Miracle-dz/UniFAR-FTUs huggingface dataset:
Big Data Analytics, Data Science, ML&AI for Connected, Data-driven Precision Agriculture and Smart Farming Systems: Challenges and Future Directions

Big data and data scientific applications in the modern agriculture are rapidly evolving as the data technology advances and more computational power becomes available. The adoption of big data has enabled farmers and producers to optimize their agricultural activities sustainably with cutting-edge technologies, resulting in eco-friendly and efficient farming. Wireless sensor networks and machine learning have had a direct impact on smart and precision agriculture, with deep learning techniques applied to data collected via sensor nodes. Additionally, internet of things, drones, and robotics are being incorporated into farming techniques. Digital data handling has amplified the information wave, and information and communication technology have been used to deliver benefits to both farmers and consumers. This work highlights the technological implications and challenges that arise in data-driven agricultural practices as well as the research problems that need to be solved.
CaNRun: Non-Contact, Acoustic-based Cadence Estimation on Treadmills using Smartphones

Running with a consistent cadence (number of steps per minute) is important for runners to help reduce risk of injury, improve running form, and enhance overall bio-mechanical efficiency. We introduce CaNRun, a non-contact and acoustic-based system that uses sound captured from a mobile device placed on a treadmill to predict and report running cadence. CaNRun obviates the need for runners to utilize wearable devices or carry a mobile device on their body while running on a treadmill. CaNRun leverages a long short-term memory (LSTM) network to extract steps observed from the microphone to robustly estimate cadence. Through an 8-person study, we demonstrate that CaNRun achieves cadence detection accuracy without calibration for individual users, which is comparable to the accuracy of the Apple Watch despite being non-contact.
A Cognitive Scaling Mixer for Concurrent Ultrasound Sensing and Music Playback in Smart Devices

Recent advances in the field of acoustic sensing have enabled various applications in different domains including mobile health (biosignals monitoring), human-computer interaction (gesture recognition), and imaging. These acoustic sensing systems typically leverage the existing speakers and microphones in COTS smart devices by transforming them into a SONAR system that can detect minute motions in the environment. Although this is beneficial, the sensing systems might negatively affect the traditional utilities of these sensors, which is to play/record music and voice. For example, transmitting ultrasonic sound signals concurrently with music on a smart speaker can lead to overload in the speaker’s mixer, resulting in a degraded quality of both music and sensing. In this paper, we address this problem by cognitively adapting the sensing systems, so that they can work in concurrence with the music playback without any degradation in the music quality that can be interpreted by the users and also achieving optimal sensing accuracy. We enable this by formalizing it as an optimization problem that maximizes the transmitted sensing signal magnitude and minimizes the distortion conditioned on the concurrent music play. Specifically, we design a deep learning model that takes a high-frequency sine wave sensing signal and generates an adapted sensing signal that ensures both the accuracy of sensing and the quality of music play simultaneously. We conduct a small pilot study to validate our approach in a downstream task of respiration monitoring. The results show that the adapted signal achieves similar accuracy in respiration signal detection with scenarios with no concurrent music.
Wordgen : a Timed word Generation Tool

Sampling timed words out of a timed language described as a timed automaton may seem a simple task: start from the initial state, choose a transition and a delay and repeat until an accepting state is reached. Unfortunately, simple approach based on local, on-the-fly rules produces timed words from distributions that are biased in some unpredictable ways. For this reason, approaches have been developed to guarantee that the sampling follows a more desirable distribution defined over the timed language and not over the automaton. One such distribution is the maximal entropy distribution, whose implementation requires several non-trivial computational steps. In this paper, we present Wordgen which combines those different necessary steps into a lightweight standalone tool. The resulting timed words can be mapped to signals used for model-based testing and falsification of cyber-physical systems thanks to a simple interface with the Breach tool.
Pattern Matching and Parameter Identification for Parametric Timed Regular Expressions

Timed formalisms such as Timed Automata (TA), Signal Temporal Logic (STL) and Timed Regular expressions (TRE) have been previously applied as behaviour specifications for monitoring or runtime verification, in particular, under the form of pattern-matching, i.e. computing the set of all the segments of a given system run that satisfy the specification. In this work, timed regular expressions with parameters (for timing delays and for signal values) are considered. We define several classes of parametric expressions (based on Boolean or real-valued signals and discrete events), and tackle the problem of computing a parametric match-set, i.e. the parameter values and time segments of data that give a match for a given expression. We propose efficient data structures for representing match-sets (combining zones and polytopes), and devise pattern-matching algorithms. All these different types and algorithms are combined into a single implementation under a tool named parameTRE. We illustrate the approach on several examples, from electrocardiograms to driving patterns.
Resources and Partisanship: Response to Commentaries

