ὅδε οἶκος, ὦ ἑταῖρε, μνημεῖον ἐστιν ζῴων τῶν σοφῶν ἀνδρῶν, καὶ τῶν ἔργων αὐτῶν

Seminar on Computer Science and Applied Mathematics

 

PROGRAM


Matematički Institut SANU, Beograd
Knez Mihajlova 36
Fakultet organizacionih nauka, Univerzitet u Beogradu,
Jove Ilica 154
IEEE Chapter Computer Science (CO-16) Belgrade, Republic of Serbia

SEMINAR ZA RAČUNARSTVO I PRIMENJENU MATEMATIKU

MI SANU, Knez Mihailova 36, sala 301f

PLAN RADA SEMINARA ZA JUN 2022. GODINE

Predavanja na seminaru mogu se pratiti na daljinu preko linka
https://miteam.mi.sanu.ac.rs/asset/YoqHWKALRkRTbK9So.
Registracija za on-line praćenje predavanja na Seminaru je na linku
https://miteam.mi.sanu.ac.rs/asset/xzGqvSp7aWbg8WpYX.



Utorak, 07.06.2022. u 14:15, Knez Mihailova 36, sala 301f i Online
Emil Jovanov, Electrical and Computer Engineering Department at the University of Alabama in Huntsville
SEAMLESS HEALTH MONITORING: OPPORTUNITIES AND CHALLENGES
Ubiquitous smart sensors and devices integrated into the Internet of things (IoT) environment transformed our homes, offices, and industries, while wearable monitoring and mobile health (mHealth) revolutionized healthcare diagnostics and delivery. Smart environments create unprecedented opportunities for seamless health monitoring and preventive diagnostics. “Things” with embedded activity and vital sign sensors that we refer to as “smart stuff” can interact with wearable and ambient sensors in Synergistic Personal Area Networks—SPANs. SPANs creates a dynamic “opportunistic bubble” for ad-hoc integration with other sensors of interest around the user, wherever they go. The synergy of information from multiple sensors can provide: (a) New information that cannot be generated from existing data alone, (b) user identification, (c) more robust assessment of physiological signals, and (d) automatic annotation of events/records. This lecture presents possible new applications, opportunities, and challenges of new environments.

Utorak, 14.06.2022. u 14:15 Online
Ilir Čapuni, Prirodno-matematički fakultet Univerziteta Crne Gore
KONSTRUKCIJA TURINGOVE MAŠINE OTPORNE NA VJEROVATNOSNI ŠUM
Pouzdanost izračunavanja odnosi se na računanje pomoću mašine koja je podvrgnuta određenom šumu. Nas prije svega interesuju prolazne greške, tj. greške koje nijesu posljedica nekog kvara ili trajnog oštećenja neke komponente, a javljaju se nezavisno jedan od drugog sa nekom malom vjerovatnoćom.
Istorijski prvi rezultat ove vrste je rad von Neumanna koji za svaki Booleovo kolo $C$ veličine $n$ konstruiše novo kolo $C'$ veličine $O(n\log{n})$ koji sa velikom vjerovatnoćom rješava isti zadatak kao i $C$, iako svaki gejt kola $C'$ može da pogreši sa nekom malom vjerovatnoćom.
U ovom predavanju, daćemo konstrukciju univerzalne Turingovog mašine sa jednom trakom koja može sprovesti proizvoljno dugačka izračunavanja čak i uz prisustvo vjerovatnosnog šuma koji se definiše kako slijedi: u svakom koraku, nezavisno od prethodnog koraka, promjena stanja glave, aktivne ćelije i kretanje glave mogu, sa malom vjerovatnoćom, biti različite od one koju diktira program mašine. Konstrukcija je iznenađujuće složena i koristi hijerarhiju simulacija: mašina $M_1$ simulira mašinu $M_2$ koji simulira mašinu $M_3$, i tako dalje.
Mašina $M_1$ može “izdržati” šuma nivoa 1, $M_2$ može podnijeti šum 2.-og nivoa, i tako dalje. Svaka od ovih mašina može se implementirati na univerzalnoj mašini koristeći program $p$ i nivo $k$ kao ulazne podatke. Program $p$ je zajednički program za sve ove mašine i on je ugrađen na mašini $M_1$, pa se isti ne može oštetiti od grešaka. Forsiraćemo sve nivoe da koriste isti program koristeći Kleen-ovu teoremu o fiksnoj tački. Ovom konstrukcijom, izračunavanje koje traje $t$ koraka može se simulirati u $t(\log{t})^{\alpha \log{\log{\log{t}}}}$ koraka uz prisustvo vjerovatnosnog šuma, za neku konstantu $\alpha$.
Koautor ovog rada je Peter Gacs.

