Effects of Daytime Electric Light Exposure on Human Alertness and Higher Cognitive Functions: A Systematic Review

This paper reports the results of a systematic review conducted on articles examining the effects of daytime electric light exposure on alertness and higher cognitive functions. For this, we selected 59 quantitative research articles from 11 online databases. The review protocol was registered with PROSPERO (CRD42020157603). The results showed that both short-wavelength dominant light exposure and higher intensity white light exposure induced alertness. However, those influences depended on factors like the participants’ homeostatic sleep drive and the time of day the participants received the light exposure. The relationship between light exposure and higher cognitive functions was not as straightforward as the alerting effect. The optimal light property for higher cognitive functions was reported dependent on other factors, such as task complexity and properties of control light. Among the studies with short-wavelength dominant light exposure, ten studies (morning: 3; afternoon: 7) reported beneficial effects on simple task performances (reaction time), and four studies (morning: 3; afternoon: 1) on complex task performances. Four studies with higher intensity white light exposure (morning: 3; afternoon: 1) reported beneficial effects on simple task performance and nine studies (morning: 5; afternoon: 4) on complex task performance. Short-wavelength dominant light exposure with higher light intensity induced a beneficial effect on alertness and simple task performances. However, those effects did not hold for complex task performances. The results indicate the need for further studies to understand the influence of short-wavelength dominant light exposure with higher illuminance on alertness and higher cognitive functions.

Recognizing Physical Activity of hospitalized Older People from Wearable Sensors Data using IoT

The IoT is a new concept that provides a world where smart, connected, embedded systems operate, giving rise to the amount of data from different sources that are considered to have highly useful and valuable information. Data mining would play a critical role in creating smarter IoT. Traditional care of an elderly person is a difficult and complex task. The need to have a caregiver with the elderly person almost all the time drains the human and financial resources of the health care system. The emergence of Artificial intelligence has allowed the conception of technical assistance where it helps and reduces the time spent by the caregiver with the elderly person. This work aims to focus on analyzing techniques that are used for prediction purposes of falls in the elderly. We examine the applicability of three classification algorithms for IoT data. These algorithms are analyzed and a comparative study is undertaken to find the classifier that performs the best analysis on the dataset using a set of predefined performance metrics to compare the results of each classifier.

Improved argumentative paragraphs detection in academic theses supported with unit segmentation

Academic theses writing is a complex task that requires the author to be skilled in argumentation. The goal of the academic author is to communicate clear ideas and to convince the reader of the presented claims. However, few students are good arguers, and this is a skill that takes time to master. In this paper, we present an exploration of lexical features used to model automatic detection of argumentative paragraphs using machine learning techniques. We present a novel proposal, which combines the information in the complete paragraph with the detection of argumentative segments in order to achieve improved results for the detection of argumentative paragraphs. We propose two approaches; a more descriptive one, which uses the decision tree classifier with indicators and lexical features; and another more efficient, which uses an SVM classifier with lexical features and a Document Occurrence Representation (DOR). Both approaches consider the detection of argumentative segments to ensure that a paragraph detected as argumentative has indeed segments with argumentation. We achieved encouraging results for both approaches.

Effects of third-language learning on inhibitory control with consideration of task complexity and language proficiency

Aims and Objectives: The present study explores the question of whether learning a third language (L3) in an English as a foreign language (EFL) classroom setting induces improved inhibitory control compared with that found in bilinguals, considering task complexity and language proficiency. Methodology: Thirty-six Chinese–English second language (L2) young adult learners and 121 Chinese–English–Japanese/French/Russian/German L3 young adult learners with three levels of L3 proficiency participated in the study. Simon arrow tasks were employed to measure two types of inhibitory control: response inhibition (the less complex task with univalent stimuli) and interference suppression (the more complex task with bivalent stimuli). Data and Analysis: Statistics using ANOVAs and multiple comparisons were employed to analyze the effects of L3 learning on the reaction time and accuracy for response inhibition and interference suppression, respectively. Findings: The results demonstrated that L3 learners did not outperform L2 learners in the two types of inhibitory control: response inhibition (less complex) and interference suppression (more complex). Moreover, L3 learners with a higher proficiency did not display better inhibitory control than those with a lower proficiency in response inhibition and interference suppression. However, as the L3 proficiency increased, some specific aspects of inhibitory control did improve and exhibited a nonlinear pattern. Originality: The present study extends bilingual advantage in inhibitory control to formal L3 learning, exploring whether bilingual advantage in inhibitory control also appears in L3 learners, considering task complexity and language proficiency. Significance/implications: The present study contributes to the theory of the relationship between multilingualism and inhibitory control by showing that this relationship may be more complex than it is understood currently. Learning an additional language to L2, particularly short-term learning, may not lead to an incremental advantage in overall inhibitory control. However, as learning time increases, changes may appear in specific aspects of inhibitory control, and may be a nonlinear one.

