% confidence interval (CI) for positive SRH, with non-participation as the reference. Results After adjustment for covariates and mutual adjustment for other LA, participation in the following types of LA was positively associated with positive SRH: sports activities among working men (OR 1.46; 95% CI, 1.07–2.00), non-working men (OR 1.33; 95% CI, 1.04–1.69), and non-working women (OR 1.74; 95% CI, 1.41–2.15); cooking among non-working men (OR 1.65; 95% CI, 1.18–2.33) and non-working women (OR 1.28; 95% CI, 1.03–1.60); musical activities among working men (OR 1.44; 95% CI, 1.01–2.05) and non-working women (OR 1.59; 95% CI, 1.29–1.95); and technology usage only among working men (OR 1.41; 95% CI, 1.01–1.96). In contrast, TV watching was negatively associated with positive SRH among non-working women (OR 0.69; 95% CI, 0.56–0.85). Conclusions Our results suggest that encouraging older adults to participate in types of LA appropriate to their gender and work status might be a key to positive SRH.
Aleph: A Leaderless, Asynchronous, Byzantine Fault Tolerant Consensus Protocol

In this paper we propose Aleph, a leaderless, fully asynchronous, Byzantine fault tolerant consensus protocol for ordering messages exchanged among processes. It is based on a distributed construction of a partially ordered set and the algorithm for reaching a consensus on its extension to a total order. To achieve the consensus, the processes perform computations based only on a local copy of the data structure, however, they are bound to end with the same results. Our algorithm uses a dual-threshold coin-tossing scheme as a randomization strategy and establishes the agreement in an expected constant number of rounds. In addition, we introduce a fast way of validating messages that can occur prior to determining the total ordering.
Implicit–explicit multistep finite-element methods for nonlinear convection-diffusion-reaction equations with time delay

ABSTRACT In this paper, two classes of implicit–explicit (IMEX) multistep finite-element methods are constructed to solve the nonlinear delay convection-diffusion-reaction equations. This equations are discretized with finite-element method in space and IMEX multistep methods (including IMEX one-leg methods and IMEX linear multistep methods) in time. The resulting schemes are efficient because it can reduce the coupling degree, and avoid solving the large-scale systems of nonlinear algebraic equations. The -norm error estimates with handling the breaking point and stability results for the proposed methods are obtained. Numerical results demonstrate the efficiency and accuracy of the approaches.
MicroRNA‐133b suppresses bladder cancer malignancy by targeting TAGLN2‐mediated cell cycle

MicroRNAs (miRNAs), a group of small noncoding RNAs, are widely involved in the regulation of gene expression via binding to complementary sequences at 3′‐untranslated regions (3′‐UTRs) of target messenger RNAs. Recently, downregulation of miR‐133b has been detected in various human malignancies. Here, the potential biological role of miR‐133b in bladder cancer (BC) was investigated. In this study, we found the expression of miR‐133b was markedly downregulated in BC tissues and cell lines (5637 and T24), and was correlated with poor overall survival. Notably, transgelin 2 (TAGLN2) was found to be widely upregulated in BC, and overexpression of TAGLN2 also significantly increased risks of advanced TMN stage. We further identified that upregulation of miR‐133b inhibited glucose uptake, invasion, angiogenesis, colony formation and enhances gemcitabine chemosensitivity in BC cell lines by targeting TAGLN2. Additionally, we showed that miR‐133b promoted the proliferation of BC cells, at least partially through a TAGLN2‐mediated cell cycle pathway. Our results suggest a novel miR‐133b/TAGLN2/cell cycle pathway axis controlling BC progression; a molecular mechanism which may offer a potential therapeutic target.
Trusted and GDPR-compliant research with the internet of things

The Internet of Things has become a key enabling technology for data-intensive research across universities and private organisations alike. However, the recent introduction of the General Data Protection Regulation (GDPR) in Europe has raised concerns that the GDPR might hamper data-intensive research. In this paper, we address the question of how to enable ethical and compliant research with personal IoT data in an academic environment. We identify three novel trust principles for GDPR compliant use of personal IoT data in science and research (private-by-default, analytics transparency and Accountable analytics) and propose an architecture for a trusted IoT research infrastructure.
The anaerobic power reserve and its applicability in professional road cycling

ABSTRACT This study examined if short-duration record power outputs can be predicted with the Anaerobic Power Reserve (APR) model in professional cyclists using a field-based approach. Additionally, we evaluated if modified model parameters could improve predictive ability of the model. Twelve professional cyclists (V̇O2max 75 ± 6 ml∙kg−1∙min−1) participated in this investigation. Using the mean power output during the last stage of an incremental field test, sprint peak power output and an exponential constant describing the decrement in power output over time, a power-duration relationship was established for each participant. Record power outputs of different durations (5 to 180 s) were collected from training and competition data and compared to the predicted power output from the APR model. The originally proposed exponent (k = 0.026) predicted performance within an average of 43 W (Standard Error of Estimate (SEE) of 32 W) and 5.9%. Modified model parameters slightly improved predictive ability to a mean of 34–39 W (SEE of 29 – 35 W) and 4.1 – 5.3%. This study shows that a single exponent model generally fits well with the decrement in power output over time in professional cyclists. Modified model parameters may contribute to improving predictability of the model.
Search-Based Concolic Execution for SW Vulnerability Discovery

Huge amounts of software appear nowadays. The more the number of software increases, the more increased software vulnerabilities are. Although some automatic methods have been proposed in order to detect and remove software vulnerabilities, they still require a lot of time so they have a limitation in the real world. To solve this problem, we propose BugHunter which automatically tests a binary file compiled with a C++ compiler. It searches for unsafe API calls and automatically executes to the program block that have an unsafe API call. Also, we showed that BugHunter is more efficient than angr through experiments. As a result, BugHunter is very helpful to find a software vulnerability in a short time. key words: search-based, concolic execution, vulnerability
Nitric Oxide Production from Nitrite Reduction and Hydroxylamine Oxidation by Copper-containing Dissimilatory Nitrite Reductase (NirK) from the Aerobic Ammonia-oxidizing Archaeon, Nitrososphaera viennensis

Aerobic ammonia-oxidizing archaea (AOA) play a crucial role in the global nitrogen cycle by oxidizing ammonia to nitrite, and nitric oxide (NO) is a key intermediate in AOA for sustaining aerobic ammonia oxidation activity. We herein heterologously expressed the NO-forming, copper-containing, dissimilatory nitrite reductase (NirK) from Nitrososphaera viennensis and investigated its enzymatic properties. The recombinant protein catalyzed the reduction of 15NO2− to 15NO, the oxidation of hydroxylamine (15NH2OH) to 15NO, and the production of 14–15N2O from 15NH2OH and 14NO2−. To the best of our knowledge, the present study is the first to document the enzymatic properties of AOA NirK.