We thank all the commentators for their diligent and thoughtful efforts on our article. The detailed and scholarly work by several of them went far beyond the call of duty, which was most gratifying. In this brief response, we seek to articulate what can be learned from them and to resolve some misunderstandings. Our article was motivated by the recognition that partisan hostility has increased in recent years, particularly in the United States of America (USA). We thought social psychologists might be well positioned to seek ways of reducing the conflict, given the field’s accumulated expertise regarding human interactions and group processes. Judging by these commentaries, we were perhaps overly optimistic about social psychology’s potential for promoting social harmony in this fashion. Indeed, only the Aquino et al. (this issue) commentary took up the theme of how to reduce partisan conflict.
Nature-derived ingredients as sustainable alternatives for tenderizing meat and meat products: an updated review

ABSTRACT Tenderness is one of the most desirable meat qualities and a significant determinant of consumers’ preference. Estimating/achieving optimal tenderness are not straightforward because of their susceptibility to complex factors. Efforts toward fostering optimal tenderness in meat are called meat tenderization and can be categorized under physical, chemical, and biological approaches. While physical tenderization involves mechanically breaking the muscle myofibrillar/connective tissues, chemical techniques apply chemical/synthetic additives. Conversely, the most sustainable biological approaches use natural products (mainly proteases) to foster meat tenderness. This study presents an updated review of natural products from plants, animals, and microbes that have gained applicability as meat tenderizers. We have discussed in detail the comparative advantages and disadvantages of the different natural sources of tenderizers and presented some overarching factors that limit the widespread acceptance of natural tenderizers over synthetic chemicals. Finally, prospects for achieving natural products as a more global choice for tenderizing meat were suggested.
Simultaneously consideration subsidy allocation and target setting in low-volume offshore ferry routes: an empirical study of Taiwan companies

Abstract To satisfy the need for resident transportation between offshore islands and a mainland, and among offshore islands, some ferry firms are asked to run low-volume routes. Since low-volume routes often bring losses to operators, the government may provide compensation to those offshore ferry routes. However, the current subsidy methods may result in operating inefficiency. Also, the previous studies lack a mechanism for auditing the performance of these ferry routes while considering subsidies. In order to solve these problems and consider the operational characteristics of ferry routes and the fairness of input and output multipliers, this study proposes a different performance-based mechanism built by a cross-efficiency two-stage network data envelopment analysis to allocate subsidies and set targets for individual offshore ferry routes. By setting targets, the government can supervise subsidy usage. An empirical example of seven offshore ferry routes in Taiwan is illustrated. The different results of allocation between stages can be found for all routes and the individual intermediate product and final output targets can be set for all routes. The results provide constructive suggestions for the policymaker on how to allocate subsidies and set targets among offshore ferry routes.
Effective queries for mega-analysis in cognitive neuroscience

Functional neuroimaging investigates the neural correlates of performing cognitive tasks. The empirical evidence in this field is constantly growing and gave rise to methods for assessment and integration of the results across different studies. A promising and suitable technique is the so-called mega-analysis. Performing mega-analysis is, however, challenging. It is a multi-step process which connects a researcher’s implicit reasoning about information processing in the brain with complex analysis of heterogenous data. Although the process of mega-analysis is well understood, it comprises many concepts and queries that lack a formal definition. Therefore, it is difficult to choose a suitable data model, design a data schema, and implement the relevant queries. A prerequisite for a successful mega-analysis is a set of studies conforming to a carefully defined experimental setting. Finding such datasets is, however, a laborious and error-prone task of keyword-based literature search. To aid understanding of the underlying issues, we propose a conceptual model of mega-analysis. The model integrates a researcher’s implicit knowledge with a systematic definition of relevant data. The nature of the data suggests a graph data model for effectively querying datasets. Consequently, we define a knowledge graph integrating the data associated with experimental setting, formally define the queries over the knowledge graph, and showcase their implementation in a graph database.
Towards a Multi-Model Approach to Support User-Driven Extensibility in Data Warehouses: Agro-ecology Case Study

Information systems have evolved into complex data platforms supporting end-to-end data-intensive needs, aimed at coping with the different V’s that characterize Big Data. In particular, multi-model databases (MMDBs) have been proposed to natively support storing and querying data in different (schemaless) models, so as to better handle Variety. In this work we envision a new data warehouse architecture in which an MMDB is used to enable on-the-fly user-driven extensions of multidimensional cubes with additional data, while ensuring support to variable and complex data and keeping the impact on ETL low. After proposing the architecture with the aid of a case study on the management of emerging plant disease, we discuss the main associated open issues.
RHINE: Robust and High-performance Internet Naming with E2E Authenticity

