Intelligence Capacity Research Articles

Penerapan Model Maturitas Digital Pada Kinerja Startup

This study aims to apply a digital maturity model to measure startup performance. The maturity model consists of dimensions and criteria, which describe the area of ​​action, and maturity stages which indicate the evolutionary path to maturity. Data collection was obtained using a questionnaire given online at the startup. The research design consisted of three steps: developing a digital maturity model dimension through literature review, expert interviews, and focus groups, online surveys among individuals, and data analysis using the Rasch algorithm and cluster analysis. From these findings, it can be said that some attention should be paid to the business intelligence capacity of startups, given its impact on company performance. In addition, the effect of network learning through business intelligence is significant and presents a positive influence on performance. The results from empirical studies show that understanding the strategic importance of digitalization, as well as using digital technology for collaboration, is already practiced in most companies. However, creating a personalized customer experience based on big data analysis or process automation is characterized by a lower level of achievement. Companies are starting to carry out digital transformation more systematically and strategically, by creating measurable goals and defining roles and responsibilities within the organization. These results help practitioners as well as researchers, better understand the processes through which organizations are truly involved in their digital transformation.

Influence of current physical exercise on the relationship between aging and episodic memory and fluid intelligence

The present study examined the effects of current physical exercise and age on episodic memory and fluid intelligence, assessed with a free-recall task and the Culture Fair Intelligence Test (Cattell, 1963) respectively, while statistically controlling for other cognitive reserve factors (educational level, leisure activities, and vocabulary level). Two hundred and eight participants aged 20 to 85 participated in the study. Physical exercise level was indexed by weekly frequency over the last 12 months using self-reported measurement (from none to 4 times a week). Overall, results show a beneficial effect of physical exercise especially from a weekly practice of 2 times, and significant interaction between physical exercise and age on episodic memory and fluid intelligence capacities indicating a reduced effect of age in more physically active participants. These results reinforce the view that physical exercise is a strong and specific reserve factor that reduces decline in some cognitive functions during aging.

Anomaly Detection Based on Convex Analysis: A Survey

As a crucial technique for identifying irregular samples or outlier patterns, anomaly detection has broad applications in many fields. Convex analysis (CA) is one of the fundamental methods used in anomaly detection, which contributes to the robust approximation of algebra and geometry, efficient computation to a unique global solution, and mathematical optimization for modeling. Despite the essential role and evergrowing research in CA-based anomaly detection algorithms, little work has realized a comprehensive survey of it. To fill this gap, we summarize the CA techniques used in anomaly detection and classify them into four categories of density estimation methods, matrix factorization methods, machine learning methods, and the others. The theoretical background, sub-categories of methods, typical applications as well as strengths and limitations for each category are introduced. This paper sheds light on a succinct and structured framework and provides researchers with new insights into both anomaly detection and CA. With the remarkable progress made in the techniques of big data and machine learning, CA-based anomaly detection holds great promise for more expeditious, accurate and intelligent detection capacities.

An intelligent capacity management system for interface converter in AC-DC hybrid microgrids

An interface converter (IC) is used in an AC-DC hybrid microgrid (HMG) and its main tasks are frequency regulation in the AC side, adjusting the DC voltage, and controlling the power flow between AC/DC sides based on the droop control method. The IC should be capable of providing ancillary services such as reactive power supply and compensation of unbalanced and harmonic components in the AC side. However, the use of the IC to provide ancillary services occupies its capacity, which may interfere with the main tasks of the IC. In addition, it is shown in this paper that in unbalanced conditions, the effective power capacity of the IC is reduced by considering the current limit of the converter. In this case, the converter may not be able to perform the main task and provide all the necessary ancillary services at the same time, otherwise, it may be exposed to an overcurrent condition. Therefore, an efficient strategy is needed to manage the IC converter capacity to facilitate optimal use of the entire IC capacity even in unbalanced conditions. Given this challenge, this paper proposes an intelligent strategy for managing the IC capacity, which prioritizes the realization of the main task and the provision of ancillary services. The proposed strategy is evaluated, and its effectiveness is proven by simulation results in Matlab/Simulink.

