Miracle-dz/UniFAR-FTUs huggingface dataset:
Variable size segmentation for efficient representation and querying of non-uniform time series datasets

Existing approaches for time series similarity computing are the core of many data analytics tasks. Given the considered data volumes, or simply the need for fast response times, they often rely on shorter representations, usually with information loss. This incurs approximate comparisons where precision is a major issue. We present and experimentally evaluate ASAX, a new approach for segmenting time series before their transformation into symbolic representations. ASAX reduces significantly the information loss incurred by possible splittings at different steps of the representation calculation, particularly for datasets with unbalanced (nonuniform) distributions. We provide theoretical guarantees on the lower bound of similarity measures, and our experiments illustrate that our method outperforms the state of the art, with significant gain in precision for datasets with unbalanced distributions.
Unifying categorical representation of multi-model data

The support for multi-model data has become a standard for most of the existing DBMSs. The tools for database design are general enough to cover multiple models, too. However, the step from a conceptual (e.g., ER or UML) schema to a logical multi-model schema of a DBMS (or their combination) is not straightforward. In this paper, we show how category theory can be used for representation of multi-model data and schema and how the mutual mapping between the categorical representation and logical models of particular DBMSs can be designed. For this purpose we define the notion of an access path which enables to specify the mapping for all the currently popular models. To demonstrate advantages of the proposal we introduce framework MM-cat which applies the proposed approaches on MongoDB and PostgreSQL.
Computing generalizations of temporal εL concepts with next and global

In ontology-based applications, the authoring of complex concepts or queries written in a description logic (DL) is a difficult task. An established approach to generate complex expressions from examples provided a user, is the bottom-up approach. This approach employs two inferences: the most specific concept (MSC), which generalizes an ABox individual into a concept and the least common subsumer (LCS), which generalizes a collection of concepts into a single concept. In ontology-based situation recognition the situation to be recognized is formalized by a DL query using temporal operators and that is to answered over a sequence of ABoxes. Now, while the bottom-up approach is well-investigated for the DL EL, there are so far no methods for temporalized DLs. We consider here the temporalized DL that extends the DL EL with the LTL operators next (X) and global (G) and we present an approach that extends the LCS and the MSC to the temporalized setting. We provide computation algorithms for both inferences-even in the presence of rigid symbols-and show their correctness.
An automatic approach to detect problems in Android builds through screenshot analysis

The number of Android smartphones has grown in recent years, generating more than twenty thousand builds daily in our industry. Expert testers manually evaluate system builds to ensure Google's approval before entering in mass-production. In this context, automated approaches are needed to speed up this manual process. This paper presents a machine-learning-based approach to automate the validation of builds through the analysis of screenshots. Our approach uses screenshots and input files to identify which rules need to be applied as a first step. Then, it performs an analysis to recognize elements present in the screenshots using Optical Character Recognition (OCR) and Support Vector Machine (SVM). Finally, it uses a rule-based algorithm to classify the validation points as correct, incorrect, or not applicable. At the end, the labeled validation points are presented on a monitor, enabling experts to review them manually. To assess our approach, we compared our method with a manually labelled data set generated by three guest reviewers, and also with ground-truth labels obtained after Google's validation. Our approach achieved an average F1-score of 0.99. The results achieved by the six guest reviewers were, in general, worse than the result of our approach. Additionally, the approach helps the reviewer to save in average 90.86% of their work time, when compared with a purely human review.
Aspect based sentiment analysis in music: a case study with spotify

Nowadays, there are more and more social networks and Web platforms that give their users the opportunity to share their opinions and tastes on items of different types. This inevitably led to a growth of data relating to the subjective sphere of each individual. This information is extremely useful for several purposes, such as providing personalized recommendation services or understanding opinions conveyed through text. Sentiment Analysis provides helpful methods to analyze these textual opinions (e.g. reviews) from a global point of view. In case we want a more detailed representation of the opinion represented in a text, Aspect-based Sentiment Analysis identifies a valuable option thanks to its fine-grained level of text analysis. In this paper, we have designed a processing pipeline aimed to extracting domain-related aspects from text by means of an unsupervised approach. We formally define Aspect Terms and Aspect Categories as well as Aspect-based Sentiment Embedding, an approach of representing documents by computing aggregated sentiment scores for each aspect. We perform experimental evaluations on the Spotify dataset to prove the utility of our technique in predicting elements strictly related to emotions and feelings. Our results show improvements on the regression task for sentiment-related features compared to the classical semantic-based representations.
Using Audit Committee and Internal Audit Function Inter-Relationships to Drive Up Effectiveness

