Reviewerly/exHarmony huggingface dataset:
Mobile Game Design in the Context of the Mobile Network

With the upgrading of mobile network and the rapid development of smart phones, mobile phone hardware devices running mobile games are getting better and better, which promotes the in-depth development of mobile game design. With the diversification and simplification of mobile game development platform, more mobile game developers join in, inject more vitality into mobile games, accelerate the innovation of games and meet the needs of people for games. This paper analyzes the development of mobile games, the common platforms of mobile games, the current situation and development prospects of mobile games.
Foreseeing Survival Through ‘Fuzzy Intelligence’: A Cognitively-Inspired Incremental Learning Based de novo Model for Breast Cancer Prognosis by Multi-Omics Data Fusion

High-precision breast cancer prognosis is crucial for early disease identification, avoiding hazardous side-effects of unnecessary therapies, and decreasing mortality rates through personalized and tailored treatment regimens. However, designing a prognosis model continues to be challenging, given the intricate relationship between distinct genetic attributes, varied clinical results of drug therapies, the noisy nature of gene expressions, and the high-class imbalance seen in multimodal cancer data. Furthermore, because labeled omics data collection is costly and requires highly-trained experts, the data available is very limited. This makes the design of the conventional machine and deep learning models incredibly challenging as they require large quantities of data for learning the underlying intricate patterns and would otherwise overfit, decreasing model precision. Moreover, all present models suffer from a ‘closed world assumption.’ These models, once trained, cannot be updated in real-time (when more omics data is available in the future) without a complete re-training. The present study is the first to introduce the ‘Fuzzy’ way towards Breast cancer prognosis, framing the task as an incremental learning problem. The proposed approach allows the model to continually update its learned feature space on a non-stationary multimodal data stream emulating the human brain’s remarkable quality to learn over time. We demonstrate the model’s ability to learn complex relationships between different multimodal attributes, training on severely imbalanced and limited data by mapping it to a high-dimensional ‘fused’ feature space. The proposed model surpasses state-of-the-art machine learning (ML) models significantly. These results suggest that prediction through ‘fuzzy intelligence’ is a promising approach towards breast cancer prognosis.
BeamAtt: Generating Medical Diagnosis from Chest X-Rays Using Sampling-Based Intelligence

Medical imaging coupled with image captioning is fuelling the possibilities of generating accurate medical reports with minimal human intervention. In economically downtrodden nations, this produces opportunity for the poor to acquire world-class treatment from around the globe with an efficient time to market. Chest X-ray images are integral to the task of diagnosis and treatment of respiratory problems. In this paper, we propose BeamAtt: an end-to-end deep CNN-RNN-based encoder-decoder framework that incorporates spatial visual attention to generate a terse diagnosis from chest X-ray films. We choose to use a GRU RNN decoder as compared to LSTMs or hierarchical LSTMs from previous literature and reason via extensive evaluation for the same. To boost performance over state-of-the-art methods with complex architectures, we employ sampling-based techniques along with beam search optimisation while generating inferences and argue that a simpler framework with intelligent optimisation is able to successfully achieve higher performance metrics. We show how vivid attention plots can provide deep insight into the region of the image on which the network concentrates to generate a word token. We compare our model with recent prior art using standard evaluation metrics BLEU-1/2/3/4 and ROUGE-L and demonstrate superiority of the proposed method. BeamAtt achieves a BLEU-1 score of 0.56 and CIDEr score of 2.077 which is a significant boost in performance over contemporary solutions.
Dense-depth-net: a spatial-temporal approach on depth completion task

Depth completion is essential functionality in the perception system of an autonomous vehicle. With various convolution neural networks (CNN), scene geometric representation has been studied extensively under supervised learning or self-supervised learning. This paper utilizes recurrent neural networks (RNNs) to investigate temporal information from camera video sequences, which can help mitigate the mismatch between two consecutive data frames. Our paper proposed an architecture consisting of two sequence processing: the spatial exploitation stage built from a two-branches network and the temporal exploitation stage, a novel convolutional LSTM (ConvLSTM). Furthermore, we take the ability of long short-term memory (LSTM)-based RNNs to estimate a one-step depth map as an additional role of the representations of objects not only in a data frame but also in its temporal neighborhood. Moreover, the proposed ConvLSTM network demonstrated to have the option to make depth forecasts for future or occluded parts of an image frame. We evaluate the performance of the proposed architecture on the KITTI dataset and achieve the result proving to improve accuracy via a supervised-learning.
Interfered Fluid Dynamical Controller for Spacecraft Rendezvous with Collision-Free

