251 research outputs found
Motivation To Play Esports: Case of League of Legends
The population of playing electronic sports has increased recently, and the most popular one is League of Legends (LoL). As a multiplayer online battle arena video game, it’s not only a game, but also a competitive electronic sport. The purpose of this study was to assess the motivations of playing League of Legends and to relate them by genders, age groups and frequency groups. The final sample comprised 111 LoL players. The study categorized 12 items into three factors: achievement, socialization and immersion. Results indicated that achievement factors were stronger motives for men than women. For different age groups, there was no significant difference on socialization factors. The immersion factors for players who spent different times on LoL were not very influential
A Reliable Web Services Selection Method for Concurrent Requests
Current methods of service selection based on quality of service (QoS) usually focus on a single service request at a time, or let the users in a waiting queue wait for Web services when the same functional Web service has more than one requests, and then choose the Web service with the best QoS for the current request according to its own needs. However, there are multiple service requests for the same functional web service at a time in practice and we cannot choose the best service for users every time because of the service’s load. This paper aims at solving the Web Services selection for concurrent requests and developing a global optimal selection method for multiple similar service requesters to optimize the system resources. It proposes the improved social cognitive (ISCO) algorithm which uses genetic algorithm for observational learning and uses deviating degree to evaluate the solution. Furthermore, to enhance the efficiency of ISCO, the elite strategy is used in ISCO algorithm. We evaluate performance of the ISCO algorithm and the selection method through simulations. The simulation results demonstrate that the ISCO is valid for optimization problems with discrete data and more effective than ACO and GA
Wakening Past Concepts without Past Data: Class-Incremental Learning from Online Placebos
Not forgetting old class knowledge is a key challenge for class-incremental
learning (CIL) when the model continuously adapts to new classes. A common
technique to address this is knowledge distillation (KD), which penalizes
prediction inconsistencies between old and new models. Such prediction is made
with almost new class data, as old class data is extremely scarce due to the
strict memory limitation in CIL. In this paper, we take a deep dive into KD
losses and find that "using new class data for KD" not only hinders the model
adaption (for learning new classes) but also results in low efficiency for
preserving old class knowledge. We address this by "using the placebos of old
classes for KD", where the placebos are chosen from a free image stream, such
as Google Images, in an automatical and economical fashion. To this end, we
train an online placebo selection policy to quickly evaluate the quality of
streaming images (good or bad placebos) and use only good ones for one-time
feed-forward computation of KD. We formulate the policy training process as an
online Markov Decision Process (MDP), and introduce an online learning
algorithm to solve this MDP problem without causing much computation costs. In
experiments, we show that our method 1) is surprisingly effective even when
there is no class overlap between placebos and original old class data, 2) does
not require any additional supervision or memory budget, and 3) significantly
outperforms a number of top-performing CIL methods, in particular when using
lower memory budgets for old class exemplars, e.g., five exemplars per class.Comment: Accepted to WACV 2024. Code:
https://github.com/yaoyao-liu/online-placebo
Class-Incremental Exemplar Compression for Class-Incremental Learning
Exemplar-based class-incremental learning (CIL) finetunes the model with all
samples of new classes but few-shot exemplars of old classes in each
incremental phase, where the "few-shot" abides by the limited memory budget. In
this paper, we break this "few-shot" limit based on a simple yet surprisingly
effective idea: compressing exemplars by downsampling non-discriminative pixels
and saving "many-shot" compressed exemplars in the memory. Without needing any
manual annotation, we achieve this compression by generating 0-1 masks on
discriminative pixels from class activation maps (CAM). We propose an adaptive
mask generation model called class-incremental masking (CIM) to explicitly
resolve two difficulties of using CAM: 1) transforming the heatmaps of CAM to
0-1 masks with an arbitrary threshold leads to a trade-off between the coverage
on discriminative pixels and the quantity of exemplars, as the total memory is
fixed; and 2) optimal thresholds vary for different object classes, which is
particularly obvious in the dynamic environment of CIL. We optimize the CIM
model alternatively with the conventional CIL model through a bilevel
optimization problem. We conduct extensive experiments on high-resolution CIL
benchmarks including Food-101, ImageNet-100, and ImageNet-1000, and show that
using the compressed exemplars by CIM can achieve a new state-of-the-art CIL
accuracy, e.g., 4.8 percentage points higher than FOSTER on 10-Phase
ImageNet-1000. Our code is available at https://github.com/xfflzl/CIM-CIL.Comment: Accepted to CVPR 202
Mnemonics training: Multi-class incremental learning without forgetting
Multi-Class Incremental Learning (MCIL) aims to learn new concepts by
incrementally updating a model trained on previous concepts. However, there is
an inherent trade-off to effectively learning new concepts without catastrophic
forgetting of previous ones. To alleviate this issue, it has been proposed to
keep around a few examples of the previous concepts but the effectiveness of
this approach heavily depends on the representativeness of these examples. This
paper proposes a novel and automatic framework we call mnemonics, where we
parameterize exemplars and make them optimizable in an end-to-end manner. We
train the framework through bilevel optimizations, i.e., model-level and
exemplar-level. We conduct extensive experiments on three MCIL benchmarks,
CIFAR-100, ImageNet-Subset and ImageNet, and show that using mnemonics
exemplars can surpass the state-of-the-art by a large margin. Interestingly and
quite intriguingly, the mnemonics exemplars tend to be on the boundaries
between different classes.Comment: Experiment results updated (different from the conference version).
Code is available at https://github.com/yaoyao-liu/mnemonics-trainin
Surgical treatment of the osteoporotic spine with bone cement-injectable cannulated pedicle screw fixation: technical description and preliminary application in 43 patients
OBJECTIVES: To describe a new approach for the application of polymethylmethacrylate augmentation of bone cement-injectable cannulated pedicle screws. METHODS: Between June 2010 and February 2013, 43 patients with degenerative spinal disease and osteoporosis (T-scor
Meta-transfer learning for few-shot learning
10.1109/CVPR.2019.00049CVPR 2019403-41
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