The Audit Committee (AC) has become a common mechanism for ensuring good corporate governance in firms. The interaction between the AC and Internal Audit Function (IAF) is important for the AC in fulfilling its oversight responsibilities. Hence, it is very important that the interaction between the respective roles of the AC and IAF is of good quality. However, little is understood about the quality of the interaction between these two roles. Extant literature suggests that a conclusive theory and/or theories are lacking that can provide an in depth understanding of how the AC and IAF foster a quality working relationship between their respective roles. Hence, we are proposing the relational coordination theory as a framework to enrich the understanding of the interaction between the AC and IAF. This conceptual paper reviews the literature on the AC effectiveness (ACE) and process, and analyses how the interaction quality can be determined using the proposed conceptual model encapsulating this theory.
Ontology-based user privacy management in smart grid

A smart grid system is one of the most complex cyber-physical systems consisting of power generation, distribution, consumption, customer domains, and millions of connected end devices. The widespread implementation of the smart grid raises concerns about the privacy of the data it collects. Since users' personal and non-personal data are going to be accessed by different entities involved in the smart grid and by third parties, the privacy concern can be a big obstacle in the adoption of the smart grid among people. Hence, there is an urgent need to provide the user with a privacy solution to support selective sharing of their usage data with different entities. In this paper, we propose an ontology-based user privacy management approach that will enable the user to release their data based on sensitivity and privacy factors, thus making an informed privacy decision on their usage data sharing. Green Button Initiative is a smart grid application that allows users to download and share their energy usage data with third parties. We present a proof-of-concept implementation extending the Green Button Initiative to test the feasibility of the proposed approach. Lastly, we discuss the results of a user study to investigate the effectiveness of our proposed approach.
Using Bounded Estimation to Avoid Nonconvergence in Small Sample Structural Equation Modeling

ABSTRACT The most frustrating outcome of an SEM analysis is nonconvergence. Nonconvergence typically happens when the sample size is small ( ) or very small ( ). To minimize the frequency of nonconvergence, this paper proposes a solution called bounded estimation. The idea is to use data-driven lower and upper bounds for a subset of the model parameters during estimation. In this paper, we provide a rationale to compute these bounds, and we study the effect of different approaches to employ these bounds on the frequency of nonconvergence. A simulation study shows that bounded estimation dramatically decreases the frequency of nonconvergence in both correctly and misspecified models, without any (negative) effects on the quality of the point estimates for the unbounded parameters.
Artificial intelligence-enabled enterprise information systems

The fourth industrial revolution has marked the end of the industrial automation era of simple and repetitive human tasks. Then, it opened a new arena for the efforts of mimicking human intelligence with a clear objective. This arena facilitates i) machinedriven decision-making, ii) seamless machine-to-machine communication using the formalisms easily expressed and understood by humans, iii) solving problems driven by uncertain and unknown variables, and iv) other activities that require more complex considerations and actions, in which humans typically implement in the past. With Artificial Intelligence (AI), Enterprise Information Systems (EIS) are becoming increasingly capable of sensing and perceive (even reach beyond the human cognitive horizon), analyse, or understand and respond, based on the acquired understanding. This combination was made possible using big data, significantly improved algorithms and sufficient computational power to train and run those algorithms with vast amounts of data. In this Special Issue, the concept of AI EIS enablement (Zdravković, Panetto, and Weichhart 2021) is introduced to provide the umbrella for a new way of thinking, architecting, designing, developing and using the EIS. It is placed on the top of the widely discussed data-enablement, sometimes in the context of so-called sensing enterprise. This enterprise continuously listens to its internal and external environments using the technologies, such as sensors, embedded electronics and multi-agent systems. Although today’s hype on AI is driven by the performance of complex deep learning architectures and models, both symbolic and non-symbolic AI applications were considered to reflect the equal importance of challenges and opportunities for logic-based and data-based methods. The Special Issue was advertised on the website of the publisher and by email. Also, the authors of the selected papers from the special session with the title ‘(Industrial) Internetof-Things for Smart & Sensing Systems’, organised at the 8th International Conference for Information Society and Technology (ICIST 2018) were invited to submit the extended versions of those papers to the Special Issue. Twenty submissions have been received, and after several rounds of reviews and revisions, six manuscripts were accepted for publication. Those manuscripts are shortly presented in the following section.
Losses in Waveguide and Substrate Integrated Waveguide (SIW) For Ku Band: A Comparison

In this paper equivalent Substrate Integrated Waveguide (SIW) for a waveguide is designed. Different types of losses in the waveguide and the optimized SIW are calculated theoretically and a comparison is done between the two. The comparison proves that at millimeter wave frequencies the choice of dielectric becomes crucial for a waveguide design and SIW is preferable to be used at these frequencies because the leakage losses decrease significantly.
Illumination variation interference suppression in remote PPG using PLS and MEMD

