Miracle-dz/UniFAR-FTUs huggingface dataset:
Subjective Vividness of Kinesthetic Motor Imagery Is Associated With the Similarity in Magnitude of Sensorimotor Event-Related Desynchronization Between Motor Execution and Motor Imagery

In the field of psychology, it has been well established that there are two types of motor imagery such as kinesthetic motor imagery (KMI) and visual motor imagery (VMI), and the subjective evaluation for vividness of motor imagery each differs across individuals. This study aimed to examine how the motor imagery ability assessed by the psychological scores is associated with the physiological measure using electroencephalogram (EEG) sensorimotor rhythm during KMI task. First, 20 healthy young individuals evaluated subjectively how vividly they can perform each of KMI and VMI by using the Kinesthetic and Visual Imagery Questionnaire (KVIQ). We assessed their motor imagery abilities by summing each of KMI and VMI scores in KVIQ (KMItotal and VMItotal). Second, in physiological experiments, they repeated two strengths (10 and 40% of maximal effort) of isometric voluntary wrist-dorsiflexion. Right after each contraction, they also performed its KMI. The scalp EEGs over the sensorimotor cortex were recorded during the tasks. The EEG power is known to decrease in the alpha-and-beta band (7–35 Hz) from resting state to performing state of voluntary contraction (VC) or motor imagery. This phenomenon is referred to as event-related desynchronization (ERD). For each strength of the tasks, we calculated the maximal peak of ERD during VC, and that during its KMI, and measured the degree of similarity (ERDsim) between them. The results showed significant negative correlations between KMItotal and ERDsim for both strengths (p < 0.05) (i.e., the higher the KMItotal, the smaller the ERDsim). These findings suggest that in healthy individuals with higher motor imagery ability from a first-person perspective, KMI efficiently engages the shared cortical circuits corresponding with motor execution, including the sensorimotor cortex, with high compliance.
Some Extensions of the Crouzeix-Palencia Result

In [{\em The Numerical Range is a $(1 + \sqrt{2})$-Spectral Set}, SIAM J. Matrix Anal. Appl. 38 (2017), pp.~649-655], Crouzeix and Palencia show that the numerical range of a square matrix or linear operator $A$ is a $(1 + \sqrt{2})$-spectral set for $A$; that is, for any function $f$ analytic in the interior of the numerical range $W(A)$ and continuous on its boundary, the inequality $\| f(A) \| \leq (1 + \sqrt{2} ) \| f \|_{W(A)}$ holds, where the norm on the left is the operator 2-norm and $\| f \|_{W(A)}$ on the right denotes the supremum of $| f(z) |$ over $z \in W(A)$. In this paper, we show how the arguments in their paper can be extended to show that other regions in the complex plane that do {\em not} necessarily contain $W(A)$ are $K$-spectral sets for a value of $K$ that may be close to $1 + \sqrt{2}$. We also find some special cases in which the constant $(1 + \sqrt{2})$ for $W(A)$ can be replaced by $2$, which is the value conjectured by Crouzeix.
Bio-Based Compounds from Grape Seeds: A Biorefinery Approach

Food and agricultural waste represents a growing problem with negative effects on the economy, environment, and human health. Winemaking produces byproducts with high added value, which can be used for new productions in several application fields. From the perspective of biorefinery and circular economy, grape seeds could be exploited by extracting bioactive compounds with high added value before using biomass for energy purposes. The markets concerned are, in addition to the food, cosmetics, and pharmaceuticals sectors, which use bioactive compounds, the sector of biopolymeric materials and of energy for the production of biohydrogen and biomethane. Generally, bioactive components should be investigated through an integrated and multidisciplinary study approach based on emerging analytical techniques; in this context, attention is addressed towards green and sustainable procedures; an update of extraction techniques, innovative technologies, and chemometrics are described. Nowadays, processes so far tested on a pilot scale for grape waste are developed to enhance the extraction yields. Here, a picture of the Italian experience applied to the byproducts of the wine industry is given.
A Delay-tolerant Proximal-Gradient Algorithm for Distributed Learning

Distributed learning aims at computing high-quality models by training over scattered data. This covers a diversity of scenarios, including computer clusters or mobile agents. One of the main challenges is then to deal with heterogeneous machines and unreliable communications. In this setting, we propose and analyze a flexible asynchronous optimization algorithm for solving nonsmooth learning problems. Unlike most existing methods, our algorithm is adjustable to various levels of communication costs, machines computational powers, and data distribution evenness. We prove that the algorithm converges linearly with a fixed learning rate that does not depend on communication delays nor on the number of machines. Although long delays in communication may slow down performance, no delay can break convergence.
Detection of Traffic Initiated by Mobile Malware Targeting Android Devices in 3GPP Networks