GENIGMA: an app to map the 3D genome of cancer cell lines through extreme citizen science

In this Scientists' Forum article, we present a new app, GENIGMA, launched by structural genomics specialist Marc A. Marti-Renom and his team, aimed at mapping the 3D genome of cancer cell lines. GENIGMA is a digital game, which was designed and tested through a co-creation process with citizens. Finally, by playing the game, citizens produce data, reaching beyond the capacity of artificial intelligence. This is an exceptional experiment of extreme citizen science, which broadens the implications of open science.

Guest editorial: AI-empowered future communication networks

Recently, the fifth-generation (5G) of wireless network has been developed to support enhanced mobile broadband (eMBB), massive machine-type communications (mMTC) and ultra-reliable and low-latency communications (uRLLC). However, with the tremendous increase of data services, especially driven by emerging applications such as holographic communications, augmented reality, virtual reality tactile reality, mixed reality, etc., 5G is far from enough to support the faster, more reliable and larger scale communication requirements of these services. In response, the investigation of future generation communication networks (B5G and 6G) has been triggered, which promises more powerful capacities of transmission, security, reliability and intelligence than 5G does.

Application of Artificial Neural Networks, Support Vector, Adaptive Neuro-Fuzzy Inference Systems for the Moisture Ratio of Parboiled Hulls

Drying as an effective method for preservation of crop products is affected by various conditions and to obtain optimum drying conditions it is needed to be evaluated using modeling techniques. In this study, an adaptive neuro-fuzzy inference system (ANFIS), artificial neural network (ANN), and support vector regression (SVR) was used for modeling the infrared-hot air (IR-HA) drying kinetics of parboiled hull. The ANFIS, ANN, and SVR were fed with 3 inputs of drying time (0–80 min), drying temperature (40, 50, and 60 °C), and two levels of IR power (0.32 and 0.49 W/cm2) for the prediction of moisture ratio (MR). After applying different models, several performance prediction indices, i.e., correlation coefficient (R2), mean square error index (MSE), and mean absolute error (MAE) were examined to select the best prediction and evaluation model. The results disclosed that higher inlet air temperature and IR power reduced the drying time. MSE values for the ANN, ANFIS tests, and SVR training were 0.0059, 0.0036, and 0.0004, respectively. These results indicate the high-performance capacity of machine learning methods and artificial intelligence to predict the MR in the drying process. According to the results obtained from the comparison of the three models, the SVR method showed better performance than the ANN and ANFIS methods due to its higher R2 and lower MSE.

STUDY ON PROJECT MANAGEMENT IN EMOTIONAL INTELLIGENCE

The regularly focused on solitary credits that sway a leader's adequacy. This investigation, all things considered, takes a gander at whether emotional intelligence directs the expected adverse consequence of appropriated presence on commitment and impact, and at last, leader viability. Buttressed by emotional intelligence, commitment, and impact speculations, the exploration question focused on how emotional intelligence abilities moderate the effect of a project supervisor's disseminated presence to deliver the leader powerful. The investigation test for this exploration came from deliberate members who work for a U.S. government office including leaders co-situated with their groups and conveyed presence leaders. Elucidating insights showed that leaders with higher emotional intelligence (EI) were more captivating and compelling than co-found leaders with high EI. Relapse investigations showed most noteworthy importance between the reliant factors commitment and impact and the autonomous factors of disseminated presence and emotional intelligence when utilizing emotional intelligence branches and undertakings for the EI factors. Information from this examination showed appropriated presence leaders with high emotional intelligence capacities impact commitment and impact decidedly. Crafted by this examination propels bits of knowledge into how emotional intelligence impacts, decidedly, project leader commitment and impact when the project administrator's quality is dispersed. The information delivered by this exploration was enlightening however just partly on the grounds that outcomes were not adequately broad. In any case, the use of this investigation applies to the down to earth world as dispersed groups is by all accounts a more perpetual piece of the business scene than impermanent, and figuring out how to more readily fill in as a project supervisor with disseminated presence is fundamental for the two associations and project chiefs.