Mitigating Cold Start Problem in Serverless Computing with Function Fusion

[sangyoon]As Artificial Intelligence (AI) is becoming ubiquitous in many applications, serverless computing is also emerging as a building block for developing cloud-based AI services. Serverless computing has received much interest because of its simplicity, scalability, and resource efficiency. However, due to the trade-off with resource efficiency, serverless computing suffers from the cold start problem, that is, a latency between a request arrival and function execution[sangyoon] that is encountered due to resource provisioning. [sangyoon]In serverless computing, functions can be composed as workflows to process a complex task, and the cold start problem has a significant influence on workflow response time because the cold start can occur in each function.The cold start problem significantly influences the overall response time of workflow that consists of functions because the cold start may occur in every function within the workflow. Function fusion can be one of the solutions to mitigate the cold start latency of a workflow. If two functions are fused into a single function, the cold start of the second function is removed; however, if parallel functions are fused, the workflow response time can be increased because the parallel functions run sequentially even if the cold start latency is reduced. This study presents an approach to mitigate the cold start latency of a workflow using function fusion while considering a parallel run. First, we identify three latencies that affect response time, present a workflow response time model considering the latency, and efficiently find a fusion solution that can optimize the response time on the cold start. Our method shows a response time of 28–86% of the response time of the original workflow in five workflows.

Automated Event Detection and Classification in Soccer: The Potential of Using Multiple Modalities

Detecting events in videos is a complex task, and many different approaches, aimed at a large variety of use-cases, have been proposed in the literature. Most approaches, however, are unimodal and only consider the visual information in the videos. This paper presents and evaluates different approaches based on neural networks where we combine visual features with audio features to detect (spot) and classify events in soccer videos. We employ model fusion to combine different modalities such as video and audio, and test these combinations against different state-of-the-art models on the SoccerNet dataset. The results show that a multimodal approach is beneficial. We also analyze how the tolerance for delays in classification and spotting time, and the tolerance for prediction accuracy, influence the results. Our experiments show that using multiple modalities improves event detection performance for certain types of events.

A COMPLEX CONCEPTION OF MANAGEMENT SYSTEM OF ORGANISATIONS AND SOCIAL RESPONSIBILITY

The difficult situation is affecting many organisations at the present time. This article mainly deals with ensuring of the quality of production on the example of a case study in one manufacturing organisation. However, it is about involving other aspects, such as: maintenance, social responsibility, process approach. Everything is based on specific findings related to the economic crisis caused by the COVID-19 pandemic. Quality management tools and responsibility tools were applied in solving this complex task. The example of the mentioned organisation shows that the conformity application of aspects of the management system above in a holistic connection to soft resources not only supports the strengthening of production quality and the growth of efficiency, but it also strengthens an internal culture of the organisation. The goal is to ensure an efficient operation of the organisation and thus to support its competitiveness.

Analysis of the Emails From the Dutch Web-Based Intervention “Alcohol de Baas”: Assessment of Early Indications of Drop-Out in an Online Alcohol Abuse Intervention

Nowadays, traditional forms of psychotherapy are increasingly complemented by online interactions between client and counselor. In (some) web-based psychotherapeutic interventions, meetings are exclusively online through asynchronous messages. As the active ingredients of therapy are included in the exchange of several emails, this verbal exchange contains a wealth of information about the psychotherapeutic change process. Unfortunately, drop-out-related issues are exacerbated online. We employed several machine learning models to find (early) signs of drop-out in the email data from the “Alcohol de Baas” intervention by Tactus. Our analyses indicate that the email texts contain information about drop-out, but as drop-out is a multidimensional construct, it remains a complex task to accurately predict who will drop out. Nevertheless, by taking this approach, we present insight into the possibilities of working with email data and present some preliminary findings (which stress the importance of a good working alliance between client and counselor, distinguish between formal and informal language, and highlight the importance of Tactus' internet forum).

An assessment of sentencing approaches to persons convicted of white-collar crime in South Africa

Assessing court sentencing approaches to persons convicted of white-collar crime is a complex task. For the purposes of this article, this research task involved assessing the appropriateness of sentences imposed within the proportionality principle during the period 2016 to 2021 in South Africa. This further involved the empirical use of both qualitative and quantitative methodologies, in order to determine how commercial courts – in this case, the Bellville Commercial (Regional) Court – impose a sentence on white-collar criminals. The article establishes that, in South Africa, categories of white-collar crime such as corruption, racketeering, fraud and money laundering are increasingly reported by the media, independent institutions and government. There is a public perception that courts are generally lenient in sentencing white-collar offenders. This article aims to determine the appropriateness of a sentence, within the principle of proportionality, for white-collar criminals, in order to deter this type of crime.