Android devices have become the most popular of mobile devices; omnipresent in both business and private use. They are virtually always on and offer functionalities exceeding those of desktop computers. These properties, as well as sensitive information stored on Android devices, render them an attractive target for mobile malware authors. As the volume of mobile malware increases, analysis is becoming challenging and, sometimes, infeasible. Additionally, current network-based intrusion detection systems are very efficient at detecting malicious IP-based traffic, but often lack functionalities for detecting circuit-switched traffic often (mis)used by mobile malware. Recognizing the increasing difficulty of analyzing mobile malware, we sought to understand trends and relationships between mobile malware samples. We conducted various analyses of large volumes of Android malware samples. Among other outcomes, we provide a chronological quantitative analysis of Android malware samples depicting the usage of obfuscation techniques and map relationships among samples known to initiate short messages. The latter analysis helped us to understand the typical traffic pattern in short messages and even the number of authors responsible for mobile malware initiating short messages. This thesis introduces three innovations in the detection of mobile malware. A new architecture, called 3GPP Mobile malware Protection (3GPPMOP), is designed to reside in the core network of any currently operated 3GPP network such as 2G, 3G and 4G network and to detect mobile malware targeting any mobile device in near real-time. The second innovation, the highly space efficient blacklist (HSEB), optimizes the space needed to store entries in the blacklist, rather than optimizing processing time, which is of critical value for managing the volume of filtered traffic processed in mobile networks. Finally, we employ supervised machine learning to successfully detect yet unknown short messages initiated by Android malware.
From signals to knowledge: A conceptual model for multimodal learning analytics

Multimodality in learning analytics and learning science is under the spotlight. The landscape of sensors and wearable trackers that can be used for learning support is evolving rapidly, as well as data collection and analysis methods. Multimodal data can now be collected and processed in real time at an unprecedented scale. With sensors, it is possible to capture observable events of the learning process such as learner's behaviour and the learning context. The learning process, however, consists also of latent attributes, such as the learner's cognitions or emotions. These attributes are unobservable to sensors and need to be elicited by human‐driven interpretations. We conducted a literature survey of experiments using multimodal data to frame the young research field of multimodal learning analytics. The survey explored the multimodal data used in related studies (the input space) and the learning theories selected (the hypothesis space). The survey led to the formulation of the Multimodal Learning Analytics Model whose main objectives are of (O1) mapping the use of multimodal data to enhance the feedback in a learning context; (O2) showing how to combine machine learning with multimodal data; and (O3) aligning the terminology used in the field of machine learning and learning science
Autonomous Model Management via Reinforcement Learning

Concept drift - a change, either sudden or gradual, in the underlying properties of data - is one of the most prevalent challenges to maintaining high-performing learned models over time in autonomous systems. In the face of concept drift, one can hope that the old model is sufﬁciently representative of the new data despite the concept drift, one can discard the old data and retrain a new model with (often limited) new data, or one can use transfer learning methods to combine the old data with the new to create an updated model. Which of these three options is chosen affects not only near-term decisions, but also future needs to transfer or retrain. In this paper, we thus model response to concept drift as a sequential decision making problem and formally frame it as a Markov Decision Process. Our reinforcement learning approach to the problem shows promising results on one synthetic and two real-world datasets.
Effect of different concentrations of pulverized mesocarp of Citrus paradisi Macf. on the bromatological characteristics of spray‐dried lemon juice powder

Abstract The aim of this research was to evaluate the effect of different concentrations of pulverized mesocarp of Citrus paradisi Macf. as a drying aid on the bromatological characteristics and yield of spray‐dried lemon juice powder. Five concentrations of grapefruit mesocarp encapsulant (0.4, 0.8, 1.2, 1.6, and 2.0% (w / w)) and maltodextrin DE 10 (1.2%, w / w) were evaluated as encapsulant agents. The highest yield (46.76%) was obtained with 1.2% of grapefruit encapsulant. Water activity and ash content were inversely proportional to the added encapsulant concentration. The highest moisture value was obtained with 0.4% and the highest soluble solids with 2.0%. For all treatments, the pH level did not change, except at 0.4% (it was lower). The concentrations of the encapsulants significantly affected the evaluated characteristics, except for the proteins.
Optimal Control of Continuous-Time Markov Chains with Noise-Free Observation

We consider an infinite horizon optimal control problem for a continuous-time Markov chain $X$ in a finite set $I$ with noise-free partial observation. The observation process is defined as $Y_t = h(X_t)$, $t \geq 0$, where $h$ is a given map defined on $I$. The observation is noise-free in the sense that the only source of randomness is the process $X$ itself. The aim is to minimize a discounted cost functional and study the associated value function $V$. After transforming the control problem with partial observation into one with complete observation (the separated problem) using filtering equations, we provide a link between the value function $v$ associated to the latter control problem and the original value function $V$. Then, we present two different characterizations of $v$ (and indirectly of $V$): on one hand as the unique fixed point of a suitably defined contraction mapping and on the other hand as the unique constrained viscosity solution (in the sense of Soner) of a HJB integro-differential equation. Under suitable assumptions, we finally prove the existence of an optimal control.
“I never thought I could get health information from the Internet!”: Unexpected uses of an Internet website designed to enable Ethiopian immigrants with low/no literacy skills to browse health information