Information fusion for edge intelligence: A survey

Edge intelligence capability is expected to enable the development of a new paradigm integrated with edge computing and artificial intelligence. However, due to the multisource nature, heterogeneity, and a large scale of the sensory data, it is necessary to improve the data processing and decision-making capacity for the edges. Hence, this paper asserts that information fusion is an important technique to power the capacity of edge intelligence in terms of collection, communication, computing, caching, control and collaboration. Specifically, it provides a comprehensive investigation of four representative scenarios assisted by information fusion at the edge, i.e., multisource information fusion, real-time information fusion, event-driven information fusion, and context-aware information fusion. Moreover, it discusses the future directions and open issues in this field.

Comparison of the Secret Service of al-Shabaab, the Amniyat, and the National Intelligence and Security Agency (Somalia)

This is a comparative analysis of the secret service of al-Shabaab, the Amniyat, and the Somali government’s security unit, the National Intelligence and Security Agency (NISA). It begins with the short history of al-Shabaab as an introduction to understand the roots of the movement and how the resurgence of the group is tied to the effective management of the Amniyat. Then it analyzes the capabilities and capacities of the intelligence apparatuses of al-Shabaab and the Federal Government of Somalia (FGS), highlighting the similarities and differences between the organizations. Viewed from the perspective of the local population, the terrorist group provides security and predictability for individuals living under its control, gained local respect for managing its forces, and uses its secret service, the Amniyat, effectively. NISA suffers from the same problems as its predecessor in the Siad Barre regime, such as lack of transparency, overpoliticization, and involvement in illegal activities. Furthermore, there is lack of cooperation and transparency between local intelligence services and police forces. While the FGS realized the importance of doing away with clan-based security systems, the dominance of clans is still constantly increasing at the expense of the government. Today, al-Shabaab’s intelligence capacities and capabilities are more advanced than those of the NISA, hence the centralization and unification of the security architecture is of chief importance.

Edible Mushroom Potency to Alleviate Stunting Through Gut Microbiota Modulation: A Review

Stunting has become a major concern in Indonesia since 30.8% of under-5-years old Indonesian children in 2018 suffer for it. Children who suffer from stunting have growth faltering and less intelligence capacity. In the long term, it will affect their adult life productivity and national human resources quality. Stunting represents a nutrient requirement for children are not achieved especially in 1000 first day of life. Despite a lot of various reason, lack nutrient intake and infectious disease are considered as the direct cause of stunting. Recent research has explored that microbe who cohabitate human intestinal can affect their host’s health and nutritive status. The composition of gut microbiota is shown different between a healthy individual and stunted individual. Hence, there is a hypothesis that stunting can be alleviated by modulating the composition of gut microbiota. Various kind of edible mushroom can be found and has been part of the diet for several Indonesian. Not only mushroom contains high dietary fiber, vitamin, and mineral, but also several mushrooms are known for its immunomodulating effect. With plentiful prebiotic potential carbohydrates, like chitin, hemicellulose, β and α-glucans, mannans, xylans, and, galactans in mushroom, Mushrooms can act as prebiotics to modulate gut microbiota, and give health benefits to the host. This paper will present several shreds of evidence that edible mushroom has potency to become a source of prebiotic, affect gut microbiota composition, and prevent stunting. It also will show any obstacles in applying edible mushroom in an attempt of combating stunting, to give future research prospect in related studies.