A novel framework based on partial least squares (PLS) and multivariate empirical mode decomposition (MEMD) is proposed to effectively evaluate heart rate from webcam videos captured during illumination changing conditions. The framework takes the assumption that both facial region of interest (ROI) and background ROI have the similar illumination variations and the background ROI can be treated as the denoising reference by using PLS to extract the underlying common illumination variation sources existing in both ROIs. Then, a number of intrinsic mode functions are decomposed by applying MEMD to the illumination-variation-suppressed facial ROI and the HR is evaluated. Compared to the experimental results obtained by the recently proposed independent component analysis and the ensemble empirical mode decomposition methods, the proposed method led to a better agreement with HR ground truth (the mean bias was 3.4 bpm with 95% limits of agreement ranging from −13.2 to 19.9 bpm), indicating a promising solution for the realistic HR estimation remotely.
Epiphytic ant‐plant obtains nitrogen from both native and invasive ant inhabitants

Ant‐plants have been extensively used as model systems in the study of the evolution and ecology of mutualisms. Using a 15N isotope labeling experiment, we found that both a native ant mutualist (Philidris cordata) and an invasive ant (Pheidole megacephala) provide nitrogen to the Australian ant‐plant Myrmecodia beccarii.
Overexpression of UCP4 in astrocytic mitochondria prevents multilevel dysfunctions in a mouse model of Alzheimer’s disease

Alzheimer’s disease (AD) is becoming increasingly prevalent worldwide. It represents one of the greatest medical challenge as no pharmacologic treatments are available to prevent disease progression. Astrocytes play crucial functions within neuronal circuits by providing metabolic and functional support, regulating interstitial solute composition, and modulating synaptic transmission. In addition to these physiological functions, growing evidence points to an essential role of astrocytes in neurodegenerative diseases like AD. Early stage AD is associated with hypometabolism and oxidative stress. Contrary to neurons that are vulnerable to oxydative stress, astrocytes are particularly resistant to mitochondrial dysfunction and are therefore more resilient cells. In our study, we leveraged astrocytic mitochondrial uncoupling and examined neuronal function in the 3xTg AD mouse model. We overexpressed the mitochondrial uncoupling protein 4 (UCP4), which has been shown to improve neuronal survival in vitro. We found that this treatment efficiently prevented alterations of hippocampal metabolite levels observed in AD mice, along with hippocampal atrophy and reduction of basal dendrite arborization of subicular neurons. This approach also averted aberrant neuronal excitability observed in AD subicular neurons and preserved episodic-like memory in AD mice assessed in a spatial recognition task. These findings show that targeting astrocytes and their mitochondria is an effective strategy to prevent the decline of neurons facing AD-related stress at the early stages of the disease.
Plasma miRNA-214 is a predictive candidate biomarker of progression speed in patients with ALS

This study was designed to develop and validate a reliable biomarker to predict the progression speed reflecting immune function of amyotrophic lateral sclerosis (ALS). After establishing the induced microglia model (iMGs) derived from peripheral blood monocytes, comparative studies to find factors related to phagocytic differences between iMGs of patients with rapidly progressive ALS [ALS(R)-iMGs, n = 15] and those of patients with slowly progressive ALS [ALS(S)-iMGs, n = 14] were conducted in the discovery cohort. To validate discovered candidate and whether it could be used as a reliable biomarker predicting the progression speed of ALS, we recruited 132 patients with ALS and 30 age-matched healthy controls as the validation cohort. ALS(R)-iMGs showed impaired phagocytic function. Transcriptomic analysis revealed that the perturbed phagocytosis in ALS(R)-iMGs was related to the decreased expression of NCKAP1 (NCK-associated protein 1) and NCKAP1 overexpression rescued the impaired phagocytic function. miRNA-214-3p targeting NCKAP1 in ALS-iMGs was correlated with progression speed in the discovery cohort. The validation cohort revealed that plasma miRNA-214-3p levels were significantly increased in ALS patients (p < 0.0001, AUC = 0.839), correlated with disease progression speed (p = 0.0005), and distinguished the rapidly progressive subgroup (Q1) from the slowly progressive (Q4, p = 0.029), respectively. Plasma miRNA-214-3p can predict the progression speed in ALS. Plasma miRNA-214-3p could be used as a simple and easily accessible biomarker for predicting the future progression speed related to phagocytic dysfunction in ALS patients.
A systematic approach to study protein-substrate specificity enables the identification of Ssh1 substrate range

