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Accepted Papers
A Memory Based Approach for Digital Implementation of Tanh using LUT and RALUT

Samira Sorayassa and Majid Ahmadi, Department of Electrical and Computer Engineering,University of Windsor, Windsor, Ontario, N9B3P4, Canada

ABSTRACT

Tangent Hyperbolic (Tanh) has been used as a preferred activation function in implementing a multi-layer neural network. The differentiability of this function makes it suitable for derivative-based learning algorithm such as error back propagation technique. In this paper two different memory-based techniques for accurate approximation and digital implementation of the Tanh function using Look Up Table (LUT) and Range Addressable Look Up Table (RALUT) are given. A thorough comparative study of the two techniques in terms of their hardware resource usage on FPGA and their accuracies are explained. The schematic of the synthesized design for special cased are given as an example.

KEYWORDS

Tanh Activation function, Tanh Implementation on FPGA, Approximation methods, Lookup Tables (LUT) Range Addressable Lookup Tables (RALUT).


Predictions in Pre-Hospital Emergency Transport in France: A State of the Art

Christophe Guyeux, University of Burgundy, France

ABSTRACT

For a number of years now, the regional fire department centers have been recording their interventions numerically. Such databases are under-utilized and are mainly used for statistical and management purposes. However, such a history of interventions can be very useful, if used in conjunction with artificial intelligence algorithms, for predictive purposes. Such work has recently been done in France through a series of articles investigating the various aspects of the problem, and has been put into production at the Doubs center. The objective of this review is to take stock of all the work that has been done so far, to list the successes and the stumbling blocks, and to draw up a roadmap on this theme for the years to come.


Problems with Regression-line in Data-mining Applications and A Better Alternate Linear-Model

Sukhamay Kundu, Dept of Computer Science and Engineering Louisiana State University, Baton Rouge, LA 70803, USA

ABSTRACT

The regression-line for a set of data-points pi = (xi,yi),1 = i = N, lacks the rotation-property in the sense that if each pi is rotated by an angle ? around the origin then the regression-line does not rotate by the same angle ? except for the special case when all pi’s are collinear. This makes the regression-line unsuitable as a linear model of a set of data points for applications in data mining and machine learning. We present an alternative linear model that has the rotation property. In many ways, the new model is also more appealing intuitively as we show with examples. The computation of the new linear model takes the same O(N) time as that for the regression-line.

KEYWORDS

perpendicular distance, regression-line, rotation property, application to data mining.


Streamline Border Control with Blockchain Towards Self-Sovereign Identity

Pekka Koskela, Anni Karinsalo, Jori Paananen and Laura Salmela, 1-4VTT Technical Research Centre of Finland Ltd, P.O. Box 1000, FI-02044 VTT, Finland

ABSTRACT

Since the mid-2000s, the digitalisation of border checks has often referred to the increased adoption of automated border control (ABC) solutions at border crossing points in all border environments from airports and seaports to land border crossings. Key prerequisites for the operational implementations of the so-called eGates have been the electronic machine-readable travel document together with biometric technologies that have facilitated the automation of much of the tasks performed by border guards at manual control booths for selected groups of nationalities. Now, the next wave of major changes is emerging with the development of electronic identification (eID), with certain implementations particularly designed for cross-border use cases supplementing and possibly replacing the traditional physical identity document in a long-term future. The evolution of eID strongly aligns with the increased demands for data privacy to ensure that individuals can better control how much information is shared about themselves, with whom and for what purpose. One possible technology to provide the so-called data self-sovereignty is distributed ledger technology(DLT), including blockchains. DLT is being developed for instance by the Linux foundation, dispensing several distributed ledger projects and associated solutions for digital and self-sovereign identity. One of these projects is Hyperledger Indy. In this study, we present a distributed ledger implementation based on Hyperledger Indy applied as a border check use case. Our aim is to investigate the suitability of DLT in providing data self-sovereign facility in border checks, and to discuss the benefits and disadvantages the technology might entail for this security domain.

KEYWORDS

Blockchain, Self-sovereign Identity, Border Control.


Dynamic Protocol Blockchain for Practical Byzantine Fault Tolerance Consensus

Ali Asad Sarfraz and Shiren Ye, Department of Computer sciences, Aliyun school of big data, changzhou University, china

ABSTRACT

This work details the byzantine fault-tolerant protocols that dynamically allow replicas to join and exit. Byzantine fault-tolerant (BFT) protocols and the blockchain now play an essential role in achieving consensus. There are numerous drawbacks to PBFT, despite its multiple positives. The first thing to note is that it runs in an environment completely isolated from the rest of the world. The entire system must be shut down before any nodes can be added or removed. Second, it ensures liveness and safety if no more than (n-1)/3 out of total n replicas, PBFT takes no action to cope with ineffective or malicious counterparts. This is bad for the system and will lead to its eventual failure. These flaws have far-reaching consequences in real life. The Randomization PBFT is an alternative way of dealing with these issues. In recent decades, as computer technology has advanced, so has our reliance on the products, services, and capabilities that computers provide.

KEYWORDS

DRBFT, PBFT, protocol, Blockchain, Byzantine fault tolerance, and Dynamic PBTF.