Democratizing business intelligence and machine learning for air traffic management safety

The ways in which Air Navigation Service Providers (ANSPs) monitor safety performance is strongly influenced by international regulations, standards, and agreements, although each State may also add its own local requirements. Particularly in the case of more mature ANSPs, the regulatory safety performance obligations are merely the tip of the iceberg in the undertaken safety performance activities. Much of the indicators, methods and tools are over and above what is required by regulations, either national or international. In modern settings, the usage of Business Intelligence and Machine Learning solutions can be enumerated under the continuous chasing of strategies to foster ANSPs’ safety intelligence capacities towards higher standards.This manuscript shows the development process of an integrated data-driven framework for self-service BI and ML on safety reporting data for the air traffic management system. The proposed framework firstly focuses on the development process of a BI architecture to extract meaningful knowledge from multiple data sources. Then, it progresses discussing how ML solutions may support gaining a deeper understanding of system’s performance and delineating specific safety recommendations. The explorative application of the proposed framework in multiple European ANSPs provides the basis for sharing lessons learned and outlining a possible path to start democratizing safety intelligence in aviation.

Forms of explanation and understanding for neuroscience and artificial intelligence.

Much of the controversy evoked by the use of deep neural networks as models of biological neural systems amount to debates over what constitutes scientific progress in neuroscience. To discuss what constitutes scientific progress, one must have a goal in mind (progress toward what?). One such long-term goal is to produce scientific explanations of intelligent capacities (e.g., object recognition, relational reasoning). I argue that the most pressing philosophical questions at the intersection of neuroscience and artificial intelligence are ultimately concerned with defining the phenomena to be explained and with what constitute valid explanations of such phenomena. I propose that a foundation in the philosophy of scientific explanation and understanding can scaffold future discussions about how an integrated science of intelligence might progress. Toward this vision, I review relevant theories of scientific explanation and discuss strategies for unifying the scientific goals of neuroscience and AI.

CAMNet: A controllable acoustic model for efficient, expressive, high-quality text-to-speech

Spoken language is becoming one of the key components of human–machine interaction, both to send information to the machine – e.g. voice control – and to receive from it – e.g. virtual assistants. In this scenario, text-to-speech (TTS) models have become an essential artificial intelligence capacity. Even though this interaction can be based on neutral style speech, generating speech with different styles, pitches and speaking rates may improve user experience. With this in view, this paper presents CAMNet, a controllable acoustic model for efficient, expressive, high-quality TTS. CAMNet is based on deep convolutional TTS (DCTTS), a state-of-art acoustic model which is efficient and produces neutral speech. DCTTS was first adapted to generate Bark cepstrum acoustic features in order to integrate well with the LPCNet (linear prediction coefficient) neural vocoder and to remove the reduction factor which demanded the presence of an upsampling network before the vocoder – i.e. the CAMNet output can be directly fed into LPCNet. Next, style transfer functionality was added by means of a novel characterisation of the prosodic information from the Bark cepstrum acoustic features and a new approach to inject this information into the convolutional layers. Finally, controllability is provided via a variational auto-encoder module which creates a smoothed disentangled latent space which allows interpolation and extrapolation of reference styles as well as independent and simultaneous control of two generative factors: pitch and speaking rate. Moreover, this controllability is implemented using a simple offset-based approach. To sum up, CAMNet is an efficient acoustic model which provides a simple but consistent controllability on coarse-grained expression, pitch and speaking rate while still providing high-quality synthesised speech.

Design and Development of Heuristic Utility Management Algorithm for Chinese Library Management System

Utility Management in a library is the programmatic tool with the synthetic mental program ability, along with Artificial Intelligence capacities, headed to manage a high volume of books, articles, and assignments, which help to ease the manual significance of librarians. This computerized machine code helps librarians to deal with various databases of the library management system. This framework keeps the records of all the resource details in an optimized manner. It uses a utility management software code with an optimized search classifier that helps to deal with the resource of the library. In this work, the Heuristic Utility Management Algorithm (HUMA) has been used to keep track of resources in the library using mathematical modeling and standardized programmatic computation on tags, which relates the decode scanner to parse the input information. HUMA helps to reduce the manual routine work done by the librarians, and it has been analyzed in this research with prominent survey outcomes based on experimental validation.