The variety and severity of recent DNS-based attacks under-score the importance of a secure naming system. Although DNSSEC provides data authenticity in theory, practical de-ployments unfortunately are fragile, costly, and typically lacks end-to-end (E2E) guarantees. This motivates us to rethink authentication in DNS fundamentally and introduce RHINE, a secure-by-design Internet naming system. RHINE offloads the authentication of zone delegation to an end-entity PKI and tames the operational complexity in an offline manner, allowing the efficient E2E authentication of zone data during online name resolution. With a novel logging mechanism, Delegation Transparency, RHINE achieves a highly robust trust model that can tolerate the compromise of all but one trusted entities and, for the first time, counters threats from superordinate zones. We formally verify RHINE’s security properties using the Tamarin prover. We also demonstrate its practicality and performance advantages with a prototype implementation.
On the Modeling of an Emitting Cylindrical Transducer With a Piezoelectric Polymer Membrane

This work aims at providing a theoretical description of an ultrasonic emitting transducer, which consists of a cylindrical Polyvinylidene fluoride (PVDF) membrane coupled to an air-filled back-cavity. Based on the fundamental equations of piezoelectricity and linear acoustics, and assuming axisymmetrical geometry, an analytical model which describes the coupling of the displacement field of the membrane with the pressure fields on both of its sides is presented. Solutions are obtained using modal expansion theory for the displacement field of the membrane and for the acoustic pressure field in the axial-direction of the back-cavity. This model permits to predict the resonances of the system, which are not taken into account in an earlier work [1]. A lumped elements network is then derived from the analytical solution, valid in the lowest frequency range, when keeping only one mode and when considering two levels of approximations. Comparison between measurement results and theoretical ones of the pressure radiated by two prototypes of emitter shows an overall good agreement.
How Music features and Musical Data Representations Affect Objective Evaluation of Music Composition: A Review of CSMT Data Challenge 2020

Tools and methodologies for distinguishing computer-generated melodies from human-composed melodies have a broad range of applications from detecting copyright in-fringement through the evaluation of generative music systems to facilitating transparent and explainable AI. This paper reviews a data challenge on distinguishing computer-generated melodies from human-composed melodies held in association with the Conference on Sound and Music Technology (CSMT) in 2020. An investigation of the submitted systems and the results are presented first. Besides the structure of the proposed models, the paper investigates two important factors that were identified as contributors to good model performance: the specific music features and the music representation used. Through an analysis of the submissions, important melody-related music features have been identified. Encoding or representation of the music in the context of neural network modes are found no-ticeably impacting system performance through an exper-iment where the top-ranked system was re-implemented with different input representations for comparison purposes. Besides demonstrating the feasibility of developing an objective music composition evaluation system, the investigation presented in this paper also reveals some important limitations of current music composition systems opening opportunities for future work in the community.
A Tool for Visual Exploration and Analysis of Solar Photovoltaic Module Data

Solar photovoltaic (PV) modules are a popular source of clean energy, and effective monitoring and optimization of their performance requires the ability to explore and analyze the measurement data from their sensors. In this work, we introduce a tool for the visual analysis of such data stored in raw measurement files, offering efficient interactive visualization directly on the files. The tool includes a soiling detection module, and novel UI modules that combine time series data visualization with advanced solar panel analytics presentation.
Computational principles of adaptive multisensory combination in the Drosophila larva

Many sensory systems have evolved to optimally combine signals from multiple sensory modalities to improve perception. While theories have been proposed to explain how this process is accomplished through probabilistic inference using large neural populations in vertebrates, how animals with dramatically smaller nervous systems such as the Drosophila melanogaster larva achieve multisensory combination remains elusive. Here, we systematically characterize larval navigation in different configurations of odor and temperature gradients with optogenetically-controlled noise. Using a data-driven agent-based model, we find that larvae adapt to the reliability of individual sensory signals, and in some cases minimize the variance of the combined signal. Besides firmly establishing that probabilistic inference directs natural orientation behaviors in the Drosophila larva, our results indicate that the exact mechanism underlying the combination of sensory information may be modality-dependent. By underscoring that probabilistic inference is inherent to insect nervous systems, our work opens the way for studying its neural implementation.
Isolated loss of the AUTS2 long isoform, brain-wide or targeted to Calbindin-lineage cells, generates a specific suite of brain, behavioral and molecular pathologies