The Internet is a major source for health information but contributes to the digital divide and health disparities. Minorities with low literacy skills are at a particular disadvantage in obtaining online information. A website was created with health information presented through videos in Amharic and an interface that does not require reading skills to enable users with low/no literacy to navigate among topics. In all, 225 Israeli Ethiopian immigrants were asked to use the website, most with low/no literacy skills. Participants were excited about it, but those with low/no literacy felt they needed support and training for future use. Some felt it was too difficult. The findings point to unexpected potential sociocultural uses for the website for immigrants with different levels of literacy skills. The analysis yielded two user typologies that can help identify user needs and segmentation, a culture-centered adaptation of the technology acceptance model, and implications for communication infrastructure theory.
A Neural Approach to Pun Generation

Automatic pun generation is an interesting and challenging text generation task. Previous efforts rely on templates or laboriously manually annotated pun datasets, which heavily constrains the quality and diversity of generated puns. Since sequence-to-sequence models provide an effective technique for text generation, it is promising to investigate these models on the pun generation task. In this paper, we propose neural network models for homographic pun generation, and they can generate puns without requiring any pun data for training. We first train a conditional neural language model from a general text corpus, and then generate puns from the language model with an elaborately designed decoding algorithm. Automatic and human evaluations show that our models are able to generate homographic puns of good readability and quality.
Instant 3D photography

We present an algorithm for constructing 3D panoramas from a sequence of aligned color-and-depth image pairs. Such sequences can be conveniently captured using dual lens cell phone cameras that reconstruct depth maps from synchronized stereo image capture. Due to the small baseline and resulting triangulation error the depth maps are considerably degraded and contain low-frequency error, which prevents alignment using simple global transformations. We propose a novel optimization that jointly estimates the camera poses as well as spatially-varying adjustment maps that are applied to deform the depth maps and bring them into good alignment. When fusing the aligned images into a seamless mosaic we utilize a carefully designed data term and the high quality of our depth alignment to achieve two orders of magnitude speedup w.r. t. previous solutions that rely on discrete optimization by removing the need for label smoothness optimization. Our algorithm processes about one input image per second, resulting in an end-to-end runtime of about one minute for mid-sized panoramas. The final 3D panoramas are highly detailed and can be viewed with binocular and head motion parallax in VR.
Learning and Memorization

In the machine learning research community, it is generally believed that there is a tension be-tween memorization and generalization. In this work, we examine to what extent this tension exists, by exploring if it is possible to generalize by memorizing alone. Although direct memo-rization with a lookup table obviously does not generalize, we ﬁnd that introducing depth in the form of a network of support-limited lookup tables leads to generalization that is signiﬁcantly above chance and closer to those obtained by standard learning algorithms on several tasks derived from MNIST and CIFAR -10. Furthermore, we demonstrate through a series of empirical results that our approach allows for a smooth tradeoff between memorization and generalization and exhibits some of the most salient characteristics of neural networks: depth improves performance; random data can be memorized and yet there is generalization on real data; and memorizing random data is harder in a certain sense than mem-orizing real data. The extreme simplicity of the algorithm and potential connections with generalization theory point to several interesting directions for future research.
A technology acceptance model for the perception of restaurant service robots for trust, interactivity, and output quality

Robotics has entered food services for orders and deliveries. Robots not only attract customer's attention, but also solve the problems of human resource shortages, training, and turnover rates. For these reasons, restaurant robots may become staple in future culinary experiences. The present study attempts to study the perception of restaurant robots using technology acceptance model (TAM) with trust, interactivity, and quality of output as the variables. The interviewed subjects are general managers or deputy managers in more than 100 restaurants in Taiwan. The results indicate that attitude positively influences acceptance with perceived usefulness and perceived ease of use positively nudges attitude. Perceived ease of use significantly increases perceived usefulness. Trust and the quality of output significantly affect perceived usefulness, as does interaction has a positive influence on perceived ease of use. Finally, we hope this research result will shed some light on the future implementation of robotics in restaurant services.
Towards an Online Emotional Support Agent: Identifying Emotional Support Strategies via Crowdsourcing