Artificial Intelligence in hair research: A proof-of-concept study on evaluating hair assembly features.

The first objective of this study was to apply computer vision and machine learning techniques to quantify the effects of haircare treatments on hair assembly and to identify correctly whether unknown tresses were treated or not. The second objective was to explore and compare the performance of human assessment with that obtained from artificial intelligence (AI) algorithms. Machine learning was applied to a data set of hair tress images (virgin and bleached), both untreated and treated with a shampoo and conditioner set, aimed at increasing hair volume whilst improving alignment and reducing the flyway of the hair. The automatic quantification of the following hair image features was conducted: local and global hair volumes and hair alignment. These features were assessed at three time points: t0 (no treatment), t1 (two treatments) and t2 (three treatments). Classifier tests were applied to test the accuracy of the machine learning. A sensory test (paired comparison of t0 vs t2 ) and an online front image-based survey (paired comparison of t0 vs t1 , t1 vs t2 , t0 vs t2 ) were conducted to compare human assessment with that of the algorithms. The automatic image analysis identified changes to hair volume and alignment which enabled the successful application of the classification tests, especially when the hair images were grouped into untreated and treated groups. The human assessment of hair presented in pairs confirmed the automatic image analysis. The image assessment for both virgin hair and bleached only partially agreed with the analysis of the subset of images used in the online survey. One hypothesis is that treatments changed somewhat the shape of the hair tress, with the effect being more pronounced in bleached hair. This made human assessment of flat images more challenging than when viewed directly in 3D. Overall, the bleached hair exhibited effects of higher magnitude than the virgin hair. This study illustrated the capacity of artificial intelligence for hair image detection and classification, and for image analysis of hair assembly features following treatments. The human assessment partially confirmed the image analysis and highlighted the challenges imposed by the presentation mode.

Improving Health Security and Intelligence Capabilities to Mitigate Biological Threats

ABSTRACT The COVID-19 pandemic has fostered a greater appreciation of the significance of health security and intelligence capabilities. This article examines health security and intelligence capabilities in the United States and other countries, with a view to raising awareness of biological threats across the Five Eyes community. New biotechnologies offer vaccines, therapies, and healthcare solutions, but also present biological threats that must be managed. This article begins by discussing the rise of biological threats and the relative lax regulatory environment in the United States and overseas. The second part then examines the growing importance of health security intelligence and the role it can play in response to this potential threat. The third part focuses on intelligence collection, capability, and threat mitigation, and how this could be mobilized to address harmful impacts of biological threats. The final part of the article provides recommendations for building an intelligence capacity within ICs as well as associated regulatory reforms.

Intelligence test items varying in capacity demands cannot be used to test the causality of working memory capacity for fluid intelligence

There is a strong relationship between fluid intelligence and working memory capacity (WMC). Yet, the cognitive mechanisms underlying this relationship remain elusive. The capacity hypothesis states that this relationship is due to limitations in the amount of information that can be stored and held active in working memory. Previous research aimed at testing the capacity hypothesis assumed that it implies stronger relationships of intelligence test performance with WMC for test items with higher capacity demands. The present article addresses this assumption through simulations of three theoretical models implementing the capacity hypothesis while systematically varying different psychometric variables. The results show that almost any relation between the capacity demands of items and their correlation with WMC can be obtained. Therefore, the assumption made by previous studies does not hold: The capacity hypothesis does not imply stronger correlations of WMC and intelligence test items with higher capacity demands. Items varying in capacity demands cannot be used to test the causality of WMC (or any other latent variable) for fluid intelligence.

