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NEWS

Our Graduate Nadin Kökciyan will be Featured as New Faculty

Our Ph.D. graduate Nadin Kökciyan will be featured as one of the 18 new AI faculty at the New Faculty Highlight of the AAAI’2021. Dr. Kökciyan is the only faculty selected from Read more...
Lale Akarun Elected Vice President of International Association of Pattern Recognition

International Conference of Pattern Recognition (ICPR), organized by the Association of Pattern Recognition (IAPR) was held virtually this year.  The 2020-2022 Executive Committee Read more...
Success at Online Programming Contest

All three CMPE student teams managed to get into the first four at the Online Programming Contest, a national competitive programming contest organized by inzva. Kod Yazmalım— Read more...
First Place at IEEE TR Student Branches Algorithm Competition

In the IEEE TR Student Branches Algorithm competition Mesut Melih Akpınar, Halil Utku Çelik, İsmail Tarık Erkan, Atakan Yaşar from the Computer Engineering Deparment and Tarık Can Read more...
Cognitive Robots that Imagine Other’s Dreams and Make Them Come True

In the Boğaziçi University Department of Computer Engineering, under the assistance of Asst. Prof. Dr. Emre Uğur, there has been ongoing projects regarding cognitive robots and Read more...

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CmpE Events

Today

  1. MSc. Thesis Defense: Bidirectional multi-step prediction with affordances by of Utku Bozdoğan
    • Start time: 01:00pm, Monday, August 8th
    • End time: 02:30pm, Monday, August 8th
    • Where: AVS Conference Room, BM
      • MSc. Thesis Defense
      • Title: Bidirectional multi-step prediction with affordances
      • Speaker: Utku Bozdoğan
      • Advisor: Emre Uğur

      Abstract: Affordances are action possibilities of an object, directlyperceived by an actor based on their capabilities. Affordances arelearned from goal-free exploration of the actor's capabilities throughobserving the effects of their actions on objects in an environment. Theactor can then use the learned affordances to make plans to reach a goalsince they now know which actions on a certain object are possible andwhich one results in the desired effect. The affordance principle isalso followed in robotics to learn to distinguish which actions in therepertoire of a robot are applicable to an object in its environment andto what effect. This information can then be utilized in goal-directedplanning, either directly or with the aim of reducing the search spacefor possible solutions. In this work, the problem of making multi-steppredictions for object manipulation is investigated in the continuousdomain. Several types of actions are defined in a robot's repertoire,and the interactions of the robot with a number of objects possessingdiffering qualities in a tabletop setting are recorded. Relativedistance quantities are used for representing actions and effects whichallow generalizability, alongside a top-down centered depth image of theobject. This data is used to train a model which can be conditioned onactions to predict the effects, conditioned on effects to predict theapplied actions, or conditioned on both to predict the actions andeffects. By using a planner on top of this model, the capacity to chaintogether a correct sequence of actions for an object to reach thedesired goal position is achieved. The model is verified in experiments,generating and executing reasonable plans efficiently. Setting it apartfrom previous work, using continuous effect and actions enables theplanner to also find solutions to configurations not seen in trainingwhere actions are not applied to their full extent, but only partially.

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Wednesday, August 10th

  1. PhD Thesis Defense, Ahmet Alp Kındıroğlu Transfer Learning for Sign Language Recognition
    • Start time: 10:00am, Wednesday, August 10th
    • End time: 12:00pm, Wednesday, August 10th
    • Where: Zoom Link: https://boun-edu-tr.zoom.us/j/99284579504
    • All interested are cordially invited. Abstract:Sign language is a visual language that conveys meaningthrough the arrangement and movement of hands, arms, and facial expressions.Computer vision-based sign language recognition (SLR) can assist in bridgingthe communication gap between hearing and deaf people. SLR is an umbrella termfor a variety of tasks, including isolated sign recognition, sign spotting,continuous sign language recognition, and sign language translation. Researchon sign language recognition (SLR) has made significant progress but relies onvast amounts of data to model and recognize signs. It has not yet generatedviable applications that can do translations for everyday users despite thefact that a substantial amount of effort is being devoted to generating bigannotated sign language datasets for sign languages. In addition, most SLRresearch is focused on a few popular sign languages, leaving the majority ofsign languages, including Turkish Sign Language (TID), as under-resourcedlanguages for developing sign language technologies. In this dissertation, we have highlighted a number of openresearch questions about the development of sign language recognitiontechnologies for TID and have approached the topic from a number of differentangles. We generated BosphorusSign22k, an isolated SLR dataset for TIDcontaining 22k videos from 744 different classes and provided benchmark resultsusing state-of-the-art approaches on this dataset. In order to efficientlymodel signs, we proposed aligned temporal accumulative features (ATAF) as afeature capable of representing isolated sign language gestures as dynamic andstatic subunits. Combined with methods using other modalities, the methodachieves state-of-the-art performance on the BosphorusSign22k dataset. Next, weutilized regularized regression-based multi-task learning methods and proposeda sign language alignment method called task-aware canonical time warping forisolated sign language recognition. The method aimed to align and group signsso as to minimize discrepancies between signs from different sources whileemphasizing differences in signs from different classes. Finally, we utilizedseveral data sources for training isolated sign language recognition models inorder to improve recognition performance for under-resourced languages. Weestablished a benchmark for cross-dataset transfer learning using two existingpublic Turkish SLR datasets and evaluated five supervised transfer learningalgorithms using a temporal graph convolution-based sign language recognitionmethod. Experiments with closed-set and partial-set cross-dataset transferlearning reveal a substantial improvement over combined training andfine-tuning-based baseline techniques.