Reduce++: Unsupervised Content-based Approach for Duplicate Result Detection in Search Engines

Zahraa Chreim1, Hussein Hazimeh1, Hassan Harb1, Fouad Hannoun2, Karl Daher2, Elena Mugellini2 and Omar Abou Khaled2, 1Lebanese University, Faculty of Science, Beirut, Lebanon, 2University of Applied Sciences of Western Switzerland, Fribourg, Switzerland

ABSTRACT

Search engines are among the most popular web services on the World Wide Web. They facilitate the process of finding information using a query-result mechanism. However, results returned by search engines contain many duplications. In this paper, we introduce a new content-type-based similarity computation method to address this problem. Our approach divides the webpage into different types of content, such as title, subtitles, body, etc. Then, we find for each type a suitable similarity measure. Next, we add the different calculated similarity scores to get the final similarity score between the two documents, using a weighted formula. Finally, we suggest a new graph-based algorithm to cluster search results according to their similarity. We empirically evaluated our results with the Agglomerative Clustering, and we achieved about 61% reduction in web pages, 0.2757 Silhouette coefficient, 0.1269 Davies Bouldin Score, and 85 Calinski Harabasz Score.

KEYWORDS

Information Retrieval, Websites Similarity, Graph Representation, Similarity Measures, Graph Kernel, Deduplication, Search Engines.


Meditation’s Physiological Effects on the Nervous Systems of Teachers Undergoing Stress

Hannah Cho, North Hollywood High School, 5231 Colfax Ave, North Hollywood, CA 91601

ABSTRACT

It is well known that meditation is an ef ective stress-relief technique. This can be attributed tomeditation’s ef ects on the autonomic nervous system; it activates the parasympathetic nervous system (PNS), and inhibits the sympathetic nervous system(SNS). Stress has been unprecedentedly exacerbated with the onset of the COVID-19 pandemic. This study investigates how meditation can be further personalized and adapted to enhance its ef ects on the autonomic nervous system, with a focus on teachers at a local progressive high school. Teachers wore physiological data-recording wristbands formonthly meditation sessions. Wristbands recorded electrodermal activity, blood volume pressure, heart rate, peripheral skin temperature, and motion-based activity. Collected wristband data was then exported into Kubios, a HRV analysis software, to calculate PNS and SNS indexes during the meditation portion and the interactive engagement that followed. Heart rate variability time-domain indicators that were used to calculate the indexes- Mean HR (bpm), Baevsky Stress Index, Mean RR (ms), RMSSD (ms)- and nonlinear Poincaré plot values were also compared. Focus meditations exerted higher PNS indexes and lower SNS indexes than during the engagement periods. Visualization meditations that called for active thinking exerted mixed and opposing results across participants, alluding that the type of vision participants are instructed to imagine may dif erently manipulate the ANS branches. This concept that meditation types can uniquely af ect nervous system control can be used to personalize meditation as clinical treatment. Although the ecological validity of this out-of-lab study is indeterminate, the findings of this study can lead to further research in laboratory settings.

KEYWORDS

Heart Rate Variability, Autonomic Nervous System, Stress, Meditation, Kubios.


A Computational Phonetic Comparison For Low -Resource Languages

Ali Rahama, Information Technology Specialist,6701 Seybold Rd STE 212, Madison, WI 53719, USA

ABSTRACT

The objective of this paper is to investigate the possible correlation between extinct Meroitic and local languages in todays Sudan. The researchers goal is implementing Natural Language Processing and use of phonetic comparison algorithms. Implementing phonetics to Natural language Processing is requiring great effort, our model produces both alphabetic mappings and translations of words into corresponding cognates implementing Soundex algorithm to capture character mappings and high-level morphemic correspondences. Researching this matter using Natural Language processing tools could lead to enhancement for Phonetic Comparison Algorithms in Low-Resources Languages area which currently lack researchers’ attentions.

KEYWORDS

Natural Language Processing (NLP), Phonetics, Meroitic, Phonemes, Phonetic, Soundex, Meroitic.


Object-Oriented Design of Learning Apps

Shahajd and K. Mustafa, Department of Computer Science, Jamia Millia Islamia University, New Delhi, India

ABSTRACT

In the age of Apps, there has been a widespread proliferation of Learning Apps (LA). Almost every educational institution has been affected since the pandemic. The research indicates that such apps are highly effective for the so-called touch-screen generation in a variety of contexts. Data on LAs performance show that they are associated with compelling increases in student achievement. Recognizing the significance, it is suggested that teachers and other caretakers become involved in this new trend of mobile learning. Despite this, experts generally highlight issues concerning their effectiveness (see later). As a result, we observe the emergence of several design paradigms, having no or little theoretical bases. Even though businesses grow, and new tools and technology are developed, there isnt a good app design strategy based on accepted didactics. Realizing this, we suggest a Pedagogic-Object-Oriented-Based Approach to the design and development of LA, building on the idea of IEEE learning objects and the success of the object-oriented paradigm.

KEYWORDS

Learning App, App Ingredient, Object-based Approach, Design.


Fetal Health Risk Prediction using Incremental Machine Learning

Vidyalekshmi Chandrikal and Simi Surendran, School of Engineering, Amritapuri, Kollam, Kerala, India

ABSTRACT

This paper gives complete guidelines for authors submitting papers for the AIRCC Journals. One of the significant health problems around the globe is associated with neonatal mortality or morbidity and disability in later life. The primary cause of neonatal death is preterm labor, which accounts for less than a half percent of all deaths among children under five years. Continuous monitoring and risk prediction could help provide medical assistance to the pregnant woman at the right time, substantially reducing neonatal mortality. We conducted a detailed data analysis and comparative study on various machine learning models on the cardiotocography dataset to conclude on a better accuracy of preterm birth prediction. This paper proposes an incremental learning approach to predict preterm labor risk. The response time for the medical aid can be significantly reduced using this incremental edge learning.

KEYWORDS

Preterm Birth, Machine Learning, Fetal Health Monitoring, Incremental Learning.


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