WITHDRAWN: Impact of stress on software developers by moderating the relationship through emotional intelligence in a work environment

Information systems development activities' progress depends mostly on the development team's events, which are essential for the formulation of system artefacts mandated. Developers have been facing many challenges in their purpose of completing tasks efficiently, as well as the need to build software for large-scale projects. In two samples, this study impacts the internal values, task properties and population in the burnout between software developers, focusing on Emotional Intelligence (EI) capacity. Initially, it was believed to display trends of interaction with the stress in terms of role, education, personality, EI, and burnout experience. Secondly, it has been proposed and tested a slightly more precise mediation analysis wherein the stress level mediated the combination of EI and burnout. This research aims to analyze the influence of work stress on the happiness of workers through emotional intelligence moderating the relationship. The proposed method is called the Regulating Relationship through Emotional Intelligence (RREI). The quantitative experiments demonstrated that the proposed RREI reduces burnout and promotes confidence in software developers' projects' management. Besides, the effect of burnout on performance increases with emotional exhaustion. • RREI reduces burnout and promotes confidence. • Aims to analyze the influence of work stress on happiness through EI • Effect of burnout on performance increases with emotional exhaustion

Abstract PR-10: Identifying new risk factors for early-onset CRC in population under 50 years old using EHR-based machine learning

Abstract Objective: Colorectal cancer (CRC) is the 3rd most common cancer in the United States. Despite declining incidence in patients older than 50, there has been an increasing trend of CRC in patients under the age of 50. Some risk factors such as inflammatory bowel disease and inherited genetic syndromes can lead to early onset of CRC; yet incidence of these disorders is not concomitant with the increase of CRC under 50 years old. This study utilizes machine learning models with electronic health records (EHR) data from the OneFlorida network—a clinical research network contributing to the national PCORnet to demonstrate a potential avenue for investigation of the rise in incidence. Methods: This study applied four machine learning algorithms on a cohort of 1,227 CRC cases and matched 34,157 controls, all under the age of 50 extracted from the OneFlorida network. Colon cancer (CC) and rectal cancer (RC) were modeled separately. For each case patient, we created a prediction window starting from the first recorded encounter in their EHR to end dates of 0, 1, 3, and 5 years prior to the case CC/RC index date. For each control patient we matched them to cases based on age at an encounter date to close to the case index date. The data was split into a training set (80%) for training the models, and a testing set (20%) used to measure model performance. SHapley Additive exPlanations (SHAP) was used to analyze significant features. Results: Notable trends in model prediction results were decreased sensitivity across prediction windows as data per patient decreased, in both RC and CC cohorts. Zero-year and 1-year prediction area under the curve (AUC) was significant at 0.64 to 0.75 for all algorithms across RC and CC. As the prediction window widened, the prediction performance dropped to as low as 0.35 (i.e., 5-year prediction). The best performing algorithm across all experiments was the support vector machine (SVM). Top predictors identified in the CC cohort include hypertension, cough/asthma, chronic sinusitis, anxiety d/o, and atopic dermatitis, while top predictors in the RC cohort include obesity, female gender, HIV, anxiety d/o and asthma. Conclusions: Disorders with chronic immunosuppression (e.g., HIV) or inflammation (e.g., obesity, asthma, sinusitis, dermatitis) may represent immune-axis derangements contributing to a favorable state for CRC. This preliminary study provides early insight into the capacity of artificial intelligence to uncover new risk factors in the population of patients with onset of young-onset CRC with more algorithm refinement and risk factor exploration underway. Citation Format: Michael B. Quillen, Taylor M. Parker, Jiang Bian, Thomas George. Identifying new risk factors for early-onset CRC in population under 50 years old using EHR-based machine learning [abstract]. In: Proceedings of the AACR Virtual Special Conference on Artificial Intelligence, Diagnosis, and Imaging; 2021 Jan 13-14. Philadelphia (PA): AACR; Clin Cancer Res 2021;27(5_Suppl):Abstract nr PR-10.