Utorak, 21.06.2022. u 14:15, Knez Mihailova 36, sala 301f i Online
Luka Matijević, Matematički institut SANU
VARIABLE NEIGHBORHOOD SEARCH FOR MULTI-LABEL FEATURE SELECTION
With the growing dimensionality of the data in many real-world applications, feature selection is becoming an increasingly important preprocessing step in multi-label classification. Finding a smaller subset of the most relevant features can significantly reduce resource consumption of model training, and in some cases, it can even result in a model with higher accuracy. Traditionally, feature selection has been done by employing some statistical measure to determine the most influential features, but in recent years, more and more metaheuristics have been proposed to tackle this problem more effectively. In this lecture, we will present a brief introduction to machine learning and data mining algorithms, with a focus on feature selection. We will present different approaches to feature selection, concentrating on metaheuristic wrapper methods. We will also present the results of our recent work on this topic, where we proposed the Basic Variable Neighborhood Search (BVNS) algorithm to search for the optimal subset of features, combined with a local search method based on mutual information. The algorithm can be considered a hybrid between the wrapper and filter methods, as it uses statistical knowledge about features to reduce the number of examined solutions during the local search. We compared our approach against Ant Colony Optimization (ACO) and Memetic Algorithm (MA), using the K-nearest neighbors classifier to evaluate solutions. The experiments conducted using three different metrics on a total of four benchmark datasets suggest that our approach outperforms ACO and MA.

Utorak, 28.06.2022. u 14:15, Knez Mihailova 36, sala 301f i Online
Jozo Dujmović, Department of Computer Science, San Francisco State University
STEPENOVANA LOGIKA (GRADED LOGIC)
Ljudske percepcije istinitosti, značaja, pogodnosti, preference, i mnoge druge, imaju intenzitet u intervalu od najmanjeg (0) do najvećeg (1). Te percepcije su stvar stepena, pa ih nazivamo stepenovane percepcije. Stepenovana logika je kontinualna logika ljudskog rezonovanja sa stepenovanim percepcijama. Cilj stepenovane logike je da ponudi matematičke modele koji opisuju ljudsko rezonovanje u oblasti vrednovanja i odlučivanja na način koji se može eksperimentalno verifikovati. Stepenovana logika je konsistentna generalizacija klasične Bulove (G. Boole) logike. Bulova logika funkcioniše u rogljevima jediničnog hiperkuba, a stepenovana logika funkcioniše u celom jediničnom hiperkubu. Klasične logike obično potpuno zanemaruju stepen značaja istinitosti; stepenovana logika ga eksplicitno uzima u obzir. Prvi radovi iz stepenovane logike publikovani su u Beogradu 1973. godine. Šta se dešavalo sa ovom oblasti u narednih pola veka vidi se u knjizi Jozo Dujmović, Soft Computing Evaluation Logic, Wiley, 2018. Naš cilj je da ponudimo sažetu prezentacju ove oblasti.



RUKOVODIOCI SEMINARA

MI SANU
Vera Kovačević-Vujčić
Milan Dražić

FON
Zorica Bogdanovic
Marijana Despotovic-Zrakic

IEEE
Bozidar Radenkovic