In this paper, an empirical study conducted with Amazon Mechanical Turk workers is presented that aims to make a correspondence between messages about stressful situations, which were shared via Twitter, and 5 different types of supportive replies to them. Around 10.00 tweets were collected and analyzed in the terms previously described. We performed statistical tests to determine possible correlations between causes of stress and supportive response strategies. We are about to use these findings to improve a previously implemented algorithm that automatically generates supportive messages to stressed users. This algorithm is the core of an agent, in the form of a chatbot, that would be able to interact with such stressed users as if it would belong to their social networks.
Placement of Virtual Containers on NUMA systems: A Practical and Comprehensive Model

Our work addresses the problem of placement of threads, or virtual cores, onto physical cores in a multicore NUMA system. Different placements result in varying degrees of contention for shared resources, so choosing the right placement can have a large effect on performance. Prior work has studied this problem, but either addressed hardware with specific properties, leaving us unable to generalize the models to other systems, or modeled much simpler effects than the actual performance in different placements. Our contribution is a general framework for reasoning about workload placement on machines with shared resources. It enables us to build an accurate performance model for any machine with a hierarchy of known shared resources automatically, with only minimal input from the user. Using our methodology, data center operators can minimize the number of NUMA (CPU+memory) nodes allocated for an application or a service, while ensuring that it meets performance objectives.
Tackling Code-Switched NER: Participation of CMU

Named Entity Recognition plays a major role in several downstream applications in NLP. Though this task has been heavily studied in formal monolingual texts and also noisy texts like Twitter data, it is still an emerging task in code-switched (CS) content on social media. This paper describes our participation in the shared task of NER on code-switched data for Spanglish (Spanish + English) and Arabish (Arabic + English). In this paper we describe models that intuitively developed from the data for the shared task Named Entity Recognition on Code-switched Data. Owing to the sparse and non-linear relationships between words in Twitter data, we explored neural architectures that are capable of non-linearities fairly well. In specific, we trained character level models and word level models based on Bidirectional LSTMs (Bi-LSTMs) to perform sequential tagging. We train multiple models to identify nominal mentions and subsequently use this information to predict the labels of named entity in a sequence. Our best model is a character level model along with word level pre-trained multilingual embeddings that gave an F-score of 56.72 in Spanglish and a word level model that gave an F-score of 65.02 in Arabish on the test data.
Nurse-led extubation in the post-anaesthesia care unit

Weaning of mechanical ventilation occurs in intensive care units by nurses, which stimulates the prospect of nurse-led extubation extending into the PACU environment for improved patient outcomes and reduced demand of hospital resources. Nurse-led patient extubation in the PACU, would involve specially trained nurses weaning mechanical ventilation via an established protocol for a specific patient group, prior to the patient being extubated by an anaesthetist or intensivist.
“I see your garbage”: Participatory practices and literacy privilege on “Grammar Nazi” Facebook pages in different sociolinguistic contexts

In contemporary online culture, Grammar Nazi (GN) is a derogatory term used to label individuals who practice excessive language policing but has also been ironically appropriated by groups of users who engage in evaluation of other people’s grammar for entertainment purposes. In this article, we combine approaches from media studies and sociolinguistics to analyze the adoption of the phenomenon by two GN Facebook pages in two languages: English and Czech. Our mixed-method analysis shows that while both pages can be read as examples of media participation, they also exemplify their users’ “literacy privilege” associated with standard language ideology. However, there are differences in the practices associated with the label, reflecting the specific sociolinguistic contexts. While Czech GNs act as “guardians” of the public space, collecting and displaying localized orthographic errors for collective dissection, the English page is more dedicated to sharing jokes and puns typical of international online culture.
Does School Racial Composition Explain Why High Income Black Youth Perceive More Discrimination? A Gender Analysis

Recent research has documented poor mental health among high socioeconomic status (SES) Blacks, particularly African American males. The literature has also shown a positive link between SES and perceived discrimination, suggesting that perceived discrimination may explain why high SES Black males report poor mental health. To better understand the role of contextual factors in explaining this pattern, we aimed to test whether school racial composition explains why high income Black youth perceive more discrimination. We explored these associations by ethnicity and gender. Using data from the National Survey of American Life-Adolescent supplement (NSAL-A), the current study included 810 African American and 360 Caribbean Black youth, with a mean age of 15. Ethnicity, age, gender, income-to-needs ratio (SES), skin color, school racial composition, and perceived (daily) discrimination were measured. Using Stata 15.0 (Stata Corp., College Station, TX, USA), we fitted seven structural equation models (SEMs) for data analysis in the pooled sample based on the intersection of ethnicity and gender. Considerable gender by ethnicity variations were found in the associations between SES, school racial composition, and perceived discrimination. For African American males but not African American females or Caribbean Black males or females, school racial composition fully mediated the effect of SES on perceived discrimination. The role of inter-racial contact as a mechanism for high discrimination and poor mental health of Black American adolescents may depend on their intersection of ethnicity and gender. School racial composition may be a mechanism for increased perceived discrimination among high SES African American males.