To solve the problem of obstacle avoidance in the process of spacecraft rendezvous and improve the safety of the maneuver, a new control methodology based on interfered fluid dynamical system (IFDS) and finite-time sliding mode control (SMC) is proposed. First, combining the basic principles of IFDS and artificial potential function (APF), a simple feedback control method is designed, which can be compatible with different initial speeds and converge to the target state. Then, considering the influence of obstacles and velocity error to achieve a sliding surface and design the control law. Finally, the stability of the control law is proved by using Lyapunov stability theory. The simulation results show that the new methodology has the ability to avoid obstacles in the approaching process and has good performance.
High utility pattern mining based on historical data table over data streams

Efficient high utility itemsets mining over data stream is one of the most challenging problems in the data mining literature. Due to many redundant candidate generations, the existing algorithm is hard to show good scalability over a massive data stream. Additionally, the existing literature has less considered the reference value of historical data and has less adopted a distributed architecture. This paper proposes an efficient algorithm HUMHDT to mine high utility patterns over a data stream with the sliding window technique. Besides, we use historical data to effectively prune the redundant candidates generated in the current data stream mining process. Moreover, by referring to historical data, the algorithm can discover the potential items more effectively. Furthermore, we propose a distributed system architecture that can construct and update the historical data table without affecting the data stream mining algorithm and optimize the current data stream mining algorithm through a historical data table. The complete experimental results show that HUMHDT is more efficient than the state-of-the-art algorithms in terms of execution time and memory consumption.
On-Device Object Detection for More Efficient and Privacy-Compliant Visual Perception in Context-Aware Systems

Ambient Intelligence (AmI) encompasses technological infrastructures capable of sensing data from environments and extracting high-level knowledge to detect or recognize users’ features and actions, as well as entities or events in their surroundings. Visual perception, particularly object detection, has become one of the most relevant enabling factors for this context-aware user-centered intelligence, being the cornerstone of relevant but complex tasks, such as object tracking or human action recognition. In this context, convolutional neural networks have proven to achieve state-of-the-art accuracy levels. However, they typically result in large and highly complex models that typically demand computation offloading onto remote cloud platforms. Such an approach has security- and latency-related limitations and may not be appropriate for some AmI use cases where the system response time must be as short as possible, and data privacy must be guaranteed. In the last few years, the on-device paradigm has emerged in response to those limitations, yielding more compact and efficient neural networks able to address inference directly on client machines, thus providing users with a smoother and better-tailored experience, with no need of sharing their data with an outsourced service. Framed in that novel paradigm, this work presents a review of the recent advances made along those lines in object detection, providing a comprehensive study of the most relevant lightweight CNN-based detection frameworks, discussing the most paradigmatic AmI domains where such an approach has been successfully applied, the different challenges arisen, the key strategies and techniques adopted to create visual solutions for image-based object classification and localization, as well as the most relevant factors to bear in mind when assessing or comparing those techniques, such as the evaluation metrics or the hardware setups used.
Evaluation of Generative Adversarial Networks for Time Series Data

In the last few years, several works have been proposed on Generative Adversarial Networks (GAN). At the same time, there is a lack of investigation on their evaluation and the few proposed evaluation methods have not yet been rigorously studied. By way of example, the metrics to evaluate Generative Adversarial Networks (GAN) have been exclusively developed and tested for image data, but models operating on time series data have not been studied at all. In fact, it is still unclear what are the advantages and disadvantages of each approach and what is the difference between them in terms of performance i.e. which metric can easily detect common GAN problems such as mode collapse or mode dropping. Different tests have been introduced by [6] to evaluate GAN metrics for images. Inspired by this work, we extensively study the numerous evaluation metrics proposed in the literature that are designed for time series data and compare them to each other in a structured way. To the best of our knowledge, this represents the first work that studies the existing evaluation metrics of GAN for time series data and evaluates their performance against different evaluation criteria. Moreover, we introduce MiVo, a new evaluation metric that computes the similarity between a set of real and a set of generated data and finds for each real times series a synthetic one and for each synthetic time series a real one. We show that this bidirectional check will allow to easily detect different training problems such as the ones mentioned above. At the same time, this method is computationally much more efficient as it doesn't involve any machine learning model and hence no training is needed.
Probabilistic Micropayments with Transferability