Many cellular functions are carried out by protein pairs, or families, providing robustness alongside functional diversity. For such processes, it remains a challenge to map the degree of specificity versus promiscuity. Protein-protein interactions (PPIs) can be used to inform on these matters as they highlight cellular locals, regulation and, in cases where proteins affect other proteins – substrate range. However, methods to study transient PPIs systematically are underutilized. In this study we create a novel approach to study stable as well as transient PPIs in yeast. Our approach, Cel-lctiv (CELlular biotin-Ligation for Capturing Transient Interactions in Vivo), uses high- throughput pairwise proximity biotin ligation for uncovering PPIs systematically and in vivo. As a proof of concept we study the homologous translocation pores Sec61 and Ssh1. We show how Cel-lctiv can uncover the unique substrate range for each translocon allowing us to pinpoint a specificity determinator driving interaction preference. More generally this demonstrates how CEl-lctiv can provide direct information on substrate specificity even for highly homologous proteins.
Behavioral and brain responses to verbal stimuli reveal transient periods of cognitive integration of external world in all sleep stages

Sleep has long been considered as a state of disconnection from the environment, with absent reactivity to external stimuli. Here, we questioned this sleep disconnection dogma by directly investigating behavioral responsiveness in 49 napping subjects (27 with narcolepsy and 22 healthy volunteers) engaged in a lexical decision task. Participants were instructed to frown or smile depending on the stimulus type (words vs pseudo-words). We found accurate behavioral responses, visible via contractions of the corrugator or zygomatic muscles, in all sleep stages in both groups (except slow-wave sleep for healthy volunteers). Stimuli presented during states with high (vs. low) values of neural markers indexing rich cognitive states more often yielded responses. Our findings suggest that transient windows of reactivity to external stimuli exist in all sleep stages, even in healthy individuals. Such windows of reactivity could be used to probe sleep-related mental and cognitive processes in real-time across all sleep stages.
Authentication of Electromagnetic Interference Removal in Johnson Noise Thermometry

This report summarizes off-site testing performed at the Tennessee Valley Authority Kingston Fossil Plant. This location was selected as a valid off-site test facility because the environment is very similar to the expected industrial nuclear power plant environment. This report will discuss the electromagnetic interference discovered in the environment, the removal technique validity, and the results from the measurements. The Tennessee Valley Authority Kingston Fossil Plant (KFP) is located on Watts Bar Reservoir on the Tennessee River near Kingston, Tennessee. KFP is a coal-burning power plant. Its nine units boast a summer net capacity of 1,390 MW and can generate about 10 billion kW-Hr a year [1]. This facility houses boilers, pumps, scrubbers, and many other pieces of equipment that produce mechanical vibrations and electromagnetic interference (EMI). Two locations were selected as valid locations for Johnson noise thermometry (JNT) measurement. The first location was at the feed pump discharge of a boiler. The second location was at the boiler’s feed pump suction (Figure 1). The feed pump discharge is a warm location averaging around 425 K, and the feed pump suction averages around 410 K.
Regulation of astrocyte lipid metabolism and ApoE secretion by the microglial oxysterol, 25-hydroxycholesterol

Neuroinflammation is a major hallmark of Alzheimer’s disease and several other neurological and psychiatric disorders and is often associated with dysregulated cholesterol metabolism. Relative to homeostatic microglia, activated microglia express higher levels of Ch25h, an enzyme that hydroxylates cholesterol to produce 25-hydroxycholesterol (25HC). 25HC is an oxysterol with interesting immune roles stemming from its ability to regulate cholesterol biosynthesis. Since astrocytes synthesize cholesterol in the brain and transport it to other cells via apolipoprotein E (ApoE)-containing lipoproteins, we hypothesized that secreted 25HC from microglia may influence lipid metabolism as well as extracellular ApoE derived from astrocytes. Here we show that astrocytes take up externally added 25HC and respond with altered lipid metabolism. 25HC increased extracellular levels of ApoE lipoprotein particles without altering Apoe mRNA expression, due to elevated Abca1 expression via activation of LXRs and decreased ApoE reuptake due to suppressed Ldlr expression via inhibition of SREBP. Astrocytes metabolized 25HC to limit its effects on lipid metabolism via Cyp7b1, an enzyme responsible for 7α-hydroxylation of 25HC. Knockdown of Cyp7b1 expression with siRNA prolonged the effects of 25HC on astrocyte lipid metabolism. 25HC also suppressed Srebf2 expression to reduce cholesterol synthesis in astrocytes but did not affect fatty acid levels or the genes required for fatty acid synthesis. We further show that 25HC led to a doubling of the amount of cholesterol esters and their concomitant storage in lipid droplets. Our results suggest an important role for 25HC in regulating astrocyte lipid metabolism.