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  2. PhD Thesis Defense - Serdar Metin - Design, Implementation, and Analysis of Fair Faucets for Blockchain Ecosystems
    • Start time: 01:00pm, Wednesday, August 10th
    • End time: 03:00pm, Wednesday, August 10th
    • Where: AVS Conference Room@BM & Zoom
    • The present dissertation addresses the problem of fairly distributing shared resources in non-commercial blockchain networks. Blockchains are distributed systems that order and timestamp records of a given network of users, in a public, cryptographically secure, and consensual way. The records, which may in kind be events, transaction orders, sets of rules for structured transactions etc. are placed within well-defined data structures called blocks, and they are linked to each other by the virtue of cryptographic pointers, in a total ordering which represents their temporal relations of succession. The ability to operate on the blockchain, and/or to contribute a record to the content of a block are shared resources of the blockchain systems. In commercial networks, these resources are exchanged in return for fiat money, and consequently, fairness is not a relevant problem in terms of computer engineering. In non-commercial networks, however, monetary solutions are not available, by definition. The present non-commercial blockchain networks (e.g. test networks such as Ropsten or Rinkeby, or academic networks such as Bloxberg) employ trivial distribution mechanisms called faucets, which offer fixed amounts of free tokens (called cryptocurrencies) specific to the given network. This mechanism, although simple and efficient, is prone to denial of service (DoS) attacks and cannot address the fairness problem. In the present dissertation, the faucet mechanism is adapted for fair distribution, in line with Max-min Fairness scheme. In total, we contributed six distinct Max-min Fair algorithms as efficient blockchain faucets. The algorithms we contribute are resistant to DoS attacks, low-cost in terms of blockchain computation economics, and they also allow for different user weighting policies. While four of the contributed algorithms provide scalability to unlimited number of users, two of them account for both short term and long term fairness.

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Thursday, August 11th

  1. PhD Thesis Defense - Niaz Chalabianloo - Stress Measurement and Regulation in Real-Life Using Affective Technologies
    • Start time: 10:00am, Thursday, August 11th
    • End time: 12:00pm, Thursday, August 11th
    • Where: AVS Conference Room, BM
    • Stress has become one of the main contributors to serious mental and physical health issues in today's world. Existing works in the literature have used Psychophysiological measures and proposed numerous mechanisms to detect stress and administer feedback to help users regulate it. Unobtrusive wearables' popularity is increasingly growing, intertwined with digital health notions, making them efficient, inexpensive, and easily accessible affective self-help technologies. This thesis first aims to investigate and implement stress detection mechanisms in the laboratory and everyday environments using unobtrusive wearable devices. In this regard, we investigate various scenarios, such as how to use laboratory data to improve the results of a daily life scenario. We also explore how adding contextual information such as physical activity and weather information can improve the results. Moreover, we study low-cost and practical methods for emotional regulation in stressful conditions of everyday life. In the next step, a mixed-methods study is conducted. For this, signals from multiple wearables and users' subjective opinions regarding different aspects of wearability were analyzed quantitatively and qualitatively. The next step is an in-depth study in cooperation with HCI researchers, in which we demonstrate the effects of haptic feedback on emotion regulation. As a next step for helping users choose the right device, we evaluate several wearables under completely identical conditions to compare the stress detection quality in wearables with different technologies. Finally, we utilize Explainable AI to make our models more understandable for the end users, and in particular for the psychology and clinical experts. The results of our studies indicate that an integrated detection, notification, and intervention cycle is required to ensure a reliable system for regulating stress in daily life.

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34342 Bebek, Istanbul, Turkey

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