Micropayments are one of the challenges in cryptocurrencies. The problems in realizing micropayments in the blockchain are the low throughput and the high blockchain transaction fee. As a solution, decentralized probabilistic micropayment has been proposed. The winning amount is registered in the blockchain, and the tickets are issued to be won with probability p, which allows us to aggregate approximately Open image in new window transactions into one. Unfortunately, existing solutions do not allow for ticket transferability, and the smaller p, the more difficult it to use them in the real world. We propose a novel decentralized probabilistic micropayment Transferable Scheme. It allows tickets to be transferable among users. By allowing tickets to be transferable, we can make p smaller. We also propose a novel Proportional Fee Scheme. This is a scheme where each time a ticket is transferred, a portion of the blockchain transaction fee will be charged. With the proportional fee scheme, users will have the advantage of sending money with a smaller fee than they would generally send through the blockchain. For example, sending one dollar requires only ten cents.
Digital image processing: Offline handwritten signature identification using local binary pattern and rotational invariance local binary pattern with learning vector quantization

Abstract The Handwritten signature is one of the authentication tools that humans have to determine the authenticity of a file or document. The difference between offline and online handwritten signatures is that offline signatures requires a scanner while online signatures use a stylus. The feature extraction methods used are Local Binary Pattern (LBP), uniform LBP, LBP 8 rotation, and uniform LBP 8 rotation. Learning algorithm used is Learning Vector Quantization (LVQ). This study used 200 images data, derived from 20 participants handwritten signature with contribution of 10 signatures each participants. Training data and test data will be divided based on the k-fold cross validation classification then tested based on 4 groups of data sets with certain parameters. Data set 1 consisted of 5 participants, data set 2 consisted of 10 participants, data set 3 consisted of 15 participants, and data set 4 consisted of 20 participants. The best system performance results with the highest accuracy are in training and testing of data set 1 with maximum of 100 iteration for each method. The results of system accuracy is very affected by total number of participants used, which is the more the number of participants with signatures processed, the accuracy will be reduced. The detailed results of system performances accuracy using data set 1 for the LBP extraction method obtained an accuracy of 84%, uniform LBP obtained an accuracy of 90%, LBP 8 rotation obtained an accuracy of 86%, and LBP 8 uniform rotation obtained an accuracy of 80%.
Accelerated Componentwise Gradient Boosting Using Efficient Data Representation and Momentum-Based Optimization

Componentwise boosting (CWB), also known as model-based boosting, is a variant of gradient boosting that builds on additive models as base learners to ensure interpretability. CWB is thus often used in research areas where models are employed as tools to explain relationships in data. One downside of CWB is its computational complexity in terms of memory and runtime. In this article, we propose two techniques to overcome these issues without losing the properties of CWB: feature discretization of numerical features and incorporating Nesterov momentum into functional gradient descent. As the latter can be prone to early overfitting, we also propose a hybrid approach that prevents a possibly diverging gradient descent routine while ensuring faster convergence. Our adaptions improve vanilla CWB by reducing memory consumption and speeding up the computation time per iteration (through feature discretization) while also enabling CWB learn faster and hence to require fewer iterations in total using momentum. We perform extensive benchmarks on multiple simulated and real-world datasets to demonstrate the improvements in runtime and memory consumption while maintaining state-of-the-art estimation and prediction performance.
Weakly-Supervised Ultrasound Video Segmentation with Minimal Annotations

Ultrasound segmentation models provide powerful tools for the diagnosis process of ultrasound examinations. However, developing such models for ultrasound videos requires densely annotated segmentation masks of all frames in a dataset, which is unpractical and unaffordable. Therefore, we propose a weakly-supervised learning (WSL) approach to accomplish the goal of video-based ultrasound segmentation. By only annotating the location of the start and end frames of the lesions, we obtain frame-level binary labels for WSL. We design Video Co-Attention Network to learn the correspondence between frames, where CAM and co-CAM will be obtained to perform lesion localization. Moreover, we find that the essential factor to the success of extracting video-level information is applying our proposed consistency regularization between CAM and co-CAM. Our method achieves an mIoU score of 45.43% in the breast ultrasound dataset, which significantly outperforms the baseline methods. The codes of our models will be released.
A Framework for Using Contactless Technologies in Operating Rooms

Contactless technologies, which have become widespread with technology advancement, have evolved in many fields such as industry, health, and construction. The applications of these technologies in the medical field are of great importance for the future of modern medicine. Operating rooms are one of the best examples of using those technologies in medicine. The systems to be used in operating room environments are expected to meet some medical requirements, be useful, and be prepared for critical scenarios. Sterilization is one of these requirements needs to be met during the operation. This paper aims to provide a solution to sterilization problems in operating rooms by using voice and gesture commands. In this way, doctors can complete their jobs by checking the working environment and the patient's condition during a surgery. The proposed system is developed on DICOM viewer called Weasis. OpenCV is used to detect hand movements via the camera, and Kaldi is used to detect voice commands. The detector created with Kaldi runs on Docker to integrate with other components. Commands received through hand gestures can directly change the position of the mouse and run Weasis interface commands. When sufficient environmental conditions are not provided, the error rate is reduced by using hand and voice commands together.
Exploring the Usability of the German COVID-19 Contact Tracing App in a Combined Eye Tracking and Retrospective Think Aloud Study