Rearrangements within the AUTS2 region are associated with a rare syndromic disorder with intellectual disability, developmental delay and behavioral abnormalities as core features. In addition, smaller regional variants are linked to wide range of neuropsychiatric disorders, underscoring the gene’s essential role in brain development. Like many essential neurodevelopmental genes, AUTS2 is large and complex, generating distinct long (AUTS2-l) and short (AUTS2-s) protein isoforms from alternative promoters. Although evidence suggests unique isoform functions, the contributions of each isoform to specific AUTS2-linked phenotypes have not been clearly resolved. Furthermore, Auts2 is widely expressed across the developing brain, but cell populations most central to disease presentation have not been determined. In this study, we focused on the specific roles of AUTS2-l in brain development, behavior, and postnatal brain gene expression, showing that brain-wide AUTS2-l ablation leads to specific subsets of the recessive pathologies associated with C-terminal mutations that disrupt both isoforms. We identify downstream genes that could explain expressed phenotypes including hundreds of putative direct AUTS2- l target genes. Furthermore, in contrast to C-terminal Auts2 mutations which lead to dominant hypoactivity, AUTS2-l loss-of-function is associated with dominant hyperactivity, a phenotype exhibited by many human patients. Finally, we show that AUTS2-l ablation in Calbindin 1-expressing cell lineages is sufficient to yield learning/memory deficits and hyperactivity with abnormal dentate gyrus granule cell maturation, but not other phenotypic effects. These data provide new clues to in vivo AUTS2-l functions and novel information relevant to genotype-phenotype correlations in the human AUTS2 region.
A bivalent ChAd nasal vaccine protects against SARS-CoV-2 BQ.1.1 and XBB.1.5 infection and disease in mice and hamsters

We previously described a nasally delivered monovalent adenoviral-vectored SARS- CoV-2 vaccine (ChAd-SARS-CoV-2-S, targeting Wuhan-1 spike [S]; iNCOVACC®) that is currently used in India as a primary or booster immunization. Here, we updated the mucosal vaccine for Omicron variants by creating ChAd-SARS-CoV-2-BA.5-S, which encodes for a pre- fusion and surface-stabilized S protein of the BA.5 strain, and then tested monovalent and bivalent vaccines for efficacy against circulating variants including BQ.1. 1 and XBB.1. 5. Whereas monovalent ChAd-vectored vaccines effectively induced systemic and mucosal antibody responses against matched strains, the bivalent ChAd-vectored vaccine elicited greater breadth. However, serum neutralizing antibody responses induced by both monovalent and bivalent vaccines were poor against the antigenically distant XBB.1. 5 Omicron strain and did not protect in passive transfer experiments. Nonetheless, nasally delivered bivalent ChAd- vectored vaccines induced robust antibody and spike-specific memory T cell responses in the respiratory mucosa, and conferred protection against WA1/2020 D614G and Omicron variants BQ.1. 1 and XBB.1. 5 in the upper and lower respiratory tracts of both mice and hamsters. Our data suggest that a nasally delivered bivalent adenoviral-vectored vaccine induces protective mucosal and systemic immunity against historical and emerging SARS-CoV-2 strains without requiring high levels of serum neutralizing antibody.
Anisotropic Cosmological Model in Presence of Holographic Dark Energy and Quintessence

The problem of Bianchi type-III universe filled with matter and holographic dark energy (DE) components is investigated. To obtain a determinant solution, special law of variation for Hubble’s parameter proposed by Berman (Nuovo Cimento B 74: 182, 1983) has been considered. The relationship between holographic dark energy model with the quintessence dark energy has been established. Quintessence potential and dynamics of the quintessence scalar field are reconstructed, which describes the accelerated phase of the expanding universe.
Multisensory perceptual learning in virtual reality may facilitate transfer to untrained locations but is impacted by training procedures

Visual training improves performance in visually-intact and visually-impaired participants, making it useful as a rehabilitation tool. An interesting question in rehabilitation is whether invocation of multisensory integration could increase training efficacy. To investigate, participants completed a 10-day training experiment wherein they repeatedly performed a 4-way global motion direction discrimination task. On each trial, participants were presented with a 5° diameter visual global motion stimulus placed in a gaze-contingent manner at 10° azimuth/elevation. The visual-only group was presented the unimodal visual stimulus. However, for the auditory/visual (AV) group, the visual stimulus was paired with a pulsed white-noise auditory cue moving along in a direction consistent with the horizontal component of the visual motion stimulus. Direction range thresholds (DRT) were computed daily. The motion direction discrimination learning transferred fully in both groups, regardless of the presence of a feature-based attention cue. However, the figure-ground segregation learning only fully transferred to untrained locations with the addition of the feature-based attention cue. These results speak to the multiple levels of processing on which perceptual learning can operate. PACS 0000, 1111 2000 MSC 0000, 1111