In the course of the corona virus (COVID-19) pandemic, many digital solutions for mobile devices (e.g., apps) were presented in order to provide additional resources supporting the control of the pandemic. Contact tracing apps (i.e., identify persons who may have been in contact with a COVID-19 infected) constitute one of the most popular as well as promising solutions. However, as a prerequisite for an effective application, such apps highly depend on being used by large numbers of the population. Consequently, it is important that these apps offer a high usability for everyone. We therefore conducted an exploratory study to learn more about the usability of the German COVID-19 contact tracing app Corona-Warn-App (CWA). More specifically, N = 15 participants assessed the CWA, relying on a combined eye tracking and retrospective think aloud approach. The results indicate, on the one hand, that the CWA leaves a promising impression for pandemic control, as essential functions are easily recognized. However, on the other hand, issues were revealed (e.g., privacy policy) that could be addressed in future updates more properly.
Provably Improving Election Verifiability in Belenios

Belenios is an online voting system that provides a strong notion of election verifiability, where no single party has to be trusted, and security holds as soon as either the voting registrar or the voting server is honest. It was formally proved to be secure, making the assumption that no further ballots are cast on the bulletin board after voters verified their ballots. In practice, however, revoting is allowed and voters can verify their ballots anytime. This gap between formal proofs and use in practice leaves open space for attacks, as has been shown recently. In this paper we make two simple additions to Belenios and we formally prove that the new version satisfies the expected verifiability properties. Our proofs are automatically performed with the Tamarin prover, under the assumption that voters are allowed to vote at most four times.
Morphology-Guided Prostate MRI Segmentation with Multi-slice Association

Prostate segmentation from magnetic resonance (MR) images plays an important role in prostate cancer diagnosis and treatment. Previous works typically overlooked large variations of prostate shapes, especially on the boundary area. Furthermore, the small glandular areas at ending slices also make the task very challenging. To overcome these problems, this paper presents a two-stage framework that explicitly utilizes prostate morphological representations (e.g., point, boundary) to accurately localize the prostate region with a coarse volumetric segmentation. Based on the 3D coarse outputs of the first stage, a 2D segmentation network with multi-slice association is further introduced to produce more reliable and accurate segmentation, due to large slice thickness in prostate MR images. Besides, several novel loss functions are further designed to enhance the consistency of prostate boundaries. Extensive experiments on large prostate MRI dataset show superior performance of our proposed method compared to several state-of-the-art methods.
GPS Aided Hassle-Free Cycling Using Sensors

Cycling has become part and parcel of the urban world. It is mainly used because it is one of the cheapest modes of transport and also used exercise equipment to burn calories. Here we are proposing an idea to make cycling a joyride using GPS and a heartbeat sensor. The person will plan the route using google maps, based upon which the motors attached to the cyclist arms will vibrate and guide the person. It’s a common habit for cyclists to listen to music while cycling which will enable them to overcome tiredness. The speed of the cycle measured by an accelerometer and the heartbeat rate which is measured by a heartbeat sensor will control the music play rate.
Harnessing the Conditioning Sensorium for Improved Image Translation

Multi-modal domain translation typically refers to synthesizing a novel image that inherits certain localized attributes from a ‘content’ image (e.g. layout, semantics, or geometry), and inherits everything else (e.g. texture, lighting, sometimes even semantics) from a ‘style’ image. The dominant approach to this task is attempting to learn disentangled ‘content’ and ‘style’ representations from scratch. However, this is not only challenging, but ill-posed, as what users wish to preserve during translation varies depending on their goals. Motivated by this inherent ambiguity, we define ‘content’ based on conditioning information extracted by off-the-shelf pre-trained models. We then train our style extractor and image decoder with an easy to optimize set of reconstruction objectives. The wide variety of high-quality pre-trained models available and simple training procedure makes our approach straightforward to apply across numerous domains and definitions of ‘content’. Additionally it offers intuitive control over which aspects of ’content’ are preserved across domains. We evaluate our method on traditional, well-aligned, datasets such as CelebA-HQ, and propose two novel datasets for evaluation on more complex scenes: ClassicTV and FFHQ-Wild. Our approach, Sensorium, enables higher quality domain translation for more complex scenes.